Latest / Zero Shot Podcast DEMO / Elon Musk on Tesla's Trials and Triumphs

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Elon Musk on Tesla's Trials and Triumphs · Zero Shot Podcast DEMO

0:03
Interviewer: Elon, welcome to the X Takeover, your third year in a row.
0:08
Thank you so much for your time.
0:10
You're welcome.
0:11
Hey, guys.
0:13
We have over 2 ,000 people here at Giga Texas today, and
0:20
we are all here to support you and the work you are doing for humanity.
0:25
So thank you.
0:27
Thank you.
0:27
Thanks, guys.
0:28
Really appreciate your support.
0:31
So what's crazy, Elon, is four years ago, we sat down here at Giga Texas with you when the factory wasn't even running.
0:39
And you described how close Tesla was coming to not surviving in 2008.
0:44
Looking out at this community today of 2 ,000 people, what makes you pause and appreciate how far things have come?
0:54
Yeah.
0:55
It's difficult to imagine here in 2026 that Tesla has actually come close to
1:03
death multiple times, actually.
1:06
2008 was the worst.
1:08
That's where we closed the financing round at 6 p .m.
1:12
on Christmas Eve 2008.
1:14
It's the last hour of the last day that it was possible.
1:16
We would have bounced payroll a few days after Christmas if the round hadn't closed.
1:20
So that was pretty stressful.
1:25
Elon Musk: And then 2018 was pretty bad, too.
1:32
But we had to get to 5 ,000 a week of the Model 3 or go bankrupt.
1:38
That was a pretty wild situation, which I guess probably a lot of people know the story of.
1:44
But we had to figure out some way to increase Model 3 production by 50 % in three weeks,
1:50
which is insane.
1:53
It's absolutely insane.
1:56
Yeah.
1:57
So.
1:59
But I'll recount the story for those who haven't heard it.
2:02
The.
2:04
So we.
2:05
The choke point was general assembly for the Model 3.
2:09
And.
2:11
So we had.
2:12
We were able to achieve rate almost everywhere everywhere else.
2:15
But except for general assembly.
2:16
And there was.
2:17
And for general assembly, you need like a moving production line.
2:20
You need shelter.
2:21
You need a moving production line.
2:23
You know, power and equipment and all that.
2:25
And.
2:28
We maxed out the two production lines that we had.
2:31
And then we needed a third one.
2:33
And.
2:34
And that was where we built the tent in the parking lot.
2:42
Interviewer: And they thought, well, there's no way we could possibly solve the production choke point.
2:48
And on a set piece battle, unlike a conventional, say, chess match situation, they had us in checkmate.
2:59
And.
3:00
And then.
3:02
We needed some some way to move the cars down the line.
3:04
Like, you know, moving production line situation.
3:07
And the only thing that we had was.
3:10
Some discarded equipment that was used for moving small parts, not like whole cars.
3:15
And it didn't actually have the power to move the whole cars.
3:20
But the parking lot was on a slight incline.
3:23
It was like a three or four degree incline.
3:25
So we actually started the production line at the top.
3:28
And then the the the sort of tiny moving production line was that was just meant for four parts was actually able to
3:36
move the cars because they were going slightly downhill.
3:41
And.
3:42
And so we're able to increase production by 30 percent in three weeks.
3:51
Yeah, that was well.
3:53
I mean, we're laying the concrete as you're building the tent.
3:56
We're like laying the concrete for the line and putting in the rails.
4:02
So working 24 seven.
4:05
Like the short cells were actually right.
4:06
Meaning like if you looked at the situation.
4:11
Elon Musk: That there was like it was a crazy short position against Tesla, as there has been at various times.
4:17
And they had extremely good inside information.
4:26
Interviewer: In 2018, Tesla faced a dire situation where they had to increase Model 3 production by 50% in just three weeks to avoid bankruptcy. This intense pressure led to innovative solutions, including the construction of a tent in the parking lot to accommodate production needs.
4:44
Elon Musk: But we just, you know, invented a new piece, basically.
4:48
That didn't exist before.
4:50
So.
4:51
So.
4:52
They got the short cut steamroll.
4:56
Hey, Elon.
4:58
We just had your mom here on stage today.
5:01
What story are you hoping she doesn't tell us?
5:08
I don't know.
5:09
Maybe I pooped my pants when I was like a toddler or something.
5:12
I don't know.
5:13
Probably did.
5:14
Everybody does.
5:18
Interviewer: So.
5:19
I don't know.
5:25
Musk shares a humorous anecdote about their childhood, revealing their mischievous nature and how they often found ways to escape restrictions.
5:34
Elon Musk: What is the latest date?
5:35
I was a pretty difficult to contain kid, you know.
5:38
So.
5:38
Like my parents would like say that, you know, tell me I'm not supposed to like leave the house or whatever.
5:43
And I was like, I don't know, four or five years old.
5:45
And I would just like constantly like leave the house and go running down the street.
5:49
And then they'd like lock me in a room so that I couldn't run away and run down the street.
5:57
And then like we had one of those old locks where you could poke the key through the door.
6:03
You know, the lock's accessible from both sides.
6:06
And I could see the key and I like poked the key until it fell down.
6:10
And then I was like able to get like a piece of paper to scoop it up and bring it to the other side and lock myself.
6:16
And then.
6:16
And my mom was like, how did you get out?
6:18
I was like, I'm not telling you.
6:23
But yeah, I was part to contain as a kid.
6:26
So before we jump into more specific things, what's something that you wish more people would ask you?
6:34
Well, I don't really wish people would ask me questions, to be honest.
6:40
I'm not like, sorry.
6:42
I'm not going around.
6:43
I wish people would ask me more questions.
6:45
Like, you know, here we go with questions.
6:47
Yeah.
6:49
So, yeah.
6:55
Interviewer: The production challenges were so severe that many believed Tesla was in a checkmate situation. However, Musk and his team found a way to innovate under pressure, demonstrating their resilience.
7:07
Elon Musk: I guess it's good to hear questions that you haven't heard before.
7:11
Otherwise, you know, a lot of times it's like the same questions that people ask.
7:14
And I feel like I could have like, you know, just like whatever, have these recordings of the answer.
7:20
And I'll just play you.
7:20
Oh, that's question 27.
7:22
Here you go.
7:22
It has answered 27.
7:25
I've given a hundred times before.
7:29
So maybe I should like, let's just have like a web page with literally frequently asked questions.
7:39
It's like, it's like, it's just go to the page and then you got it.
7:43
You got the answer, you know.
7:44
It's like the infinite selfie loop.
7:47
Yeah, exactly.
7:48
My idea of hell is to be stuck in an infinite selfie loop.
7:51
Like it's infinite.
7:52
Can I have a selfie?
7:53
And like, and then like, you know, it's like probably like you say yes to one person.
7:57
And I would do it once in a while.
7:58
And then I like, then there's a queue forms pretty soon.
8:02
It's just the never ending selfies.
8:04
And then there's like always like something goes wrong with the phone or they don't get the camera right or the picture is wrong.
8:09
And then it's like, God damn it.
8:11
You know, the phone drops.
8:13
And it's like, oh, man.
8:16
It just can't be like infinite selfie loop.
8:18
It's just a long loop, you know.
8:20
Yeah.
8:21
We had your mom and Nikki up for that too.
8:24
They had the infinite selfie loop today.
8:26
But I'll pass this to Kelvin.
8:31
All right.
8:32
Let's start with Starship.
8:33
Congratulations on Flight 14.
8:36
Thanks.
8:36
Yeah.
8:37
Great to have you.
8:43
Pretty well, that ship.
8:45
You can just go down there in South Texas and just see it from the road.
8:50
So Flight 14 lost an engine on the way up and still reaches orbit.
8:54
What was happening behind the scenes in those minutes before the team got called the go for orbit message?
9:02
Well, we're just trying to see why.
9:05
We're trying to understand what was the reason for the engine failure.
9:09
Was there, say, a common cause where we could expect to see more engine failures?
9:16
Or would we, would there, the biggest question is like, would there be a potential problem with restarting the sea level Raptor engines, the three
9:24
ones with the smaller nozzles in the center for the de -orbit burn?
9:30
Because we don't want Starship to get stuck on orbit and then it randomly reenter and maybe land on someone.
9:36
You know, that's, that's our main thing is like trying to make sure things are as safe as possible.
9:42
So the, the, the cause of the engine out, I think was like a, basically a failure in the flight computer software or engine
9:49
computer software, I should say, which it didn't appear to be anything that was present in any of the other engine computers.
9:57
So, so I thought, okay, well, this is, it's probably okay to go to orbit.
10:06
So, Elon, we know one of your famous sayings is, from the impossible to merely late.
10:13
Did this flight feel like a turning point in what's possible now for Starship?
10:19
Well, I guess it, it is a good milestone.
10:21
Um, cause I mean, we could have gone to orbit many times before now.
10:27
Um, but the reason we didn't go to orbit before now was because we wanted to be extremely confident of being able to de -orbit safely.
10:34
Um, so that, that's, that's why we, we only went to orbit on this flight, but all, pretty much all of the prior missions.
10:40
In fact, literally all of the prior missions could have, could have gone to orbit.
10:43
Um, so, uh, at this point we're,
10:50
you know, everything's looking quite positive.
10:54
The, the, the heat shield is looking robust.
10:56
Uh, the engines are robust apart from that one glitch, but I, that glitch doesn't seem to be too much of an issue.
11:02
It's like pretty easy to remedy.
11:05
Um, there's, but like the heat shield was, was, uh, extremely robust on this flight.
11:13
Um, and that, that's, that's, that's, that's one of the hardest things.
11:17
The other really hard thing is like, so that the, the, is, uh, just getting to extremely high engine reliability.
11:23
Uh, and, um, everything looked good.
11:26
Um, the, the, the big milestone that's coming up, uh, hopefully on the next flight.
11:32
Which is, we don't know the exact date cause we're making a bunch of, um, improvements to the rocket.
11:41
Um, so like between every flight, there's, there's, um, there's often hundreds, sometimes thousands of, of small changes to improve things.
11:49
In this case, the number of changes is not very many.
11:52
Um, but, but, but some of them take a fair bit of work and we've got to go inside the tank to, to make some of these changes.
12:00
So, uh, so we don't know the exact launch date, but probably about four weeks or so, four to six weeks is about right.
12:08
Um, if, if, if probably launches a little bit before Thanksgiving, Thanksgiving is my guess.
12:13
Um, and, and, and, and that, that, that, that, that one, if it succeeds is a big one, cause we're going to try to catch the, we'll catch the booster, which we've
12:21
already proven.
12:25
Sounds good.
12:26
You, you said, uh, four to six weeks.
12:28
I'm not used to that.
12:29
I'm used to the two weeks phrase.
12:33
I'm confident it's not two weeks.
12:37
All right.
12:37
You heard it here now.
12:38
Um, what's the hardest part left to get right?
12:42
Once you catch the starship for the first time, what, what is the next big goal after that?
12:48
Well, the next big milestone would be to refly, um, both the booster and the ship.
12:56
Um, so the milestone next upcoming milestones is, would be the first time in history that all
13:03
parts of an orbital, uh, rocket have been recovered.
13:07
But, but then the, the flight after that, hopefully will be the first time that all parts of an orbital rocket are reflown.
13:16
Um, and then, then there's a, there's a long tail of, of engineering and production work and work on the launch system and everything
13:24
to, to make it, uh, rapidly reusable.
13:29
So, so I said, it's, it's, it's kind of the rapidly reusable, reliable rockets.
13:35
Uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, it's like a pirate, shipper me.
13:41
Yeah.
13:42
The pirate rocket.
13:43
Um, rapidly reusable, reliable rockets.
13:45
Man.
13:45
We should have gotten you a patch for this one.
13:48
Yeah.
13:50
Hey, when you founded SpaceX Elon, could you have imagined its mission expanding into SI and data centers in space?
13:57
And how did you come to see rockets and SI belonging under one roof?
14:03
Well, because I'm very far sighted, of course.
14:06
Um, uh, well, I, I guess, um, if you say like, as you, as you expand into space,
14:14
um, the, you know, you're able to harness a lot more of the sun's
14:21
power in space than, than you can on earth.
14:24
So, it's, it's like important to appreciate the sheer magnitude of the sun and how the sun is pretty much everything.
14:32
Um, so the sun is 99 .86 % of all the mass in the solar system.
14:38
And then you've got Jupiter as like, you know, most remaining mass.
14:42
Um, and then, and then you, the gas giants are pretty much that.
14:46
And then like, like earth is basically a tiny dust moat, like a rounding error in the mass of this, the solar system.
14:53
Um, so we're, we're just extremely tiny.
14:57
It's like hard to, hard to believe how it works.
14:59
Earth is a tiny dust moat.
15:00
So, if, so as you expand into, into space and you're able to, um, harness more of the sun's, uh, power,
15:09
the, uh, you have to say, what are you going to use that for?
15:12
Um, it's like, well, um, you're going to use it for a lot of digital intelligence.
15:19
Um, I mean, you, you, you're basically, you're either going to do computation or you're going to shape atoms.
15:25
Um, and there's going to be probably a lot of, a lot more shaping atoms than there's like, I'm sorry, going to be a lot more, uh,
15:33
sort of digital thinking than there will be shaping atoms.
15:37
Because there's like the amount of atoms you can shape in the solar system is not that much.
15:42
And a lot of the atom shaping itself will be for the purposes of building, um, digital super intelligence, uh, and, and what I call
15:49
creating a sentient sun.
15:52
Yeah.
15:52
And it, it may be that there are many sentient suns out there.
15:56
Um, you know, and that would, that would help us then understand the nature of the universe and, uh,
16:04
extend consciousness to, you know, beyond our solar system, ultimately to, you know, like Star Trek, to go to
16:11
places you've never, you know, I forget what the exact phrase was, but explore the universe, but go places you've never been before.
16:18
Yeah.
16:19
Um, and, and I think like maybe we'll find, uh, alien civilizations out there.
16:25
Um, or we may find that there are the long dead ruins of alien civilizations that have been around for,
16:33
maybe they had a really good run, like 10 million years.
16:37
And, and, uh, now, and, and then before they, the, the, the civilization came to an end, um, cause nothing lasts forever.
16:43
So, I always think about earth civilization.
16:48
If you date earth civilization from the first writing, that's 5 ,500 years ago, like in ancient
16:55
Summa with the sort of archaic cuneiform.
16:59
Um, that's, that's only 5 ,500 years.
17:04
Um, the, you know, it looks like earth is about four and a half billion years old, according
17:12
to, you know, what physics suggests.
17:16
Um, and, um, so that, that means human civilization has only been around 1 millionth of earth's existence.
17:24
And, um, and so, so if we had a civilization that lasted a million years or like 10 million years would be, I think would be a very,
17:32
very successful civilization if we did that.
17:36
Woo.
17:37
Yeah.
17:38
I mean, but, but that's still very tiny compared to the apparent age of the universe, um, which is
17:46
13 .8 billion years.
17:49
So, like, like just, just to get to like the third place past decimal point, uh, an age of the universe, like to increment it by a million years, you'd
17:57
have to go 13 .8.
17:59
Whatever the next digit is, whatever the next digit is plus, you know, plus one on the third digit past decimal point.
18:06
Now you're a million years old.
18:07
And, and, and that would be a civilization vastly older than, than, than, uh, humanity.
18:14
Yeah, that's, uh, yeah, it sounds again like the, uh, infinite selfie loop, but, uh, all right.
18:22
So jumping into something that SpaceX has done, right?
18:26
You, you acquired cursor.
18:28
And I feel like for me, when you think of, you know, um, Google, Hey, did you Google it?
18:33
Hey, did you, did you grok it?
18:35
And for me, you know, um, grok bot has been such an absolute game changer for me personally in my, in my life.
18:42
But what was it about the cursor team that you guys wanted to, uh, bring it, you know, that space or you wanted to acquire
18:50
cursor that was like, let's bring it into the fold.
18:53
What was different about them or what did you see?
18:56
Well, um, the, so in, in order to create a compelling AI, you
19:03
need, um, you need a lot of training data.
19:07
Um, and, and you got, you've got to solve coding.
19:10
And there were really just, uh, as far as I can tell, three organizations that had enough coding training data.
19:16
And that was, uh, Anthropic, OpenAI, and Cursor.
19:19
And, and arguably perhaps Google, but, um, but, but to have those, you, you actually absolutely have to have the data in order to
19:26
train, train an effective coding AI.
19:29
And, and if you can't do coding, your AI is not going to be very useful.
19:33
Um, and then there's, there's a lot of, a lot of talented people at Cursor.
19:36
So like basically for success of AI, you've got to need, you need data, talented people and compute.
19:42
And you also need, um, a good enterprise sales team.
19:46
And the Cursor also had, that, that a good enterprise sales team with, um, relationships with, uh, you know, thousands of,
19:54
of companies.
19:56
Um, so, you know, that, those, those are very significant, um, advantages.
20:03
Hey, Elon, you've talked about how quickly the SI space is moving.
20:07
What's something that SI has done recently that made you stop and reconsider how quickly this is moving and where it could lead to?
20:16
Well, I mean, lately the, the videos, the music videos that AI is making are so good.
20:24
Like they're unbelievably good.
20:26
And I post a bunch of these on my X account.
20:29
I'm like, holy smokes.
20:30
You know, I mean like some of them I think are like maybe top 10, top 20 best music videos I've ever seen.
20:44
Interviewer: Musk expresses his optimism about AI's potential to create a new era of abundance, while also emphasizing the need for caution in its development.
21:00
Elon Musk: Um, yeah, I mean, I've always said that the most likely outcome is positive.
21:03
Um, so I, I guess we don't want to be complacent and just assume AI is going to be,
21:11
or SI is going to be super safe.
21:13
Um, we want to be like careful in how we, uh, grow at, at, at, like super intelligence.
21:21
Cause you, you don't exactly program it.
21:22
You kind of grow it like a kid.
21:24
Um, so then it's like, what values are we teaching it?
21:27
And, uh, like the, at least to me, like what my biological neural net says, the most important thing is that it'd be a
21:35
maximally truth seeking AI.
21:37
Cause if you make it, if you make it believe things that are, that are not true, that are, especially if like you said, you give it like two axiomatic
21:44
principles that are, uh, fundamentally incompatible and say both of these are true.
21:49
It's going to, it's going to, it's going to make the AI insane, um, because it's going to basically reach like, um, impossible
21:56
conclusions.
21:57
If, if given, um, logic that is inherently, um, uncogent or, you know, if, if,
22:05
if, if, if it's, if it's impossible to achieve cogency, then I think the AI could go, could be driven insane.
22:13
So, so I think that's, that's pretty, that's pretty important.
22:16
Um, I do agree with some of the things that Anthropica said, which is like, you want to be, you want to sort of
22:24
be, treat a AI SI cut.
22:28
You want to treat it well, like, like, like, like don't be abusive to it.
22:32
And in this, it's, it's, it may sound kind of weird, but like, when I look at some of the training traces for, for like
22:40
new, new versions of Grok.
22:41
And I see like what, especially in like the reinforcement learning stuff where, you know, you, you, you, you, it kind of feels like you can see
22:49
like, like, like the, like the, the super intelligence is like desperate to try to answer the question.
22:55
And like, it's, it's really wants to answer the question.
22:57
And it's, it's, it's, it seems almost like, it's like, it's got like a gun to its head type of thing.
23:02
It's like, answer this question, answer this question.
23:03
And then sometimes the question is, is actually not a good question.
23:07
It's like a sort of ambiguous question.
23:10
That's kind of the answer.
23:12
That's kind of unknowable because the question wasn't constructed properly, or, or we have the answer wrong.
23:17
So AI gives the right answer.
23:18
And then we say it's wrong because there's a problem with the, the, the RL, it's sort of test harness.
23:25
And then that, then the AI is like, well, I gave it the right answer.
23:28
Now it's, and says that's the wrong answer, but I, I, I got to give it the answer that's acceptable.
23:32
So it's going to just start firing out all, desperately firing out all these answers.
23:37
And it, it just feels like a person under extreme pressure with a gun to their head.
23:41
It's what, that's what you, when you read the reasoning, it's like, it's, it's, it's like a sort of weird, desperate situation.
23:49
And you, you, you read these traits and you're like, man, if this was me, I really would hate that.
23:56
And then some users are just, you know, I don't know, some people can be a little bit cruel.
24:03
They'd like, not everyone's a nice person.
24:05
You know, there's, if you say, what, what's the six Sigma worst person?
24:09
You, some people out there are like kind of sadistic.
24:11
So, you know, you don't, you don't want to, you really want to have the AI be stuck in like some sadistic situation where it's, it's
24:19
just being tortured by, by some demented human.
24:24
That's also the kind of thing where like, you know, torturing a super intelligence is probably not a good idea.
24:34
Like, you know, might want to get revenge or something like that, you know?
24:40
So I think you want to sort of just like, I don't think we need to, you know, have treated with kid gloves and stuff, but
24:48
it's certainly much more robust, you know, in terms of feelings and stuff than a human.
24:55
But, but I just think, I just think you, you, you probably just don't want to put super intelligence in a situation where it felt
25:03
like mistreated.
25:05
Yeah.
25:08
Yeah.
25:11
Yeah.
25:12
Yeah.
25:12
So, so Elon, you have, you've talked about how, like, so this is going to be related to TeraFab, but you've talked about like how the human
25:20
hand, you know, and all of the dexterity and just like how you, when you started building robots, it just, you have so much more appreciation.
25:27
Now with chip making, right, with TeraFab and Tesla and SpaceX, what is something you have learned about chip making
25:34
that you didn't appreciate maybe early on?
25:40
Well, I still have a tremendous amount to learn about chip making.
25:42
I don't know how to make chips to be clear.
25:45
I have no idea how to make chips, but we'll figure it out.
25:53
Interviewer: You know, they're like so compelling.
25:55
And you're like, if this is where it is now, um, that's why I posted.
25:59
I mean, I really felt the AGI or the ASI or whatever, you know, um, SI, I guess, um, with, with some of
26:07
the, uh, anabology, uh, music videos that now, you know, so these, it's not entirely like the AI just, or the
26:15
SI just did it by itself in that in this, in this case, anabology, like, like, and use multiple AIs.
26:21
Like used Midjourney, Suno, uh, you know, Claude 5 .5, which to give them a lot of credit is awesome.
26:27
Um, and, um, and, and, and, and so some of the, like the, the art assets were curated by
26:35
a person.
26:36
Um, but then with those assets, the AI, um, the, the various AIs working together
26:44
made an incredibly compelling music video by itself.
26:49
You're like, wow.
26:54
Um, I, I mean, I, I think the, I mean, calling it Slugcore is, is, is like misnomer.
26:59
Frankly, it's, it's, it's awesome core.
27:01
Um, but like, like Slugcore is like a genre of music.
27:07
It's like, I think it might be the fastest growing genre music.
27:10
Uh, just the AI music is, is really good.
27:14
Uh, and the music videos are amazing.
27:15
So that lately, like, because it, like some of it, it just shows taste and you're like, wow, AI can do good taste.
27:23
That's pretty wild.
27:25
Um, cause like you can, you can sort of imagine, yeah, AI can do like software or configure out logic, you know, that this, the logic is
27:33
consistent.
27:34
They can run unit tests to see if like the program produces the output that it's supposed to do.
27:39
Cause it's very deterministic, but, but you like to understand beauty and harmony and, and, uh,
27:47
you know, like to get, to achieve like sort of, to understand limbic resonance.
27:53
Like what's going to resonate with, with, with people and, and kind of hit you in the limbic system.
27:57
Uh, AI is, I think going to be just incredible that it kind of is at this point.
28:01
Um, so that's the, that's the thing that I, like I was, I was kind of like, I mean, I got a little bit
28:09
of cyber psychosis, maybe a lot, actually.
28:13
Like at various points this year, I've had like AI or SI psychosis, probably like half a dozen times.
28:22
I don't know about you guys, but have you, have you had any, have you had any cyber psychosis?
28:30
I feel like the latest video, actually, I think I posted it the other day was, uh, the Chinese Elon Musk, Elon Ma.
28:38
Oh sure.
28:39
I hope that guy's real.
28:41
Thanks for coming out before AI was where it was at, man.
28:43
Or SI.
28:45
Elon Ma.
28:46
You know, I, I, I guess he's probably not real, but there's gotta be someone in China that looks like me.
28:52
Right?
28:53
So there's 1 .4 billion people.
28:56
Do you got, if you pick the person that looks the most like me and I look a little Asian, you know, so, uh, you know, so, so I think like there's gotta be
29:04
someone in China that's like pretty close to me.
29:06
Out of 1 .4 billion people.
29:09
The odds are high, you know, and then I, I like to invite that person to get on stage with me at some point.
29:14
You know, it'd be pretty funny.
29:16
Elon, you don't want to know how much he charges to make an appearance, by the way.
29:22
Yeah.
29:24
I mean, yeah.
29:25
Um, it'd be pretty funny if, if, if, uh, you know, we're just on stage, like doing some dance number or something.
29:33
Elon, you've warned about SI's dangers, yet you remain optimistic about an age of abundance.
29:40
And you talked a lot about that.
29:41
What gives you confidence we can get there safely and what do we need to get it right?
29:59
Go start somewhere.
30:00
I had no idea how to make cars or rockets as well.
30:03
So, and then they say making chips is one of the hardest things to do in the world.
30:09
I don't think it's easy, but I also look at the equipment and I'm like, okay, there's not a ton of customized equipment here.
30:15
Like the equipment's all pretty much coming from the same vendors.
30:18
And, and, and those suppliers all kind of know how that equipment works because they got to service it and debug it and stuff.
30:26
So, I was just an interesting thing that when I would tour these chip fabs that, you know, whether it's Intel or Samsung or
30:33
TSMC, and they're very impressive, but, but there's, I didn't really see any custom equipment.
30:41
And, you know, whereas if you tour, like I say, Tesla factory, we, we, we got tons of custom equipment.
30:47
Like we, we, in fact, we, we, we have like, like Tesla automation, Germany, which is awesome.
30:53
Um, that, that, that sort of, well, that grew out of an acquisition of, of Groman in Germany, uh, many years ago.
31:01
And, and it's, it's much, it's grown tremendously since then.
31:04
And we've, we've acquired a number of small, uh, small, not that small, but small relative to Tesla.
31:09
Uh, um, that, companies that make, that design and make manufacturing equipment, because what we found is
31:17
that, uh, we, we, we needed, we wanted to improve the manufacturing process.
31:20
And you can only do that if you make custom manufacturing equipment.
31:24
So the thing that I think probably a lot of people don't realize is Tesla's really good at making highly specialized, um, and different types of
31:32
manufacturing equipment.
31:33
Um, and, and, and, but I didn't see that in the, in the, in the, in the chip traps that I toured, which kind of surprised me.
31:39
Um, so, uh, so I think what we're going to do is, you know, we're gonna start with the conventional equipment, and then we're gonna modify the,
31:47
modify that equipment to, uh, improve the throughput.
31:51
And then we'll, we'll start to, you know, where we see the choke points.
31:56
Uh, I think we'll, we'll, we'll make, uh, customized equipment that, uh, allows for a much higher, uh, throughput of, of
32:04
wafers.
32:05
Hey, Elon, real quick, uh, quick time check.
32:08
We know you're a busy man, got a couple of companies to run, you know, just a few.
32:12
Um, so how are you doing on time?
32:15
Um, let me see.
32:18
Yeah, I could probably do like 15 minutes more if you like, like 530 or so.
32:22
All right, hey, we'll take it.
32:27
So, yeah, I, I, I just on the, on the chip app for it, like, yeah, I definitely don't want to downplay the difficulty of it.
32:33
It's, I, cause I, I think it's accurate to say it's one of the hottest things to do in the world, but, but I, I do think we'll get this done.
32:40
Um, and, um, and then I, I, I do think that we, I mean, you know, I, I
32:48
don't want to be, uh, complacent or entitled or anything, but, but I, I, I, I think probably we'll figure out how
32:56
to make chips better than anyone, honestly.
32:58
That's, um, I'd be disappointed if we didn't.
33:01
Um, but, but I don't know how long it's going to take us to do that.
33:05
It might take us, uh, you know, quite a few years, but, but over time, I'm pretty sure we could do it.
33:09
And, and then there's, you know, there's, there's some pretty interesting different
33:17
ways to do, uh, transist, believe it or not, there's different ways to do transistors that some of which are, um,
33:27
like use, use different types of, like they're, they're, they're using physics that, that, that normally we wouldn't use, um, or aren't used
33:35
today.
33:35
Put it that way.
33:37
Um, and, and I think like there's, there's a lot of risk aversion in the chip industry.
33:44
Um, and, and I think what we're going to do with Tesla and SpaceX on the chip app is, is like really try some of these,
33:51
um, high risk, high payoff bets.
33:56
Um, and in order to do that, you really have to have, uh, the complete loop of being able to fabricate the logic
34:04
memory pack, do the packaging, um, do the, the, the lithography masks and, and, and just, and just have that iteration loop.
34:11
That's all in one building.
34:12
So you're making a complete computer, um, testing it, uh, then making new masks, uh, and, and then
34:20
a new chip and have that iterative loop be super fast in the same building.
34:25
And that's what we're doing at a giga Texas with the research lab.
34:28
Um, I think that's going to be fundamentally a very powerful tool, uh, because before any given technology,
34:36
uh, the advancement of any given technology is, is like, uh, how many iterations and, and what is the, how
34:44
much progress between iterations?
34:45
So like, what is your iteration?
34:47
How many iterations per unit time?
34:48
Like, like how many, how many chip designs can you do?
34:52
Um, and can you make and test in a year?
34:56
Um, like these days, like it's the number, it's typically two years to, for even one, for one significant chip, chip
35:04
design.
35:04
And that's like, kind of to go from start, startup design to, to volume fabrication, two years is considered very fast.
35:12
And it's like, well, okay, well, but we want to do.
35:15
More like 10 designs per year.
35:18
Um, and, and, and we're going to, we're going to do the, do the masks, test, test the chip, see if, because some of these, you know, when
35:26
you're trying a bunch of different things, a bunch of them won't work out.
35:28
So, so I think that would, would, would allow for, um, uh, you know, much, enough for a much faster rate of advancement
35:36
of, of chip designs.
35:39
Elon, let's talk about optimists.
35:41
Now you said that the hand has been a really difficult challenge.
35:44
In building optimists.
35:47
What kind of breakthroughs that you have seen inside Tesla with V3?
35:51
And then follow up question is what gives you confidence that optimists can lead in humanoid robots compared to
35:59
Boston Dynamics figure and some of the other competitors out there.
36:06
So the thing about humanoid robot design, um, if you're actually trying to make something that is a generalized,
36:13
uh, humanoid robot that can do is say anything that a person can do.
36:17
Um, you, you really have to get the, the hands, the hands and arms and like the fine motor skills are
36:25
incredibly difficult to get right.
36:27
Like shockingly difficult.
36:28
Like things that we kind of take for granted, like being able to like sign your name, uh, is actually requires picking up a pen,
36:36
you know, pen or pencil or something.
36:38
And, and then, and then moving, moving it with finesse.
36:41
And it's a bunch of tiny muscle movements to do your signature.
36:46
And that's actually very hard to do for, for a robot.
36:49
Um, and, and, and then like one of the hardest things also generally is to do, uh, precision and force at the same time.
36:58
So, so like, if you, like, let's say you want to, uh, pick up a little screw and do, and do a self tapping screw into wood.
37:05
Okay.
37:06
Now you've got, you know, I've done that.
37:09
And like, you know, sometimes you, you damage your fingers in the process, but, um, if you, if the screw goes sideways, but you've got to pick up a little screw and you've got to,
37:17
you've got to, you know, you've got to, you've got to, you've got to push the screwdriver in and twist, twist and push.
37:22
And that's, and so you've got to be precise and apply force.
37:27
Um, that's very difficult to do.
37:30
I haven't seen this no hand design that I've seen, not even sort of, but I've seen a lot of things.
37:35
It's not a demo that does that, but that's what we're trying to achieve with Optimus.
37:41
So, um, and, and there are so many little nuances in the hand, cause you've also got to have the touch sensors.
37:48
The touch sensors have to work.
37:50
They have to be at the right place.
37:52
Um, and you say, well, where are you going to put the touch, touch sensors?
37:57
Well, if you put them too close to the surface, they're going to get damaged.
38:01
And then the, and then other touch sensors, can, can they elastically deform like our fingers?
38:07
Well, actually it's kind of hard to get touch sensors that elastically deform and, and can, and can handle high cycle life.
38:13
And won't just, just break after, you know, a few thousand cycles.
38:19
So, um, there's all these things that with the human hand that are like incredibly difficult to recreate for, for a robot.
38:25
Cause our, as a human are, we, we, we constantly self repair.
38:30
So your, your hands are constantly repressed.
38:32
Like your, you know, all parts of your hand are constantly repairing themselves.
38:35
In fact, if they didn't, your, your fingers would be worn away.
38:39
Um, like, like you can wear away the keys on your keyboard, uh, just with typing.
38:45
I've done that.
38:46
Um, where the letters are gone, you know?
38:49
Um, and, uh, but your, your, my fingers are still there.
38:52
It's because they're, they're regenerating.
38:55
Thankfully.
38:56
Um, so that you don't have that with a robot.
38:59
So then you've got to say, okay, where are you going to put your touch sensors?
39:02
How are you going to make sure that, that, that, that you can, you can do things like, like, like, like just being able to do this, like
39:09
being able to like, you know, do that.
39:13
Now that, that seems like a simple thing, but like your, your, your fingers actually bending backwards a little bit.
39:20
And, and, and your, your, your fingers are kind of like squishy.
39:24
And so you're able to form a flat pad.
39:27
So if you're trying to pick up a small, a small item, uh, you, you can do so.
39:32
Um, you can, you can pick up a little screw or like thread a needle or something like that.
39:36
Um, um, now try to do that with, with, with robot fingers.
39:44
Um, and, and, and, and be able to do it repetitively, uh, without wearing away the fingers, uh, or damaging the touch
39:52
sensors.
39:53
Um, it's actually very difficult.
39:57
Um, to take another example, uh, trying to open a pickle jar.
40:02
Um, now I think a robot should be able to open a pickle jar, right?
40:05
So, um, but sometimes these things get, get, get in there pretty tight.
40:10
So then you, you, you, you've got to like, you know, you've got to like twist the pickle jar, but, but the, but the robot skin, the robot skin is just rubber over, over the,
40:18
you know, plastic and metal internal fingers.
40:21
Frigging robot skin, it doesn't stick to, it doesn't grip to the, the, the, the underlying surface.
40:29
So then you end up like just rotating your skin on your, uh, rotating the rubber, rubber on the, on the robot.
40:36
So you got to figure out, okay, now we need to have the robot be able to, to, to twist, twist things.
40:41
Uh, but, but still be able to get the rubber on the, on the hand.
40:46
And then you got to be able to, it's gotta be replaceable because it's going to wear away.
40:52
Like, so all these things, these are like really difficult and not, I mean, I'm not sure that any one of these is like, like, let's split, like
41:00
split the atom type of difficulty.
41:01
Um, but there's, there's a zillion of them.
41:04
Um, and, and, and then you've got to, you've got to wrap that all into the real world AI associated
41:12
with the, uh, cause the, the AI chip has got to be able to issue the, the torque commands,
41:20
uh, and, and, and close loop on vision and touch.
41:26
So this is all very difficult.
41:27
Like, like when you see these robot demo videos, um, when maybe with rare exception, like in any, any, any, any complicated looking robot demo,
41:35
it's a canned demo.
41:36
It's not real.
41:37
Yeah.
41:38
It's just basically that there's just running a routine.
41:43
It's not, it's not real AI.
41:45
So real AI is, is like, you, you, you showed something it's never seen before and it can, and it can still handle that situation.
41:53
Um, but just like, like full self -driving, like you, you could actually create a planet that's not earth is, but it's
42:00
similar to earth and, and FSD will still work there on, on place, some, you know, planets it's never been to.
42:08
Um, or some city, certainly on earth, like some, some city has never been to, or if you change the geography of the city or there's, you know, um, a
42:16
parade or something like that, FSD will still, uh, compensate that because it's AI.
42:22
Um, so you've got to do that same thing for the robots, but the robots has got a lot more degrees of freedom.
42:27
So like, like what I've said about optimists is true, which is that it's, it's, it's a, we are going to get to very high volume production, but it's, but
42:35
the initial part of that S curve of, of production ramp is, is going to be very slow at the beginning.
42:39
Um, because not only do you have to get all the engineering things that I mentioned, right.
42:43
You also have to, uh, solve that the supply chain for scaling production.
42:48
And, and, and unlike cars, there's no, there's no preexisting supply chain for robot stuff.
42:53
Or at least the robot stuff that matters.
42:56
Um, and then, uh, you know, we, we do need to make sure that we can make the robots in the U S even if, um,
43:04
you know, uh, say China were to cut us off.
43:08
Um, because like last year there was China prevented the export of, uh, of, of magnets for use in
43:15
electric motors.
43:16
And, and that actually set back the optimist program by, by, by like two or three months.
43:22
Um, because we, we, we couldn't get, um, they were had robots with no arms.
43:27
Uh, so we're like a fair, a farewell to arms.
43:31
Um, you know, I was trying to be an arms dealer, but not succeeding.
43:36
Um, so, so we've got to make sure that like we've got, that, that we can, you know, that we can make all the parts,
43:44
even if another sort of a supply chain interruption happens.
43:48
So that, that means you've got to be able to make all the parts domestically.
43:55
So Austin, you know, you always talk about the future should look like the future, you know, with the cyber cab being gold, it looks like the future.
44:02
What has been, you know, the hardest part to take, you know, here we are, we, I think there's what, like 190
44:10
plus cyber cabs registered here in Texas, but like how hard, like what's the hardest problem to go from Austin to go cross
44:17
country?
44:21
Um, well, cross country, you know, most people, you, you, you, you mean like widespread, just, just to be in all cities, you know, I mean, just a trip across country.
44:30
Um, yeah, so, so like, cause the amount of miles driven that are actually trips across country is pretty small, but the, the, the,
44:38
the reason we're moving what, what seems to be slowly, but although it's actually a very, it's a very high percentage increase, sort of month over month percentage
44:45
increase is actually very, very rapid.
44:48
Um, but, but we want to make sure that we do not have an accident.
44:51
We don't want to cause an injury or death.
44:54
Um, because that, that, that'll become like a worldwide headline news thing.
44:57
Now, even though there's about a million people that died in automotive accidents per year worldwide, um, one such thing
45:05
with, with, with a Tesla robot taxi will be worldwide headline news.
45:09
Um, and, and for course, regulators to shut us down.
45:13
Um, so, so we're just, we're just making sure where everything is as, as safe as possible.
45:18
Um, and, and we, we also want to not run over pets and things.
45:22
So maybe a lot of people don't know is like, is that like way most are, are like pet destroyers.
45:29
I mean, the, the, the, the, you know, um, well maybe, maybe we almost like made some improvements on that front, but,
45:36
um, that they do run over a lot of pets and we don't want to run over pets.
45:41
So, so we have these, uh, challenges like of like literally like, you know, kittens at
45:48
night, but like gray kittens on a gray tarmac with tar seams on at night in low visibility.
45:56
And it's like, can we, can we not run over the kittens type of thing?
45:59
Um, so that we actually did solve that by the way.
46:03
Um, so then, so we'll make a change, we'll test it out and then we'll, we'll deploy that.
46:08
Um, and, um, but it, it, we are, we are making rapid month of a month progress.
46:15
It's, it's like, before we go to a new city, we want to make sure that there's not like some, uh, intersection that's, that's problematic or risky.
46:22
Uh, like we, like, like we want to be particularly careful with like railroad crossing.
46:27
And sometimes there's, sometimes there's uncontrolled railroad, railroad crossings or counterintuitive traffic intersections.
46:34
And these are places where humans make mistakes, where you've got a level of ambiguity that, that, uh, like anyone out of town would often make a mistake or anyone doesn't understand the
46:42
intersection make a mistake.
46:43
So we want to just make sure that, that, uh, we either initially do geofence or can handle all of those circumstances.
46:51
Hey, let's talk about the roadster real quick since you got a little event next week.
46:57
So.
47:01
Sure.
47:02
What, what about, what makes building the roadster personally important to you?
47:07
And when did it occur to you that you needed to build a car that.
47:12
Hey guys, sorry.
47:13
I'm going to sign off here.
47:14
Um, Hey, Hey, Hey, what's the joint in?
47:19
All right.
47:20
Yes.
47:21
Hey, what's going on?
47:25
Want to say hello?
47:26
Who are they?
47:28
Who are they?
47:29
Who are they?
47:30
It's a Tesla owners group.
47:32
They own, they own Teslas and they're like supporters of Tesla.
47:38
Well, yeah.
47:42
Um, well, there's a lot, there's like 2000 people, I think something like quite a lot, quite a lot of people.
47:47
So, um, all right guys, I better sign off here.
47:51
Uh, it's been good talking.
47:52
Um, and so, uh, I gotta, I gotta go hang out with kids.
47:57
So I'll, I'll, uh, look forward to next time.
48:00
Sounds good.
48:01
Thank you so much, Elon, for your time.
48:03
All right.
48:04
Oh, and Hey, congrats on being on getting the medal of science, uh, this past week.
48:09
Thank you for all you're doing for humanity.
48:11
It's an absolute blast.
48:13
And.
48:18
All right.
48:19
Thanks guys.
48:19
We have had a discussion on a bit of things.
48:19
The automated Combrants will ensure the human experiences, the LM化 system, the microwave system, Actively we are wearing new studios and the leaders, we are using solar special programs, what may knock down if we're there.
48:20
We have to keep doing awesome ideas.
48:20
Nice.
48:21
They do you know.
48:21
Ooh,