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Speaker 1: So too for the great groundbreaking and opening. So let's

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see with that we can move to I guess Q

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and A. We've obviously got significant bench strength here should

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probably hit like a bench and anyway, which we maybe

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have too many people on stage, but we'll try to

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answer questions within reason, you know, which this is meant

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to be kind of more of a long term sort

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of discussion as opposed to, uh, you know, what will

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be the production for the rest of the quarter type

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of thing. So let's try to orient our questions towards

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long term value creation and with that far away.

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Speaker 2: Rob.

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Speaker 3: All, it's a road lash for the Wolf research. Very

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exciting plans about the the next generation vehicle and power

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trains and batteries. I was hoping you can maybe talk

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to us a little bit about the timeline for deploying this.

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And it sounds like it's more than just a vehicle.

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This is a kind of a paradigm change on how

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vehicles are assembled, how batteries are put together and everything,

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and does that also just get reconfigured into everything that

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you do. So the model wise that are being built

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here will be built very differently in the future. Maybe

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just give us some feel for what happens from here

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and what's the timeline for implementation.

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Speaker 2: Well, talk a little bit about that, but.

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Speaker 1: Broadly speaking, the most profound architectural changes will be in

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future vehicles. Retooling a factory means bringing the factory down

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for an extended period of time, and that's I would

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preferred not to do that, I think. But but there

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are variants in how Model Y is produced. So we've

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got variants where there's rear casting, where there's a front

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and rear casting, and.

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Speaker 2: We have the.

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Speaker 1: Structural battery pack, and then there are a number of

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smaller improvements that occur. But I think for really really

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big changes those would be future vehicles.

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Speaker 2: Yeah. I know, you guys want to add.

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Speaker 4: Lars maybe, I mean yeah, so, I mean, as far

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as I agree with you one hundred percent, Elan, it's

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it's it's really easy to put innovations in new vehicles,

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but long term we'll obviously bring them back. We've always

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talked about that, but we don't want to take our

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factories down. As far as the timeline goes, you know,

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we're gonna go as fast as we can left or right.

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As always, you know, Elon alluded to the fact that

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Mexico will build our next end vehicle, but we will

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also be doing that in other plants, and so it's

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really about getting them all up and running. We expect

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that to be a huge volume product and yeah, we're

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going to move that quickly over the next couple of years.

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Speaker 5: Let's go through all them.

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Speaker 6: Thanks first in not Buena Mexico, Marmos, that's great, congratulations.

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Uh elon a question on applying first principles thinking and

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innovation to an area that up to now has seemingly

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been outside of your control, and that is on mining

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and extraction of some of the key materials.

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Speaker 7: I believe a couple of.

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Speaker 6: Years ago you you had a patent on sodium chloride

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to extract lithium from some from some clays and spodyming

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clay and things of that nature. Any How, how does

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that fit into the plan of maybe bringing real innovation

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into a mining sector that could use a little you know,

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maybe waking up and getting those costs down, because that

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could be a relegating factor.

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Speaker 1: It seems well, we're gonna address whatever we think the

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limiting factor is at any point in time, so we

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would like to do the least amount possible, So we

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don't want to get into the mining or refining sector.

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Speaker 2: We will do that if we have to.

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Speaker 1: I do think the focus really should be on refining capacity.

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You know, we need to make just a very giant

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amount of anode cathode lithium, latinatruxide, liatin combinates. It's really

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the refining capacity that is the biggest choke point. Yeah,

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so that's that's why we're building a lithium refinery in

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Corpus Christy.

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Speaker 8: In terms of the mining companies that are out there,

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uh and looking at that part of the value chain.

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And so we do have large suppliers of lithium right

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now and they are aware of you know, how we're

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approaching the Corpus refinery and the technologies we're trying there.

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And the reason we're making them aware of it is

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because we think they're fundamentally more scalable. And as we

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prove them out, we plan to share that with them because,

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as Elon says, like, it's not really like we want

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to do these things. We're doing them because it's not

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happening fast enough. So if we can prove that it

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can be done faster, the intention is to transfer that

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knowledge to our large our current suppliers, and the same

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is actually true. It was actually a clay process that

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we were playing with and we continue to work on

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the same is true on that. So we've worked with

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our suppliers as well on trials and we're sharing knowledge

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there and the intention is just to help the whole

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world do this better. And ultimately this here is getting

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the lithium or whatever it is out of the order.

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Speaker 1: And we're obviously building a cathode processing facility just adjacent

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to this building. So a little further down the road

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you'll see another large construction that's for cathode refining. But

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like I said, we'd really prefer if others did that.

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We're doing it because we have to, because we want to.

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Speaker 8: Yeah, And in that case, there just isn't really any

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large scale cathode production in the United States and it

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needed to be done. And again, if we're going to

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do it, we're going to try to do it from

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a first principles perspective. So we have tried a bunch

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of new things there. We're confident that they will work,

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and as they prove out again, we want to bring

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them back to our supplier so they can build new

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facilities more quickly with less investment.

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Speaker 2: Right, let's go to ben Hi ben Cala for bair.

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Speaker 9: So similar On the renewable side, Is there more that

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you can do in Tesla to nudge the rest of

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the renewable industry to speed up since it's such a

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big a pillar of the master plan Three?

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Speaker 1: Well, I mean, I don't know more we can do,

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but I can say that if there's like entrepreneurs out

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there that wanted to have like a guaranteed chance of success,

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it would be refining lithium or anode and cathodes or

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any any materials whatsoever for uh leathium on cells as

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no brainer.

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Speaker 9: Yep.

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Speaker 2: I think we're doing everything we can. So yeah.

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Speaker 8: On the on the reneable energy thing, the thing that

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we can do Tesla is the more we reduce the

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cost of storage, the more we reduce the cost of

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stationary storage, and the more we bring like flexible load

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to the grid in the form of like cars charging

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at the right times, the more valuable renewables are. Because

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in like Texas right now, I wasn't joking like that's

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off or whatever. So so bringing really low cost storage

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onto the grid makes renewables more valuable. Which ultimately will

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accelerate their deployment. So that's how we focus on it.

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Speaker 2: I guess.

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Speaker 10: Felippe, thank you. It's Sidi Bouscher Jeffries. I've got two questions.

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The first one, when I think about in this industry,

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everybody wants to be Tesla. Every comic er is trying

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to emulate what you're doing well done. The one thing

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they don't do is focus on having as much growth

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as possible with as hew models as possible. So I'm

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just trying to understand, as you aim for twenty million

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units in twenty thirty, how many models do you think

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you need to get there, and how does it fit

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into your drive to hyper scale?

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Speaker 2: How do you manage this?

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Speaker 10: And also the fact that probably consumers at some point

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I don't want to see your tests out the same

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test at every street corner, So I'm just trying to

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get your your sense of that.

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Speaker 2: And my other question is on.

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Speaker 10: Bidirectional I mean, you talked a lot about making you

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know a world more renewables and better use it of cars.

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I think bidirectional charging is one way of better using cars,

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but you seem to have been reluctant about doing that

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in the past, so I'm just wondering what your latest

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views are on the topic.

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Speaker 8: Sure, on bi directional it it wasn't like a conscious

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decision to not do it. It just wasn't a priority

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at the time. I think is maybe the way to

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think about it. As we looked, as we continue to

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improve the para electronics in our vehicle, we've found ways

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to bring brighter actionality while actually reducing costs of para

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electronics in the vehicle. And at allten Tesla, the goal

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is usually to get more for less, and so we

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are in the middle of kind of like a power

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electronics retool. I would say that we'll bring that functionality

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to all of our vehicles over the next you know,

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two years, let's say.

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Speaker 2: But but it's.

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Speaker 8: Yeah, I guess that's how I'll say there.

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Speaker 1: I don't think very many people are going to use

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my directional charging unless you have a power wall, because

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if you unplug your car, your house goes dark and

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this is extremely inconvenient.

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Speaker 8: Yeah, most of the value it comes in charging the

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car at the right time. It's not really about sending

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energy the other way.

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Speaker 2: Yeah.

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Speaker 1: I mean, if if you have a power wall that

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can take the house load then you can use your

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car as supple as a supplementary energy source to the

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power wall, and then you know you're not gonna drive

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one crazy by unplugging your car and having the house goal.

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Speaker 2: So I think there's some value there as.

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Speaker 1: A supplemental energy source down the road where if you

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have a power wall.

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Speaker 2: You've not diminished the convenience of the people in the house.

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Speaker 11: And the question of number of models, how many models?

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Speaker 1: But if not that many, I'm really ten, I don't know,

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not that many. There's I mean what's happened with the

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conventional cars is people have run out of things to do.

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So if you run out of things to do, they

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just end up reshuffling the deck and you have pretty

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much the same. I mean, how many variants of a

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car are there on the road. There's like hundreds. But

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there are they good variants? No, mostly not. They're just

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variants for the sake of variance. But look at how

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have things converged with the phone. I mean, there used

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to be hundreds of flip phones.

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Speaker 2: Now what do we have? It'll be like that.

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Speaker 12: George George Genericas from Cannon Coordenuity just had a question

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about your plans to grow market share in China, and

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also whether or not the political tensions between the United

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States and China impact your long term ambitions there.

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Speaker 2: Thank you. I don't know, Hey, Tom Nott, do don't

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don't swear too hard.

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Speaker 13: Yeah, well, we're still growing up our markets here in

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China quite strongly, actually, especially you know early this year

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we had this price adjustment. After that we actually generated

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a huge demand more then we can produce. Really, and

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as Elon said, as long as you're offer a product

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with value at affordable price, you know, you don't have

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to worry about demand. That's basically the philosophy that we

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we follow. We try everything to cut cost from supply chain,

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from improve the efficiency in the factory, and to pass

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down that value to our customers. I think you as

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long as we'll continue to do that, I'm not too

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concerned about the market share in China. In the second part, Geopolic,

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no one else.

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Speaker 2: We do our best.

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Speaker 13: We we we actually create a lot of jobs to

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look low community, and we tender also.

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Speaker 2: With at our suppliers.

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Speaker 13: Factories will create a lot of jobs as well, and

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we'll contribute with a lot of to the local economy.

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I think you know, as long as we're needed in

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this country. I don't see there much.

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Speaker 11: Of the risk of that, Colin, Colin Rush from Oppenheimer.

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You know, one of the things I was struck by

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in the presentation was the operational efficiency metrics that you

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guys are talking about. Could you talk a little bit

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about the velocity of learning cycles and how you guys

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track that and think about that as an organization as

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you work into a variety of other areas from a

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innovation perspective, if anyone wanted to try that, I.

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Speaker 8: Mean, I'll say one very high level thing, which is

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you can't improve something you don't measure. And we're like

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ruthless measurers at Tesla, and once you start measuring things

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that contribute to operational efficiency, you actually have a path

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to doing something about it. So I guess the key

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is a good measuring stick.

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Speaker 1: Okay, it's something I should say with respect to demand,

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which I've said a few times over the years, but

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it sometimes it needs to be set again, which is.

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Speaker 2: The overwhelmingly the uh.

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Speaker 1: There's the desire for people to own a Tesla is

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extremely high. The limiting factor is their ability to pay

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for a tesla, not do they want to eat Tesla.

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It's easy for people in this room to lose sight

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of that. If your income is far in excess of

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what a car costs, then you look at value for money,

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but you do not consider affordability. But for the vast

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majority of people it is affordability driven. This is why

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we cannot simply double the price of the car. Or

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you could say and think about things in the limit

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where if you hadn't you know, an infinitely desirable car,

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but it costs ten million dollars, it wouldn't matter because

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people can Most people do not have that, So demand

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is very much a function of affordability, not desire. Very important.

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One of the things we weren't sure about was the

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price elasticity of demand for Tesla's, so like, as we

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lower the price, how much does demand increase? And we

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found that even small changes in the price have a

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big effect on demand, very big. So that was a

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good thing to learn. Yeah, and then this autonomy question.

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Speaker 2: Is very very big.

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Speaker 1: Because it could potentially have I don't know, five times

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the utility of the asset that you currently have.

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Speaker 2: So passenger car.

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Speaker 1: Is ten or twelve hours a week of usage plus

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a lot of parking expenses, an autonomous car could be

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fifty sixty hours a week or something like that, and

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you could get rid of a lot of parking expenses.

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So you know, if this, if this is true, then

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as autonomy is effectively turned on for the fleet, it

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may be that it probably will be the biggest asset

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value increase in history overnight, Emmanuel, Thank you so much.

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Speaker 14: Emanuel Raznov from Deutsche Bank. So, as you start launching

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this next generation vehicle and ramping up volume, what will

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be your nearest term priority in terms of segment or

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vehicles focus. The slide that you showed with two vehicles

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under wrap seemed based on form factor one of them

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maybe he looks like a van, another one looks like

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maybe like a smaller vehicle, like potentially a model. To

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what is the nearest focus for you in terms of

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ramping up the next gen vehicle and how do you

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make sure that by lowering the price point so much,

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because the costs is going down fifty percent, you're not

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cannibalizing demand, you know for your existing vehicles, I mean

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demand for our vehicles in terms of desire to own

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them may as well be infinite. It's indistinguishable from infinite

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at this point, affordability is what matters. So as you

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make the car more affordable, we will have demand go crazy. Basically,

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the issue is how do we build the cars. The

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hard part is building the cars. I can't emphasize that enough.

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The hard part is building the cars and the entire

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supply chain that goes with the cars.

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Speaker 9: This is a.

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Speaker 1: Logistics challenge of extraordinary difficulty. All the things that have

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to go into the car have to scale with the

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car while everything is doing an exponential ramp. And if

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you miss even one of those things doesn't matter. Why earthquake,

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flood fire, revolution, I thought I've heard them all.

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Speaker 2: I mean, it's.

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Speaker 1: Any part of that supply chain gets interrupted, you're now

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then you have a seizure. The hard part is building

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the cars, by far, and the supply chain that goes

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with it. You guys want to talk about the supply

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chains though, yeah they might, I think, you know.

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Speaker 15: And the presentation I'd mentioned perfection is a passing great.

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We really need everything to happen perfectly, and the strategy

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for mitigating the different risks, some of which were anticipated

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and some weren't, is really bespoke to the situation. And

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then really the unique subject matter expertise and a deep

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knowledge of the particular supply chain you're managing to come

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up with those strategies. So we've seen everything from as

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Elon mentioned, you know, first the tariffs that flew back

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and forth, then the ocean logistics issue, a chip shortage,

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COVID floods, there was a fire and a fab in

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Japan that knocked down. There was a massive COVID spike

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in Malaysia where a lot of the chips to the

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back end of a lot of chips was down there.

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In more, this was one that Elon was involved with

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as well. And yeah, that's it's nerve wracking, but somehow

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it works.

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Speaker 7: Yeah, and although it is difficult, I think we have

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been laying the foundation to be as intimate to all

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the tiers of the supply chain, building that control and

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you know, directing the different tiers of supply chain. That's

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the way we are trying to make it. The risks

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that come with it, you know, again, dual sourcing, triple sourcing,

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having redundancy is the way we've been trying to mitigate

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for it.

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Speaker 2: Yeah.

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Speaker 4: On that point, when we think about vehicles, when you

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think about three y as an architecture SX as an architecture,

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our next generation platform is more than one segment, and

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really we're thinking about all the segments that are available

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that we haven't captured and where the market would be

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and designing it with our supply chain partners so that

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we can go quickly through those segments for where we need.

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But to Elon's point, if you make a car desirable

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and affordable, you know, oftentimes it doesn't necessarily matter what

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segment it's in because it's one that you want. And

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we've seen that with Model three when a lot of

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people thought that sedan was not going to be a

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great hit, but we sell tons of them. So the

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next generation platform is not one vehicle, it is multiple,

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and it's on the segment that we will, you know,

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really try and focus on that affordability and desira ability

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point more over than where we start it.

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Speaker 1: Yeah, I mean there's like, I think it's an old saying,

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like battles the one with tactics or is the one

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with logistics. The logistics challenges here are enormous, and when

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you start like being a very significant percentage of an industry,

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you can't overcapacitize. It's not realistic, and some of these

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things like you say, like well you dual source or

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triple source. You can do that maybe for small things,

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but you can't do it for big things because if

359
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you're if you're triple sourced and one of it's like

360
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having a plane with three engines, where if any of

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the three engines fails, you crash.

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Speaker 2: It's like, you know, so.

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Speaker 1: You have to eat the overcapacitizes, which drives your cafex

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up and it has idle suppliers somewhere and big warehouses,

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or you're designed to some overage that's you know, reasonable,

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and then you have expedite costs because inevitably there's something

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goes wrong somewhere and you've got to fly things around.

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So it's really just the rate of progress is the

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rate of which we are able to scale a ten

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thousand logistics problems.

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Speaker 2: The most significant of those is the.

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Speaker 1: Cell production, and so we actually liberately try to overdo

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cell production or self supply.

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Speaker 2: To have that exceed what is needed in vehicles.

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Speaker 1: Because if it goes below what's needed in vehicles, then

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with the factories stall. But then what do you do

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with all these extra cells? It's like, well, okay, so

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the much easier thing to scale up and down is

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is powerwall and megapac output stationary storage. So we can

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then overcapacities and sells and packs and scale production of

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stationary storage, which is much easier to scale than vehicle production.

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Speaker 2: So that's strategically, I think, a good thing.

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Speaker 1: Yeah, I mean the capex for megapac is tiny compared

384
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to capex for vehicles.

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Speaker 2: And also megapac demand is quasi infinite.

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Speaker 1: It basically as long as we are competitive with utilities,

387
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we can sell as many like it's yeah, quasi infinite

388
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demand for that really for multi tier many Taro White hours,

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and we got a long way to go to get

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to many Taro White hours per year.

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Speaker 2: Then let's go to down.

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Speaker 16: Thank you, Dan Leevy Barclays. I think we know competitive

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dynamics differ significantly by region. Cost dynamics different by region.

394
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We saw that with your China Giga factory, far superior

395
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cost to what you had for a month I realized

396
00:25:06,039 --> 00:25:09,000
it was a new factory. But uh, they're clearly different dynamics.

397
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So to what extent are the cost strategies that you've

398
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laid out today, Do they differ by region or or

399
00:25:16,160 --> 00:25:18,599
is there more of a global, one size fits all

400
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approach on reducing costs?

401
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Speaker 13: Yeah, I think we're pretty consistent under the strategy here.

402
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We'll try it as as much localized as possible. Like

403
00:25:37,279 --> 00:25:40,279
in China, ninety five percent over ninety five percent of

404
00:25:40,440 --> 00:25:43,200
our supplying are localized all the way from a first

405
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tier to secondarium that the third tier, and that gives

406
00:25:47,359 --> 00:25:54,000
us general detrimentous savings on cocks. And we also have

407
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a very localized labor force, and we have access to

408
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scale the labor force in the region and Young's adulta

409
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region in Shanghai, and particularly referring to and among over

410
00:26:09,039 --> 00:26:15,079
thirty thousands of employees in China, we have probably less

411
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than twenty expats. So it's uh very deep localization we

412
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have done there to be able to have such cost structure,

413
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and we try to replicate this approach elsewhere and in

414
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different give factories. Of course, you know the supplier base

415
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that are different, the you know, labor market are different

416
00:26:38,319 --> 00:26:40,839
region by region, the country back country, but which are our.

417
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Speaker 2: Best to to localize.

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Speaker 16: Yeah, and and.

419
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Speaker 2: I think that's that's absolutely.

420
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Speaker 13: Give us advantage to compete with the all the ms

421
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around the globe. Another thing is you know, we have

422
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this direct selling mode like Zach's that you know will

423
00:27:00,839 --> 00:27:04,039
save with tremendous money on op acces as well. We

424
00:27:04,160 --> 00:27:09,839
have food controller all expenditures across the the company, and

425
00:27:10,480 --> 00:27:15,480
you know we don't spend money on marketing or advertising.

426
00:27:16,599 --> 00:27:21,839
Everything we saved and will become you know, the value

427
00:27:21,880 --> 00:27:25,200
we can offer to the consumers. So that part, on

428
00:27:25,319 --> 00:27:28,920
that part, we also followed pretty consistent strategy over the years.

429
00:27:31,920 --> 00:27:34,480
Speaker 4: I mean, certainly the manufacturing stuff that Colin Pete and

430
00:27:34,559 --> 00:27:37,079
myself talked about, whether you make it here, you make

431
00:27:37,119 --> 00:27:38,720
it in Europe, or you make it in Asia, it

432
00:27:38,759 --> 00:27:40,920
applies everywhere. We think about that from the ground up,

433
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not not specific to any region.

434
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Speaker 17: Yeah, just one thing I would add to Tom's point

435
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about localization and to Lars's point here, I would actually

436
00:27:52,240 --> 00:27:54,559
say that we're moving in a direction of more standardization

437
00:27:54,720 --> 00:27:56,799
in terms of our factories and our processes and our

438
00:27:56,839 --> 00:27:59,720
cost reduction approaches. So as we've been thinking about the

439
00:27:59,759 --> 00:28:03,880
next generation platform, we've been thinking about the volume that

440
00:28:04,039 --> 00:28:08,079
we aspire to build against that. How many individual factories

441
00:28:08,119 --> 00:28:10,599
do we need to build and what is the fastest

442
00:28:10,680 --> 00:28:15,240
possible way to expand that footprint around the world. What

443
00:28:15,359 --> 00:28:17,680
we've done with Model why is each factory is an

444
00:28:17,720 --> 00:28:21,720
incremental improvement of the previous factory, which requires engineering hours

445
00:28:21,759 --> 00:28:24,440
engineering spend for each factory design, and then you end

446
00:28:24,559 --> 00:28:26,640
up with factories that are slightly different from each other.

447
00:28:27,319 --> 00:28:30,599
And as we move towards the next generation platform, I

448
00:28:30,680 --> 00:28:34,599
think the term you use, Lars is copy paste, so

449
00:28:34,839 --> 00:28:38,079
to get it right from the first time, you know,

450
00:28:38,200 --> 00:28:40,000
certainly there will be things that we learn and we

451
00:28:40,119 --> 00:28:43,200
replicate and we make adjustments to but try to have

452
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as much standardization and commonization as possible which allows us

453
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to go as quickly as possible with expansion.

454
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Speaker 2: O raka.

455
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Speaker 18: On the sides, thanks Two questions, once for Zak one

456
00:28:59,680 --> 00:29:03,079
flush self for Zach. How do you think about the

457
00:29:03,160 --> 00:29:07,559
long term growth and operating margins for the business And

458
00:29:07,839 --> 00:29:09,640
when we look at one hundred and fifty one hundred

459
00:29:09,680 --> 00:29:13,640
and seventy five billion in capex as the remaining balance

460
00:29:13,720 --> 00:29:17,000
of that is that essentially the capex guidance through twenty

461
00:29:17,079 --> 00:29:22,039
thirty for a shock the multi trip reconstruction that you highlighted,

462
00:29:22,519 --> 00:29:24,640
how is that different from what mobil eyed does with

463
00:29:24,799 --> 00:29:28,039
the IRAM map that they have And can you give

464
00:29:28,079 --> 00:29:30,480
us any color on the AP four hardware.

465
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Speaker 17: So with respect to operating margins, you know, we intend

466
00:29:38,920 --> 00:29:41,920
to continue to improve operating expenses as a percentage of

467
00:29:42,000 --> 00:29:45,680
revenue over time, and we're continuing to take cost out

468
00:29:45,720 --> 00:29:48,720
of our products and try to keep gross margins in

469
00:29:48,799 --> 00:29:51,799
a place that's healthy as well. And so you know,

470
00:29:52,160 --> 00:29:55,519
from the hardware perspective of the business, you know, it's

471
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our expectation that will continue to stay in a healthy

472
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place over time. And then there's a software portion that's

473
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added on top of that. And so Elan has commented

474
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on this many times about the impact that full self

475
00:30:07,039 --> 00:30:09,480
driving software can have on the economics of the business,

476
00:30:10,200 --> 00:30:14,000
and in that space, you know, profitability operating margins would

477
00:30:14,000 --> 00:30:18,720
be impacted dramatically. Specifically to your point about CAPEX guidance,

478
00:30:19,079 --> 00:30:20,759
you know, the intent of that slide was not to

479
00:30:20,839 --> 00:30:24,519
provide specific guidance, but to just be transparent about internally

480
00:30:25,319 --> 00:30:28,880
what our rough estimates are and to provide context that

481
00:30:29,240 --> 00:30:31,799
we think getting to twenty million vehicles per year in

482
00:30:31,880 --> 00:30:34,880
one taara what hour of energy storage is very feasible

483
00:30:34,960 --> 00:30:38,640
relative to our expected cash generation for the business. That's

484
00:30:38,680 --> 00:30:41,400
a number that we'll move as we learn. We may

485
00:30:41,480 --> 00:30:44,119
choose to vertically integrate more into things, we may find

486
00:30:44,160 --> 00:30:47,680
efficiencies elsewhere. To the whole conversation we were having earlier

487
00:30:47,680 --> 00:30:49,680
about do we do mining, do we not do mining,

488
00:30:50,279 --> 00:30:53,200
But the entire point is to say that this is

489
00:30:53,240 --> 00:30:55,440
something that's entirely possible based upon our.

490
00:30:55,359 --> 00:31:01,119
Speaker 19: Forecast regarding the monkey to preconstruction. My point I was

491
00:31:01,160 --> 00:31:04,400
trying to make was that we want to auto label

492
00:31:04,480 --> 00:31:06,519
most of the data, and we can collect data from

493
00:31:06,519 --> 00:31:08,839
the fleet and then reconstruct local areas that can act

494
00:31:08,839 --> 00:31:12,000
ass supervision for those clips. Since we want to build

495
00:31:12,000 --> 00:31:14,960
a scalable self driving system, we don't want to really

496
00:31:15,000 --> 00:31:17,359
relay on stamps or anything, even though we could really

497
00:31:17,400 --> 00:31:20,720
build something using the same technology. But this is mostly

498
00:31:20,759 --> 00:31:24,720
for auto labeling, and this provides like precise three D

499
00:31:24,799 --> 00:31:27,839
labels for all the video sequences involved in the reconstruction.

500
00:31:31,960 --> 00:31:35,519
Speaker 1: It's something that's I don't know if we touched on

501
00:31:35,599 --> 00:31:38,960
this much. But in terms of training, we have one

502
00:31:38,960 --> 00:31:41,640
of the biggest neural net training systems in the world,

503
00:31:41,839 --> 00:31:46,240
and that we expect to increase that capablely by an

504
00:31:46,279 --> 00:31:49,200
order of magnitude by the end of this year and

505
00:31:49,559 --> 00:31:52,880
probably another order of magnitude by the end of next

506
00:31:52,960 --> 00:32:00,559
year with some combination of Invidia and Dojo. So like

507
00:32:00,720 --> 00:32:04,359
much of the scale of that is quite going down people,

508
00:32:04,759 --> 00:32:05,519
and it's enormous.

509
00:32:08,640 --> 00:32:12,279
Speaker 5: Maybe the final two questions one from Alex and one

510
00:32:12,359 --> 00:32:13,039
from Chris.

511
00:32:17,880 --> 00:32:22,200
Speaker 20: Okay, Alex Potter with Piper, So I definitely want to

512
00:32:22,759 --> 00:32:25,960
ask Drew or anybody else up there for an update

513
00:32:26,079 --> 00:32:29,279
on dry battery electrode. Right, So, if you're gonna try

514
00:32:29,319 --> 00:32:34,519
to overcapacitize towards cells scale a lot of this Clearly,

515
00:32:34,599 --> 00:32:36,559
there's a lot of moving pieces and it's a complex

516
00:32:36,680 --> 00:32:39,119
sort of orchestra with the supply chain, but a lot

517
00:32:39,160 --> 00:32:41,519
of it comes down to dry battery electrode, at least

518
00:32:41,559 --> 00:32:41,720
to me.

519
00:32:43,039 --> 00:32:44,079
Speaker 2: So how are you trending?

520
00:32:44,559 --> 00:32:46,359
Speaker 20: That was the most fascinating part of the factory tour

521
00:32:46,440 --> 00:32:48,680
for me, and looking through that window and seeing that

522
00:32:48,839 --> 00:32:52,160
this is clearly not a science project, but anything that

523
00:32:52,200 --> 00:32:56,200
you're willing to disclose yields progress where you are today

524
00:32:56,279 --> 00:33:02,400
versus where you thought you were going to be. Yeah, thanks, right.

525
00:33:07,000 --> 00:33:09,039
Speaker 8: Uh yeah, I mean as you saw right, Like it's

526
00:33:09,319 --> 00:33:11,720
this is this is a real factory making a lot

527
00:33:11,799 --> 00:33:15,960
of dry electrode in an automated fashion. We've made a

528
00:33:16,000 --> 00:33:21,119
lot of progress. Uh, it's it's a it's a spectrum.

529
00:33:21,440 --> 00:33:25,119
Like we we're perfectionists, and we we have clear end

530
00:33:25,160 --> 00:33:30,720
goals and we are every week that goes by making

531
00:33:30,799 --> 00:33:33,799
progress towards those end goals, whether it's speed of the tool,

532
00:33:33,960 --> 00:33:36,839
yield of the of that process, or the downstream process.

533
00:33:39,759 --> 00:33:42,200
We we haven't stalled out yet on the rate of

534
00:33:42,279 --> 00:33:44,920
progress either, and that's both on the eNode and the

535
00:33:44,960 --> 00:33:49,720
cathode side. And I think the great thing about where

536
00:33:49,759 --> 00:33:53,000
we are with the overcapacity that Elon mentioned is it's

537
00:33:53,119 --> 00:33:58,079
giving us the opportunity to experiment as we go rather

538
00:33:58,160 --> 00:34:01,400
than just being like stuck to something that we happen

539
00:34:01,480 --> 00:34:03,519
to kick off a year and a half ago. And

540
00:34:03,599 --> 00:34:06,240
it is here something that Elon has said to the

541
00:34:06,319 --> 00:34:10,639
team many times is so okay to scrap equipment or money,

542
00:34:10,800 --> 00:34:13,960
it's not okay to scrap time. So the way we've

543
00:34:13,960 --> 00:34:17,320
been approaching it is probabilistically, what do we think is

544
00:34:17,320 --> 00:34:20,480
the most likely thing to succeed. And even actually in

545
00:34:20,599 --> 00:34:23,000
the factory here you saw more than one anode line

546
00:34:23,920 --> 00:34:26,840
that they are actually operating two slightly different versions of

547
00:34:26,960 --> 00:34:29,800
the final process step of the powder entering the tool,

548
00:34:30,199 --> 00:34:33,000
and it's a competition, you know, which is going to

549
00:34:33,039 --> 00:34:34,840
be more higher yield, which is going to perform better,

550
00:34:35,119 --> 00:34:36,599
And we have the luxury to be able to do that,

551
00:34:37,639 --> 00:34:39,559
and we're taking full advantage of it to advance the

552
00:34:39,599 --> 00:34:41,000
technology as quickly as possible.

553
00:34:42,880 --> 00:34:45,679
Speaker 2: The dir electrode problem is really quite a hard problem.

554
00:34:46,519 --> 00:34:49,320
Speaker 1: I mean, we acquired Maxwell really just for the dry

555
00:34:49,320 --> 00:34:55,639
electrode technology, but just illustrates what a gigantic gap there

556
00:34:55,760 --> 00:34:58,880
is between something working at small scale and at large scale.

557
00:35:00,639 --> 00:35:02,679
Speaker 2: And we've had a an.

558
00:35:02,559 --> 00:35:07,639
Speaker 1: Extremely talented team of engineers working on scaling the dry

559
00:35:07,639 --> 00:35:15,119
electrode process and having to be reliable, consistent, and we've

560
00:35:15,159 --> 00:35:19,280
been grinding hard, literally and figuratively on this for quite

561
00:35:19,280 --> 00:35:22,960
a while. It seems likely that we will be able

562
00:35:23,000 --> 00:35:24,719
to scale it to volume this year.

563
00:35:26,480 --> 00:35:29,079
Speaker 8: Yeah, I mean we're we're basically increasing the output week

564
00:35:29,119 --> 00:35:32,880
over week, like roughly one k week per quarters our

565
00:35:32,960 --> 00:35:36,679
internal target, and we're you know, we're tracking to that.

566
00:35:37,119 --> 00:35:41,840
I think the what you saw is effectively like a

567
00:35:42,000 --> 00:35:45,440
ton of material per hour per tool. It's kind of

568
00:35:45,480 --> 00:35:49,400
like hard to like rationalize like what that really means,

569
00:35:51,039 --> 00:35:54,639
but it's it's it is. It's different than like, oh,

570
00:35:54,800 --> 00:35:56,480
something works in a lab, it's like, well, when it's

571
00:35:56,559 --> 00:35:59,960
tons of material per hour, there's just different kinds of problem.

572
00:36:00,440 --> 00:36:03,679
Like even if you have zero point one percent escape

573
00:36:03,880 --> 00:36:08,320
of like finds into the enclosure that your equipment is in,

574
00:36:08,840 --> 00:36:11,320
now you have a dust problem and like that could

575
00:36:11,320 --> 00:36:13,599
short out some electronics. It's like just things like this

576
00:36:14,480 --> 00:36:16,719
where when you're on a lab scale you don't even

577
00:36:16,760 --> 00:36:19,440
notice it, but when you're doing thousands of tons over

578
00:36:19,480 --> 00:36:21,400
the course of months, it's like, oh, a new failure

579
00:36:21,440 --> 00:36:23,960
mode we found, and that's where we're at. But we're

580
00:36:24,239 --> 00:36:28,519
we're knocking those out though, and the team is grinding

581
00:36:28,599 --> 00:36:31,119
through it, but progress every week.

582
00:36:33,960 --> 00:36:37,920
Speaker 5: And the last question from Chris, Thanks for taking a question.

583
00:36:38,239 --> 00:36:41,599
I have a few follow ups on the next gen vehicle. First,

584
00:36:41,920 --> 00:36:44,159
when do you think we'll get a look at it?

585
00:36:44,320 --> 00:36:48,000
Maybe a prototype? Second, are there any details that you

586
00:36:48,079 --> 00:36:51,159
think you can share in terms of the size, the content,

587
00:36:51,280 --> 00:36:53,920
the performance. And Then third, I think you mentioned that

588
00:36:54,079 --> 00:36:56,960
you would produce it in other plants in addition to Mexico.

589
00:36:57,599 --> 00:36:59,760
Should we take that to mean that you can launch

590
00:36:59,840 --> 00:37:02,760
it at an existing plant before you're finished constructing the

591
00:37:02,840 --> 00:37:03,920
new plant in Mexico.

592
00:37:06,480 --> 00:37:10,679
Speaker 1: I think we'll actually have to probably decline that answer.

593
00:37:13,119 --> 00:37:16,559
We will have a proper sort of product event, but

594
00:37:17,480 --> 00:37:18,880
we would be jumping the gun if we were to

595
00:37:18,920 --> 00:37:23,719
answer your questions. Maybe another question if this yeah, yeah,

596
00:37:24,360 --> 00:37:25,400
I don't know anyone.

597
00:37:27,320 --> 00:37:28,440
Speaker 5: Will willed enough.

598
00:37:32,199 --> 00:37:33,079
Speaker 2: I have two questions.

599
00:37:33,159 --> 00:37:35,159
Speaker 21: This has really been a very impressive afternoon.

600
00:37:35,239 --> 00:37:35,920
Speaker 2: Thank you so much.

601
00:37:36,519 --> 00:37:36,800
Speaker 9: Elon.

602
00:37:36,880 --> 00:37:40,599
Speaker 21: I'm curious, as you've doubled and you'll double again, how

603
00:37:41,519 --> 00:37:44,760
what you've learned about sort of managing a larger enterprise

604
00:37:44,880 --> 00:37:47,320
and what you might have to you know, do to

605
00:37:48,119 --> 00:37:51,079
manage a bigger enterprise. And then secondly, I'm curious on

606
00:37:51,199 --> 00:37:55,039
your thoughts on how you know generative AI and you

607
00:37:55,119 --> 00:37:59,119
know these rapid breakthroughs in AI in the last months

608
00:37:59,719 --> 00:38:04,440
could help you make cars sort of you know, less

609
00:38:04,599 --> 00:38:05,199
hard to make.

610
00:38:05,960 --> 00:38:06,239
Speaker 2: Thank you.

611
00:38:09,760 --> 00:38:12,519
Speaker 1: I don't see AI helping us make cars anytime soon

612
00:38:15,000 --> 00:38:19,679
at that point, I mean, at no point in any

613
00:38:19,679 --> 00:38:23,639
of us working anything big problems, So we'll just chill out.

614
00:38:26,320 --> 00:38:29,360
I mean, I'm a little worried about the AI stuff.

615
00:38:32,800 --> 00:38:36,119
I think it's uh something I don't know which we

616
00:38:36,199 --> 00:38:37,239
should be concerned about.

617
00:38:38,039 --> 00:38:42,039
Speaker 2: Uh, I don't know.

618
00:38:42,039 --> 00:38:43,880
Speaker 1: I think we should need some kind of like regulatory

619
00:38:44,360 --> 00:38:48,880
authority or something that's overseeing AI development and just making

620
00:38:48,920 --> 00:38:52,760
sure that it's operating within the public interests. And you know,

621
00:38:52,920 --> 00:38:57,920
it's quite a dangerous, quite a dangerous technology, and I

622
00:38:58,800 --> 00:39:01,920
I fear I may have done some things to accelerate it,

623
00:39:02,079 --> 00:39:09,960
which is I don't know. So, I mean, some of

624
00:39:10,000 --> 00:39:12,920
the AI stuff I think is just obviously useful, like

625
00:39:13,039 --> 00:39:18,400
what we're doing with self driving, which is you know,

626
00:39:18,480 --> 00:39:22,199
some people think is an AGI type problem. I don't

627
00:39:22,199 --> 00:39:25,000
think it's quite an a GI problem, but it's certainly

628
00:39:25,199 --> 00:39:31,599
requires very sophisticated neural nets because the road system is

629
00:39:31,679 --> 00:39:36,719
designed for eyes and biological neural nets, so naturally the

630
00:39:37,119 --> 00:39:38,079
analog to that is.

631
00:39:39,880 --> 00:39:43,719
Speaker 2: Cameras with digital neural nets.

632
00:39:45,719 --> 00:39:50,199
Speaker 1: And yeah, the thing we've we found is just there's

633
00:39:50,199 --> 00:39:53,079
a sick if you actually look at our neural net

634
00:39:53,199 --> 00:39:58,719
architecture in the carts, kind of insane, frankly, nets upon

635
00:39:58,840 --> 00:39:59,840
nets upon nets upon.

636
00:40:00,079 --> 00:40:04,239
Speaker 19: That's the visualization toold crashes open. Yeah, it's like so

637
00:40:04,320 --> 00:40:06,559
complicated that no one can misulace this right now.

638
00:40:06,760 --> 00:40:09,679
Speaker 1: Yeah, it's hard to visual just like literally it looks insane.

639
00:40:11,199 --> 00:40:13,920
So I guess something like that's happening in our brains

640
00:40:14,039 --> 00:40:17,880
while we drive around, which is pretty wild, So.

641
00:40:19,559 --> 00:40:27,960
Speaker 2: You know, I don't know. I think Tesla's doing good

642
00:40:28,000 --> 00:40:35,119
things in AI. I don't know. This one stresses me out,

643
00:40:35,199 --> 00:40:36,320
so I don't know what to think you should say

644
00:40:36,320 --> 00:40:39,199
about it, you know.

645
00:40:42,119 --> 00:40:44,480
Speaker 5: Okay, given it's seven o'clock, I think that's all the

646
00:40:44,519 --> 00:40:47,199
time we have. Thank you very much for coming and

647
00:40:48,280 --> 00:40:48,360
se