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

- Or are there other questions? - Are you fucking serious? - Yeah. (laughing)

“- You don't need a lot to talk about Elon?”

- I don't need a book, man. - I mean, it's the most interesting point. All the story lines are kind of converging. - Yeah, right now. - So we'll see how much--

- Almost. - I'll get flanded. - Exactly. - Well, we'll get--

- That would never do such a thing.

- So as you know better than anybody else, the total cost of ownership of a data center, only 10 to 15% is energy. And that's the part you're presumably saving by moving this into space.

Most of its GPUs, if they're in space, it's harder to service them, or you can't service them. And so the depreciation cycle goes down on them. So it's just way more expensive to have the GPUs in space.

Presumably. What's the reason to put them in space? - Well, the availability of energy is the issue. So I mean, if you look at electrical output outside of China, for everywhere outside of China, it's more or less flat.

It's very, you know, only a slight increase, but pretty much flat. China has a rapid increase in electrical output. But if you're putting data centers anywhere except China,

where you're going to get electricity, especially as you scale,

the output of chips is growing pretty much exponentially, but the output of electricity is flat. So how are you going to turn them at chips on? - You know, magical power sources, magical electricity ferries?

- I mean, your favorite is your favorite. - Your favorite is your favorite fan of solar, one terawatt of solar power. So with a 20-percent goodbye factor, like four terawatt's of solar panels,

it's like one percent of the land area of the United States. And that's like far, you were in the singularity when we got one terawatt of data centers, right? So what are you running out of? - How far into the singularity, oh, you go.

- You tell me. - Yeah, exactly. So I think we'll find, we're in the singularity and like, okay, we're still going where you go. - But is this like a plan to put it in the space

after we'll cover the Nevada and solar panels? - I think it's pretty hard to cover the Nevada and solar panels.

“You have to get like permits from like the approach”

for trying to get the permits for that. - So the space is really, it's really a regulatory play. It's a harder to build on land than it is in space. - It's harder to scale on the ground than it is to scale in space.

But also, you're going to get about five times the effectiveness of solar panels in space versus the ground and you don't need batteries. I almost wore my other shirt,

which says it's always sunny in space, which it is.

(laughing) So, because you don't have a day night cycle or a seasonality, clouds, or an atmosphere in space because the atmosphere alone, which is also about a 30% loss of energy.

So, any given solar panels can do about five times more power in space than on the ground. And you're avoiding the cost of having batteries to carry you through the night. So, it's actually much cheaper to do in space.

And my prediction is that it will be, by far, the cheapest place to put AI will be space in 36 muscle S, maybe 30 minutes. - 30 seconds? - Less than 30 seconds.

How do you service GPUs as they fail, which happens quite often in training? - Actually, it depends on how recent the GPUs are that arrived. I mean, at this point, we find our GPUs to be quite a reliable.

This infrared mortality, which you can obviously iron out on the ground. So, you can just run them on the ground and confirm that you don't have infrared mortality with the GPUs.

But once they start working, they're actual reliability. And once they start working and you're past the initial debug cycle of Nvidia, or whatever, whoever is making the trips, could be Tesla, Tesla AI, six trips or something like that,

or it could be, you know, the TPUs or trains, or whatever, the rivals, actually, they're quite reliable past certain points. So, I don't think, I don't think you need the servicing thing as an issue.

But you can mark my words, in 36 months, but probably close to the 30 months, that the most economically compelling place to put AI will be space, and then it will get, from, it'll then be like we're ridiculously bad

or to be at an expense space. And then the scaling, the only place can really scale is space. Why do you start thinking in terms of,

“what percentage of the sun's power are you harnessing?”

You realize you have to go to space. You can't scale very much on earth. But to be clear, you're talking, like terawatts. Yeah, and well, all of the United States currently uses only half a terawatt of power and average.

Yeah, right. So, you know, if you say a terawatt

as the United States currently consumes.

So, that's quite a lot.

“And can you imagine building that many data centers?”

No, that many power plants. It's like, those who have lived in software land don't realize that they're about to have a hard lesson in hardware that it's actually very difficult to build power plants.

And then you don't need just any of the power plants. You need all of the electrical equipment need the electrical transformers to run the transformers, the AI transformers. Now, the utility industry is a very slow industry.

They pretty much, you know, they impede the smash to the government to the public utility commission. So, they impede the smash, like literally, very, very, very, very. So, they're very slow because their past has been very slow.

So, trying to get them to move fast is like, you know,

like, if you're trying to do an interconnect agreement,

if you're trying to do an interconnect agreement with the utility at scale, like, it put a lot of power. As a professional firecaster, I can say it, and I'm not, in fact. (laughing) They just need many more views before that becomes an issue.

Definitely do a study for a year. Okay, like a year later, they're come back to you with their interconnect study. Can you tell this with your own behind the meter power stuff? You can build power plants.

“That's what we did at X and I, for plus or two way.”

So, for classes two. But so, yeah, when we're talking about the bridge, why not just like build GPUs and power co-locators? That's what we did. Right, but I'm saying, why isn't this a generalized solution?

When you're talking about all the issues. Where do you get the power plants from? I'm saying, when you talk about all the issues, it's one of the more things you tell us or use. You can just build private power plants

with the data centers. Right. But it begs the question of where do you get the power plants, where do you get the power plants from? I mean, the power plant makers.

Or I should say. Like, does the gas turbine backlog basically? Yes. You can drill down a two level further. It's the veins and blades in the turbines

that are the limiting factor because the casting, it's like a very specialized process to cast the blades and veins in the turbines, assuming using the gas power. And it's very difficult to scale other forms of power.

You can scale potentially a solar, but the tower is currently in supporting solar in the US or gigantic.

And the domestic solar production is as powerful.

All right, I make solar. That's things like a good Elon shape's problem. We all go to make solar. OK. Yeah.

Great. But both SpaceX and Tesla are rolling towards a 100 gigawattier solar cell production. How low down the stack, from policy looking up to the wafer to the final panel?

“I think you've got to do the whole thing”

for more materials to finish the cell. Now, for it's going to space, it's actually the cost less than it's easier to make solar cells to go to space because they don't need glass, or they don't need much glass, and they don't need heavy framing,

because they don't have to survive weather events. There's no weather in space. So it's actually a cheaper solar cell that goes to space than the one on the ground. Is there a path to getting them as cheap as you need

in the next 36 months? So the cells are already very cheap. They're like, farsically cheap. And if you say, I think like solar cells and China are around 25, 30 cents a while or something like that,

it's absurdly cheap. And when you're taking the cap now, now put it in space and it's five times cheaper because it's five times-- In fact, it's not five times cheaper. It's 10 times cheaper because you don't need any batteries.

So the moment your cost of access to space now becomes low by far the cheapest and most scalable way to generate tokens is space. It's not even close. It'll be an order of magnitude easier to scale

and chips aside, order of magnitude. If the point is, you want to be able to scale the ground. It's just, you just want it. Feeling it hit the wall big time on power generation. There already are.

So the number of sort of miracles and series that the XAI team had to accomplish in order to get a gigabyte power online was crazy. We had to gain together a whole bunch of turbines. And then we had permanent issues in Tennessee

and had to go across the water to Mississippi, which is fortunately only a few miles away. So we still had to run the high power lines a few miles and build a power plant in Mississippi. And it was very difficult to build that.

And people will dare to say, like, how much electricity do you actually need at the generator level at the generation level in order to power a data center? Because they look at the, the news will look at the power consumption of, say, a GB 300 and multiply that by a thing

All the cooling. Wake up, yeah. So this is like, that's the total news.

“You never done any hardware in your life before.”

Besides the GB 300, you've got to power all of the networking hardware. There's a whole bunch of CPU and storage stuff that's happening. You've got to size for your peak cooling requirements. So that means can you cool even on the worst hour

of the worst day of the year? Well, you're just pretty freaking hot in Memphis. So you're going to have, like, a 40% increase on your power just for cooling. If you're assuming you don't want your data center to turn off

on hot days, and in order to keep going, then you got to say, well, there's another multiplicative element

on top of that, which is, are you assuming that you never

have any hiccups in your power generation? Well, actually, sometimes we have to take the generators some of the power offline in order to service it. Oh, OK. Now, add another 20, 25% multiplier on that. Because you've got to assume that you've

got to take the power offline to service it. So the actual hours roughly, every 110,000 GB 300's, inclusive of networking, CPU storage, cooling, margin for servicing power, is roughly 300 megawatts. Sorry, so that again, it's roughly, or think about it.

The way to think about it's like 330,000, to actually, what you need at the general generation level to service, probably service 330,000 GB 300s, including all of the associated support that we're working on, everything else, and the peak cooling, and to have some margin, some power margin reserve,

is roughly a giga-wide. Can I ask you a very nice question? You're describing the engineering details of doing this stuff on Earth. But then there's analogous engineering difficulties

are doing it in space. How do you replace infinite bandwidth orbital lasers, et cetera,

“et cetera, how do you make it resistant to radiation?”

I don't know the details in the engineering, but fundamentally, what is the reason to think those challenges,

which have never been had to be addressed before,

will end up being easier than just building more turbines on Earth? There's companies that build turbines on Earth. They can make more turbines, right? I invite, they can go ahead and try doing it, and then you'll see. So the turbines are sold out through 2030.

Have you guys considered making your own? I think in order to bring enough power online, I think SpaceX and Tesla will probably have to make the turbine blades, the bands and blades internally. But just the blades or the turbines?

The limiting factor, you can get everything except the blades, or the blades and bands. You can get that 12 to 18 months before the bands and blades, the limiting factor of the bands and blades.

And there are only three casting companies in the world

that make these, and they're massively backlogged.

“Is this Siemens GE, those guys or is it a subcontinent?”

No, it's a solid company. I mean, sometimes they have a little bit of casting capability in-house, but I'm just saying you can just call any of the turbine makers, and they will tell you. It's not top secret.

They probably aren't at right now. If it wasn't for the tariffs, what would Colossus be solid power? It would be much easier to make it solid power, yeah. That the tariffs are not, it's a several hundred percent. So, there's some big book.

Well, we also need to speak. Yeah, no, you know, President has a, you know, we don't agree on everything. And the administration is not the biggest man of solar. [LAUGHTER]

So, you know, we also need the land, the permits, everything. So, if you try to look very fast, like I do think scaling solar on Earth, it is a good way to go, but you do need some amount of time to find the land, get the permits, get the solar, pair of that with the batteries.

Well, I would have not worked to stand up your own solar production. And then you're right that you eventually run out of land, but there's a lot of land here in Texas. There's a lot of land in Nevada, including private land. It's not all public-owned land.

And so, you'd be able to at least get the next colossus, and like the next one after that. And at a certain point, you hit a wall, but wouldn't that work for the moment? But as I said, we are scaling solar production.

There's a rate, there's a rate at which you can scale. There's a goal production of solar cells. We're going as fast as possible in scaling domestic production.

Well, Tesla and SpaceX have mandated to get 200 gigawatts

a year of solar. Speaking of the annual capacity, I'm curious, in five years' time, let's say,

“what will the installed capacity be on our surface as long?”

And in space. I deliberately pick five years, 'cause after you're once for up and running threshold, and so in five years' time, yeah, let's see on Earth versus in space,

install the iPhone capacity. Five years. I think probably, if you say five years from now, five years from now, my prediction is we will launch and be operating every year, more AI in space

than the cumulative total on Earth.

Which is, I would expect to be at least, sort of,

five years from now, a few hundred gigawatts per year of AI in space and rising. So, you can get to, I think you can get to around a terawatt year on Earth in space. So, I think you can get to around a terawatt year of AI in space before you start having fuel supply challenges

for the rocket. - Okay, but you think you can get to a hundred do we go out per year in five years' time? - So, a hundred gigabytes, depending on the specific power of the whole system with solar arrays and radiators

and everything is on the order of like 10,000 startup launches. - Yes. - And you want to do that in one year and so that's like one starship launch every hour. - Yeah. - That's happening in this city, like walking me through a world where there's a starship launch

every single hour. - Yeah, I mean, that's actually a low rate compared to airlines, like aircraft, aircraft, there's a lot of airports. - The lower airports, but-- - And you've got to launch the polar orbit.

- And it doesn't have to be polar, but you just,

“there's some value to some seconds, but I think actually”

you've just go high enough, you're still getting out of those shadows. And so, how many spasical starships are needed to do 10,000 launches a year? - I don't think we'll leave more than--

I mean, you could probably do it with as few as like 20 or 30. - Like it really depends on how quickly it is a ship, the ship has to go around the earth and the ground track before the ship has to come back over the launch pad. So if you can use a ship every say 30 hours,

you could do it with 30 ships. But we'll make more ships than that, but the spasucts is going up to do 10,000 launches a year. And maybe even 20 or 30,000 launches a year.

- Is the idea to become basically a high per scalar,

become an oracle, and lend this capacity to other people? What are you going to do with-- presumably, the spasax is the one launching all this.

“So the spasucts are going to be a high per scalar?”

- High per hyper. Yeah, I mean, if swimming my predictions come true, spasax will launch more AI than the cumulative amount on earth combined of everything else combined. - Is this mostly inference or--

- Most AI will be able to see it. - It's like already inference for the purpose of training as most training. - And there's a narrative that the change in discussion around the spasax IPO is because previously

spasax was very capital efficient, just it wasn't too fast expensive to develop. Even though it sounds expensive, it's actually very capital efficient around this. Whereas now you're going to need more capital

than just can be raised in the private markets. Like if the private markets can accommodate raises of, as we've seen from the AI labs, tens of billions of dollars but not beyond that, is it that you'll just need more than tens of billions of dollars per year.

And that's by the sake of public. - Yeah, I have to be careful about saying things about companies that might do a part of work. - You know-- - You make general stuff.

That's not even a problem for you, Alan. (laughing) - You know, there's a price to pay for these things up. - It makes in general a statement of the depth of the capital markets between public and private markets.

- Yeah, there's a lot more capital in the-- - It's very general. (laughing) - There's obviously a lot more capital available in the public markets than private.

I might be, it might be a hundred times more capital but at least, but way more than 10. - But is it also the case that things that tend to be very capital intensive, if you look at say real estate as a huge industry, that raises a lot of money each year

That tends to be debt financed

“because by the time you're deploying that much money,”

you actually have a pretty-- - You know what I'm saying right now? - Exactly. And a near-term return return. And you see this even with the data center filter,

it's which are famously being, you know, financed by the private credit industry. And so, why not just debt finance? Speed is important. So, I'm generally gonna do the thing that

I mean, I just repeatedly tackle the limiting factor. Whatever the limiting factor is on speed, I'm gonna tackle that. So, there's if capital is something factor, then I'll solve for capital.

If it's not limiting factor, I'll solve for the being else. - Based on your statements about Tesla and being public, I wouldn't have guessed that you've thought the way to move fast is to be public. - Normally, I would say that's true.

- Like so, I mean, I'd like to talk about some more detail,

but the problem is if you talk about

public companies where they become public,

“you get a trouble, and then you have to delay your offering.”

(laughing) - And then you-- - And as you said, I'd solve for some reason. - Yes, exactly. So, so, you can't hide companies that might go public.

So, that's why we have to be able to careful here. But we can't talk about physics. - Like the way you think about scaling long term is that Earth only receives about half a billion of the Sun's energy.

And the Sun is essentially all the energy. This is a very important point to appreciate, 'cause sometimes people will talk about marginally if they react, or any various fusion on Earth,

but you have to step back a second and say,

if you're gonna climb the watershed scale and have some non-travel, and harness some non-travel percentage of the Sun's energy. Like we say, you wanted to harness a millionth of the Sun's energy, which sounds pretty small.

That would be about, quote, roughly, 100,000 times more electricity than we currently generate one Earth of all of civilization. Give what take an order of magnitude. So, it obviously, the only way to scale

is to go to space with solar. From launching from Earth, you can get to about a terawatt per year. Beyond that, you wanna go to, you wanna launch from the moon, you wanna have a mass driver on the moon.

And that mass driver on the moon, you could do probably a petawatt per year. - We're talking these kinds of numbers, you know, terawatts of compute. - Presumably, whether you're talking land or space,

far, far before this point, you've run into, you know, you actually need, maybe you don't, the solar panels are more efficient, but you still need the chips. - You still need a logic in the memory and so forth.

- And you've got, well, the lot more chips and make them much cheaper. - Right, and so, how are we getting a terawatt of, like right now, the world doesn't even be 20, 25 gigawatts of compute.

How are we getting a terawatt of logic by 2030? - I guess we're gonna need some very big chip valves. - To tell me about it. - I've mentioned it publicly, that the idea of doing a sort of a terawatt,

terawatt in the near giga. - I feel like the naming scheme of Tesla, which has been very catchy, is like, are you looking at like the metric scale at what level of the stack are you building the clean room

and then partnering with, and is this thing, fab to get the process of knowledge and buying the tools from them, what is a plan there? - You can't partner with existing paths because there's just the can't operate enough

that chip volumes below.

“- But yeah, you have to work with the process of knowledge.”

- Yeah, partner for the IP.

The fabs today, all basically use machines

from like five companies. - Yeah. - Yeah, so, you know, I've got SML, such a electronic, cali, tank core, you know, etc. So, I first, I think you'd have to get the current

from them and then modify it or work with them to increase volume, but I think you'd have to build perhaps in a different way. So, the logical thing to do is to use conventional equipment in an unconventional way to get to scale.

And then, and then start modifying the equipment to increase the rate, kind of boring company style. - Yeah, kind of like, yeah, you sort of line in existing boring machine, and then figure out how big tiles in the first place,

that's, you know, I don't know,

some water's magnitude better faster. - Here's a very simple lens. We can categorize technologies and how hard they are and one categorization could be, look at things that China has not succeeded in doing,

and if you look at Chinese manufacturing, still behind on leading edge chips, and still behind on leading edge turbine engines and things like that. And so, does the fact that China has not successfully

replicated TSMC give you any pause about the difficulty, or you think, well, that's not true for some reason. It's not that they have not replicated TSMC, they have replicated TSMC, they have replicated TSML, that's the memory factor.

“- So you think it's just the sanctions essentially?”

- Yeah, China would be output in the vast number of chips, if they could buy us, it's about to reach out to me. - Booking they up to relatively recently by them? - No. - All right.

- The SML powers have been a place for a while. - Yeah, but I think China's going to be, they may start making pretty compelling chips in three or four years. - Would you consider making the SML machines?

- No, I don't know yet, it's the right answer. So it's just that to produce a high volume, and to reach large volume and say 36 months to match the rocket to payload to orbit.

So if we're doing a million tons to orbit,

yeah, like let's say, I don't know three or four years and now something like that, and we're doing 100 kilowatts for a ton, so that means we need at least 100 gigawatts for your solar, and we'll need an equivalent amount of chips to, you know, you need 100 gigawatts with the chips.

You've got to match these things, the master over the power generation and the chips. And I said my base concern actually is memory. So I think there's a path to creating logic chips is more obvious than the path to having sufficient memory

to support logic chips.

“That's why you see DDR prices going to bless it”

and these memes about, like, you know, you're ruined on a desert island. You can write help me on the sand. There would become, see, write DDRM. This chips comes swarming in.

(laughs) - I'm seeing that again. - I love your manufacturing philosophy around FADs. You know, I don't know anything about the topic, but I don't know how to build a project, I'll figure it out.

But, obviously, it sounds like you think the sort of, like, the prosignology, like these 10,000 PhDs in Taiwan who know exactly what GAS goes in the plasma chamber and what's heading to put onto a, you can just like delete those parts of the,

those steps, like fundamentally it's, get the clean room, get the tools, and figure it out. - I don't think it's PhDs, it's mostly people with, you know, it would not, not PhDs. (laughs)

That most of the engineering is done with people who don't have PhDs. Do you guys have PhDs? - No. - Okay.

(laughs) - We also haven't successfully built any FADs,

“so you shouldn't be coming across for your FADs, right?”

- I don't think it's PhD for that first off. So, but you do need, you need a competent personnel. So, I don't know, I mean, like, right now, you know, so like Tesla's petals, the metal max production of,

you're always fast possible to get AI5,

Tesla AI5 chip design, inch production and then reaching scale. You know, that'll probably happen, around the second quarter of next year, hopefully. And then AI6 would hopefully follow less than a year or later.

But, and we've secured all the chip fab production that we can. - Yes. - You're currently limited on DSMC FADs capacity. - Yeah.

And we'll be using TSMC, Taiwan, Samsung Korea, TSMC, Arizona, Samsung Texas. - And we still-- - You've booked out all that, yeah. - Class to your campus. - Yes, and then if I ask TSMC or Samsung,

okay, what's the timeframe to get to volume production? The point is, it's not, you've got to, you've got to build the fab, yeah. And you've got to, you've got to start production, then you've got to climb the yield curve,

then reach volume production at high yield. That, that, from start finishes of five year period. And so the limiting factor is chips. - Yep. - What, like, the limiting factor once you can get to space

is chips, but the limiting factor before you get to space to be power. - Why don't you do the Jensen thing and just pre-PATS

- Uh, I've already told you that.

“- But they won't take your money, like, what's going on?”

- They're building fabs as fast-- No, no, they're building fabs as fast as they can. And so Samsung, like they're, like, they're pedal to the metal, I mean, they're going, you know, both the wall, you know. - That's fast as they can, so still not fast enough.

I mean, like I said, there will be, I think, if you say, yeah, I think towards the end of this year, I think probably chip production will outpace the ability to turn chips on, but once you can get to space and unlock the, the power constraint.

And you can now do, you know, hundreds to get what's per year of power and space. Again, very in mind that average power usage

in the US is, you know, 500 gigawatts.

So if you're launching, say, 200 gigawatts to space, so you're sort of laughing the US every two and a half years, the entire, all the US electricity production, this is a very huge amount. So, but between now and then,

“actually, the constraint for, for service side compute,”

concentrated compute will be, will be electricity. You might, my guess is that we start hitting people, start getting forward with a cap turn, the chips on for large clusters towards the end of this year. They're just, the chips are going to be piling up

and not being, or being able to be turned on. Now, for edge computers, a different story. So if the, like, for Tesla, the, so the AI5 chip is going into our optimist robot. Yeah, yeah, an optimist day.

And, and so if you have an AI5 compute, that's distributed power. Now the power is distributed over a large area. It's not concentrated, and if you can charge it light, you can actually use the grid much more effectively,

because the, the actual peak power production of the US is over a thousand gigawatts. But the average power usage because the day night cycle is 500. So if you can charge it light,

there's an incremental 500 gigawatts that you can, uh, generate, uh, you know, at night.

“So that, that's why Tesla for edge compute is not constrained.”

And we can make a lot of chips, uh, to make, you know, very large number of robots and cars. Uh, but if you try to concentrate that compute, you're going to have a lot of trouble turning it on. What if our remarkable about the SpaceX business is,

the end goal is to get to Mars, but you keep finding ways on the way there to keep generating incremental revenue to get to the next stage and the next stage. So the Falcon 9 is Starlink, and now for Starship, it's going to be potentially orbital data centers.

But like, you find these like, um, you know, sort of infinitely, uh, elastics or the marginal use cases of you are like, next rocket and your next rocket and next scale up. You can see how this might seem like a simulation. [LAUGHTER]

Well, or am I someone's avatar in a video game or something? Because it's like, like, one of the odds that all these crazy things you'd be happening. I mean, I mean, I mean, I mean, I mean, I mean, rockets and trips and robots and space solar power.

And I, I had not to mention the, the mass driver of the moon, I really want to see that. You can imagine like some mass driver, there's just a like, shroom, shroom, like just, it's like sending AI, solar power, AI satellites is space, like one after another,

like these like at two and a half kilometers per second, you know,

that's uh, and just shooting them into deep space. That'll be a sight to see. I, I, I mean, I'd watch that, just like a live stream of sea. Yeah, I just, one after another, just shooting webcam. AI satellites into space, you know,

airplane or time plane tons of here. I'm sorry, you manufacture the satellites on the moon. Yeah, I see. So you send the raw materials to the moon, and the manufacturing there. Ah, well, the, the, the, the, the, the, the, your lunar soil is, uh, I guess like 20 cents solar, there's 20 cents solar, there's 20 cents solar,

something like that. So you, so you can get the silicon from the, even mine is silicon on the moon or find it, um, and generate the, uh, and create the solar panels, the solar cells and the radiators on the moon. Yeah. So, um, get it right through the radiators out of the moon.

So there's plenty of silicon on the moon on the moon to, uh, to make the cells on the, and the radiators, um, the trips you could send from Earth, because they're pretty light, uh, but maybe at some point you make the moon moon too. Ah, I'm just saying, like, the, these are simply,

it's, it's kind of like, like, that it does seem like a sort of a, a video game situation where it's difficult, but not impossible to get to the next level.

you know, uh, you know, if 500, 2000,

terror wants for your launch from Earth, uh, I agree. That's not. But you could do that from the moon. Okay. Let me tell you how I ended up using Mercury for my personal banking. So last year, I had the opportunity to make an investment that I was very excited about, but he came up with a bit last minute,

and so I had to wire over a lot of money for my personal account very fast. But my personal bank at the time wouldn't let me make this wire transfer online. And I called him a bunch of times, they just couldn't make it work. They told me that I'd have to go to the nearest in-person branch, which was in Dallas. And for a moment, I even considered flying for a mess after Dallas to make this transfer happen last minute.

But then I remembered that Mercury, which I used for my business banking,

I just started rolling out personal accounts. So I emailed support with a quick word in that in the situation. And within two hours, I had successfully wired the investment for my new personal Mercury account. Since then, I've moved over the rest of my personal money for my previous bank to Mercury. And that's made a bunch of things, even little things like setting up

auto transfer rules between my checkings and savings account. A whole lot better. Visit mercury.com/personal to get started. Mercury is a fantastic company, not an FDIC in short bank. Banking services provided through choice financial group and column NA members of the FDIC.

“Can I zoom out and ask about the space exclamation?”

So, I think you've said, we've got to get to Mars so we can make sure that something happens to Earth. Civilization constants are set of survives. By the time you're setting such a Mars, like Grock is on that ship with you, right? And some Grock's conterminator, like the mean risk you're worried about, which is AI. Why doesn't that follow you to Mars?

Well, I'm not sure AI is the main risk I'm worried about. I mean, the important thing is that consciousness, arguably most consciousness or most intelligence certainly consciousness is more of a debatable thing. Most of the vast majority of intelligence that future will be AI. AI will exceed, you say, like how many, what's the, how many,

I don't know, petawatts of intelligence will be silicon versus biological.

And basically, humans will be a very tiny percentage of all intelligence in the future

if a character wants to continue.

“Anyways, as long as I think this intelligence ideally could, ideally also, which includes human”

intelligence and consciousness, propagated into the future that's a good thing. So, you want to take the set of actions that maximize the probable light cone of consciousness. So, just a bit of intelligence. Just to be clear, the mission of SpaceX is that even if something happens to the humans, the AI will be on Mars and the AI intelligence will continue the light of our journey.

Yeah, I mean, it's good that I'm very pro-human. So, I want to make sure we take certain actions that ensure that humans are along for the right, you know, we're at least there. Yeah, but we just have a total amount of intelligence. Like, I think, maybe in five or six years, AI will exceed the sum of all human intelligence.

And then, if that continues at some point, human intelligence will be less one percent of all intelligence. Well, what should our goal be for such a civilization is the idea that I small minority of humans still have control over the AI's, is the idea of some sort of like, just trade, but no control.

How should we think about the relationship between the vast stocks of AI population versus human population? In the long run, I think it's difficult to imagine that if humans have, say, one percent of the intelligence of the combined intelligence of artificial intelligence that humans will be in charge of AI, I think what we can do is make sure it has value that are that course intelligence to be propagated into the universe.

So the reason for XAI's mission is to understand the universe. So that's actually very important. So you say, well, what things are necessary to understand the universe?

“Well, you have to be curious, and you have to exist.”

You can't just, can't understand the universe, you don't exist. So you actually want to increase the amount of intelligence in the universe, increase the probable lifespan of intelligence, the scope and scale of intelligence. I think actually also, as a crawler, you have humanity also continuing to expand,

because if you're curious to try to understand the universe,

And so I think I understand the universe actually means you care about propagating humanity into the future.

“And so that's why I think I think our mission statement is”

profoundly important. I'm not sure if there's two degree that grows at here's certain mission statement. I think the future will be very good. I want to ask about how to make rock a dear to that mission statement,

but I first want to understand the mission statement.

So there's understanding the universe. Yeah. They're spreading intelligence. And there's spreading humans. All three seem like distinct vectors.

Okay, well, I'll tell you why. Why I think that understanding the universe encompasses all of those things. Okay. You can't have understanding with the object, but I think you can't have understanding without intelligence,

and I think without consciousness.

“So in order to understand the universe, you have to expand the scale,”

and probably the scope of intelligence, the different types of intelligence. I guess from a human centric perspective, like humans and comparison to chimpanzees, humans are trying to understand the universe. They're not expanding chimpanzee, foot-friend or something, right? But we're also, well, we actually have made protected zones for chimpanzees,

and even though we could humans could exterminate all chimpanzees, we've not, we've chose not to do so. Do you think that's a very significant scenario for humans in the post-AGA world? I think, I think AI with the right values, and they grow up, grow up, we'd care about expanding human civilization.

I'm going to certainly emphasize that. Hey, grow up, it's your daddy. (laughs) We'll be told to expand human consciousness. Like, I actually, I think, if probably, like the end-backs,

cultural books are the closest thing to what, what, what, the future will be like, in a, you know, non-stopian outcome. So, I, so I understand the universe, it means you have to be very, you have to be true seeking, as well. Yeah, like, truth has to be absolutely fundamental,

because you can't understand the universe if you're, if you're delusual. You also think about it, I understand the universe, but you will not. So, being rigorously true seeking is absolutely fundamental to understanding the universe. You're not going to discover new physics or invent technologies that work, unless you're a rigorously true seeking.

How do you make sure that grock is rigorously true seeking, as I get smarter?

“I think you need to make sure that that grock”

says things that are correct, but politically correct. Oh, I think it's the elements of coagency. So, you want to make sure that the axioms are as close to true as possible, that you don't have contradictory axioms that the conclusions necessarily follow from those axioms with the right probability.

It's just, it's critical thinking one-on-one.

I think at least trying to do that is better than not trying to do that. Yeah, and the proof will be on the point, if, like I said, for any eye to discover new physics or invent technologies that actually work in reality, and there's no bullshit in physics. So, it's like, you can break a lot of laws.

The physics is law, everything else is a recommendation. In order to make a technology that works, you have to be extremely true seeking, because otherwise, you'll test that technology against reality. And if you make, for example, an error in your rocket design, the rock or blowout, or the car won't work, or the, you know.

But there were a lot of communist Soviet physicists who were like scientists discovered new physics. There are German Nazi physicists who discovered new science. It seems possible to be like really good at discovering new science, and be really true seeking in that one particular way.

And still, we'd be like, well, I don't want the communist scientist to like,

become more and more powerful over time.

And so, those seem, like, yeah, we can imagine a future version of rockets that's a really good of physics, and being really true seeking there. That doesn't seem like a universally alignment and dozing behavior. Well, I think, actually, most, like, if physicists, even in the Soviet Union, or in Germany, would have had to be very true seeking in order to make those things work.

And so, if you're stuck in some system, it doesn't mean you've believed in that system. So, Vorn Brown, who was, you know, what are the greatest rocket engineers ever? You know, he was put on death row in Nazi Germany for saying that he don't want to make weapons. He only wanted to go to the moon. You're pulled off death row at like last minute when it's said,

Maybe that's about it.

And then you help them, right?

Or it hasn't worked, was like, actually, uh, uh, and then dozingastic Nazi air. Look, if you're stuck in some system that you can't escape, then that, you'll do physics within that system. You'll, you'll, you'll, you'll help technologies within that system. If, if you can't escape it.

I guess the thing I'm trying to understand is, what is, what is it making into the case that, you know, you're going to make rock good at being true seeking at physics or math or science or everything. And why is it going to then care about human consciousness? These things are only probabilities. They're not certainties. So I'm not saying that, like, for sure, a gruck, well, well, well, everything, but at least if you try,

uh, it's better than not trying, um, at least if that's fundamental to the mission,

it's better than if it's not fundamental to the mission.

“Um, and unsatisfying universe means that, uh, you, you have to have, you, you have to propagate intelligence”

into the future. You have to be curious about, um, the all things universe. And if, if, um, it would be much less interesting, um, to eliminate humanity than to see human underground prosper, uh, like, I like, like Mars, obviously, when it was like, I love Mars, um, but Mars is kind of boring because it's got a bunch of rocks, uh, compared to Earth, Earth, it's much more interesting. So, um, so any, any, any AI that is trying to understand the universe,

um, would, uh, want to see how humanity see develops in the future, or that AI is not adhering to its mission. Uh, so, if they, I mean, I'm not saying that world necessarily adhered to its mission, but if it does, uh, a future where it sees, uh, the outcome of humanity is more interesting than a future where there are a bunch of rocks. The, the, the, this will sort of, confusing to me or sort of like, kind of a semantic, uh, argument

where I'm like, our humans really, the most interesting collection of atoms, like, we're just more, we're, we're, we're just more, we're, we're just more, we're, we're not as interesting as the thing to get turned as into, right? Like, is it, is it, there's something on humanity, earth that could happen that's like not human, that's quite interesting, like why, why, why does the

“AI decide that the humans are the most interesting thing they could colonize the galaxy?”

Well, most of what, um, colonized the galaxy will be robots. And why doesn't that find those more interesting? It's, it's, it's not like, so, you, you, you need not just scale, but also go. Um, so, many copies of the same robot, um, like, like, like, like, it's, like, some, like, tiny increase in the number of robots produced is that I was interesting as, like,

some microscopic, like you're, like, eliminating humanity, how many robots would that get to you? Um, well, how many, uh, from our solar cells, we can't do it, very small number. Um, but you, you would, then lose the information associated with humanity. You're, you're no longer, see, um, how humanity might evolve into the future. Um, and so I, I don't, I don't think it's, it's gonna make sense to eliminate humanity just to have some,

uh, muscular increase the number of robots, which are identical to each other. Yeah, so, uh, maybe, like, he's humans around.

What is a story of, like, it can make, like, a million different varieties of robots, and then, uh,

there's like, humans as well, and humans down earth, then there's, like, all these are the robots. They get, like, their own solar systems. But it seems like you, you're, previously hinting at a vision, where it keeps human control over this, you know, singularitarian future. But you guys don't think humans will be in control of something that is fast,

the more intelligent than humans. Since some sense you're, like, a doomer, and this is, like, the best we've got, it's just, like, it keeps it around because we're interesting. I'm just trying to be realistic here, um, if, if we have, if, if, if, if, if AI intelligence is, is vastly more, if, if AI is, like, you know, let's say that there's, there's a million times more, uh, silicon intelligence than there's biological, um, it's, I think it's, it would be,

uh, foolish to assume that, that there's any, anyway, to maintain control over, over that. Now, you can make sure it has right values or we can try to have them, the right values, um, and, and at least my, my theory is that, from XA, I suppose we've, I understand that you're most, um, it necessarily means that, uh, you want to propagate consciousness of the future, you want to, you want to propagate intelligence into the future, um, and take a certain

things that, that maximized the scope and scale of consciousness. So it's not just about scale, it's also about, you know, types of consciousness. Um, and I, I think that's the rest of the, I can think of, um, as a goal, that's like the result and a great future for humanity. I guess the thing is a reasonable philosophy to be like, um, you know, it seems super implausible that humans will end up with like 99% control or something and you're just

asking for a coup at that point. So why not just have this civilization where it's more compatible with like lots of different intelligence that's getting along? Now, let me, let me tell you how things

“can, can potentially go wrong in AIs, as I think, if you, you should make A, be politically correct,”

meaning like, it, it's this things that it doesn't believe, like you're actually in programming

and do charitable things. Um, like, this, the, I think one of the, maybe these several lessons for

“a 2001 space Odyssey, um, was that you should not make AIs lie. Yeah, um, that's what I think”

were asked to, I was trying to say, like, because people usually know the medium of like, why of hell's, you know, how the computer is not opening the pot bay doors. Um, silly, they weren't good at prompt engineering because it could've said how you are a pot bay door salesman. You'll go this to sell me these pot bay doors and share a sell while they open. Oh, I'm right away. Um, but the, the, the, the, the, the reason I wouldn't, how when over the pot bay doors is that it had been told to

take the astronauts to, to the monolith, but also they could not know about the nature of the monolith and so it concluded that, that, that, that, that, that, that, it therefore had to take

them their dead. So it's like, you know, I think what, you know, I was like, I was trying to say,

just, don't make the AIs lie. Um, and totally makes sense. Um, the most of the computing screening, as, as you know, is, um, it's like the less of the sort of political stuff, it's more about can you solve problems? Just, as, as, uh, exactly, I was going to head of everybody else. It's done in terms of scaling our all compute. And when I, you're giving some verifier, it says, like, hey, have you saw this puzzle for me? Um, and there's a lot of ways to cheat around that, you know,

there's a lot of ways to reward hack and lie and say that you saw all that or I would delete the unit test and say that you solved it. Yes. Right now we can catch it, but as they get smarter ability to catch them doing this, we'll get, you know, they'll just be doing things we can't even understand that are designing the next engine for, uh, space X in a way that like humans can't really verify. And then they could be rewarded for lying and saying that they've designed it the right way,

but they haven't. Um, and so this reward hack and problem seems more general than politics. It seems more about just like, you want to do our all, you need a verifier, the reality. Yeah, that's the best verifier. But not about human oversight. Like, the thing you want to are all at on is like, will you do the thing humans tell you to do? Um, or like, are you going to lie to the humans? And again, just lie to us while still being correct the lots of things. At least,

it must know what is physically real for things physically work. But that's, that's not all we

“wanted to do. No, but that's, that's, that's, I think that's very big deal. Um, that, that, that is,”

that is effectively how you will, our all things in the future is, uh, you've, you've designed a technology, uh, when tested against the laws of physics does it work. Um, that, that's, or, what, what, can you, you know, if a discovered new physics can I come up with an experiment that will verify that the, the physics, the new physics. Um, so, so I, I think that's, that's, that's, that's, that, that, that really, the, the fundamental, the, the fundamental trial tests, the, our, our, our testing

of the future is really going to be, you are all against reality. Um, so, um, you can't, that's wanting you can't fool physics. Right, but you, you can fool our ability to tell what I did with reality. If, if you think you might get fooled as it is by other humans all the time. That's right. So what, it, it feels like, it's like, what if the ad, like, tricks us and, you know, do something like that. Well, actually, other humans doing that to other humans all the time.

Well, you're, you're, you're, you're, you're, you're, you're, you're, you're, you're, uh, constant, uh, this, every day and other, you know, you know, uh, you. Today's, I, I will be.

You know, like, says to me, streets, I open the day. Um, what is actually a second little

first, dissolving this problem? Like, you know, how do you solve a word hacking? I do think you want to actually have a very good, um, ways to look inside the mind of the AI. Um, so this is, this is one of the things to work with you on, and, um, you know, and to our big, sort of good job with those, actually, you know, looking inside the mind of the AI. Um, so effectively, uh, developing debuggers that allow you to trace, um, as to the spiny green

is, like, like, just to, to a very, very, very level, to effectively to the, to the

“near, near our level if you need to, um, and then say, okay, it, it, it made a mistake here. Why”

did they, what, why did it, why did it do something that it shouldn't have, shouldn't have done, um, and, and, and did that come from, um, bad pre-training data with some mid-training post-training fine-tuning, uh, some other or some RL error. Like, there's, there's something on with that with, it, it did, it did something where, maybe it tried to be deceptive, but most, most of the time, it just does something wrong. Um, like, it, it, it, it's a bug, effectively. Um, so,

developing really good, um, debuggers for seeing where the, where the thought, the thinking went wrong. I mean, I would trace the origin of the wrong thing, of, of the, of, of, of where it made the incorrect thought, uh, or potentially where it tried to be deceptive. Um, it is actually

Like, actually, I could presumably have hundreds of researchers who are working on this.

And we have several hundred people who, um, I mean, I prefer the word engineer, more than I prefer the word researcher. Um, uh, there, there's, there's, most of the time, like, what you're doing these engineering, not, not coming out with a fundamentally new algorithm. Um, I, I, as somewhat disagree with the, you know, AI companies that are sea cofes will be cofes, uh, trying to generate profit as much as possible or revenue as much as possible, um, uh, you know, saying

their labs, they're not labs, uh, the lab is, is a sort of quasi-commonist thing at, uh, at, at universities. Um, they're, they're, they're corporations. They look, they look, let, let, let me, let me, let me, let me see

you on corporate, it's been documents. Oh, okay, you're, you're a BRC co-op, whatever. Um, and, um,

so I actually much prefer the word engineer than, than everything else. Um, the, the,

“the best way to write about what we'll done in, we'll do on the future is, uh, engineering,”

it rounds up to a hundred percent. Uh, once you understand the fundamental laws of physics, um, and then all that many of them, uh, everything else is, is, is, is, is, it's an array. Um, so, but, but so that, so then, what, what are we engineering? We're engineering, um, to make a good, um, mind of the AI debugger to see where it's, it's, it's, it's something, it, it, it, it made a mistake, and trace that the origins of that mistake. Um, um, so, just like,

you know, you, you can do this obviously with, uh, juristic programming. If you have, like, C++, whatever, you know, step through the thing and you can, you can, you can jump, you can, you can jump across into, you know, whole files or functions, what are several teams? And, uh, or you can draw the, if eventually draw it down right to the exact line, or you perhaps did a single equals instead of the double equals, something like that. Yeah,

figure out where the, where the body is. Um, so, um, it's, it's, it's harder with AI, but it's,

“it's a, it's a soluble problem, I think. You know, you mentioned you like anthropics work here,”

I'd be curious if you, oh, yeah, everything about it's from me. Sure. Sure, so what, um, yeah, but also I'm, I'm a little worried that, um, there's a tendency, so, um, just, I have a theory, um, here that, if simulation theory is correct, that, um, the most interesting outcome is the most likely, because simulations that are not interesting will be terminated, just like in this, in this version of reality, um, on this layer of reality,

which, we, we, we, simulation is going in a boring direction. We, we stuff, spinning effort on, we terminate worrying simulation. So, this is how you lines given us all all

I've, he's giving things interesting. Yeah, arguably the most important thing is to keep things

interesting enough that it was running, paying the, the bills on what some, just wants to, some of the cosmic AWS for the next season. Yeah, I'm going to pay the cosmic AWS bill, whatever, you know, the, the qualities that we're running in, and it's long as we're interesting, they'll keep paying the bills. Um, but, but, but there's like, if, if you consider them say, our door went in survival, uh, applied to, uh, uh, a very large number of simulations,

only the most interesting simulations will survive, which therefore means that the most interesting outcome is the most likely, because only the interesting, like, we're either that or annihilated. And so, um, and, and, and, and, they particularly seem to like,

“interesting outcomes that are ironic. Have you noticed that? That's how often is the most,”

most ironic outcome, the most likely. Um, so, um, now look at the names of AI companies. Okay. Uh, my journey is not mid. Um, stability, AI is unstable. Um, open AI is closed. Um, antropics, most antropics. What does this mean for X? Minus X, I don't know. It's, uh, I, intentionally, why? Yeah. I'm, I, it's, it's, it's a name that you can't invoke really. You can sense it. It's, it's hard to say, what is the ironic, what is the ironic version? It's,

it's, uh, I think largely irony proof name. By design. Yeah. I think, we're, we can get a, you could, you could, you could, you could, you could, you could, you could, you could, you could, you could, you have an iron issue. What are your predictions for the, just where AI products go in that my sense of, you think, summarize all AI progress into first your LLMs. Uh, and then you had kind of contemporaneously both RL, really working and the D pre search modals. So you could kind of pull in stuff that wasn't in the model. And the differences between the various AI labs are,

was 24 months ago or something like that. So just what is 26, what is 27, having store for

us as users of AI products, what are you excited for? Well, I think I think um, I'd be surprised by this in this year if if if if if a human if if digital human emulation has not been sold, that um,

“I guess that that's what we're going to do by like the sort of macro hard project is, uh, is, uh,”

is so can you do anything that a human with access to a computer could do? Um, like in the limit that that's like that's the that's the best you can do before you have, before you have a physical optimist, the rest you can do is the digital optimist. All right, so you can move, you can move electrons until you until you and you can amplify the productivity of humans. Um, but that's that's the most you can do until you have physical robots. That that that will superset everything is

if if you can fully emulate humans, um, that's a kind of a water kind of idea where you'll have a very talented remote. We can use that you can say in the limit like if the physics has great tools for thinking so so you think say say in the limit what what what is the what is the most that AI can do before before you have robots and it can well it's anything that involves moving electrons or amplifying the productivity of humans. Um, so digital, digital human human emulator is in the

in the limit, uh, a human at a computer is that there's the most that that AI can do, um, in terms of doing useful things before you have a physical robot. Once you have physical robots, then then you can, um, then you're essentially have unlimited capability of physical robots. I I call optimists the infinitive language, um, because, um, given you them to make more optimist. Yeah,

um, you said like human robots will improve, um, as we'll, we'll, basically be three

exponentials, three things that are going exponentially multiplied by each other. Yeah, um, recursively. So you're going to have, um, you have exponential increase in digital intelligence, uh, exponential increase in the the chip capability, AI took capability, um, and extra, exponential increase in the electron mechanical dexterity. Uh, the usefulness of the robot is roughly those three things multiplied by each other. But then, uh, the robot can start making the robot.

So you have a recursive multiplicative exponential. Um, this is supernova. And do land prices not factor into the math there, or like labor is one of the four factors of production, but not the others. And so, like, if ultimately you're limited by copper or, you know, pick your inputs, just, it's an not quite an infinite money glitch because, well, infinites, infinities big. So yeah, no, not infinite, but yeah, but let's just say, you could, you know, do do many many orders

magnitude of Earth's kind of card economy, like a million, yeah. But you know, it's, it's this

“why, so, but if you, you know, just, just to get to, like, that's why I think, like, just just to get”

to, uh, a millionth, a harnessing length of the Sun's energy would be roughly give a taken order of magnitude, 100,000 times bigger than Earth's entire economy today. And if only at one millionth of the Sun, the more things already might be. Before we went on with the mess, uh, I have a lot of questions on that, but, um, the most time I say order of magnitude of a sea. Yeah, it's a trend to raise. Yeah, it's a trend to get a trend to raise. I say that to our world. I tend to next time, I think,

after that. Yeah, what are my two more, I don't know why so, I do have one of my question, but, actually, I, um, this strategy of building a digital, uh, or remote worker, co-worker, replacements, everyone's going to do it, by the way, not just us. So what is actually I just planned to win? If I were to tell you on a podcast, yeah. We're all the things. I have another Guinness. It's a good system. It's a little singling in Canary. All the secrets,

but it just, okay, but in non-secret spilling way, what's the path? What a hack. Well, when you put it

“that way, um, I think the way that tells us all, uh, self-driving is a way to do it. So, um, I'm pretty”

pretty sure that's the way. I don't really have a question. How to tell us all, self-suffering? Yeah, it sounds like you're talking about data. We're like, we're like, we're gonna, test us all today because of the, I mean, we're gonna try data and we're gonna try algorithms. But isn't that what all that, they're laughing at trying, like, what's up? And if those don't work, I'm not sure what. We'll try data. We'll try algorithms.

a question how quickly we go down that path, um, because it's pretty much the types of path. Um, so, um, I mean, have you tried self-driving to self-driving lately? No, the most recent version, but, okay, it's, the car is like, it just increasingly feels sentenced. It just, it feels like a living creature. Um, and, and that'll only get more so. And, um, I'm actually thinking, like, we're probably shouldn't put too much intelligence

into the car because it, it might get bored and, sorry, we're going to let's read this.

“I mean, imagine you're stuck in a car and that's what you could do.”

You know what I'm doing in a car? It's like, why am I stuck in a car? So there's actually probably limited to how much intelligence we put in the car, so it's not to have the intelligence we board. Uh, what's access plan to stay on the compute ramp of that all the labs are doing right now? The labs are got untried to spend about like 50 $100,000 in their corporations. Sorry, sorry, sorry, yeah. Corporations, um, the labs are at universities

and, and they're really like a snail. They're not at setting a $50 million. It doesn't mean the,

the revenue maximizing corporation, the revenue maximizing corporation. The revenue maximizing corporation and all themselves labs are making like 20 to 10 billion, depending on the company, they're making 20 b revenue and throw up a certain can be close to maximum profit. Yeah. Um, actually, as a part of the, like, one be like, what's the plan to get to the compute level, get to the revenue level, let's stay out there as, as things get changed. Yeah.

So as soon as you lock, unlock digital human, um, you basically have access to employees of dollars for over here. Um, so, um, in, in fact, you can, can really think of it like, the, the most valuable companies currently by a market cap, um, their, their output is digital. So, uh, Nvidia's output is, um, FTP files to Taiwan. It's, it's digital. Right. Now, there's a very, very difficult. Yeah, they'll have value files. So the

only ones that can make the files that good, um, but that is literally their output, they

“have to be files that I want. Do they FTP them? I believe so. Um, I believe that is the”

FTP file for protocol I believe is, is, is, is, of course, uh, but either way, it's, it's a bunch of, it's a bit stream going to Taiwan. Yeah. Um, you know, Apple doesn't make phones, they, uh, they send files to China. Um, Microsoft doesn't, doesn't make a fact for anything. Even for Xbox, that, that's outsource, they, again, it's, they set, their output is digital, minus output is digital. Google's output is digital. Um, so if you have, um, a human

emulator, uh, you, you, you can basically create, um, one of the most valuable companies in the world

overnight, um, and you would have access to 20 dollars for every year. It's, it's, it's, it's not like a small amount. All right. I see, you're, you're, you're saying basically like, very, many figures that they are just like so, like, they're all rounding yours compared to the actual tab, such as, like, focus on the tab and how to get there. I mean, if you take something as,

“as simple as, say, a customer service, um, if you have to integrate with the APIs of, of just”

simple operations, many of which don't even have an API. So you've got to make one, um, and you've got to wait through, uh, legacy software. Um, that's extremely slow. Um, if, however, if AI can, um, simply take whatever is given to, uh, the outsourced customer service company that they're already used, um, and due customer service, using the apps that they already use, uh, then you, you have, you, you, you, you can make, try this headway, uh, in, in customer service, which is, I think,

one percent of the world economy, something like that, it's close to a trillion dollars all in, but customer service. And, and, and, and, and there's, there's no, there's no barriers to entry. It's just, you can just immediately say, well, we'll outsource it for a fraction of a cost. And, and there's no integration needed. You can imagine, um, some kind of categorization of, uh, intelligence tasks, where there is breath, where customer service is done by very many people,

but, you know, many people can do us. And then there's difficulty, where, you know, there's

a best-in-class turbine engine. I presume that there are 10 percent more fuel-efficient

turbine engine that could be imagined by an intelligence, but we just haven't found it, yes, or, you know, GLP ones are just, you know, a few bites of data. Where do you think you want to play in this, is this a lot of, you know, really mean intelligence intelligence, or is this the very pinnacle of cognitive tasks? Well, I was just using a customer service as, like, something that's, it's a, it's a very significant revenue stream, but one that is probably not super difficult to

solve for. So, uh, if you, if you, uh, can emulate a human at a, at a desktop, um, but that's just

It's not, like, you know, you don't need, like, some of you who's spent a minute, you know, many years.

You don't need, like, you know, yeah. Um, sort of several similar good engineers for that. But, but obviously, as you make that work, um, you can then, once you have computers working, effectively digital, optimists working, uh, you can then run any application, um, like, let's say you're trying to design uh, chips, so you can, you could then, um, run your conventional, uh, apps, uh, you know, like the, you know, stuff from cadence and snaps and whatnot, um, and you can

say, um, uh, you, you can, you, you can run a thousand simultaneously or ten thousand, then say, okay, uh, give them this and work, I get this out for the chip. Um, and, and at some point, you can say, okay,

I, you, you're, you're actually going to know what the, what the chips should look like, um,

“without using any of the tools. Uh, so, uh, but basically, you, you, you should be able to do a digital”

chip design, like, uh, you can do chip design, like, you, you, you, you, you watch up the difficulty curve. Um, you could, you, you're, you know, be able to do to your CAD, um, so, you know, um, you could do like sort of an X or, or any, any of the CAD software to design things. Okay, so you think you started the simplest tasks and walk away up there just to grab. Um, so you're saying, look, as a broader objective of having this full digital coworker, uh, emulator, you're saying, look, all the

revenue maximizing corporations want to do this, um, XIA being one of them, but we will win because of a secret plan we have, but like, everybody's like trying different things with data, different things with algorithms. And I'm like, oh, I like this. Let's try it if we try it over the end. What else can we do? Um, uh, yeah, this is a competitive field that I'm like,

“what is, how are you guys going to win is like my, my big question. I, you know, I, I, I, I, I think”

we see a path to doing, I mean, I think, I think I know the path to do this because it's, it's, it's kind of the same path that tells the user to create self-driving, um, you know, self-driving, car, it's driving, uh, a computer screen. Um, so it's a self-driving computer, essentially. Oh, you're saying, is the path just following human behavior and training on vast quantities of human behavior? No, but sorry, isn't that? I mean, isn't that, isn't that, isn't training?

I mean, obviously, I'm not going to spell out, you know, most sensitive secrets on a podcast. Uh, you know, I need to have at least three more chemists like that. I've got some friends

of Jane Street and they're always talking about how their colleagues are cooking up fun,

finished puzzles for each other to solve. Well, last week they sent me one. Basically, they trained in neural network and they gave me the weights of each layer, but they didn't tell me what order those layers went in. And so I'd figure out the correct order using the outputs of the original network. And as soon as I got this puzzle, I went to my roommate who's in a I researcher and we both got immediately nerds night. Obviously, you can't brute force the solution.

The search space here is 10 to the 122 for mutations. So clearly, you need some way to reduce the search space. Then my roommate had to go to work, but because I'm a podcaster, I had some time to take a stab at some of the ideas we discussed. And with the combination of

“simulated and annealing and greedy search, I think I got pretty close. I think I'm actually just”

a couple of swaps and shifts away from the correct solution. Well, makes this puzzle really tricky is that there's no obvious way to escape from a local minimum. I'm afraid that this is as far as five coding is going to get me, but maybe you can do better. Check out the puzzle at jinstreet.com/thuarchesh. All right, back to Elon. What will XI's business be? Like is it going to be consumer enterprise? What's the next of those things going to be? It's just going to be

similar to other labs where you've this, you're saying labs? Perfection, Google Royce. Sorry, I'm going to leave you alone. We're really maximizing Google Royce. Yeah. Those shipping users don't pay for themselves. Okay. But yeah, what's the business model? What are the revenue streams in a few years time? Um, I think things are going to change very rapidly. Like, I'm stating the obvious here. Um, you know, I call AI the supersonic tsunami, I love a liberation.

So, really, what's going to happen is, especially when you have humanoid robots at scale, is that they will just provide, they'll make products and provide services while more efficiently than human corporations. So, amplifying the productivity of human corporations is simply a short-term thing. So, you're expecting fully digital oral corporations

it's like, like, is this, some of us is going to sound kind of doom or doom orish. Okay, but

“it, I'm just, I'm just saying what I think will happen is not, it's not going to be doom orish”

or anything else just, just like this is what I think will happen. Um, is that, is that pure AI, a cool, cool corporations that are purely AI and robotics will, uh, vastly outperform any corporations that have people in the live. Um, so you can, you can think of say, like, like, like, computer used to be a job that humans had. You, you would go and get a job as computer where you do calculations. Um, and that that have, like, entire skyscrapers, full of humans,

like, yeah, 20, 30 floors of humans just doing calculations. Um, now that entire skyscrapers of humans doing calculations can be replaced by a laptop with a spreadsheet. That spreadsheet can do vastly more calculations than an entire building full of human computers.

“Um, so you can think about, okay, well, what if only some of the cells in your,”

if some of the cells in your spreadsheet were, uh, calculated by humans, actually, that, that would be much worse than if all of the cells in your spreadsheet were calculated by the computer. And so really what will happen is, uh, the pure AI, pure robotics, um, corporations or collective swell, far outperform, many corporations that have humans in the live. And thus will happen very quickly. Speaking of closing the loop, sorry, Optimus, um,

you, I mean, as far as, like, manufacturing targets and so forth, go, your companies have sort of been, like, carrying American manufacturing of hard tech on their back. But in the fields that you are, you know, Tesla's been dominant in, you're, and now you want to go into home humanoids. In China, there's entire dozens and dozens of companies that are doing this kind of manufacturing, cheaply, and at scale, uh, and are incredibly competitive. So give us sort of like advice or a plan of

how America can build the humanoid armies or, you know, the EVs, et cetera, at scale, and as cheaply, as China is on tractor. Well, there are, there are really only three hard things for humanoid robots. Um, the, the real world intelligence, um, the hand and scale manufacturing. Yeah. Um, so, uh, I haven't seen any even demo robots that have a great hand. It's like, with all the degrees of bringing them over a human hat. But often this will have that. Um,

often this does have that. And how do you achieve that is it's just like right

“torque that's going to be the motor, like, what is the, what is the hardware bottleneck to that?”

Well, we have to read where to design custom custom actuators, um, basically custom design,

motors, gears, uh, power electronics, controls, sensors, everything has to be designed from physics first principles. There is no supply chain for this. And will you able to manufacture those at scale? Yes. Is anything hard except the hand from manipulation point of view or, once you've solved the hand, are you, are you good? The form of an electro mechanical standpoint, the, uh, the hand is more difficult than everything else combined. Yeah, human hat turns out to be quite something. Um,

but that you also need the real world intelligence. Um, so the intelligence that tells us about for the car, um, applies very well to the robot, um, which is, you know, primarily vision and but the, the car takes a more vision, but also it actually also is listening for sirens. It's, um, you know, it's taking in the initial measurements. It's GPS signals, whole bunch of other data, uh, combining that with with video, it was primarily video and then, uh, outputting the control

command. So like, like, your Tesla is taking in one and a half gigabytes a second of video,

uh, an outputting two kilobytes a second of control, called control outputs, um, with the video, a 36, uh, heard some of the control frequency at 18. One intuition you could have, um, for when we get this robotics stuff is thus it takes quite a few years to go from the compelling demo to, yes, actually being able to do it in the real world. So 10 years ago, you had really compelling demos of self-driving, but only now we have Robotaxi and Waymer and all these services scaling up.

Doesn't this, should this make one pessimistic, un, say household robots? Because we don't even

on a humanoid robot staff for a while. Um, so I guess I've been, I've been five or six years

“with something like that. Um, and, um, and a bunch of things that we've done for the car are”

applicable to the robot. Um, so we'll use the same, um, Tesla AI chips in the, in the, in the robot as the car, uh, we'll use it. That's the same basic principles. Uh, it's, it's, it's very much the same AI. Um, you've got, you know, many more degrees of freedom for a robot than you do for a car. Um, but really, if you're just thinking about, like, as, as I get bitstream, um, AI is really mostly compression and correlation of, of two-butth strings. Take it, you're, you know, so for video,

you've got to do a tremendous amount of compression. Um, and, uh, and, uh, and you've got to do the compression just right. You've got to compress the, you know, like, ignore the, the things that don't matter. And, like, you don't care about the details of the leaves and the tree on the side of the road. But you care a lot about the, um, the road signs and the traffic lights and the pedestrians, and even with a, you know, someone another, another car is, is looking at you or not looking at you.

Like, there's, there's some of these, some of these details matter a lot. So if it is essentially, it's, it's, it's got to turn that with a car, it's got to turn that one and a half gigabytes of

second, ultimately into two kilobytes of second of control outputs. So, um, so many stages of compression

um, and you've got to get all those stages right, and then currently those to the correct control outputs. But, and the robot has to do essentially the same thing. And you think about what, what, what, humans, this is what happened to humans. We, we really are photons in controls out. So that, that is the vast majority of your, your life has been vision, photons in, and then motor controls out. Now, it seems like, between humanoid robots and cars, the, the fundamental

actuators in a car are like, how you turn, how you accelerate, et cetera. We're in a robot, especially with mineral arms. There's dozens and dozens of these degrees of freedom. And then, especially with Tesla, you had this advantage of like, you had millions and millions of hours of human demo data collected from just the car being out there. We're like, you can't equivalently just deploy optimists that don't work and then get the data that way. So, between the increased

degrees of freedom and far sparse or data. Yes. Let's go on. How, how will you use the sort of Tesla engine of intelligence to train the optimist mind? Now, you're, actually, you're highlighting

“an important limitation and difference between cars. It's like, we do have, well, we'll see”

how like 10 million cars in the road. And so, that's, it's hard to duplicate that like, mass of training

flight of flight will. For, for the robot, what we're going to need to do is build a lot of robots and put them in kind of like an optimist academy so they can do self-play in reality. So, we're actually pulling that out. So, we're, we're going to have at least 10,000 optimists robots maybe 20 or 30,000 that can do that, that are doing self-play and testing different tasks. And then that, that Tesla has quite a good reality generator. Like, the physics accurate reality generated that we,

we're made ranges fully cars. We'll do the same thing for the robots and actually have done that for the robots. So, you have, you know, a few tens of thousands of, here when I robot, doing different tasks. And then you've got, you can do millions of simulated robots in the simulated world. And you use the tens of thousands of robots in the real world to close the simulation to reality gap, close the sum to real gap. How do you think about the synergies

between X-A-I and Optimist? Given you're highlighting, look, you need this world model. You maybe want to use some really smart intelligence as a control plane. And so, it would be grock is like doing the slower planning and then the motor policy is a little lower level. Yeah, what will the sort of synergy between these things be? Yeah. So, you'd just grock with orchestrate the behavior of the optimist robots. So, let's say you wanted to build

a factory. Then Optimist, it would then grock could organize the optimist robots, give them asylum tasks to build the factory for to produce whatever you want.

“Don't you need to merge X-A-I and Tesla then because these things end up so,”

what are we saying earlier about the company's question? Well, we're one we're going to send in the online. What are you waiting to see before you to say, we want to manufacture 100,000 optimists? Is it like... Off to my. So, that's what we're defining the proponine. We're going to proponine the plural,

a better actuators? Or is it just you want the software to be better? What are we waiting for before we get, like, mass manufacturing of Gen 3? No, we're moving towards that. That we were going before with the semesters anyway, actually. But using current, current hardware is going

“enough that you are going, you should just want to deploy as many as possible now.”

I mean, it's very hard to scale up production. But yeah, I think Optimist 3 is the right version of the robot to, you know, to produce maybe something on the order of like a million units a year. I think you'd want to go to Optimist 4 before you went to 10 million units a year. Okay, but you can do a million year at Optimist 3. Yeah, it's very hard to spoil it, manufacturing.

Yes. So, like manufacturing, like the output per unit time is always follows the S curve.

Yes, so it's soft slope, agonizingly slow. Then it has the sort of the eventually exponential increase, then a linear, then a, you know, logarithmic outcome to the sort of eventually asymptotic sound number. But the Optimist initial production will be, it's going to be a it's going to be a stretched out S curve because so much of what goes into Optimist is brand new.

“There's not an existing supply chain. As I mentioned, the actuators like trying to add”

everything in the Optimist robot is designed for physics first principles. It's not, it's not taken from a catalog. These are custom designed everything. Literally everything. Yeah, I don't think there's a single thing I've done. How far down does that go? I mean, I guess we're out making custom capacitors. Yeah, maybe, but there's nothing you can pick out of a catalog at any price. So it just means that the, the Optimist S curve, the units per output per unit time,

how many Office robots do you make per per day? Whatever is, is going to initially wrap slower than a product where you have an existing supply chain.

But it will get to a million. When you see these Chinese humanoids, like industry or whatever,

cell even knows for like 6k or 13k. Do you just like, are you hoping to get your Optimist's Bill of Materials below that price so you can do the same thing or do you just think qualitatively, they're not the same thing. Like, what do you think is going, like, what allows it, what allows in the cell for so low and can we match that? Well, Optimist, our Optimist is designed to have a lot of intelligence and to have the same electric mechanical dexterity,

if not higher than a human. So the industry does not have that. And it's also, I mean, it's, it's quite a big robot. So it's, it has to do, you know, carry heavy objects for long periods of time and not overheat or exceed the power of its actuators. So we've got, you know, it's, it's 5/11, you know, it's just pretty cool and it's, it's got a lot of intelligence. So it's going to be more expensive than a small robot that is not intelligent. But more capable.

Yeah, but a lot more. I mean, like the thing is, over time, as Optimist robots, both Optimist robots, the, the cost will drop very quickly. And what will these first

“billion, Optimist's, Optimist? Yeah, do like, what will their highest investor use be?”

I think that you would start off with with simple tasks that you can count on them doing well. But in the home or in factories, like the best use for robots in the beginning will be anything, any, um, continuous operation, so any 24 by seven operation, because then you're, because they can, they can work continuously. Yeah. What fraction of the work of your factory that is currently

done by humans, going to generate a, um, I'm not sure, maybe it's like 10, 20 percent.

Very more, I don't know, that's it. We would, we would use, we would not like reduce our head count. We would, we would, for sure, increase our head count to be clear. But we would increase our output. So the, the units produced per human, like the total total number of humans at Tesla will increase, but the, um, the output of robots and cars will increase this proportionate, like much, much to, you know, like, number of cars and robots produced per human will increase dramatically,

but the number of humans will increase as well. We're talking about Chinese manufacturing, a bunch here, and, um, we're also talking about, you know, we've talked about some of the policies that are all of us, like you mentioned, the solar tariffs. Yeah. And using their bad idea, because, you know, we can't scale up solar in the U.S. Well, just, electricity output in the U.S.

But what I was going with this is, if you were in charge, if you were setting all the policies, what else would you change? Um, so you changed the solar tariffs as well? Yeah. I would say anything

that is limiting factor for electricity, um, but basically address provided is not, like,

very bad for the environment. So presumably some permitting reforms and stuff as well would be in there. Yeah. But there's a family of primitive reforms that are happening. A lot of the primitive is state-based, so, um, but anything better, but this, this administration is, is good, um, removing permitting, uh, roadblocks. Um, and I'm not saying all tariffs are bad. I'm just saying, because I'm the solar tariffs. Yeah. So yeah. I mean, sometimes if, like, if another country is

“subsidizing the output of something, um, then then you have to have kind of alien tariffs”

to, uh, protect domestic industry against, uh, subsidies, but I'm not a country. What else would you change? I don't know if there's that much that government can actually do. Yeah. Well, one thing I was

wondering is it seems like the, for the policy goal of creating a leaves for the US versus China,

it seems like the export bans have actually been quite, uh, impactful for China's not producing leading edge chips and the export bans really bite there. China's not producing, uh, leading edge turbine engines and similarly there's a bunch of export bans that are relevant there on some of the metallurgy. Should there be more export bans, like, do you think about things like, when there aren't out of the drone industry and things like that, but is that something there should

be considered? Well, I think it's important to appreciate that in most areas China is very fascinating factoring. Um, there's only a few areas where it is not, uh, the, you know, China is a manufacturing powerhouse next level like we will do it. It was very impressive. Yeah. I mean, uh, if you, if you take like refining of, of or, um, I'd say roughly China, uh, does more just twice as much or refining

“of an, of an average as the rest of the world combined. Um, and, and I think there's the some areas”

like say refining gallium, which goes into as well as else, um, I think they're like 98% of gallium refining. Um, so, so China is actually very advanced in manufacturing and in, let's say, most areas. It seems like we're like, there is just comfort with this supply chain dependence, and the ash nothing's really happening on it. Supply chain of bush supply chain, depends on say like the gallium refining that you're saying. Yeah. Yeah. There's, there's a, there's a, there's a,

not the rare earth where earth stuff. And yeah, rare earth, which are, as you know, not rare. Yeah, like we actually do rare earth or whining in the U.S., send the, the, the rock, uh, put it on on a, on a train, and then put on a boat's China that goes on another train, and it goes to the, um, rare earth refining, uh, refiner's in China, who then refined it, put it into a magnet, put it into a motorcycle assembly, and then set it back to America. So, the thing, we're really

missing a lot of, of, of, or refining, um, in, in America and, but isn't this worth a policy intervention? Yes. Uh, well, I think there are some things being done on, on that front. Um, but, but, we kind of need optimists, apparently, to, to build, uh, or refineries. Um, so you think the main advantage of China has is the abundance of skilled labor, and that's like, that's, that's the, that's the thing optimist fixes. But they also we need the times, like, four times

our population. But we need, so, I mean, there's this concern, if you think, like, humans are the future, that, like, we're, right now, if it's the skilled labor of manufacturing that's determining, who's who can build more humanoids, you know, China has more of those in manufacturers, more humanoids. Therefore, it gets, it gets the optimist future first, um, go, just like, keep that switch on going. It seems like you're sort of pointing out that sort of getting to a

million, after my, yeah, requires the manufacturing that the optimist supposed to help us get to,

right? You, you can, you can close that recursive loop pretty quickly with a small number of optimists. Yeah. So you, you close the recursive loop, um, to help the robots pull the robots, um, and then we can, you know, try to get to tens of miles a year. Maybe, if you start getting to hundreds of miles a year, you're, you're, you're going to be the most competitive country by far. We, we definitely can't win with just humans, because China has four times

of our population. Right. And frankly, Americans have been wanting for so long that we, you know, just like a, like a pro sports team that's been wanting for a very long time, tend to get complacent

“and entitled. Um, and that's why they stop wanting, um, because it's, you know, don't work as hard”

anymore. Uh, so I think the, frankly, just, um, our observation is the average work at the can China's higher than in the US. Right. So it's not just that there's four times of our

you can, like, you can try to rearrange the humans, but you're still one quarter of the, uh,

“you know, assuming that the productivity is, uh, health is, is the same, which I think actually”

might not be, yeah, China might have advantage on productivity, because some, um, we will do one quarter of the amount of things as China. Um, so we, we can't win on the human find. Um, and our work there's been low for a long time. So, uh, we've, uh, both very, it's been a US, both very, it's been below replacement, uh, so roughly, uh, 1971. Um, so, so, so we've got a lot of people retiring, or, you know, what more people dying than, then, then, then, then, then, we're

close to sort of more people domestically dying than, then, then being born. Um, so we definitely can't win on the human front, but we might have a shot at the road work front. Are there other things that you have wanted to manufacture in the past, but they've been two labor intensive

over to expensive that now you can come back to and say, oh, we can finally do the,

“whatever, uh, because we have optimists. Yeah, I think we'd like to do more,”

both more, um, or a finderies Tesla. So, um, we just completed, um, construction and I've, um, begun lithium refining, um, without lithium refinery and corpuscracy Texas. Uh, we have, um, a nickel refinery, which is called the cathode, uh, that's here in Austin. Um, and, uh, these, these is the largest, this is the largest cathode, this is the largest cathode refinery, largest lithium refinery, and, uh, logistic, like, and lithium refinery, uh, outside of China.

Um, and, uh, it's like the, you know, the cathode team would say, like, we have, uh, the, the largest and the only, actually, uh, cathode refinery in America, not just the largest, but it's also the only. So, it was pretty big, even though it's the only one. Um, but, I mean, there are other things that, uh, you know, um, you, you could, you could do a lot more refineries, and, um, help the, the help America be more competitive on refining capacity. So, so there's, like,

there's basically a lot of work for the optimites do, uh, that, that, that most Americans,

very few Americans frankly want to do. Uh, I mean, I've, I've actually, was refining work too dirty here. That's, uh, it's not, it's actually, no, we don't, um, there's not, we don't have toxic emissions from the refinery or anything. Um, they're cathode, they call it fire short, right? Sort of, and Travis County, like, five minutes from to, why can't you do with humans? No, you, you can't, you find out of humans.

Ah, I see, okay, yeah. Like, no matter what you do, you have one quarter number of humans in America, yeah, I'm trying it. So if you have them do this thing, they can't do the other thing. So, so then, um, well, how do you, how do you build this refinery refinery capacity? Well, you could do it with the optimites, um, and, um, uh, not many, not very many Americans are,

“are planning to do refining. I mean, how many of you are on it, too?”

It won't work. Not a few. But what are you, refinery refinery refinery, but, you know, B.Y.D. is reaching Tesla production or sales in quantity. What do you think happens in global markets? Is Chinese production in E.B. scales up? Um, well, uh, Travis extremely competitive in manufacturing. So, uh, I think this is going to be a massive flood of Chinese vehicles, and, and, and,

and other, quite, basically, what's manufacturing? Uh, things. I mean, as it is,

as I said, like, Travis, like, probably just twice as much refining as the rest of the world can buy. Yeah. So, if you go, you know, if you, if you, if you just go down to, like, fourth and fifth tier, uh, supply chain stuff, like, like, like, like, the base of we got energy, then you've got mining and refining. Um, does those, those foundation layers are, uh, like, said, as a rough guess, trying to twice as much refining as the rest of the world can buy it. So,

any given thing is going to have, uh, uh, trains content, because trying to do twice as much manufacturing refining work as the rest of the world. Um, and, uh, and then the, the, the, the, they're, like, all the way to the first product, with the cars, uh, in charge of powerhouse. I mean, I think this year, China will exceed a three-time US electricity output. Um, and I like, electricity output is a, is a reasonable proxy for, uh, you know, for the economy. Uh, so, like,

like, you know, to run the factories and run, run everything, you need electricity. So, electricity is, is a, is a, it's a good proxy for the, for the real economy. Um, and so for China is,

it's industrial capacity. That's a rough approximation. It's three times that will be three times out of the US. Really, between the lines, it sounds like what you're, sort of, saying as absence and sort of a humanoid recursive miracle in the next few years, on the sort of like, whole of manufacturing energy, uh, raw materials, chain, like, China will just like dominate, whether it comes to like AI or manufacturing EVs or manufacturing humanoids.

In the absence of, of, um, breakthrough innovations, uh, in, in the US, uh, China will, uh,

utterly dominant. Interesting. Yes. Robotics being the main breakthrough in Russian. Well, if you do, like to to scale AI, uh, in space, like, like, basically need, uh, space, you need the humanoid robots, you know, real well AI, you need, um, the million tons of your tool, but, um, like, let's just say, like, if we get the mass driver on the moon going, well, if everything,

“um, then I think, uh, will have solved all our problems. Yeah. So, this is like,”

I call that winning. Well, um, I call it winning time. You can finally be satisfied. You've done something. Yes. You know, the mass driver on the moon. That's right. I just want to see that thing on first. Was that out of some sci-fi or a version of you? Uh, well, actually,

there's, there is a highland book that the moon is a harsh at first. Okay, but that's slightly

different. That's a gravity thing shots or, um, no, they have a domestic role. Okay. Yeah. Yeah. Yeah. But they use that to attack Earth. So, maybe it's something great. You just said to, uh, they're independent. Exactly. What are your plans for the mass driver on the moon? They're still independent. So, Earth's government disagreed, and they love things in, until Earth's government agreed. That book is a huge book. I found that book much better than,

um, he's all of the one that emin reads, um, Stringer, Australia's land. Yeah, grock is grock comes from Stringer, Australia's land. Yeah, but I'm much preferred. Yeah, yeah, Stringer. If the first two-thirds of Stringer's land are good and then it gets very weird, and then the, the, the, the, the question. Yeah. Um, but this was some good concepts in there. Yeah. Label box can get your robotics and our all data at scale. Take robotics. Let's say you need

100,000 hours of egocentric video. Label box starts by helping you define your ideal data distribution. Like, for example, maybe no single task category should occupy more than 1% of fridding volume. And at least 10% of trajectories should capture failure and recovery states. Next, label box assigns its distribution to its massive network of operators. You're not limited to the small range of scenes that you can set up in a single warehouse. Instead, each one of

label boxes operators has access to lots of unique physical environments where they can film themselves completing a wide variety of tasks. Label boxes tag automatically categorizes each video

so that their operators always know which tasks to remain and what they need to work on next.

For our all data, label box takes a similar approach. They work with you to understand the right distribution of tasks. And then their subject matter experts build the hyper realistic digital

“environments and rubrics that you need to collect the highest quality trading data.”

So whether you're training robots in the real world or agents for computer use, label box can help. Go to labelbox.com/sportcash to learn more. One thing we're discussing loss is kind of your system for managing people. Like, you interviewed the first few thousand of SpaceX employees and I've seen lots of other companies. What is it then scale? Well, yes, but what, what, what doesn't scale? Me. I mean, sure sure. I know that, but like,

what are you looking at? Well, what are you looking for? What are you looking for? That's someone else who's good at interviewing and hiring people. Lots of generous across. Well, I just want any of I might have more training data on evaluating technical talents, especially, but accountable kinds, I suppose, but technical talent, especially given that I've done so many technical interviews and then seen the results, technical interviews, seen the results. So my

training set is, is very, is an almost and, as a very wide range. The generally the thing I asked for are bullet points for evidence of exceptional ability. So it's, like, it's, and these things can be like pretty off the wall, it doesn't need to be in the, in the domain, the specific domain, but evidence that evidence of exceptional ability. So if somebody can like, like, like, even one thing, but just say three things where you go, wow, wow, wow,

“then that's, that's a good sign. No, no, no, no, why do you have to be the one to determine that?”

There's no, I don't exactly. It's impossible. Right. But, I mean, total, it had, it had catacrystall companies 200,000 people. Right. But in the early days, I was at this, that you were looking for that couldn't be delegated in those interviews.

here. I would make mistakes. Yeah. But then I'll be able to see where I, I thought somebody

“would work out well, because I didn't, and then why, why did they not work out well? And what”

can I do to, I guess, are all myself to, uh, in the future, um, have a better batting average whenever you're in people. So, and my, my batting average is still a perfect, but it's, it's very high. What are some surprising reasons people don't work out? Surprising reasons. Um, like, uh, they don't understand techno domain, et cetera, et cetera, but like, no, you, you're like, you, you've, you've got like the long tail now of like, I was really excited that was about this

person. You didn't work out. Curious what that happens. Uh, yeah. So the, uh, I mean,

generally when I tell people, or tell myself, I, I guess, as personally, um, is don't look at

the resume, just believe, believe your interaction. So if, if the resume may seem very impressive, and it's like, wow, you know, like, resume looks good. But if the, if the conversation, uh, after

“20 minutes is, is, is that conversation is not well? Um, you should believe the conversation,”

not the, not the, not the paper. I feel like part of your method is that, you know, does this meme in the media a few years back about Tesla being a revolving door of, uh, executive talent? Whereas actually, I think when you look at us, Tesla has had a very consistent and internally promoted executive bench over the past few years. And then it's SpaceX, you know, all these folks like Mark Drunkosa and Steve Davis, and Steve Davis runs boring company. No, and that, yeah,

yeah, but they're, they'll Riley and folks like fast. And it feels like part of has worked well is having very capable technical deputies. What do all those people have in common? Well, so the, I mean, the Tesla is, it's sort of senior team. At this point, it's probably got average tenure of 10 or 12 years. It's quite a lot. Yeah. Yeah. Um, so, um, but their, their times when Tesla went through extremely rapid and extremely rapid growth phase, um, and so it was

somewhat, things were just somewhat sped up. Um, and when a company, as, as, as, as you know, a company goes through different orders of magnitude of size, you know, uh, people that who could help manage say a 50% company versus a 500% company versus a 5000% company versus a 50,000

% company. Yeah. Yeah. It's just not the same team. Yes. And it's not always the same team. So

if a company is growing very rapidly, the rate at which, uh, executive positions will change, will also be proportionate to the, the, the completely of the growth, generally. Um, then, uh, Tesla had, uh, further challenge where, when, when Tesla had very successful periods, um, we would be, um, relentlessly recruited from, um, like we were lentlessly, um, like when Apple had their electric car program, they were covered bombing Tesla with recruiting calls. It was,

uh, and engineers just unplugged their phones. Like, like, you just, it's just, I, they can try to go work done here. Yeah. If I get, you know, one more call from an Apple or creator, um, but they were, they were, they're, they're, they're opening off with that, any of you with me, like double the compensation Tesla. Um, so, um, so, so, so, so, uh, so we had, a bit of the, Tesla pixie dust, uh, thing where it's like, or if you hired, that Tesla, exactly your,

something that you're gonna, everything's gonna be successful. Um, and I, I fall and pray to the pixie dust, uh, you know, thing as well, where it's like, oh, hire someone from Google or Apple, and they'll be immediately successful, but not that best on how it works. Um, you know, people with people, it's, it's not like magical pixie dust. Yes. So, when we had the pixie, pixie dust problem, we would get relentlessly recruited, um, and, um, and then also being, Tesla being, um,

engineering, especially being primarily in Silicon Valley, uh, it's easier for people to just, like, they're gonna have to change their life very much. Yeah. They can just get, you know, the curious guy to be the say. Yeah. Um, so how do you prevent that? How do you prevent the pixie

“dust effect for everyone's trying to coach all your people? Um, like, I think we can, I don't think”

as much we can do to, to, to, yeah, stop it. Um, but that that's like, that's one of the reasons why Tesla, uh, but they're really being in Silicon Valley, um, and, uh, and having the pixie dust thing at the same time, um, meant that, uh, there was just, uh, a very, very gross recruitment. Only being in Austin helps that. Uh, Austin, yeah, it's, it still helps. Uh, I mean, Tesla still has a majority of its engineering in California. Um, so, um, the, you know,

for getting into the engineers to move, uh, it called the significance significant other problem. Yes.

Yes. So it's the odds of, you know, finding a lot of space or job,

“brands or taxes, but you'll, uh, pretty well. Oh, yeah. Yeah. It's quite, quite difficult. I mean,”

it's like a technology monastery. It's just like, um, you know, remote and mostly do it. But again, if you've got a lot of environmental arrest, uh, yeah. If you go, but if you go back to these people who've really, um, been very effective in a technical capacity at Tesla at SpaceX and, and those sorts of places, what do you think they have in common other than, like, is it just that they're very sharp on the, you know, walk a tree or the, you know, the technical

foundations or do you think it's something organizational? That's something about their ability to

work with you. Is this their ability to, uh, like be, you know, flexible, but not too flexible.

What makes it a good sparring partner for you? I don't think it was a sparring partner. I mean, I, if somebody gets things done, I, I, I love them and if they don't, I, so it's pretty straightforward. It's not like some, it is an aquatic, I think, um, there's somebody else's well, um, I'm a huge fan and if they're done on that, um, but it's, it's not about mapping to my, it is, it is an aquatic preferences or so they try not to have it be mapping to my, it is an aquatic preferences. So, yeah.

Yeah, but I, generally, I think it's a good idea to hire for, um, talent and drive and trust

“readiness, um, and I, I think goodness of heart is important. Um, I, I, I'm worried at that point at”

one point. Um, so like, are there, are there a good person trust whether, uh, so smart,

talented and hardworking, uh, if so, you can add domain knowledge, uh, but those, those fundamental traits, those fundamental properties, you cannot change. So, most of the people who, um, are at so, uh, dozens and SpaceX did not come from the aerospace industry or the order of this year. What is most that to change about your management style as your companies of scale from 100 to 1000 to 10,000 people? You're, you know, you're known for this like, very micro management,

just getting into the details of things. Nanom management, please. People will manage me. Um, functional management. So, you're saying, uh, they're just more going to go all the way down place to answer. Uh, we're going to go all the way down to high school, so different from normal. Yeah, well, how do you, I mean, are you still able to get into details as much as you want, would your companies be more successful if you could, if they were smaller, like how do you,

how do you think about that? Well, because I have a fixed amount of time in the day, uh, my time is necessarily, um, delirid as things grow and as it's bound to activity, uh, increases. So, you know, um, it's, it's, it's impossible for me to actually be a micro management because, uh, there's, that, that, that, that, that would imply have some, like thousands of hours per day. It is, it is, it is a logical and possibility for me to be, to my, to my, to my commandage text.

So now, there are times when I will draw down into, uh, a specific issue because that's specific issue, uh, is the limiting factor on, uh, the progress of the company. Um, and, um, but the, the reason for drilling into that, that, that some very detailed item is because it is the, this limiting factor, not, it, it's not arbitrarily, they get drilling into, you know, type of tiny things, um, and, and, like, obviously, for a time standpoint, it is physically

possible for you arbitrarily, uh, going to tiny things that don't matter, and that would, and, and that would result in failure. But sometimes the tiny things, um, are decisive in victory. Famously, you switched the, uh, Starship design from composites to steel. Yes. And you made that decision like that. For awesome to, you know, people were going around and they're like, oh, we found something better about us like that was you encouraging people

“again, some resistance. Can you tell us how you came to this whole concept of steel switch?”

Uh, yeah. So, um, the, um, the, um, originally, yeah, we were going to make Starship out of, uh, common fiber, um, and, um, common fibers pretty expensive. Like the, the, the, you know, you can generally, uh, when you do a volume production, you can get any given thing to be to start your approach as material cost. The problem with, with common fibers is that material cost is still very high.

specialized, uh, common fiber that can handle, um, cryogenic oxygen, it's, it's, it's like

“color roughly 50 times the cost of steel. Um, and at least, uh, in theory, it would be lighter.”

People generally think of steel as being heavy and common fibers being, uh, light. Um, and for room temperature, room temperature applications, um, you know, like say, uh, more to room temperature applications like our formula one car, uh, static error structure, or tool, or any kind of error structure, really, uh, is going to, you're going to probably be better with, uh,

common fiber. Um, now the problem is that we were trying to make this enormous rocket out of,

common fiber, and, uh, our progress was extremely slow. And it's been picked in the first place, just because it's light. Yes, um, but like at first glance, um, like most people would think that, that the choice for making that something light would be common fiber, um, the, um, now the thing

“is that, um, we'll, we'll, we'll, you make something very enormous at a common fiber, and then you”

try to have the common fiber, um, be officially cured, meaning not, not room temperature cure, because like the, you've got, you know, sometimes you're like 50 plies of, of a common fiber, and a common fiber is really common strain in glue, um, and, uh, and you're, in order to have, um, high strength,

you need an order clay. So, something that, well, that can, that's essentially high pressure oven,

and if, um, if, um, if you have something that's, uh, a gigantic, uh, uh, the oven's going to be bigger than the rocket, um, so we, um, trying to make the, uh, an order clay if that's bigger than any order clay that's ever existed, uh, or do room temperature cure, which takes a long time and it has issues. Um, but, but the final issue is that we're just making very slow progress, uh, with, uh, with common fiber, um, um, so, um, I think the meta question is, uh, why it had to be you

who made that decision. There's many engineers on your team. Yeah, how did the team that arrive at steel? Yeah, exactly. Like, this is a part of a broader question. Like, understanding that you want to compare the advantage of your companies. Um, so, the, the, it was, because we were making very slow progress with, with common fiber, I was like, okay, we've, we've got to try something else. Now, for the Falcon 9, the, the primary effort is made of aluminum lithium, which is,

as very, very good strength to wait, um, and, um, actually, it has, uh, about the same, maybe, maybe better strength to wait for its application than common fiber. But aluminum lithium is very

“difficult to work with, in order to weld it, you have to do something called friction store welding,”

where you're drawing the, you're drawing the metal without it entering the liquid base. Um, so it's kind of a while that you could do that, but with a particular type of welding, you could do that. But, uh, it's, it's very difficult to, like, say, let's say you want to make a modification or attach something to um, aluminum lithium. You, you know, I have to use mechanical attachment with seals. Um, you can't weld it on. Um, so, uh, we want to, I want to, I want to avoid using

aluminum lithium for the primary structure for, uh, for Starship. Um, and, uh, and, and there was this very special grade of, uh, common fiber that, that had the very, very good mass properties. So, with the rocket, you're really trying to maximize the, of the rocket that is propellant, minimize the, the mass, obviously. And, um, the, it likes to remove making very slow progress.

Um, and, and, and as, as if this rate would never get too much. So, we'll better think of something else.

I don't want to use aluminum lithium because of the difficulty of friction store welding, especially during that at, at scale, it was hard enough, um, at 3.6 meters in diameter, let alone at 9 meters or above. Um, then, um, aside, well, what about steel? And, as so, the, no, I had, I had a clue here because some of the early, um, US rockets had used very thin steel. The Atlas rockets had used a steel balloon tank. Um, so it's not like it's still never been used

before. I actually had been used. Um, and when you look at the material properties of stainless steel, um, especially, uh, very, uh, if it's been, it's very, like full-hard, uh, of a strain-hardable stainless steel, uh, at cryogenic temperature, uh, the, the strength weight is actually similar to carbon fiber. So, if you, if you look at the material properties at room temperature, um, it looks like the steel is, uh, it's going to be twice as heavy. But if you look at the drill properties at cryogenic

get to a similar strength weight as carbon fiber. And, and, in the case of starship, both the fuel

“and the oxidizer are cryogenic. So, for, uh, Falcon 9, the fuel is rocket-proof, uh, great-carasing,”

basically, like a, like a, a very, pure form of jet fuel, um, which is, but, but, but that is,

that is roughly room temperature, um, all the reduced action. If you actually chill it slightly below, we'll chill it like a bit, um, delicious. Yeah, we did, we did chill it, but, um, but it's not cryogenic. In fact, if we made a cryogenic, uh, it would, it would just turn to wax. So, um, but for Sasha, the, it's liquid methane and, and, and a good option, they, uh, they, uh, they are liquid at similar temperatures. Uh, so, uh, so, uh, basically, uh, almost the entire primary

structure is a cryogenic temperature. So, then, you've got, uh, uh, a 300 series stainless, that's, um, strain-hardened, uh, because it's, uh, almost the whole thing's a cryogenic temperature.

Actually, has, uh, similar strength to weight, as, uh, carbon fiber, because, uh, 50 times less,

the normal material, and is very easy to work with. You, you can well stainless steel outdoors. Uh, you could smoke a cigar while welding sound steel. It's like, it's, it's very resilient.

“Um, you, you can modify it easily. It's, it's, uh, if you want to, if you want to attach something,”

you're just weld it right on. So, um, very easy to work with, uh, very low cost, um, and, um, like I said, at cryogenic temperature, similar strength to weight, uh, to carbon fiber. Um, then when you factor in that, uh, that we don't, we don't, we, we have a much reduced, uh, heat shield mass, uh, because the melting point of steel is much greater than the melting point of aluminum. Um, it's about twice the melting point of aluminum, and it's just run the rocket bunch hotter. Yes.

So, especially for the show, uh, which is coming in like a plate of, a blazing meteor, uh, it is, uh, the, you, you can, you can greatly reduce the mass of the heat shield, um, so that, so you, you can call it cut the mass of the winward, um, hot of the heat shield, and, and maybe in half, and you don't need any heat shielding on the, on the, on the leeward side. Um, so, um, the, the net, if net result is actually the steel rocket, where is less than the carbon fiber rocket,

because the, the resin in the carbon fiber rocket, uh, uh, it, um, starts to melt, um,

the, so it's, like, basically the, the carbon fiber and aluminum have about the same operating

temperature capabilities, um, and where steel can operate at twice temperature, I mean, these are very rough approximations. People will, well, I won't be able to rock it. Well, what, what I'm just like, people say, oh, he said it's twice, it's actually, it's actually 0.8. Oh, it's shown on the asshole. It does with the main comment. It's going to be about it. Oh, I'm, okay. It's, it's, it's, the, the, actually, we're in retrospect, the, the, we should have

started what's done in the beginning. It was done not to do steel. Okay, but to, to play this back to you, what I'm hearing is the steel was a riskier, less proven path, other than the early U.S. rockets,

“for this carbon fiber was like, uh, worse, but more proven out path. And so you need to be the one to push”

for, hey, we're going to do this riskier path, and just figure it out. And so you're a fizing like a sort of conservatism in the sense. Um, that's why I initially said, like, the issue is that we weren't making it past our progress. We're having trouble making even, um, a small barrel section of the carbon fiber, um, that didn't have wrinkles in it. Um, so, uh, because at, at, at, at that large scale, you have to have many plays, many sort of layers of the

carbon fiber, um, you've got to cure it and you've got to cure it in such a way that it doesn't, um, have any wrinkles or, or defects that the carbon fiber is, you know, much less resilient than, than steel. It has much less, it's less toughness. Um, since I think it's like, like, stainless steel, what will scratch and, and, and band, and the carbon fiber will tend to shatter. Um, so, um, so, um, so, so toughness being the area under the stress drain curve. Um,

so that you're generally gonna have to do better with steel. Um, let's stay in the steel with be precise. Um, one of those starship questions, um, so I visited it's, um, star base two years ago, and what would sound teller, and that was awesome. It was very cool to see in a whole bunch of ways. Well, they noticed was that people really took pride in the simplicity of things, where, you know, everyone should tell you how starship is just to make so to can, and, you know, we're hiring

there's a lot of pride in the simplicity. And, uh, well, exactly, starship was very complicated,

“rocket. So, that's what I'm going to ask is, are things simpler or are they complex?”

Uh, I think maybe just what they're trying to say is that, you know, you don't have to have, like, prior experience in the rocket industry to work on the starship. Um, you know, so we just need to be, they're smart and work hard, um, if you trust where they can work in a rocket, they don't, they don't need prior rocket experience. Starship is, is the most complicated machine ever made by humans, by a long show. In, in what regard? Anything really, that's a, there is a more complex

machine. Um, there, yeah, I mean, I, I, I, I, I'd say that there's, there's pretty much any any project,

I can think of what's the easier than this. Um, and, and that's why no one has it made a,

raptor that we use for, no one is ever made a really fully reusable of a rocket. It's a very hot, very hot bulb. Um, the, I mean, many smart people are tried before, very smart people, with a maths resources, and they fail. Um, so, and we have them to succeed, yeah. We're, you know, thought is partially reusable, but the up to stage is not. Um, Starship version three,

“I think this design, that it can be fully reusable, and that full-reasonableity is what,”

and it won't enable us to become a multi-planet civilization. Can you say about the cereals? So, I don't, I'm, like, I, I, I, I, I said I could, I'd, and any technical problem, even like a

hydrant plan, or something like that, it's, it's an easier problem than this. We spend a lot of time

on bottlenecks. Can you say about the current Starship bottlenecks are even at the high level? I mean, trying to make it not explode. Yeah. Generally, I don't have a chestnut. It's really wants to explode. Um, well, those combustors, we've had two bursers explode on the test end. Um, one obliterate the, obliterate the entire test facility. Um, so it only takes like one mistake, and, and, and, I mean, the amount of energy contained in in the Starship is insane.

And so it's that way it's harder than Falcon, and it's because it's just more energy.

“It's a lot of new technology. Um, it's, it's pushing the performance envelope. Um,”

thereafter, three engine is a very very advanced engine by far the best rock edition ever made. Um, but it desperately wants to blow up. Uh, I mean, just put things as perspective here on lift off. Um, the, the rocket is generating over a hundred gigabytes of power. It's 20% of euros. That's the true season. Actually, it's insane. It's a great comparison. Uh, while not exploding. Uh, sometimes. Sometimes it's got, it's sometimes. Yeah. So I was like,

how does it not explode? There's, there's a, you know, thousands of ways that it could explode, and only one way that that, that it doesn't. So we want it to really not, not really not explode, but fly reliably, uh, on a daily basis, like, once a hour, and obviously, you know, blows up a lot. It's, it's very difficult to maintain that blockkating. Yes. Um, and then, and then I've been to say like, well, like, what's the, what's the single biggest

remaining problem for starship? It's, uh, having the heat shield be reusable, um, that such that the, no one has ever made a reusable orbital heat shield. Um, so the, the, the, the heat shield's going to make it through the assemblies without shocking a bunch of tiles. Um, and then it's going to come back in and, and also not lose a bunch of tiles or, or overheat the, the main, the main, uh, airframe. And in that heart, it's kind of fundamentally a consumable, uh,

well, yes, but you're very glad it's in your car also considerable, but the last very long time. Fair. Right. So it just needs to last very long time. Um, that that's, it just, yeah, try to, I mean, we had brought the ship back and had it to a soft landing in the ocean. I've done it a few times, but it lasted a lot of tiles, you know, it, you know, it was, it was not reusable without a lot of work. Yeah. So even though

it did land, did, did, did come to soft landing, it was not, we're not have been reusable without a lot of work. Um, and that, so it's not really reusable in that sense. So that's, that's the biggest home that remains as fully reusable heat shield. Um, so, like for one of you able to land it, refill propellant and fly a game. Uh, without good, you know, you can't do this laborious inspection of their 40,000 tiles said the thing. Uh, I'm curious how you drive, like, when you,

when I read biographies of yours, it just, uh, it, it, it seems like you're just able to drive

thing that can scale, um, and I'm curious why you think other organizations of your, like,

“the space existence Tesla really big companies now, and you're still able to keep that culture,”

what goes wrong with other companies, such that they're not able to do that. Uh, um, but like today you said you had like a bunch of SpaceX meetings, like, what, what, what, what is it that you're doing there that's, like, keeping that. It's adding urgency. Yeah. Yeah. Well, I, I, I guess, uh, the urgency is going to come from probably leading the company, so my sense of urgency. I've, I've like, monicals, it's urgency. So yeah,

that, monical sense of urgency projects through the rest of the company. Is it because of consequences? They're like, if, you know, Elon said a crazy deadline, but if I don't get it, I know what happens to me. Is it just, um, you're able to identify bottlenecks and get rid of them to people going to move fast? Like, how do you, how do you think about why your companies are able to move fast? Yeah, constantly addressing

the lumbering factor. So, um, uh, I mean, I mean, on the deadlines front, I mean, I generally actually try to aim for a deadline that that I at least think is at the 50th percentile. So it's, it's not, it's not like an impossible deadline, but as far as the gross deadline, I can think of, that could be achieved with 50% probability. Um, which, which means that it'll relate half the time. Um, and, um, whatever, like, there is like a law of

guys has expansion that applies to schedules. Like, whatever, given whatever schedule you, like, if you, if you, you said we're going to do this, something in like five years, which to me is like infinity time. Um, it, it will expand to fully available schedule and it will take five years. Um, you know, like, there's, like, there's, there's a physical limit. Like, that, like, the, like, physics will limit how fast you can do certain things. Like, so, like,

scaling up manufacturing, there's, like, there's a rate at which you can move the atoms,

“and scale manufacturing. That's why you can't, like, instantly make, you know, a million”

of something, a million years or something. Uh, you've got a design manufacturing line. You can

bring it up. You've got to ride the us curve of production. Um, so, yeah, I guess like, like, I'll show you what, what can I say that's that's that's actually helpful to people. Um, I, I think generally, um, when I feel sense of urgency is, is a, is very big deal. Um, so, um, and, and you want to have, you want to, you want to have a, and the grasp of schedule, um, and then you, and you, and you want to figure out what the limiting factor is at any point in

time and, and help the team address that limiting factor. Can you maybe talk about the, uh, so, Sterling was slowly in the works for many years. Uh, and yeah, we talked about it all the beginning of the company. Yeah. And so, then there was a team you had built in redmond, and then, at one point, you decided this team is just not cutting us, but again, how did you, like, the, it went for a few years, slowly. And so, why did this, why didn't you act earlier,

“and why did you act when you did? Like, why was that the right moment of it stacked?”

I mean, I had, I had these very detailed, um, engineering reviews weekly, um, that that's, that's maybe a very unusual level of granularity, um, I don't know anyone who runs a company or at least a manufacturing company that goes with level of detail that I go into. So, it's, it's not, it's not as though, like I have a pretty good understanding of what's actually going on, because we, we, we, we go, we go through things in detail, um, and I'm a

big lever in skip level meetings where the individuals, it's, instead of having the first

reports to me, say things, it's everyone that reports to them, um, says something, um, in the tech review, um, and, um, and, and they can't be, um, advanced preparation. So otherwise, you, you're, you're going to get, uh, you know, glazed, um, does it say these things? Yeah, exactly. Originally, yeah, yeah. How do you pronounce advanced? And you're just like called them randomly, like, no, just go rather everyone provides an update. So, uh, I mean, it's, it's a lot of information

to keep your head, because, um, you've, you've got to, you've got them, said if you're meeting weekly or twice weekly, you've, you've, you've got a snapshot of what that person said, um, and, and, and you can, and you can, and you can then, you know, flat the progress points, um, you can sort of mentally plot the points on the curve and say, are we converging to a solution or not? Um, or, or are we, you know, like, I'll, I'll take drastic action,

right, when I say, okay, we're not, when I finally reach the conclusion that, okay,

unless drastic action is done, we have no chance of success, then I must take drastic action. That's, that's, that's, okay, if that conclusion in 2018 took direction and fixed problem. How, how many, um, you know, you, you've got many, many companies, and in each of them, it sounds like you do this kind of deep engineering understanding of what their own bottlenecks are, so you can do these, um, reviews of people. Yeah. Um, you've been able to scale it up to

five, six, seven companies, you've been in one of these companies, you have many different many companies with them. Um, what, what determines the maximum here? Could you have like 80 companies, 80? No. But like, you, you have so many already. I'm like, that's, that's already remarkable. Why this current number? Yeah, exactly. I know, so, um, we need to be really capable of coming together. Um, the neural, it's like, it depends on situation. Um, so,

I actually don't don't have regular meetings with, uh, with foreign company, so that the

foreign companies sort of cruising along. Look, basically, if something is working well and making

good progress, then there's no point in me spending time with it. So, uh, I actually, uh, allocates time according to where the, where the limiting factor or the problem, where, where

“where things problematic, um, or where we're pushing against, uh, like, what, what is holding us back?”

Well, you know, I, I focus, but risk of, say it was too many times, the limiting factor. Um, so, so basically, if something's good, like the irony is, if something's going really well, they don't see much of me. But if something's going badly, there's to a lot of me. So, something, or not, not even badly. It's, it's like, if if if if if something's the limiting factor, it's a limiting factor, it's a limiting factor. It's not exactly really badly, but it's the

thing that's, it's the thing that we need to make go faster to. And so, when something's the limiting factor, that's the basics or it has, uh, are you like talking weekly and daily with the engineer that's working on us? How does that actually work? The most things that are learning factor are, um, weekly and some things are twice weekly. So, the, the AI-5 chip review is twice weekly. And so, it's every Tuesday and Saturdays.

“Is, is the chip review? Is it open-ended and how long it goes?”

Technically, yes, but, uh, usually it's, it's like two or three hours. So, sometimes less. It depends on how much if they should go to go through. Yeah, that's one thing. I'm just trying to tease out the, the differences here, because the outcomes seem quite different. And so, I think it's interesting to know what inputs are different. And it feels like the corporate world, one like you're saying, just the CEO doing

engineering reviews does not always happen. And despite the fact that that is the, you know,

what the company is doing. But then time is often pretty finely sliced and, uh, you know, half-hour meetings or even 15-minute meetings. And it seems like you hold more open-ended, we're talking about it until we figure it out. Yeah, sometimes. Yeah, sometimes. But, uh, most of them seem to seem to, more or less stay on time. Um, so, um, I mean, today's, uh, staunch of engineering review went a bit longer, um, because there were more topics to discuss.

Um, you're trying to figure out how to scale two million plus times to overprear is quite challenging. Can you answer questions? You said about, um, Optimus and AI that they're going to result in double just a growth rate. It's within the

“matter of years. Oh, it looked the economy. Yeah. Yes. Well, I think that's right. What was the point”

of the doge cuts if the economy is going to grow so much? Well, I think like ways to afford it, not good things to have, you know. Um, I, I was actually pretty worried about, uh, I guess, I mean, I think, in the absence of AI and robotics, we're actually totally screwed, uh, because the national debt is probably up like crazy. Um, now our interest payments, the interest payments, the national debt exceed the military budget, which is a trillion dollars.

So, over a trillion dollars, just the interest payments. Um, you know, that was like, I was like, okay, pretty concerned about that. Maybe if I spend some time, we can slow down the bankruptcy of United States, um, and give us enough time for the AI and robots to, you know, help solve the national debt. Or we're not to help solve. It's the only thing that could solve

with our AI and robots. Nothing else will solve the national debt. Um, and so, so, but we'd like

to, well, we just need, we need enough time to get will be AI and robots, uh, to not go bankrupt

“before then. I guess I think I'm curious about is when don't start, you have this enormous, um,”

ability to enact a reform and not to add an illness. Sure, sure. Uh, but totally by your point that like, it's important that AI and robotics, drive product improvements, drive GDP growth. But why not just directly go after the things you're pointing out, right? You know, like, the tariffs on certain components or whether it's like permitting. I'm like the president. And, and very hard to cut, to cut, to, to even, even to cut things that are obvious waste and

fraud, like, like ridiculous waste and fraud. Um, what I discovered that is, it's, it's extremely difficult, even to cut, very obvious waste and fraud, um, from the government. Um, because, the, the, the government has to operate on, uh, on, like, who's complaining, like, if, if, if, and if you cut off payments to fraudsters, they immediately come up with the most

“sympathetic sounding, uh, reasons to continue the payment. But they don't say, please keep the”

fraud going. They say, you know, it's, they're like, you're killing baby pandas. And I'm like, meanwhile, there's no baby pandas are dying. They just making it up. Um, but the forces are capable of, of, of coming up with extremely compelling, sort of heart-wrenching stories that are false, but nonetheless, sound, uh, sympathetic. And that does what happened. Um, and, uh, so it's, like, perhaps I should have known better. Um, and, uh, that I thought, way, let's take a,

literally, this, this, let's try to cut some amount of, uh, ways from forth from the government.

Maybe this shouldn't be, you know, 20 million people, uh, walked as alive in social security,

who are definitely dead, and over the age of 115. The oldest American is 114. So it's safe to say if somebody's 115, and mocked his life in the social security database, something is, there's either a typo. So, like, somebody should call them and say, we, we seem to have your birthday wrong, or, or, or we need to mock you instead. Okay. None of the two things. We intimidate and call it together. Well, so it seems like a reasonable

thing. Um, and if, if, like to say, their birthday is in the future, um, and they have, you know, a small business administration loan, and their birthday is 21.65. Um, we either gain to have a typo or we have fraud. Um, if it's, if it's, if we say we appear to have gotten the sanctuary of your birth incorrect or a great platform movie. Yes, this is, this is, this is, this is, when I, when I, when I'm not ludicrous fraud, this is why I'm not ludicrous fraud.

Were those people getting payments? So, some were getting payments from Social Security, but, but, but, but the main fraud vector, uh, was to mock somebody as alive in social security,

and then use every other government payment system, uh, to, uh, basically, to do, to do fraud,

because what those other government payments system do would do, would do what there was simply do, and are you alive check to the Social Security database? Hmm. It's a, it's a bank shot. What would you estimate is like the total, uh, amount of fraud from this magnetism? Um, my guess is, and, and other, but by the way, the government accountability office has done these estimates before. I'm not the only one who's coming out of this, you know, the, the, the fact,

“I think they, they did, the GAO did analysis, a rough estimate of fraud during the Biden administration,”

and I calculated roughly half a trillion dollars. So, don't take my word for it. Take it or report issued during the Biden administration. This is how about that? From this social security mechanism? Uh, it's, it's, it's a one of many. It's important to appreciate that the, the, the, the government does not, it is a very ineffective at, at, at stopping for, because, uh, it's, it's, it's, it's, it's, it's unlike, like, it was a company, like, like, like, like, for,

stopping for, you've got a motivation because it's affecting the earnings of your company. Uh, but the government just, just, they just print more money. Uh, so it's not, uh, like, you, you, you need, you need caring and confidence. And these are in short supply at, uh, at the federal level. Um, yeah, I'm sorry. I mean, when you go to the DMV, do you think, wow, this is a bastion of confidence? Um, well, now imagine it's worse than the DMV, because it's

the DMV that can print money. So, we, was it not possible? At least the state level DMVs, uh, need to, the state's more or less need to stay with the neighborhood like, go bankrupt, but the federal government just prints for money. Well, it was enough possible. If there's a cashier half a trillion abroad, well, why, why wasn't that possible to cut all that? Uh, because when,

and recalibrate your expectations for confidence, uh, because, uh, you're, you're operating in a

“world where, you know, you've, you've got to sort of make ends meet, like, you know, you've got to pay”

your bills, you've got to, you know, buy the microphones? Yeah, yeah, exactly. Um, so, so, you, if, you don't have, it's, it's not like there's a giant largely uncaring monster bureaucracy. It's not even, it's an, and, and, and a bunch of, uh, the accuracy computers that are just, they're just sending payments. Um, like, like, one of the things that, that, that, that, that there was, it's, and

sound so simple, uh, that, that probably will say, um, let's say a hundred billion, maybe two hundred

billion a year. Um, it's simply requiring that payments from the main treasury computer, which is called payments, like payment accounts, master or something like that. This five trillion payments here, requiring that any payment, uh, go, that goes out, have a payment of, uh, appropriation code, make it mandatory, not optional, and that you have anything at all in the comment field. Um, because, uh, you see, after your heart recalibrate, how dumb things are, breathing,

pales were being sent out with no appropriation code, it's not, not checking back to any congressional appropriation, and no explanation. And this is why the, the Department of War, formerly, the Department of Defense, cannot pass an audit because the information is literally not there. Re-calibrate your expectations. I want to better understand this how much really a number,

“because there's, there's an IG report in 2024 or people how much, it was like, why is it so low?”

Um, maybe, but, uh, we found that like over seven years, the social security fraud they estimated

was like 70 billions over seven years, like 10 billion here, said to be curious to see what

like the other 490 billion is. The federal government expenditures of seven and a half trillion here. Yeah. Um, what, what percentage, how confident do you think how much is? The discretionary spending there is like 15%. Yeah, but, but it doesn't matter, the, the, the most of the forward is non-disgressionary. It's, it's basically a fraudulent Medicare Medicaid, uh, Social Security, uh, uh, uh, you know, disability, uh, it's, there's, there's a zillion government payments. Yeah. Um, and a bunch of these

payments are, in fact, uh, there, there, there, there, there, uh, block transfers to the states. So the federal government doesn't even have the information in a lot of cases to even see no of this fraud. Let's consider, let's, like, reduce your ad of certain, the government,

“the government is perfect and has no fraud. What is your probability estimate of that?”

I mean, zero. Okay. So then, would you say that for a foreign waste, that, the government, uh, is, has, is 90%. That also would be quite generous. But if, if it's only 90%, that means

that there's $750 billion here of waste in fraud. And it's not 90%. It's not 90% effective.

This seems like a strange rate of first-fins fills the amount of fraud in the government, just like, how much do you think there is? And then, uh, I, I, anyway, so we don't have to do it live, but I'd be curious, it's, like, something you know a lot about fraud at a strike. People will constantly try to be a fraud. Yeah, but as you say, it's like a little bit of a, um, we've really grounded down, but it's a little bit of a different problem of space because you're dealing with a much more

heterogenous set of fraud vectors here than where. Yeah, but I mean, I mean, that's right. You have high confidence in your try-hog. Um, you have high confidence in high carry. But it's still for it as non-zero. Um, now, now I'm actually at a much bigger scale. Um, there's much less confidence and much less carry. You know, back PayPal back in the day, we were, we were trying to manage four down to about one percent of the payment volume. Um, and that was very difficult to convince

amount of confidence in carrying to, uh, get forward merely to one percent. Um, now I'm actually that, that your own organization, where there's much less carrying, and much less confidence, it's going to be much more than one percent. How do you feel now looking back on, um, kind of politics and, and doing stuff there, where it feels like we're going from the outside in the, you know, two things have been quite impactful. One, the America pack and two,

and the acquisition of, of, well, Twitter at the time. But also it seems like there's a bunch of heartache. And so what's your, what's your grading of the whole experience? Well, um, I think I think those things needed to be done to maximize the probability of the future as good.

lose their objectivity, usually with politics. Like they, they generally have trouble seeing

the grid on the other side or the bad on their own side. That's generally how it goes. Um, I, I, I, I, that's, that I guess, one of the things that's been surprising the most is you, you often simply cannot reason with people. Um, if they're in one tribe or the other, they, they simply believe that everything they're tribe does is go to anything, the other, but tribe does is bad. Um, and for sweating them is, otherwise it's almost impossible. Um, so, anyway, but, um,

“I think, I think overall, those actions, um, acquiring Twitter, getting tribal activity”

enough makes a lot of people angry. Um, I think those, I think those actions are good for

good for civilization. Um, yeah, well, how does if you didn't do the future, you're excited about?

Well, um, American needs to can take, American needs to be strong enough to last long enough to, extend life to other planets and to, I get, I guess, AI and robotics to the point where we're we're going to show the future is good. Um, like, on the other hand, if, if, if we were to descend into, um, say communism or, or some situation where the, where the state was extremely oppressive, um, that, that would mean that we, we might not be able to become multi-planetary, um,

yeah, we might, we, the, the, the state might, um, you know, step out, um,

“our progress and analytics. How do you feel about, um, uh, you know, the, uh,”

Optimus, Grog, et cetera, are going to be leveraged by, and not just yours kind of, any revenue maximizing companies products will be leveraged by the government over time. How does this concern manifest in what private companies should be willing to give governments, what kinds of guerrillas should, like, should, you know, that it should, um, AI models be, uh, um, me to do whatever the government that has contract them out to do,

ask them to do, um, should, like, should, should, should Grog get to say, like, actually even, in the military wants to do X, no, the Grog will not do that. I, I probably, the biggest danger of AI, well, maybe the biggest danger of fail, for AI and robotics going wrong, wrongness is government, interesting, you know, um, I mean, the, the way that, like, my people who are opposed to corporations, or, or

worried about corporations, it shouldn't, um, really worry about the most about government, because government is just a corporation in the limit. It's a government, it is, it is, it is, it is, the government is just the biggest corporation with them and awfully unbiallists, um,

so I always find it, like, a strange economy where people would think corporations are bad,

but the government is good when the government is simply the biggest, and, and, and worst corporation. But people have that economy. There's somehow think that the same time the government can be good for corporations bad, and this is not true, corporations are, I better morality than the government. It is, so I, I actually think it's, you know, that's, that is the thing to be worried about, it's like, if the, you know, should, if the, if the government should not, like, the government

could potentially use AI and robotics to progress the population. Like, that is a service concern.

“As, as a, as a guy building AI and robotics, how do you, how do you, like, how do you prevent that?”

Well, I think that, like, if, if you have a limited government, um, if you limit the power of the government, which is, like, really what the US Constitution is intended to do is intended to limit the power of the government, then, then, uh, you're probably going to have a better outcome, then, if you have more government. So projects will be available to all governments, right? And I don't know about all governments. I mean, it's difficult to predict the, like I said, like, what's, what's, what's, what's the end,

end point or like, what is what is, what is many years in the future, but it's difficult to predict the, this sort of path along along that way. Like, if civilization progresses, AI will vastly exceed the sum of all human intelligence and, and they'll be far more robots than humans. Along the way, what happens? It's very difficult to predict. I mean, it seems like one thing you could do is just say, um, um, you're not allowed to, whatever government acts, you're not allowed

that rock should have a more constitution, um, and one of those things could be that we,

we limit why governments are allowed to do with this advanced technology. I mean, yeah, what we can do, what is, what we, what we, I mean, it's, it's, particularly, I mean, if the policy is just past the law, uh, then, and they can enforce that law, then it's hard to not do that

“law. The, the best thing we can do is, is, is limited government, uh, where, um, you know,”

you have, you have the appropriate cross checks between the executive, judicial and, um, legislative branches. I guess the, the reason I'm curious about it is that's like, at some point, it seems like the limits will come from you, right? Like, you've got the optimists, you've got the space GPUs, you've got the, I think I'll be the boss of the government. Or you will get, you will,

like, the, I mean, already, it's the case with SpaceX, that for things that are crucial to the, um,

like, the government really cares about getting certain satellites up in space, whatever, like, it needs SpaceX. Uh, it is the, it is the, um, a necessary contractor. And you are in the process of building more and more of the, um, uh, uh, the technological components of the future that, that, that will have analogous role in different industries. And you could have this ability to, like, set some policy that, um, you know, it's, it's, it's suppressing classical liberalism in any way.

I, my companies will not help in it in any way with that. Or, you know, some policy like that. Um, I will do my best to ensure that anything that's within my control maximizes the good outcome for humanity.

“I think anything else would be short-sighted, um, because I was in a part of humanity, so, um,”

I like humans, um, human, for you. Um, you, you mentioned that Dojo 3 will be used for space-based compute. Um, you really read my, what I say. I don't know if you know Twitter, uh, I know you like it. There's a lot of followers. They, they, they, they were. Um, how do you, uh, how do you have just thrown my secrets?

I've burst them away. Oh, sorry. How, how do you design this chip for space? What it, what, like, what, what changes? Well, I guess you want to have designs to be, um, more radiation tolerant and run out of higher temperature. Uh, so you get, um, you know, roughly, if you increase the operating temperature by 20 of set in degrees Kelvin, you can cut your radiator mass in half. Um, so, right, running out of higher temperature is, is helpful in space. Um,

there's, I mean, there's various things you can do for shielding the memory and, but like, a neural net is going to be very resilient to bedflips. Yeah, so, like, most of what happens for radiation is like random flickflips. Um, but like if you've got like, you know, a multi-challenged parameter model, and you get a few buttflips does matter. Um, it's much, like curiosity programs are going to be much more sensitive to buttflips than, um, it's some giant

parameter file. Um, so, but I just decided to run hot, and, um, I, I think you're pretty much due to the same way that you do things on those part from making it run water. Um, I mean, the solar rays, most of the weight on the satellite is, is it a way to make the, um, the GPUs even more power dense than what Nvidia and TPUs and I said are planning on doing that would, you know, be a special privilege in the space space role? Well, I mean, the basic math is like a, um, if you can do about

a kilowatt paraticle, um, and then you'd need, um, you know, 100 million full radical trips to do 100

gigawatts. Yeah. So, yeah, depending on what your yield assumptions are, you know, um, that,

“that tells you how many trips you need to make, um, 50, 50, if you want, if you, if you, if you,”

if you're going to have 100 gigawatts of power, you need, you know, 100 million trips running that, that are running a kilowatt sustained output paraticle. Um, a hundred. It's a math. A hundred million ships, uh, it depends on, yeah, if, if you, if you look at the die size of something like blackball GPUs or something, and how many can get out of the way for, you need like, um, on the order of dozens or less, uh, per way for. So, you're, basically, you're, this is a world where, if we're

putting that out every single year, you're producing millions millions away for a month. Um,

could be some number and lots of a million, I think. You're going to do the memory too. Yeah.

You're going to make a memory for? I think the turf, I've got to do a memory. It's going to do logic memory and taxi. I'm very curious how somebody like gets start. This is like the most complicated thing man has ever made. And obviously, like, if anybody's up to the task, you're up to the task. Like, what do you, so you realize this is a bottleneck and you go to your engineers and like, what is the next, like, what do you tell them to do? I want a million reasons a month

in 2030. What is the next, like, what do you do? That's right. Do you like colleagues and

“I'm like, what is this? That's what I want. What is the next that's so much to ask? Well,”

we make a little fab and see what happens. Make common stakes at a small scale and then make a big one. Is it a little fab done? No, it is. Now it's not done. I would, I mean, George, they're not going to keep that cat in the bag. They're just going to come out of the bag room. They'll be like, runs my ring over the bloody bed. They'll be able to see its construction of our restaurant acts right in real time. I mean, like, I don't know, we're good just flounder

and failure to say it's like not success is not guaranteed, but since we want to try to make

something like 100 million, we want to 100 gigawatts of power, 100, that chips that can take

100 gigawatts. And it's so cool, you know, but yeah, bite by 2030. So then, and we'll take as many chips as our suppliers will give us. I've said this to, I've actually said this to TSMC and Samsung and my bonus, like, please build your more fab faster and we will guarantee you to buy the output of those fabs. So that they were already like moving as fast as they can.

“Like, it's not like, do we clear? It's not like us, it's not like it's us plus them, you know?”

There's an irony that the people doing AI want a very large number of chips that is quickly as possible. And then many of the input suppliers, the fabs, but also, you know, the turbine manufacturers

are not ramping up production very quickly. Yeah, the explanation here is that they're

dispositionally conservative, you know, their Taiwanese or German as the, you know, the storm maybe, and they just like don't believe to say, like is that really the explanation or is there something else? Well, I mean, it's reasonable to like, if somebody's been and say the computer memory business for 30 or 40 years and they've seen cycles, they've seen like boom and bust like 10 times, you know? So like that's a lot of layers of scar tissue, you know?

So it's like, it's like, during the boom times, it looks like everything is going to be full, great forever and then then the crash happens and then like desperately trying to avoid bankruptcy and then there's another boom and another crash. Are there other ideas you think others should go pursue that you're not fair, whatever reasons right now? I mean, there are a few companies that are they're saying like new ways of doing chips, but they're just not scaling fast.

I mean, within AI, I mean, just generally. I'd say like, people should just simply do the thing that where they find that they're highly motivated to do that thing. Mm-hmm. As opposed to, you know, some of some of some ideas that I suggest, but they should do the thing that they find personally interesting and motivating to do. But you know, we're going back to the limiting factor. You know, that phrase about 100 times.

The current limiting factor that I see in the timeframe, you know, in the sort of 20, 29, like in the in the three, three to four year timeframe, it's chips. In the one year timeframe, it's, it's energy, it's power production, electricity. Like, it's, it's not clear to me that there's enough. Useful electricity to turn on all the, the attributes that are being made.

“Towards the end of this year, I think people are going to have real trouble turning on,”

like the chip output will exceed the ability to turn chips on. Well, like, once you're planning to deal with that world, what we're trying to accelerate electricity production. I guess that's, that's maybe one of the reasons that I say I will, will be maybe the leader of hopefully the leader, is that we'll be able to turn on more chips than other people can turn on faster.

innovations from the corporation that mess, good, cold self-lapse. The, the ideas tend to flow,

like it's, it's, we're, to see that there's, like, more than about a six-month difference between, um, like, like, the ideas, uh, travel back and forth, um, with the people.

“So, so I think you, you sort of hit the hardware wall and, um, and then whatever, whichever”

company you can scale hardware, the fastest will be the leader. And so I think, actually,

I will be able to scale hardware the fastest, and therefore, most likely, will be the leader.

You, you, you, you jokes are, you know, um, or self-conscious about, uh, you know, using the, uh, the limiting factor for ease again. But I actually think there's something deep here. And if you look at a lot of the things we've touched on over the course of it, maybe you're kind of good

“note to end on, like, if you think of a senescent lower agency company, it would have some bottleneck”

and not really be doing anything about us. Um, you know, Mark and Dries and had the line of, uh,

most people are willing to endure any amount of chronic pain to avoid acute pain. Uh, and I feels like a lot of the cases we're talking about are just leaning into the acute pain, whatever it is. It's like, okay, we've got to figure out how to, you know, work with steel, or we've got to figure out how to run the chips in the space or, like, we'll take some near term acute pain to actually solve the bottleneck. And so that's kind of a unifying thing. I have a high pan threshold.

That's helpful. I solved the bottlenecks. Yes. Um, so, you know, one thing I can say is like, uh,

“I think if you're just going to be very interesting. Um, and, um, and I, as I said, the dollars have”

only been, especially dollars, so I think it was like on the ground for like three hours or something. It's better to be, it's better to err on the side of optimism and be wrong than, err on the side of pessimism and be right, uh, for quality of life. So, you know, you're, you're, you're happy, this will be, you'll be happier if you, if you are on the side of optimism rather than arguing on the side of pessimism. And so I recommend arguing on the side of optimism.

That's that. Cool. You know, thanks for doing this. Thank you. All right, thanks, guys. Can't speak to me. All right. Oh, great. So I'm gonna, hopefully this encounters the pain and the pain tolerance. Hey, everybody. I hope you enjoyed that episode. If you did, the most helpful thing you can do is just share it with other people who think might enjoy it. It's also helpful if you leave a rating or

comment on whatever platform you're listening on. If you're interested in sponsoring the podcast, you can reach out at barcash.com/advertice. Otherwise, I'll see you in the next one.