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Elon Musk's Tesla 2025 q3 investor relations · Elon Musk Podcast

0:01
Hey, everybody. Welcome back to the Elon Musk
0:04
Podcast. This is a show where we discuss
0:07
the critical crossroads, the Shape, SpaceX, Tesla X, The
0:11
Boring Company and Neurolink. I'm your host, Will Walden.
0:15
Good afternoon, everyone, and welcome to Tesla's third quarter
0:18
2025 Q&A webcast. My name is Travis Axelrod, Head
0:23
of Investor Relations, and I'm joined today by Elon Musk,
0:26
Vaibhav Taneja and a number of other executives.
0:29
Our Q3 results were announced at about 3:00 PM Central Time in
0:33
the update deck, we published at the same link as this webcast.
0:37
During this call, we will discuss our business outlook and
0:40
make forward-looking statements. These comments are based on our
0:43
predictions and expectations as of today.
0:45
Actual events or results could differ materially due to a
0:48
number of risks and uncertainties, including those
0:53
mentioned in our most recent filings with the SEC.
0:56
We urge shareholders to read our definitive proxy statement,
0:58
which contains important information about the matters we
1:01
voted on at the 2025 Annual Meeting.
1:05
During the question and answer portion of today's call, please
1:07
limit yourself to one question and one follow up.
1:10
Please use the raise hand button to join the question queue.
1:14
Before we jump into Q&A, Elon has some opening remarks.
1:17
Elon. Thank you.
1:22
We're we're at a critical inflection point for Tesla and
1:27
our strategy going forward as we bring AI into the real world.
1:30
I think it's important to emphasize that Tesla really is
1:34
the leader in real world AI. No one can do what we can do
1:39
with real world AII. Have pretty good insight into AI
1:45
in general I think. That Tesla has the.
1:47
Highest intelligence density of any AI out there in the car and
1:51
that is only going to get better.
1:54
And really just the beginning of scaling at a, at a quite
1:57
massively full self driving and robo taxi and fundamentally
2:02
changing the nature of transport.
2:03
I think people just don't don't quite appreciate the degree to
2:06
which this will take off where the the it's honestly, it's
2:12
going to be like a shockwave. So it's it's because because the
2:23
cars are all out there, they're. You know, we have millions of
2:26
cars out there that with a software update become full self
2:33
driving cars. And, and you know, we're making
2:38
a couple million a year. And, and in fact, with the
2:43
advent of with, with what we see now as as a clarity on achieving
2:49
full self driving, unsupervised full self driving, I should say
2:54
I feel confident in expanding Tesla's production.
2:58
So that is that is our intent to expand as quickly as we can our
3:04
future production. So I was, I was resident to do
3:08
that until we had clarity on, on achieving unsupervised full self
3:14
driving. But at this point, I, I feel
3:17
like we've got clarity and it, it makes sense to expand
3:22
production as as fast as we reasonably can.
3:26
We're also making huge make making impact, impact on the
3:33
energy sector with, with battery storage.
3:38
So with both Powerwall and especially with the Mega Pack,
3:43
we are dramatically improving the ability to generate more
3:49
energy from the grid. Let me sort of talk a little bit
3:52
about that, which is if you, if you look at total US energy
3:58
capability for example, there's roughly a terawatt of of
4:03
continuous power available in the US, but the average usage
4:07
over 24 hour cycle is only half a terawatt because of the.
4:11
Big difference between day and night usage.
4:15
If you buffer that buffer the energy with batteries, you can
4:18
effectively double the energy output in the United States just
4:21
with batteries building, no incremental power plants.
4:24
And it's very. Difficult to build power plants,
4:26
so they take a long time. There's a lot of permitting and
4:30
it's not an industry that's used to moving fast.
4:32
So we see the potential there for Tesla battery packs to
4:40
greatly improve the, the energy output per year for any given
4:45
grid, US or otherwise. We're also on the cusp of, of
4:53
something really tremendous with optimists, which I think is
4:59
likely to be or has potential to be the biggest product of all
5:02
time. And it's it's a difficult
5:09
project. And it's worth noting that it's
5:12
not like it's it's just automatic.
5:15
I'm unaware of any robot programmed by Ford or GM or, you
5:18
know, and by US sort of car. Companies, people like I think
5:22
maybe think of Tesla as as a car company.
5:24
We mostly make cars and battery packs, but so it's not like it's
5:32
not just like an obvious. Fall off a log thing to make
5:35
Optimus, but but we do have the ingredients with of real world
5:40
AI and exceptional electrical mechanical engineering
5:46
capabilities and the ability to scale production, which I don't
5:51
think anyone else has all of those ingredients.
5:57
So yeah, with with version 14 of the of self driving.
6:06
Which people you can see the reactions of of people online,
6:11
they're quite amazed actually anyone in the US can get version
6:16
14 if they just go and select I want the advanced software in
6:20
their car. So if if you're listening right
6:23
now and you'd like to try it out, just go in in settings and
6:28
say I want the advanced software and you will get version 14.
6:35
And yeah, so on the Mega Pack front, we've we unveiled Mega
6:41
Block, Mega Pack 3. We also have exciting plans for
6:45
Mega. Pack 4, Mega Pack 4 will
6:48
incorporate a lot of the, a lot of what is normally in a
6:54
substation and be able to output at probably 35 kilovolts
7:04
directly. So this, this greatly improves
7:07
our ability to deploy Megapack because it's not dependent on
7:11
building a substation of 335 KV or Megapack 4.
7:15
So that that'll be next. That's the that's the
7:19
engineering priority for Megapack.
7:25
And we look forward to unveiling Optimus V3, you know, probably
7:32
in Q1. I think it'll be ready for to,
7:37
to show off. And that that I think is going
7:41
to be quite remarkable. If you, it won't even seem like
7:47
a robot. It'll seem like a person in a
7:50
robot suit, which is kind of how we started off with Optimus.
7:55
But it it'll seem so real that you'll need to like poke it, I
8:00
think to believe that it's actually a robot.
8:06
And and obviously, like the the real world intelligence we've
8:09
developed, we've developed for the car.
8:13
Most of that transfers to Optimus.
8:16
So it's a very good starting point.
8:19
In conclusion, we're excited about the, you know, updated
8:23
mission of Tesla, which is sustainable abundance.
8:27
So going beyond sustainable energy to say.
8:31
Sustainable abundance is the mission where we we believe with
8:39
with optimist and self driving. That you can actually create a
8:47
world where there is no poverty, where everyone has access to the
8:54
finest medical care. Optimist will be an incredible
9:00
surgeon, for example, and imagine if everyone had access
9:04
to an incredible surgeon. So, so I, I think there's, you
9:12
know, of course we need to make sure Optimus is safe and
9:14
everything, but, but I, I do think we're headed for a world
9:18
of sustainable abundance and that I'm excited to work with
9:21
the Tesla team to make that happen.
9:27
Great. Thank you very much.
9:28
Elon Vebub also has some opening remarks.
9:31
Thanks, Travis. Q3 was a special quarter at
9:35
multiple levels. We set new records not just for
9:37
deliveries and deployments, but also around a range of financial
9:42
metrics from total revenues, energy, gross profit, energy
9:46
margins to fresh free cash flow. This was the result of continued
9:51
confidence of our customers in our products and the relentless
9:54
efforts by the Tesla team. The strength and deliveries was
9:58
attributed to strong performance across all regions.
10:02
Greater China and APEC were up sequentially 33 and 29%
10:06
respectively. North America was up 28%, while
10:10
EMEA was up 25%. The base in deliveries was the
10:13
function of continued excitement around the new Model Y We had
10:17
previously talked about 2025 being the year of the Y and have
10:21
since delivered on that promise with the new Model Y released in
10:25
Q1. Followed by Model Y, long
10:28
wheelbase and performance and more recently Standard Y in
10:32
North America and EMEA. We're now operating a robotaxi
10:36
in two markets, Austin and most Bay Area cities.
10:40
We've already expanded our coverage area in Austin three
10:43
times since the initial launch and are on pace to continue
10:47
expanding further. Unlike our competitors, our
10:50
robotaxi fleet blends in. The markets we operate in since
10:55
they don't have extra sensor sets or peripherals which make
10:59
them stick out. This is an under appreciated
11:02
aspect of our current vehicle offerings which are all designed
11:06
for autonomous driving. We feel that as experience, as
11:11
people experience the supervised FSD at scale, the demand for our
11:16
vehicles, like Elon said, would increase significantly on the
11:20
FSD adoption front. We we've continued to see decent
11:24
progress. However, note that total paid
11:27
FSD customer base is still small, around 12% of our current
11:30
fleet. We're.
11:32
Moving or we're working with regulators in places like China
11:36
and EMEA to obtain approval so that we can get FSD in those
11:40
regions as well. Now covering a little bit on the
11:44
financial side, automotive revenues increased 29%
11:48
sequentially in line with the growth and deliveries.
11:51
While regulatory credits declined sequentially, we
11:55
entered into new contracts and continued delivery on previously
11:59
entered contracts. Our automotive margins excluding
12:04
credits increased marginally from 15% to 15.4, which was
12:08
attributed to improvements in material cost and better fixed
12:12
cost absorption due to higher volumes.
12:16
The energy storage business continue to deliver with record
12:19
deployments, gross profit and margins.
12:22
As discussed before, this business has a bigger impact
12:25
from tariffs as measured by percentage of COGS, since
12:29
currently all sales procured are from China while we're still
12:33
working on other alternatives. However, as the ramp of mega
12:39
factory Shanghai is happening, this is helping us avoid
12:43
tariffs. Because we are using this
12:45
factory to supply the non-us demand, like Elon said, you know
12:50
grid scale storage, the only way we can get to electricity
12:55
fastest is by using storage. The other thing to keep in mind
13:00
is we are seeing headwinds in this business given the increase
13:04
in competition and tariffs, the total tariff impacts for Q3.
13:08
For both businesses was in excess of 400 million generally
13:13
split evenly between them. Services and other demonstrated
13:19
a marked improvement sequentially.
13:20
This was a function of improvements primarily in our
13:23
insurance and service center businesses.
13:26
Note that while small, our robotaxi costs are included
13:30
within services and other along with our other businesses like
13:33
paid supercharging, used car parts and merchandise sales
13:37
etcetera. Our operating expenses increase
13:41
sequentially, the largest increase included in
13:45
restructuring and other related to certain actions undertaken to
13:48
reduce cost and improve efficiency to convergence of our
13:52
ARAI chip design efforts. Additionally, we incurred legal
13:58
expenses related to proceedings in certain legal cases as well
14:02
as incremental cost incurred in preparation for our shareholder
14:07
meeting. Such costs are recorded within
14:10
STNA. Further, our employee related
14:13
spend is increasing especially in R&D as we have recently
14:16
granted various performance based equity awards to employees
14:20
working on AI initiatives and therefore such spend will
14:24
continue to increase going forward.
14:27
On other income, our other income decreased sequentially
14:31
primarily from mark to market adjustments on BTC holdings,
14:34
which was much smaller gain of 80 million in.
14:37
Q3 versus 284,000,000 in Q2. With the rest of the movement
14:41
attributable to FX movements in the quarter, our free cash flow
14:46
for the quarter was approximately 4 billion, which
14:49
was yet another record. Our total cash and investments
14:54
at the end of the quarter were over 41 billion.
14:57
On the CapEx front, while we are expecting to be around 9 billion
15:01
for the current year, we're projecting the numbers to
15:04
increase substantially in 2026 as you prepare the company for
15:08
the next phase of growth in terms of not just our existing
15:11
businesses, but our bets around AI initiatives including
15:15
Optimus. In conclusion, note that
15:19
bringing AI into real world is hard, but we have never shield
15:23
away from doing what is hard. We are extremely excited about
15:27
the future and are laying down the foundation, the benefits of
15:31
which will be realized over years to come.
15:34
I would like to end by thanking the Tesla team, our customers,
15:38
our investors and supporters for the continued belief in us.
15:44
Thank you very much, Vaibhav. Now let's go to
15:46
investorquestionsfromsay.com. The first question is what are
15:51
the latest robo taxi metrics? Fleet size, cumulative miles
15:55
rides, completed intervention rates, and when will safety
15:58
drivers be removed? What are the obstacles still
16:01
preventing unsupervised FSD from being deployed to customer
16:04
vehicles? I'll, I'll start.
16:07
Off with that and then Shark can elaborate.
16:10
But we we are expecting to have no safety drivers in at least
16:19
large parts of Austin by the end of this year.
16:21
So within a few months, we expect to have no safety drivers
16:25
at all in at least in parts of Boston.
16:28
We're obviously being very cautious about the deployment.
16:32
So, so our goal is to be actually paranoid about
16:36
deployment because obviously even one accident will be front
16:40
page headline news worldwide. So I guess it's better for us to
16:45
take a cautious approach here. But we do expect to have no, no
16:49
safety drivers in the car in Austin within a few months.
16:55
I think that's perhaps the most important data point.
16:58
And then we, we do expect to be operating Robotaxi in I think
17:06
about 8 to 10 metro areas by the end of the year.
17:11
You know, it depends on various regulatory approvals and, but
17:17
you, you can actually, I think most of our regulatory
17:19
applications are online. You can kind of see them because
17:21
they're they're public information.
17:24
But we expect to be operating in Nevada and Florida and Arizona
17:30
by the end of the year. Ashok Yeah.
17:34
We we continue to operate our fleet in Austin without anyone
17:38
in the driver's seat and we have covered more than a quarter
17:41
million miles with that. And then in the Bay Area where
17:45
we still have a person in the driver's seat.
17:47
So the regulations we cross more than 1,000,000 miles.
17:51
So and we continue to see that the fleet robotaxi fleet works
17:55
really well. Customers are really happy and
17:59
there's no notable issues on the customer side via customers have
18:03
used FSE supervised for a total of 6 billion miles as of
18:08
yesterday. So that's like a big milestone.
18:10
And overall the safety continues to be very good.
18:14
And as I mentioned, we are on on track to remove the person from
18:19
inside the car all together starting with Austin.
18:25
Great. The next question is what is the
18:29
demand and backlog for Megapack, Powerwall, solar or energy
18:34
storage systems? With the current AI boom, is
18:37
Tesla playing to supply power to other hyperscalers?
18:42
Thanks. Demand for Megapack and
18:44
Powerwall continues to be really strong into next year.
18:47
We received very strong positive customer feedback on our Mega
18:52
Black Mega block product, which will begin shipping next year
18:55
out of Houston. And we're seeing remarkable
18:58
growth in in the demand for AI and data center applications as
19:01
hyperscalers and utilities have seen the versatility of the
19:04
Megapack product to increase reliability and receive and and
19:08
relieve grid constraints. As Elon was talking about, we
19:12
we've also seen a surge in residential solar demand in the
19:15
US due to policy changes, which we expect to continue into the
19:18
first half of 2026 as we introduced a new solar lease
19:21
product. And we also began production of
19:24
our Tesla residential solar panel in our Buffalo factory and
19:28
we will be shipping that to customers starting Q1.
19:30
The panel has industry leading aesthetics and shape performance
19:34
and demonstrates our continued commitment to US manufacturing.
19:42
Great. Thank you, Mike.
19:44
Unfortunately, the next question is related to future products.
19:47
This is not the appropriate venue to cover that, so we're
19:49
going to have to skip it. The question after that is what
19:53
are the present challenges in bringing Optimist to market
19:56
considering app control, software engineering, hardware
20:00
training, general mobility models, training task specific
20:03
models, training voice models, implementing manufacturing and
20:07
establishing supply chains. Yeah, I mean bringing office
20:15
Optimist market is an incredibly difficult task.
20:17
To be clear. It's not like some walk in the
20:19
park at some point. I mean at this actually
20:22
technically Optimist can walk in the park right now.
20:25
And we do have Optimist robots that walk around our offices at
20:31
our engineering headquarters in Palo Alto, CA basically 24 hours
20:36
a day, seven days a week. So any visitors that come by you
20:39
actually you can, you can stop to stop one of the Optimist
20:43
robots and ask it to take you somewhere and it'll literally
20:46
take you to that meeting room or that location in the building.
20:51
So I don't want to downplay the difficulty of Optimist.
20:55
It's, it's an incredibly difficult thing, especially it's
20:59
difficult to create a, a hand that is as dexterous and capable
21:04
as the human hand, which is an incredible, the human hand is an
21:07
incredible thing that the more you study the human hand, the
21:11
more incredible you realize the human hand is and, and why you
21:14
need 5, you know, 4 fingers in the thumb.
21:17
Why the why the fingers have certain degrees of freedom.
21:20
You know why, why the, the various muscles are of different
21:25
strengths. The fingers are of different
21:26
lengths. And it turns out actually that
21:31
all that, those are all there for a reason.
21:35
And so making it, making it that the hand and, and forearm,
21:41
because most of the, most of the actuators, just like the human
21:43
hand, the muscles of that control your hand are actually
21:46
primarily in your forearm. The optimist hand and forearm is
21:50
an incredibly difficult engineering challenge.
21:54
It's, I'd say it's more difficult than the rest of, from
21:57
an electromechanical standpoint, the forearm and hand are, it's
22:00
more difficult than the entire rest of the robot.
22:05
So, but really in order to have a, a useful generalized robot,
22:10
you, you do need this, you do need an incredible hand and, and
22:15
then you need the real world AI and you need to be able to scale
22:20
up that production to have it be relevant, because it's not
22:24
relevant if it's just a few 100 robots.
22:28
But so you need to be able to make Optimus robots at volumes
22:32
comparable to vehicles, if not significantly higher.
22:38
So if you're trying to make a million or something per year,
22:42
I'm trying to make a million Optimus robots per year.
22:45
That manufacturing challenge is immense considering that the
22:48
supply chain doesn't exist. So with with cars, you've got an
22:52
existing supply chain. With computers, you've got an
22:55
existing supply chain. With, with a humanoid robot,
22:59
there is no supply chain. So in order to, to manufacture
23:04
that, Tesla actually has to be very vertically integrated and
23:09
manufacture very deep into the supply chain, manufacture the
23:15
parts internally because there just is no supply chain.
23:20
So this is this is the kind of thing where I'm like, if I put
23:23
myself in the position of a startup trying to make an
23:25
humanoid robot, I'm like, I don't know how to do it without
23:29
and an immense amount of manufacturing technology.
23:35
So that's that's why I think like Tesla's in almost a unique,
23:39
I think, I think unique position when you consider manufacturing
23:42
technology, scaling real world AI and the and a truly dexterous
23:49
hand. Those are the generally the
23:52
things that are missing when you read about other robots that
23:56
they just don't have those three things.
24:00
So now I think we can achieve all those things, those those
24:04
three things with an immense amount of work.
24:08
And, and that, that is that is the game plan.
24:11
So, you know, my, like my, you know, fundamental concern with
24:20
regard to how much voting control I have in Tesla is if I
24:25
go ahead and build this enormous robot army, can I just be ousted
24:28
at some point in the future? That's my biggest concern.
24:32
If I that's, that, that is the, that is really the only thing
24:34
I'm trying to address with with this.
24:37
So it's called compensation, but it's not like I'm going to go
24:40
spend the money. It's just if we build this robot
24:44
on me. Do I have at least?
24:48
A strong influence over that robot on me.
24:51
It's not control, but a strong influence.
24:54
That's that's what it comes down to in a nutshell.
24:57
Like I don't feel comfortable building that robot on me if I
24:59
don't have at least a strong influence.
25:04
Great. Thank you.
25:06
We've already covered robotaxi expansion.
25:10
Unfortunately, the question after that is another future
25:12
product question. So we're going to have to skip
25:14
that. The next one though is can you
25:16
update us on the $16.5 billion Samsung chip deal?
25:20
And Taylor, given the importance of semiconductors to autonomy
25:23
and Tesla's AI driven future, what gives you confidence
25:26
Samsung can fulfill AI 6 at Tesla's timelines and achieve
25:30
relatively better yields and cost versus TSTSMC?
25:35
OK, so I'm going to give quite a long answer to this question
25:39
because I have to unpack this question and then and then
25:42
answer the unpacked version. So first of all.
25:49
I have nothing but great things to say about Samsung.
25:51
They're an amazing company and Samsung is worth noting this
25:57
manufacturer our AI4 computer and does a great job doing that.
26:03
So now with the, the AI5 and here's I, I need to make a point
26:12
of clarification relative to some comments I've made publicly
26:16
before, which is we're actually going to focus both TSMC and
26:21
Samsung initially on AI5. So the, the AI5 chip design by
26:28
Tesla is I, I think it's an amazing design.
26:35
I've spent almost every weekend for last last few months with
26:41
the chip design team working on AI5 and I, I don't hand out
26:47
praise easily, but I have to say that I think, I think the Tesla
26:52
chip team is, is really designing an incredible chip
26:56
here. This is by some metrics, the AI5
27:00
chip will be 40 times better than the AI4 chip, not 40%,
27:07
forty times because we, we, we have a detailed understanding of
27:13
the entire software and hardware stack.
27:15
So we're designing the hardware to address all of the pain
27:20
points and software. So I don't think there's, there
27:23
really isn't anyone that's doing this the entire stack all the
27:28
way through real world, you know, calibrating against the
27:33
real world where you've got cars and robots in real world that
27:36
like we know what the chip needs to do and we know what, just as
27:40
importantly, we know what the chip doesn't need to do.
27:46
You know, to sort of give you some examples here with the AI
27:50
5:00, we, we deleted the the legacy GPU or the OR the
27:57
traditional GPU, which is it, it's in AI 4, but AI 5 does not
28:03
have, we just just deleted the legacy GPU because it basically
28:09
is a GPU. So we also deleted the image
28:14
signal processor. And there's, there's like a long
28:20
list of actually of deletions that are very important.
28:25
As a result of these deletions, we can actually fit AI5 in 1/2
28:31
reticle and. With with with good margin for
28:37
the traces from the memory to the the trip, the Tesla trip
28:43
accelerators. The ARM, the ARM CPU course and
28:51
and the PCI. Sort of the PCI blocks.
28:56
So this, this is a beautiful chip.
29:04
I've poured so much life energy into this.
29:06
Chip personally and I'm I'm confident this will be this is
29:10
going to be a winner next level. So it makes sense to have both
29:17
Samsung and TSMC focus on AI5 and so like the technically the
29:25
Samsung fab has slightly more advanced equipment than the
29:28
TSMC. Fab, these will be.
29:30
Both be made in in the US that in one TSMC in Arizona, Samsung
29:35
in Texas and but but it's it it we're going to make.
29:43
Starting off, just to be confident of having our, our,
29:47
our goal, explicit goal is to have an oversupply of AI5 chips.
29:54
Because if, if we, if we have too many AI5 chips for the cars
29:57
and, and and robots, we can, we can always put them in the data
30:00
center. So we already use AI4 for for
30:06
training in our in our data center.
30:08
So we use a combination of AI4 and NVIDIA hardware.
30:13
So we're not about to replace NVIDIA to be clear, but but but
30:18
we do use both in combination AI four and NVIDIA hardware and the
30:24
AI5 excess production we can always put in in our data
30:28
centers. NVIDIA keeps keeps improving
30:34
that. The challenge that they have is
30:35
that they've got to satisfy a large range, a lot of
30:38
requirements from a lot of customers, but Tesla only has to
30:42
satisfy requirements from one customer with Tesla that that
30:45
makes the design job radically easier and means we we can
30:49
delete a lot of complexity from the chip.
30:53
Like I can't emphasize how important this is.
30:56
So like when when you look at the various logic blocks in the
30:58
chip, as you increase the number of logic blocks, you also
31:02
increase the interconnections between the logic blocks.
31:05
So you can think of it like there's just highways, like how
31:09
many highways do you need to connect the various parts of the
31:12
chip? And especially if you're not
31:14
sure how much data is going to go between each, you know, logic
31:19
block on the chip, then you, you kind of end up having giant
31:22
highways going all over the place.
31:25
It's a very, it like it becomes an almost impossibly difficult
31:29
design problem. And Nvidia's done an amazing job
31:31
of dealing with almost an impossibly difficult set of
31:33
requirements. But in our case, we, we, we're
31:37
going for radical simplicity. And the net effect is that I, I,
31:43
I think AI 5 will be the best performance per Watt, maybe by a
31:50
factor of two or three and the best performance per dollar for
31:54
AI, maybe by a factor of 10. So you know, that's you know.
32:03
We'll have to. The proof's in the pudding, so
32:05
obviously we need to actually get this chip made and made it
32:08
scale, but that's what it looks like.
32:15
Great. Thank you, Elon, we've already
32:18
covered unsupervised FSD. So the next question is, instead
32:22
of trying to replace hardware 3 with hardware 4, why not give an
32:25
equal incentive to trade in for a new vehicle?
32:29
Yeah, not completely given on upon hardware three.
32:33
However, over the last year we've offered the customers the
32:36
option to transfer FSD to their new vehicle with which at times
32:41
you've been running some promotions if if they got FSD,
32:45
they can get better preferential rates.
32:48
So we've been definitely taking care of this.
32:51
But we do want to solve autonomy first and then we'll come back
32:54
with a way to take care of these customers.
32:56
These customers are very important.
32:58
They were the early adapters. For what it's worth.
33:01
My daily commuter is a hardware 3 car which I use FSD on a daily
33:05
basis, so we will definitely take care of you guys.
33:12
Great and thank you. Once the V14 release series is
33:17
fully done, we are planning on working on AV14 Lite version for
33:22
Hardware 3, probably expected in Q2 next year.
33:29
Awesome. Thanks Ashok.
33:31
Alrighty, our final question from say is how long until we
33:36
see self driving Tesla semi trucks and could you see this
33:39
technology replacing trains? Yeah.
33:42
So I guess I'll start with that. In terms of the semi production
33:46
plan and schedule, so the factory is is going on schedule.
33:49
We've, you know, completed the building and are installing the
33:52
equipment. Now we've got our fleet of
33:54
validation trucks driving on the road.
33:58
We'll have larger builds towards the end of this year and then
34:00
our first online builds in the first part of next year ramping
34:03
into you know the Q2 timing with real volume coming in the back
34:06
half of the year. So that's going quite well and
34:09
and that's the first step to obviously getting autonomous
34:12
trucks on the road. In terms of trains, you know,
34:15
they're really great for long point to point deliveries are
34:18
super efficient, but you know, that last mile, the load unload
34:23
can be better served for in shorter distances with
34:25
autonomous semis. And that would be great.
34:28
And so we do expect that to probably shift.
34:29
And as we, you know, really, as Elon said, change the way
34:33
transportation is considered. And so we're looking forward to
34:36
that timeline. And Ashok, I know you can can
34:39
take the the full self driving part.
34:41
Currently, the team is super focused on solving for passenger
34:44
vehicles autonomy. That said, the same technology
34:47
will extend quite easily to the semi truck once we have a little
34:51
bit of data from the semi trucks.
34:56
Great. And now we will move over to
34:59
analyst questions. The first question comes from
35:02
Emmanuel at Wolf. Emmanuel, please go ahead and
35:05
unmute yourself. Great.
35:10
Thanks so much. Hi, everybody.
35:12
So Ilan, you talked about expanding production of vehicles
35:18
as fast as possible. Now that you have confidence in
35:21
the unsupervised autonomy. How should we think about that
35:25
in the context of your existing capacity of 3,000,000 units?
35:30
Is that where you're hoping to get volume to?
35:33
What sort of timeline are we talking about?
35:36
And would this require, you know, some level of boosting or
35:42
incentivizing demand? Like would this basically be
35:45
prioritizing volume over near term profitability given the
35:49
longer term opportunity? Well, our.
35:54
Capacity isn't quite 3,000,000, but it, it, it will be 3,000,000
36:02
at some point. You know, aspirationally, you
36:05
know, it could be 3,000,000 within we could probably hit an
36:08
annualized rate of 3,000,000 within 24 months, I think maybe
36:12
less than 24 months. Bearing in mind like this,
36:16
there's an entire like supply chain, like a vast supply chain
36:19
that's got to also move in tandem with that.
36:23
So I think we're going to, we're going to expand production as
36:28
fast as, as, as, as we can and as fast as our suppliers can,
36:33
can, can sort of keep up with it.
36:37
And then we're going to think about where, where do we build
36:41
incremental factories beyond that, Like the single biggest
36:47
expansion in production will be the the Cyber Cab, which starts
36:49
production in Q2 next year. That's, that's really a vehicle
36:54
that's optimized for full autonomy, in fact does not have
36:58
a steering wheel or pedals and is really an engineering
37:02
optimization on minimizing cost per mile, if you like fully
37:05
considered cost per mile of operation.
37:09
So that's. You know, for the other, for
37:12
the, for other vehicles, they're still, they still have a little
37:15
bit of the horseless carriage thing going on where you know,
37:17
obviously you've got if you're still, if you've got steering
37:19
wheels and pedals and. And and you're designing a car
37:22
that people might want to go, you know, very direct past
37:27
acceleration and tight cornering, like high performance
37:30
car, you know, cars. Then you're going to design a
37:33
different car than one that is optimized for a comfortable
37:35
ride, but doesn't expect to go, you know, past sort of 85 or 90
37:40
miles an hour. And it's just aiming for a
37:43
gentle ride the whole time. That's what cybercap is.
37:48
So yeah, so so it's do I think we'll sacrifice margins?
37:56
I don't think so. I think the demand will be
37:59
pretty naughty. But like here's the here's the
38:02
killer app. Really what it comes down to is
38:04
can you text? Can you text while you're in the
38:06
car? And if you tell someone yes that
38:09
the car is now so good, you can you can you can be on your phone
38:16
and text the entire time while you're in the car.
38:18
It's anyone who can buy the car will buy the car and A and a
38:23
story. So that's what everybody wants
38:29
to do. In fact, not everyone wants to
38:31
do they do do that. And that's why, in fact, the
38:33
reason you've seen like there's been an uptick in accidents very
38:38
much worldwide is because people are.
38:40
Texting and driving. So Autopilot actually
38:44
dramatically improves the safety here.
38:47
Because if somebody's looking down at their phone, they're not
38:49
driving very well. So that's, that's really the,
38:54
the game changer. And you know, we, we do see like
39:01
at this point, I feel, you know, essentially 100% confident.
39:06
I say not essentially 100% confident that we can set that
39:10
we can solve unsupervised full full self driving at a safety
39:14
level much greater than human. So we've released 14.1.
39:22
We've got a technology road map that's I think pretty amazing.
39:27
We'll be adding reasoning to the car.
39:31
Our world simulator for for reinforcement learning is is
39:37
pretty incredible. Like our like our when you see
39:40
it that the Tesla reality Simulator it's you can't tell
39:43
the difference between the video that's generated by the Tesla
39:47
reality simulator and the actual video looks exactly the same.
39:52
So that allows us to have a very powerful reinforcement learning
39:58
loop to further improve the Tesla AI, we're going to be
40:02
increasing the parameter count by an order of magnitude that
40:07
that's not in 14.1. There are also a number of other
40:11
improvements to the AI just. That, that are that are quite
40:16
radical. So it's this, this car will feel
40:23
like it is a living creature. That's how good the the AI will
40:26
get with the AI4 computer with this before AI5 and then and
40:31
then AI5, like I said, is by some metrics 4040 times better.
40:35
But let's just say safely it's a 10X improvement.
40:40
So it might almost be too much intelligence for a car.
40:42
I do wonder, like how much intelligence should you have in
40:44
a car? It might get bored actually.
40:51
And then one of the things I thought like, well, if we got
40:54
all these cars that maybe are bored, well, while they're,
40:56
while they're sort of, if they are bored, we, we could actually
41:00
have a giant distributed inference fleet and say like,
41:03
well, if they're not actively driving, let's just have a giant
41:06
distributed inference fleet. You know, at some point if, if
41:11
you've got like 10s of millions of cars in the fleet or maybe at
41:14
some point 100 million cars in the fleet.
41:17
And let's say they had at at that point, you know, like I
41:25
don't know, a kilowatt of inference capability of, you
41:30
know, high performance inference capability, that's 100 gigawatts
41:34
of inference distributed with, with power and cooling taken
41:39
with, with, with cooling and, and power conversion taken care
41:42
of. So that seems like a pretty
41:49
significant asset. Great.
41:54
Thanks, Elon. The next question comes from
41:56
Adam from Morgan Stanley. Adam, please feel free to unmute
42:00
yourself. Adam, go ahead and ask your
42:08
question. Seems like we might be having
42:14
some audio issues with Adam, so we'll come back to you.
42:17
The next question will then come from Dan from Barclays.
42:26
Hi, good evening. Thank you for taking the
42:29
question. Elon, I, I know that Tesla's
42:33
really focused on with master plan for bringing AI into the
42:37
physical world. And I think we've seen over the
42:39
past, you know, this willingness for Tesla to engage and, and go
42:44
into new markets, new Tams. So when you think about the
42:48
growth prospects, how do we define the areas that are really
42:52
within Tesla's core competency versus where do you draw the
42:56
line for markets or AI applications that are outside of
43:02
Tesla's core competency? Actually, I'm not sure what you
43:07
mean by AI applications outside of Tesla's core competency.
43:12
But and we, we kind of, we didn't have any of these core
43:17
competencies when we started. You know, so it's like we had 0
43:22
core competencies, total competency of 0 actually.
43:26
So, I mean, you can think of Tesla as like, I don't know, a
43:29
dozen start-ups in one company, you know, and, and I've
43:34
initiated every one of those start-ups.
43:36
So it's, it wouldn't used to make battery packs, so
43:41
stationary battery packs, but now we're due make them for the
43:44
home, make them for, you know, utility scale with power wall
43:47
mega pack. We've created the Supercharger
43:52
network globally. No one, no one else has created
43:55
a global Supercharger network. In fact, that North American
43:58
Supercharger network's so good at that, that basically that
44:02
yeah, every other manufacturer in North America is converted to
44:04
our standard and uses our the Tesla Supercharge network.
44:10
But if it was so easy, why don't they just do it?
44:14
And the chip design team started that from scratch.
44:20
The Tesla AI software team was started from scratch.
44:24
I literally just say, hey, we're going to start this thing I
44:27
posted on Twitter now X and then, you know, join us if you'd
44:32
like to build it. In fact, Ashok was, I believe,
44:36
the first person I interviewed for the the Tesla autopilot
44:39
team, which we now call the Tesla AI software team, which
44:42
because it is the AI software team.
44:46
So you know, it's core companies, competencies created
44:49
while you wait and you know, Optimus at scale.
44:57
It is the infinite money glitch. It's like this is a, it's
45:03
difficult to express the magnitude of like if you've got
45:09
something that like that, like if I.
45:14
Optimist, I think probably achieve 5X to the productivity
45:16
of a person per year because it can operate the 24/7.
45:22
It doesn't even need to charge. It can operate it tethered.
45:24
So it's plugged in the whole time and whatever.
45:32
So it it that's that's why I call it like if, if you're true
45:34
of sustainable abundance, we're working will be optional.
45:40
You know, there's there's a limit to how much, how much AI
45:42
can do in terms of enhancing the productivity of humans, but
45:47
there is not really a limit to AI that is embodied.
45:53
That's why I call it the infinite money glitch.
45:59
I mean, one thing which I'll further add is I mean, people
46:02
forget like our first iteration of Autopilot was 10 years back.
46:07
So you know, Elon had started this way back in the day.
46:10
We've got the tweets to prove it exactly.
46:13
And then even even on the optimist side, right, as much as
46:17
people think, OK, this is a new thing, I still remember, was it
46:20
4 plus years back? We were in a finance meeting
46:23
with Elon and Elon said, hey, our car is a robot on wheels and
46:28
that's where we started developing.
46:30
In fact, most of the engineering team which is working on Optimus
46:34
has come from the vehicle side. And that's why, you know, when
46:38
we talk about manufacturing progress, we have the
46:41
wherewithal because the same engineers who worked in the back
46:45
in the day on drive units are working on actuators now.
46:49
So that's where we can do if there's any company which can do
46:52
it at scale, that is going to be us.
46:55
But we we also have actually added a lot of new engineers as
46:58
well to the team. So there's actually a lot of the
47:01
credit for the Optimus Engineering is, is actually also
47:05
new, new engineers, many of them that are just out of college
47:08
actually. So the Optimus engineering team
47:13
is a. Very talented engineering team,
47:17
I'd say like wow, actually so and you know, the optimist
47:25
reviews at this point are the the engineering review and then
47:31
there's the manufacturing review being done.
47:33
It's simultaneously with an iterative loop between
47:39
engineering, design and and manufacturing, because then we
47:42
see. We, we.
47:43
We. We.
47:43
Designed something and we see like, oh man, that's really
47:45
difficult to make. We need to change that design to
47:48
make it easier to manufacture. So we've made radical
47:51
improvements to the design of Optimus while increasing the
47:55
functionality, but making it actually possible to manufacture
47:58
like I'd say Optimus 2 is almost.
48:00
Impossible to manufacture, frankly, but by two bipolars
48:06
point we've gone from, you know, a person in a robot outfit to
48:12
what what people have seen with Optimus 2.5 where it's doing
48:15
Kung Fu. You know, it was like after this
48:18
was at the at the Tron premiere doing Kung Fu, you know, just
48:24
out in the open, you know, like with Jared Leto, like there
48:28
wasn't nobody was controlling it.
48:31
It was just doing Kung Fu with Jared Leto, you know, at the
48:36
Tron premiere. You can see the videos online
48:41
and actually The funny thing is like a lot of people walked past
48:43
it thinking it was just a person.
48:48
Even though with Optimus 2.5, you can see that it has, you
48:52
know, a waist that's three inches wide that results in not
48:55
a human. So but the movements were so
49:00
human like that people didn't realize.
49:01
A lot of people didn't realize they were looking at a robot.
49:04
So, and what I'm saying is like Optimus 3 will be a giant
49:11
improvement on that and made it scale.
49:17
But like I said, a very difficult thing.
49:20
Yeah, the the optimist sort of engineering and manufacturing
49:26
reviews. And there's the Friday night
49:28
meeting with optimists, which sometimes goes till midnight.
49:32
And then my Saturday meeting is, is with the is, is the Saturday
49:37
afternoons with the the AI5 chip design team.
49:43
So those two things are crucial to the future of the company.
49:52
Great. And Dan, did you follow up?
49:53
Yeah. Just as a related, maybe you
49:57
could just talk about to what extent are the AI efforts at
50:00
Tesla and XAI complimentary or are they just different forms of
50:05
AI? Maybe you can just help
50:07
distinguish for the audience? Thank you.
50:09
Yeah, there there are different. Forms of AI so the you know the.
50:20
The ex AI so Grok is like a a giant model that that you could
50:25
not you could not possibly squeeze Grok onto a car, that's
50:29
for sure. It is a giant piece of a model.
50:31
It's with Grok. It's trying to say solve for
50:34
artificial general intelligence with a massive amount of AI
50:38
training compute and and inference compute.
50:43
So for example, for Croc 5 will actually only run effectively on
50:47
a GV300. That's, that's how much of A
50:49
beast that Croc 5 is. So, you know, whereas Tesla's,
50:59
you know, models are, I don't know, maybe about less than 10%
51:04
the size, maybe closer to 5% the size of, of, of Croc.
51:10
So yeah, they're, they're they're, they're really coming
51:14
at the problem from very different angles.
51:19
X and Grok are are you know they're competing with.
51:25
You know, Google Gemini and opening I ChatGPT and that kind
51:28
of thing. So, and some of it's
51:33
complementary, for example, for grok voice, being able to
51:37
interact with grok in the car is cool Grok for you know, Optimus
51:44
voice recognition and voice generation is grok.
51:49
So that's that's helpful there. But they are.
51:52
Coming at it from kind of opposite ends of the spectrum.
51:56
Already, Adam, let's give it another try.
52:01
When you're ready, please unmute yourself for the next question.
52:09
Already unfortunately still having audio issues, so we're
52:13
gonna move on to Walt from Light Shed.
52:20
Walt, please go ahead and unmute yourself can.
52:26
You hear me now? Yes, Perfect.
52:29
Thank you. Just getting back to Austin, if,
52:33
if you can remove the safety driver at year end, is the
52:37
limitation the Bay Area just regulatory or is it kind of the
52:40
market by market learning process?
52:42
And I guess similarly in the 8 to 10 markets that you mentioned
52:45
to get added, is the decision there to put, you know a safety
52:49
attendant in the passenger seat or the safety driver?
52:52
And is that like your step by step process to opening up a
52:57
market or is it really just the regulation in the individual
52:59
market? Well, it's, I think, I think
53:03
even if the regulators weren't making us do it, we'd still do
53:05
that as the, as the sort of right, sort of cautious,
53:09
cautious approach to a new market.
53:11
So just to make sure that we're being, you know, paranoid about
53:15
safety, I think it makes sense to have a sort of a sort of a
53:21
either safety driver or safety occupant in the car when we
53:25
first go to new markets. To just to confirm that there's
53:28
not something we're missing. Because all it.
53:31
Takes is like one in 10,000 trips to go wrong and and you've
53:34
got you've got an issue. So it's it just to make sure
53:39
like is is there some peculiarity about a city like a,
53:42
a very difficult intersection or I don't know, something that's
53:47
that's an unexpected challenge in in a city for that one in
53:51
10,000 situation. So, right, I think we, we, we
53:59
probably could just let her loose in the, in this, in these
54:01
cities, but we just don't want, we don't want to take a chance.
54:03
And, and, and like, you know, what we're talking about here
54:05
is, you know, maybe 3 months of safety driver in, in a new metro
54:11
to confirm that it's good. And then we take the safety
54:13
driver off that, that kind of thing.
54:16
OK. And then on, on FSD 14, it has a
54:20
different feel than 13 and it's also I think a little different
54:23
than what it feels like in in Austin.
54:25
Are you, is it basically def different development pass path
54:29
that you're doing in terms of the robotaxi stuff versus what
54:32
you're dropping to the early adopters?
54:34
And when you and when you push these new builds, is it that
54:38
you're you're looking for notable improvements in
54:40
intervention rates or is that largely solved and it's more
54:43
about adding the functionality like the parking, the Dr. modes
54:46
or or just the overall comfort? Now the the first priority when
54:50
we release a a major new software architecture of.
54:55
For autopilot is safety. So, so it's, it starts off with
54:59
safety, obviously safety prioritized and then we've, and
55:03
then we solve comfort thereafter, which is why I don't
55:06
recommend people take the, the initial version.
55:09
Like like that's why I say like, yeah, most people should wait
55:12
until 14.2 for before they actually download version 14
55:19
because by 14.2 we'll have addressed many of the comfort
55:21
issues. The priority is, is very much
55:26
safety 1st and then thereafter the comfort issues.
55:29
That's why most people are like, probably it'll be a little like
55:34
it'll be safe but jerky. And we just need time to kind of
55:40
smooth the rough edges and solve for comfort in addition to
55:43
safety with a with a major news autopilot architecture change.
55:52
But it really is, I mean, I, I, I know what the, you know, the
55:57
road map is for the Tesla real world AI and, and a very
56:02
granular detail. Obviously Ashok is leading that.
56:05
And, and I mean, I spent a lot of time with the team going, you
56:09
know, in, in like excruciating detail here on, on what, what,
56:17
what we're doing to improve the real world AI.
56:21
And like I said this, this car is going to feel like it is a
56:24
living creature. And that's with AI 4 before even
56:27
AI 5. Yeah, the road map is super
56:30
accelerating like it's like so like waiting so much like
56:34
released all the stuff we're working on in terms of like, you
56:37
know, what we ship to customers versus robotaxi, it's mostly the
56:41
same. Obviously customers have
56:42
somewhat features like you know, they can choose the car wants to
56:45
park in a spot or driver or something like that, which is
56:48
not super relevant for Robotaxi. But there's only a few minor
56:51
changes like those ones. But the majority of the
56:54
algorithms and architecture, everything is the same between
56:56
those two platforms. Yeah.
56:59
But and as I mentioned earlier, like we'll be adding reasoning
57:02
to, I don't know, Shrek, is that like reasoning in like 14.3,
57:07
maybe 14.4, something like that? Yeah.
57:10
Or by end of this year for sure. Yeah, so with reasoning, it's
57:14
literally going to think about which parking spot to pick at
57:18
the. So it's going to say this is the
57:19
entrance, but actually probably there's not a parking spot right
57:23
at the entrance if it's a full, you know, if the if the parking
57:27
lot is fairly full, the probability of an open parking
57:29
spot right at the entrance is very low.
57:32
But actually what it'll simply do is drop you off at the
57:34
entrance of the store and then go find a parking spot.
57:39
But it's it's going to get very smart about figuring out a
57:41
parking. Spot it's going to spot.
57:43
Figure out it's going to spot Empty spots.
57:46
Much better than human. It's got 360° vision.
57:50
And it's gonna, yeah. Yeah, like I said, just it's.
57:54
It's gonna use reasoning to solve things.
57:58
And putting that all inside the computer that has a four is the
58:02
actual challenge. And that's what the team is
58:03
working on. Because obviously you can do
58:05
reasoning on the server that takes whatever, but then in car
58:07
you need to make real time decisions.
58:10
So putting on the computer that's in the car, that's the
58:13
challenge. Yeah, that's why I say like,
58:15
like I have a pretty good understanding of like AI, you
58:19
know, the, the sort of the, the giant model level with Grog and
58:22
with, with Tesla. And like I'm confident in saying
58:25
that Tesla has the, the Tesla AI has the highest intelligence
58:28
density when you look at the, the intelligence per GB.
58:32
I think like Tesla, AI is probably an order of magnitude
58:35
better than anyone else and doesn't.
58:38
Have any choice because that that AI has got to fit in the AI
58:41
for computer. The but the the discipline of
58:46
having that level of AI intelligence density will pay
58:51
great dividends when you go to something that has an order of
58:54
magnitude, order of magnitude more capability like AI5.
58:58
Now you have that same intelligence density but but you
59:01
got 10 times more capability in the computer.
59:06
Great. The next question will come from
59:08
Colin at Oppenheimer. Colin, please unmute yourself
59:12
when you're ready. Alan, go ahead and unmute
59:20
yourself please. Thanks so much guys.
59:22
You know, I appreciate you bringing up the, the challenges
59:25
of hand dexterity and humanoids, you know, along with the
59:28
complexity of the supply chain and the, the vertical
59:30
integration you guys are pursuing.
59:32
You know, I'm just trying to harmonize the, the timeline for
59:34
the start of production, you know, next year with the current
59:37
state of the supply chain and what sounds like a fair amount
59:39
of work remaining on the dexterity before you can really
59:42
freeze the hardware design and and start to scale up
59:45
production. Well.
59:51
We're not the, the hardware design will not actually be
59:53
frozen even through startup production.
59:57
There'll be continued iteration because a bunch of the things
1:00:00
that you discover are very difficult to make.
1:00:02
You only find that pretty late in the game.
1:00:05
So we'll be doing rolling changes of of for the optimist
1:00:09
design even after startup production.
1:00:12
But I do think that the the you know, the new hand is.
1:00:18
An an incredible piece of engineering and you know,
1:00:23
that's, well, actually we'll have a production intent
1:00:27
prototype ready to show off in, you know, Q1, probably February
1:00:32
or March and then we're yeah. We're we're, we're going to be.
1:00:40
Building a, you know, million unit Optimus production line,
1:00:46
you know, hopefully with the production start towards the end
1:00:49
of next year, but that that production ramp will take a
1:00:54
while to get to annualized rate of 1,000,000 because it's going
1:00:58
to move as fast as the the slowest, dumbest, least lucky
1:01:03
thing out of 10,000 unique items.
1:01:08
But it, but it will, it will get to 1,000,000 units and then
1:01:13
ultimately, you know, we'll do Optimus 4.
1:01:16
That'll be, you know, 10 million units, Optimus 5, maybe 50 to
1:01:23
100 million units. I mean, it's really pretty
1:01:24
nutty. Yeah.
1:01:30
All righty, that is unfortunately all the time we
1:01:33
have for Q&A today. Before we conclude though,
1:01:36
Vaibhav has some closing remarks.
1:01:39
Thanks, Davis. I want to take the time to talk
1:01:42
about an extremely important vote which is being held on
1:01:45
November 6th. The meeting will shape the
1:01:49
future of Tesla and we are asking you as our shareholders
1:01:53
to support Elon's leadership through the two compensation
1:01:56
proposals and the re election of Ira, Kathleen and Joe to the
1:02:01
board. Note that it is a team sport and
1:02:04
here at Tesla, the board is an integral part of the winning
1:02:07
team. Shareholders are at the center
1:02:11
of everything we do at Tesla, and a special committee has laid
1:02:15
out a compensation package. Like Elon said, don't we don't
1:02:19
even want to call it a compensation package.
1:02:21
Yeah. So the point is that I just like
1:02:23
there needs to be enough voting control to give a strong
1:02:25
influence, but not not so much that I can't be fired if I go
1:02:28
insane. But I you know, and I think
1:02:33
that's sort of numbers in the mid 20s approximately.
1:02:36
As a company that has already gone public, there's no.
1:02:39
That we've investigated every possible way to how do you
1:02:42
achieve increased voting control without, you know, is there some
1:02:50
way to have like a super voting stock?
1:02:51
But there there really isn't, is there is no way to have a super
1:02:54
voting stock after you've gone public.
1:02:57
But for example, Google, Meta, you know, many other companies
1:03:04
have this, but they, they had it before they went public.
1:03:09
And so it sort of gets, I guess grandfathered in.
1:03:12
Tesla does not have that. So it's just like I said, I just
1:03:18
don't feel comfortable building a robot army here and not and
1:03:20
then, you know, being ousted because of some ASEAN
1:03:26
recommendations from ISS and Glass Lewis who have no freaking
1:03:30
clue. I.
1:03:31
Mean those guys are corporate terrorists and.
1:03:35
And the problem yeah. So let me like explain.
1:03:36
Like the core problem here is that so many of the index funds,
1:03:42
the passive funds vote along the lines of what whatever Glass
1:03:47
Lewis and ISS recommend. Now, they've made many terrible
1:03:53
recommendations in the past that if those recommendations have
1:03:56
been followed, would have been extremely destructive to the
1:03:59
future of the company. But if you've got passive funds
1:04:02
that essentially defer responsibility for the vote to
1:04:08
Glass Lewis and ISS, then you can have extremely disastrous
1:04:13
consequences for a publicly traded company.
1:04:15
If if too much of the publicly traded company is controlled by
1:04:19
index funds, it's de facto controlled by Glass Lewis and
1:04:23
ISS. This is a fundamental problem
1:04:27
for corporate governance because they're not voting along the
1:04:31
lines that are actually good for shareholders.
1:04:37
That's the that's the big issue. I mean, that's what it comes
1:04:41
down to. ISS, Glass Lewis Corporate
1:04:44
Terrorism? Yeah.
1:04:47
And I would say, you know the special committee did an amazing
1:04:49
job in constructing this plan for the benefit of the
1:04:53
shareholders. There is no nothing which gets
1:04:56
passed on till the time shareholders make substantial
1:05:00
returns. So that's why, you know, in the
1:05:02
end, I would say, I would urge you to not only work on the
1:05:07
plan, but also work on all the three directors because of their
1:05:11
exceptional knowledge and experience.
1:05:13
And literally, you know, we at Tesla work with these directors
1:05:18
day in, day out. I mean, there is not even a
1:05:21
single day that one of the directors I haven't spoken to or
1:05:26
one of my colleague hasn't spoken to.
1:05:28
And we're the even the directors out here are not just reading
1:05:32
out of. You know, PowerPoint
1:05:34
presentations, they're actually working with us day in, day out.
1:05:38
So again, I just urge you guys as shareholders to work along
1:05:42
the board's recommendation. Thank you guys.
1:05:45
Great. Thank you, Vaibhav.
1:05:47
We appreciate everyone's questions today.
1:05:48
We look forward to talking to you next quarter.
1:05:50
Thank you very much and goodbye. Hey, thank you so much for
1:05:53
listening today. I really do appreciate your
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1:06:22
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