[00:01:42] Preston Pysh: Awesome to have you. Let’s start off here because we’re going to be talking a lot about Elon Musk, about what Tesla’s doing, and maybe a little bit of Bitcoin in there as well. But where I want to start is he had this Dojo for his own basically competition. I don’t know if you would frame it as competition to NVIDIA.

[00:02:02] Preston Pysh: So he was building his own chips and he had the ability to own the entire stack, clear down, from the chips that are doing the training to the collect of all the different data sources that he has to building the models and the whole bit.

[00:02:17] Preston Pysh: But back in August, he announced that he’s done with Dojo. He’s shutting it down and he’s just banning it. What impact does that really have on the grand scheme of things? Is this concerning as a Tesla shareholder or just give us your take on it?

[00:02:32] Cern Basher: Yeah, at first blush it feels like a conCern because they’re taking something, they’ve been working on for years, and we all got excited about this idea of this Dojo supercomputer that would be this amazing training computer that would reduce the reliance on NVIDIA’s systems.

[00:02:47] Cern Basher: That all sounds good, right? Not to have to pay NVIDIA’s 75% gross margins, but I think the reality is that, one, it shows how amazing NVIDIA is and their development timeline. So these big training systems, Elon clearly is able to get the allocation that he needs. Jensen seems very impressed with Elon and his capabilities and in standing up these data centers very quickly.

[00:03:10] Cern Basher: That’s a really nice customer for NVIDIA, but I also think that more importantly, it’s probably this. Tesla has always made its own AI inference chips that are essentially in the car, the hardware three now AI four, soon to be AI five, and then eventually AI six. These are very powerful chips that Tesla has designed that TSMC historically has made for Tesla.

[00:03:35] Cern Basher: And they’re now for at least AI five moving with Samsung to develop this new version of this inference chip. So Elon recognizes that it’s one thing to build a model, it’s another thing then to run it efficiently on a vehicle or in a robot. And ultimately, you’re going to need billions. Billions of inference chips.

[00:03:57] Cern Basher: And also it turns out that maybe you can take enough inference chips and stack them together and build your own AI supercomputer with that. So I think Dojo has shifted from what they were working on to now focusing on the inference side. And Tesla knows exactly what they want from the inference chip because they’re building the software that’s going to run on the chip.

[00:04:18] Cern Basher: Whereas NVIDIA doesn’t know what Tesla’s needs are in that regard. So make a long story short, I’m actually excited about the direction that they’ve pivoted to with Dojo. I think we’re going to see a new Dojo system based on Tesla’s own inference chips

[00:04:32] Preston Pysh: And Cern, correct me if I’m wrong, I’m just trying to frame this for the listener so that they understand this term inference. So you have got to chip that’s taking all the inputs and it’s quickly coming up with what the output or the usefulness of like what it’s discerning.

[00:04:47] Preston Pysh: So for like a car, it’s looking at the imagery that it’s going down the road. That input is then going into this inference chip that’s running the model that was already generated and compressed and built, but then it goes into this inference chip so that it can become useful outputs of like how the car should be steered or whatever, whether it should be breaking, right?

[00:05:08] Preston Pysh: That output from the model that’s already been trained months or years ago or whatever. That conversion to produce the output is what you’re saying might be actually the secret sauce specifically for, driverless cars in this example, but it could be for anything, whether it’s a humanoid robot, that inference step, it comes down to the quality of the output and the speed of the output. And if you’ve got a specialized inference chip for that, that really might become the secret sauce. Is that kind of where you’re going with your comment?

[00:05:41] Cern Basher: Very much so. If you think about, you’ve got a car driving itself. You cannot have that car communicate with some central data center somewhere for instructions on the next turn. It has to be, millisecond instant.

[00:05:53] Cern Basher: So you need that compute on the vehicle. Same thing with humanoid robots, same thing with really anything else that’s going to be running AI inference, that’s doing something in the real world. You might have a connection to the data center fine, but not to operate that vehicle or that robot.

[00:06:09] Cern Basher: So it all comes down to energy efficiency. Think about two of the topic that Elon has been talking about lately, this competition to Microsoft, this idea of what he’s calling macro hard, the exact opposite of Microsoft. So instead of having pre-developed software, you basically have AI run the software on the fly.

[00:06:29] Cern Basher: And in order to do that, again, you’re going to need very powerful inference chips to be able to do that kind of thing. And certainly if you’re going to compete against Microsoft on price, it’s going to be have to be very cheap.

[00:06:40] Preston Pysh: Yeah. That makes a lot of sense. Yeah. This is fascinating, this whole rate, and I wonder how much of this, the thing that the market was talking about that just blew everybody’s mind is how did Elon convince Jensen to give him so many chips and scale up X AI so quickly?

[00:06:57] Preston Pysh: I wonder if there was somewhat of a, Hey, I’m going to shut down the Dojo. I’m not going to compete with you in this space. You’re just going to be the winner. I’m going to step away from it. But by the way, I need this many chips from you like tomorrow.

[00:07:10] Preston Pysh: I wonder, have you heard any types of rumors, dating back, and I know that this Dojo shutdown was in August, was like when this happened and when he trained the last version of the X AI. It was prior to that, but I wonder if there was some type of, I’m not going to play in this space, or if he was just truly he knew he was shell slack.

[00:07:28] Cern Basher: Yeah, it’s a good question, Preston. My guess is that this evolved over time. I think they were probably seeing that they couldn’t really keep pace with where NVIDIA was. Members of the team started leaving. So I think it just became difficult and I think maybe Elon saw perhaps another pathway to, again, through the inference side, and if you build this amazing inference chip, then you can scale that up to become a training supercomputer as well.

[00:07:53] Cern Basher: So, yeah, I, don’t know exactly whether he would’ve actually said to Jensen, we will do this if you do that. I don’t know if that was necessary. I think Jensen knows what Elon is capable of. And wouldn’t you love to have Elon Musk as your one of your largest customers?

[00:08:07] Preston Pysh: Oh my God, yeah. By the way, we just read this book, it’s called The Thinking Machine. It’s about Jensen Huang on the show. It was amazing. I mean, that guy is an operator of operators.

[00:08:17] Preston Pysh: Speaking of which, when you look at like a competitive advantage or like a competitive moat in this space, so much of it just comes down to the execution of the production line. You hear Elon say this all the time, and so when you look across Tesla, I would imagine, I think it’s probably discounted because I think a lot of people don’t have an appreciation for just operational excellence on a production line, especially one that’s a producing a complex end item.

[00:08:45] Preston Pysh: That’s where Elon really shines is just in the execution of these production lines, and I can’t even imagine how many he has like in his stack at this point. It’s unimaginable. Is this something that you ever cover or that you get into or something that you think is in fact a competitive moat of Elon’s versus some of the competition in the space?

[00:09:06] Cern Basher: Yeah, very much so. And maybe let’s maybe broaden out the question a little bit here. I have talked about at length, about this notion that Elon, single-handedly is taking us from one system, this old system that we have to a new system. And there’s many different levels, right?

[00:09:26] Cern Basher: So it starts at if you think about the production line, one of the Tesla’s innovations is building the machine that builds the machine. They’re able to sort of redesign the production line, they build the machines so that they can make these vehicles more efficiently and they’ve got this efficiency in their manufacturing system that really no other company can really achieve.

[00:09:47] Preston Pysh: The recursiveness of that is of that statement is just so insane. Yeah, so insane. We’ve never seen anything like that in the history of business.

[00:09:56] Cern Basher: That’s right. The amazing thing about Tesla is if there’s, if there isn’t a company out there that provides the equipment, they just build it themselves, right? And then people try to copy them.

[00:10:05] Cern Basher: So we’re going from this world of manual manufacturing and manual can include robots. It’s sort of this hyper automated manufacturing system. And we’ve seen that with SpaceX when the Starlink terminal in Bastrop, Texas. I forget what the, I think it’s 10 million terminals a year or something, or maybe it’s more, but it’s basically highly automated.

[00:10:26] Cern Basher: The raw materials go into one side of the factory and the starlink terminal comes out the other side. We’re going to see that same kind of manufacturing for the humanoid robots, and we’re going to see probably dozens of factories around the world start to build these things at some point, but just broadly, SpaceX, for example, is going to take us from a single planet economy to eventually a multi-plan economy.

[00:10:47] Cern Basher: We’re in early, early stages of that, right? The Tesla energy bringing us from this fossil fuel world to renewables. And that’s very important. There’s so many different things. If you think about the boring company today we have a two dimensional urban transportation network roads on land. Eventually it’s going to be 3D.

[00:11:08] Preston Pysh: And you know what, Cern? I, so I was literally in Nashville last week and I had a person telling me that they have already started work on the boring company. On a tunnel in Nashville, out to the airport. And this is something, I had really haven’t heard, and maybe that’s my own lack of focus in the area, but it almost seems like people saw a demo. They were like, oh wow, that’s really interesting. But they don’t realize that they’re actually moving out on projects in cities now. Like it’s already happening.

[00:11:39] Cern Basher: Yeah, there’s a big project in Vegas. It’s ongoing. That Nashville. And I think there’s some other areas as well that are in the works. We’re still in the early phases of this because the tunnel boring machine really needs to become faster.

[00:11:49] Cern Basher: These things move at literally a snail pace. And actually slower than a snail pace. So if they need to make some further innovation to make sure that, first of all, you can take humans out of the tunnel, very dangerous, or a few minutes to be in the tunnel when these things are working, but you want this machine that can continuously bore the tunnel.

[00:12:07] Cern Basher: And build out the tunnel behind it as it’s going through. And the boring company has really made some great strides in that, so that would be very interesting. Just think about if we can put a lot of the vehicle traffic underground. Just imagine how beautiful and quiet cities would be if you could do that. That’s coming. I don’t know what the timeframe on that is. It might be, 10, 15 years from now in terms of when we really start to see an acceleration in that.

[00:12:31] Cern Basher: From a big picture standpoint, all these technologies is driving us to a situation where the cost of energy is going to come down and trend towards zero. The cost of transportation is going to come down on trend towards zero. AI is going to cause the cost of intelligence to come down on trend towards zero. And then a humanoid, robots we haven’t talked about yet, but humanoid robots, it’s going to drive down the cost of labor. Trend towards zero over time.

[00:12:58] Cern Basher: The combination of all that takes us from a world that is very limited and constrained by energy, intelligence, labor. To a world then that has an abundance of all those things. If we think about energy, just focus on energy. because energy is so critical to everything. Everything. Imagine if you had unlimited energy, what you could do.

[00:13:19] Cern Basher: You could power up all the data centers in the world. You could power up the Bitcoin network without any worry about using too much energy. Those arguments have gone out the window, thankfully. But you could do other things that are really important, you could desalinate as much water as you wanted to.

[00:13:32] Cern Basher: You would have no water shortage or scarcity anywhere in the world anymore. You could grow forests in the deserts if you wanted to. With unlimited energy you can. The food would also be more abundant. So we are on the cusp of, again, moving from one system to another. And Elon, through his various companies is pushing the forefront in many areas.

[00:13:53] Preston Pysh: I’m with you on all of that 100%. But the naysayer that would be listening to this is saying, yeah, but you’re going to completely displace humans as to like, how do they add value and get paid in this system? because they’re not going to be able to compete with an optimist robot.

[00:14:09] Preston Pysh: Humanoid robot, Potentially, Which then comes up the UBI conversation, which then points to Bitcoin and like all these other things. But yeah, I think the bigger conCern for people when they hear all this is just, I think they’re looking internally and they’re saying, I’m going to lose my job. And like then what in the world am I going to do?

[00:14:26] Preston Pysh: because I don’t have a competitive advantage against these humanoid robots. How do you have that conversation? And honestly, sir. Where I hear this the most is when I get all excited about this stuff and I’m telling my wife, and my wife like always comes back to this point and I don’t have a good answer for her. I don’t really know what to say.

[00:14:45] Cern Basher: Well, imagine sitting in a house sometime ago when everybody used to work in farms. And you come along and you say, I’ve seen a tractor, right? And it’s going to take our jobs. What are we going to do? You wouldn’t have had the answer. You wouldn’t have been able to tell that person that was worried about losing their farm job and tractors taking it over and plowing the fields way more efficiently than humans ever could and oxen ever could. The answer’s not clear in terms of what humans are going to do and where the new jobs will come from.

[00:15:14] Preston Pysh: Yeah.

[00:15:15] Cern Basher: So that’s one thing. Throughout humid history, every time there’s been a massive technological advancement, we’ve always created more jobs than we displace. Think about all the women that were switchboard operators with a telephone, and then we invent a automatic switchboard.

[00:15:31] Cern Basher: Three quarters of a million people were put outta work pretty quickly. They found jobs in other areas. So that’s the first thing to recognize is that it’s murky. It’s unclear. When we look forward, we can’t predict where the new jobs were. Who would’ve said that when the internet was built, that you and I would be doing something like this.

[00:15:48] Cern Basher: Yeah. That there’d be people that spend time on YouTube and do shows and talk about things. Yeah. No one would’ve guessed it. No. So in the world of ai, I think there’ll be some surprises along that front as well. But even so, let’s go down that darker road where we say, okay, AI is more capable.

[00:16:05] Cern Basher: And humans on every level, whether it’s labor or even jobs that don’t require labor. A job like mine, for example, is a financial advisor, let’s say. The AI is just so much smarter and more efficient than I am and can do everything that I can do and puts me outta business. Well, on one level, the world is actually better off.

[00:16:23] Cern Basher: If we can do things more cheaply and more efficiently. It just becomes a question of how do I survive? And then that becomes a question of then how do we distribute the benefits of these technologies to humanity at large? When I hear Elon talk about it, he says, we’re actually headed for a world of not universal basic income, but universal high income.

[00:16:43] Cern Basher: Now, he hasn’t really shared much detail on sort of how he sees that transitioning, but I understand it from a, certainly from a supply perspective, we should be able to produce anything and everything we want at massive abundance if we can drive down the cost of all those things we just talked about, energy, labor, transportation.

[00:17:00] Cern Basher: Everything should become abundant. Yeah, and the one example that’s a really good one is if you look back at like telephone market, the communications market we used to pay by the minute for long distance calls. Yeah. Now it got, it gets to the point where it’s too cheap to meter, you just pay a flat fee.

[00:17:16] Cern Basher: Yeah, it doesn’t matter anymore whether you’re calling from New York, California, who cares? It’s just one flat fee per month. Yeah, and that may well be the case too, with things that we pay a lot for today. Like food. If food becomes abundant to cheap, you just pay a monthly flat fee to the grocery store and you walk in and take whatever you want.

[00:17:33] Preston Pysh: These ideas are so foreign, but I think your point is pretty salient. It’s when you go back in time. And you look at these other moments where it looked like technology was going to displace a whole lot of people, and I’ve heard Munger and Buffet cover this In some of their shareholder meetings through years.

[00:17:49] Preston Pysh: They’ve talked about this idea and that’s the one thing that they also keep coming to is. It’s really hard to even remotely understand what opportunities or value props pop out naturally for humans to then take advantage of and to act like it hasn’t happened time and time again, is somewhat of a fool’s errand.

[00:18:10] Preston Pysh: I think the contrarian to that would say, well, we’ve never built intelligence that is smarter than humans, and so this, therefore this is different, which who knows? Maybe it’s a valid argument, but I’m an optimist. I guess to my own detriment, maybe sometimes, but. I’m an optimist.

[00:18:27] Cern Basher: Yeah. I think you have to be Preston, because you do, let’s look at this. This is like Pandora’s box. The box has been opened and it’s not going to be closed. No. So if you just think about it from this traditional adversarial situation where let’s say it’s us against China, for example. Okay. If we don’t. Pursue the technologies, whether it’s Bitcoin, whether it’s what Elon is doing across those companies with Tesla, SpaceX, the Chinese will.

[00:18:53] Cern Basher: So do you want Chinese to have a better AI than us? Do you want the Chinese to have better rockets than us? Do you want the Chinese to have. A better form of digital security and digital money than us. The answer is no. We have to make sure we’re competitive with everybody else, and so we can’t really afford to stick our head in the sand and on any of this stuff.

[00:19:14] Cern Basher: no. This is, whether we like it or not, there’s an element of sort of warfare and battle to all these technologies. Elon has talked about that. If we lose superiority, for example, in drones, then we become very vulnerable to drone attacks. Yeah, And same thing with energy production. If we let the Chinese run away with growing the energy production and we don’t, then suddenly we’re not going to be competitive against the Chinese. It’s really in anything.

[00:19:37] Preston Pysh: So let’s zoom out and look at the intricacy of like what he’s building. I want to look at it from a business standpoint. Which one of these technologies do you see really starting to take off in the coming, call it three to five years, when you just look across the whole array of things that he’s, I mean, he’s working on.

[00:19:57] Preston Pysh: Let me just name some of this for the audience so that they can really wrap their head around the big picture of what he’s doing. You got Tesla Auto, you got Tesla Autonomy, you got Tesla Robo Trucking and the cars. You have Tesla Energy. You have the Optimist, humanoid Robot. Dojo just got shelved.

[00:20:15] Preston Pysh: You have SpaceX, you have Starship, you have starlink, all the internet coming from the sky. You got X ai, you got Neuralink, you got X where he is collecting all the data, he’s stepping into X payments. You got the boring company, you have the Hyperloop. The list just goes on and on and where I would say your expertise certain is you’re able to piece all of these different technologies together and you can see the system of systems value.

[00:20:42] Preston Pysh: That’s coming and emerging out of that systems level thinking, and that’s where I think Wall Street is missing it. I’m assuming you agree. Yes. They’re just looking at each thing individually and saying, oh yeah, well, I don’t know. But when you piece it together as a puzzle, you’re saying there’s this emergent value above and beyond the individual value that comes out of the hole.

[00:21:03] Preston Pysh: So really long question, which one of those puzzle pieces are the next S curve kind of thing that you think is going to really pop in the coming three to five years?

[00:21:13] Cern Basher: The short answer I think is Robotaxi, but in reality they’re going to make incredible progress on everything you just mentioned. So, but let me just talk about the situation as it stands today.

[00:21:25] Cern Basher: Today, Tesla makes and sells vehicles and sells them to people and makes a little bit of profit. Let’s say Tesla makes a car that they sell for $40,000. They might make a $4,000 net profit on that car. Okay? And some of that might include government subsidies and tax credits and that kind of stuff that benefit Tesla.

[00:21:42] Cern Basher: It’s like a 10% margin. Yeah, if they’re lucky. Okay. And further, you might buy a new car, let’s just say, to make this easy every five years. So Tesla’s making $4,000. Once every five years if they sell cars per person. Yeah. Move to robo taxi situation. Okay. The cars that they’re going to use for that have a battery capacity of about half of a model Y.

[00:22:06] Cern Basher: So the cars will be cheap to make. Okay. And let’s say the vehicle over a five year period. Produces $200,000 in profit. Not per year, but just over the five year period. Yeah. Okay. So you’re looking at a massive increase. $4,000 once every five years to 200,000. You moving from selling cars to consumers and consumers are picky and taste change to making cyber cabs. A very utilitarian, basic machine that there’s a, that can give you a rough.

[00:22:36] Preston Pysh: There’s advantage that they all look the same, right? So not to think about any of that.

[00:22:40] Cern Basher: Yeah. And so if you adjust for the fact that the battery content is half, then actually it’s like equivalent to like $400,000 over a five year period compared to 4,000. It’s a hundred x the profitability of selling car. That’s the kind of unlock that Tesla’s on the cusp of with Robotaxi. And in terms of the market opportunity, it’s not just about disrupting Uber and rideshare companies. It’s about disrupting all transportation. Your personally owned vehicle. There are people that own, families that own two or three vehicles.

[00:23:15] Cern Basher: Initially, you’ll get rid of the third vehicle because you can take Roboto Taxii, and that makes sense. As the cost of Roboto Taxi comes down, people are going to ditch the second vehicle and the cost of robot taxi keeps coming down. They’re going to ditch the first vehicle. They’re just going to use Roboto taxi basically in every situation once they become ubiquitous and super cheap.

[00:23:33] Cern Basher: And I’ve always said that economics wins here. If you can save, 5,000 bucks a year on your transportation costs, you are going to do it. People will discover that they can save money. People will happily not own a car, pay insurance and deal with all the headaches of parking the car, and, paying for all the payments. This idea that we have to own our vehicles actually is it’s going to be an antiquated idea very quickly.

[00:23:56] Preston Pysh: Cern, and I want to stick some numbers on this for people so that they can think in their own terms. So when you go out and correct me if any of these numbers are off, if you go out and you get an Uber today, you’re paying anywhere from, call it two to $3 per mile in order to get that Uber.

[00:24:13] Preston Pysh: If you buy your own car, and let’s say you buy a $40,000 car, you hold it for five years, you sell it, you get whatever you know it’s worth at that point after five years and you drive it an average American amount over those five years, this would cost you about 65 cents to 80 cents per mile. So Uber’s, in that two to $3 range.

[00:24:36] Preston Pysh: If you own your own car and then you sell it after five years, it’s about 65 cents to 80 cents. Elon’s thinking he can get these robo taxis down to about 25 to 30 cents per mile, which I mean, you’re very generically, it’s half the cost of going out and owning your own vehicle and dealing with all of that personally.

[00:24:56] Cern Basher: The other thing too, Preston, that happens is that this becomes a platform to layer other services on. So if you’ve got this ubiquitous network of vehicles driving around, they can deliver food, they can deliver packages, you can layer on all kinds of other services on top of this platform.

[00:25:11] Cern Basher: You can also use it as an advertising platform. So I’ve actually said that eventually you may be able to get free rides on this network if you are willing to subject yourself to advertising. Because the costs of the car is so cheap to operate, instead of getting paid by the customer, you can just get paid by the advertisers.

[00:25:27] Cern Basher: Restaurants might pay Tesla to advertise in the car, right? Or restaurants may even pay Tesla to say, Hey, we’d recommend going to X, Y, Z pizza place. And it just takes ’em there. Like the kind of experiences you can have in a robot taxi, you are actually unlimited.

[00:25:42] Preston Pysh: Cern, can you imagine the fact that he has X AI and already has a very good understanding of what it is you like? What it is you want to be advertised on? Like he’s going to know all of that.

[00:25:55] Cern Basher: And you’ll be able to talk to the cars too, Preston. You’ll have a conversation with the Roboto taxi.

[00:26:00] Preston Pysh: These things are going to get all your geo, like where you typically eat. It’s, yeah. Holy crap.

[00:26:06] Cern Basher: Let’s say you travel to a city, you fly to Chicago and you say, take me to the best pizza place in the city.

[00:26:11] Cern Basher: And then Grok is talking to you and saying, well, do you like this? Do you like that? You can have a conversation and then the car then just takes you there. I’ve seen, and along the way, it connect as a tour guide and say, oh, by the way, this building was built in 1938 and it was once the second tallest building in the world, or whatever you’re interested in. It becomes a platform for all these different kinds of services.

[00:26:29] Preston Pysh: I saw some of the videos of people using the grok AI in the car, and the feedback at least that I saw online. I don’t know if I was being fed this for marketing purposes or what, but the feedback that I was seeing online was extremely favorable.

[00:26:44] Preston Pysh: People were like, it felt like I was having a conversation with a real person as I was, people were telling it, Hey, take me somewhere that you think I would like. That I’ve never been before in my own town. And it literally took them somewhere and then they went in and they were like, this was so, like some of the feedback that I’ve seen online was actually really favorable. And I think we’re just on the cusp of like where this is all going.

[00:27:08] Cern Basher: This is not, yeah. Right now, grok is not connected directly to the operation of the car. So you can talk to Grok and it’s like talking to, this laptop or a computer or your phone, but it’s not controlling the car. Eventually, I think there’ll be a links between the two.

[00:27:23] Cern Basher: You’ll be able to have an interesting conversation with GR about, Hey, why did you cut that other car off back there? Didn’t you see it? Or can you drive a little slower? I’m not comfortable with the way you’re driving right now. Or, whatever it is. You’ll be able to have this conversation with the vehicle. In addition to just being able to query it with information about restaurants and different things, that’s going to be a neat moment, I think, when that happens.

[00:27:44] Preston Pysh: Okay, so the cars is one piece of it. The other part that I think is maybe a little bit further behind help me out understanding kind of the timeline, but the trucks. So, yeah, go ahead.

[00:27:54] Cern Basher: Yeah. Tesla is building a factory in Nevada that has a capacity of 50,000 semi trucks a year. That factory will likely be finished, maybe the end of this year, early next year, they’ll start ramping up production. So let’s say by 2027, they’re producing 50,000 semis a year. there’s quite a few trucks in the United States.

[00:28:14] Cern Basher: It’s going to take a while to replace the fleet with electric trucks. At the same time, they will also be like the robot taxi. They will be autonomous trucks at some point, and just like how that’s going to revolutionize the cost of transportation, it’s going to revolutionize the cost of trucking. We can really get massive savings.

[00:28:33] Cern Basher: On the cost of trucking and of course trucking costs. Cause you know, inflation in everything we buy, right? If trucking is expensive and moving goods around the country’s expensive, makes everything, we buy that much more expensive. So this is going to be a massive deflationary force throughout the economy that’s going to be good for all consumers.

[00:28:51] Cern Basher: So every American will benefit from Tesla being able to reduce the cost of trucking. The trucking industry right now has a real problem and the cost to operate. Most trucking companies are not very profitable. The cost to fuel the cost of the driver, cost of the driver, I think works out to something like a dollar a mile.

[00:29:07] Preston Pysh: Yeah, that’s what I’m saying. It’s about 70 cents to 90 cents per mile.

[00:29:10] Cern Basher: Yeah. Including benefits, maybe a buck. Yeah. And so Robo Trucking gets rid of that. And then of course the diesel cost also is huge. If they’re electric trucks, you have massive savings on the fuel costs. Yeah. And then of course the maintenance.

[00:29:21] Cern Basher: The maintenance on these trucks is pretty high because these engines wear out, electric trucks won’t have as much maintenance as fewer moving parts. It’s just less stuff to maintain.

[00:29:30] Preston Pysh: We’ll just swap the battery and. New tires on it and keep rolling. Yeah, so the all in cost per mile that I’m seeing for the line haul is like anywhere from a buck 60 to $2 a mile. And the Tesla Robo Trucking is expected to come down to about 60 cents to 80 cents.

[00:29:50] Preston Pysh: So we’ll say about a 50 to 65% reduction in cost. Not as much as the individual car for people getting around, which you know, was mind-blowing, like 10 x improvement. This is about 50%. But I guess my understanding is that this is a much more reliable stream of income because you’re dealing with big vendors and like once they come into this, call it Amazon or Walmart or whoever, like once they come into this space and they stay there and they see that it’s a lot cheaper, it’s like it’s humming along.

[00:30:20] Cern Basher: Yeah. I mean, a 10% reduction in trucking costs would be huge. I mean, would drive massive change. Yeah. That a 50% is an absolutely enormous. Yeah. The thing that’s different about the trucking market is you’ve got, it’s all commercial operators, right? It’s not like we personally own our own vehicles and we can operate them pretty cheaply.

[00:30:38] Cern Basher: And Uber and Lyft right now are way more expensive to use than just driving our own personal vehicles. You don’t really have that in the trucking market. It’s all commercially operated. The industry’s earning an average, say $2 to 2, 2 30 a mile. And as long as you can come under that, you’re going to gather market share.

[00:30:56] Cern Basher: And as you keep sort of driving that down a little bit, all those players are going to be, they’re not going to be economic anymore. You’re going to drive them out of business, and the whole industry’s going to shift towards electric trucking and autonomous trucking pretty fast.

[00:31:08] Preston Pysh: On the Robotaxi piece, who’s the competitors who could give him a run for his money at this point?

[00:31:14] Cern Basher: Well, that’s a good question because there might be some competitors in China, and I think the Chinese might be a little hesitant to have Tesla control that market. So I would expect the Chinese to ensure that they’ve got some homegrown held there. And there’s some companies that are showing some pretty impressive results there.

[00:31:31] Cern Basher: But Tesla objectively is way ahead of most of those companies. Yeah, you’ve got a lot of companies that want to be part of this. If you look at Waymo, for example, in the us. The problem with Waymo is that they have a very expensive solution. Yeah. A car with a lot of sensors and a lot of bells and whistles.

[00:31:48] Cern Basher: Tesla is basically just using cameras in a computer and Waymo has thousands and thousands of dollars of other equipment that you have to take existing cars, modify them. Add ’em to the fleet. Tesla makes their own vehicles and they roll off the assembly line ready for service. They drive themselves to where they need.

[00:32:05] Cern Basher: They drive themselves off autonomously. Yeah, they’re doing that today in the factory. They drive themselves to the lots to be picked up by the trucks. So Waymo really can’t compete with Tesla on cost, and I don’t know that they’ll ever be able to. Because again, they don’t have their own manufacturing plants.

[00:32:21] Preston Pysh: Yeah. You’re getting into the highly debated LiDAR. Do we need LiDAR on the car or not? And Elon has always been of the firm opinion that you just, Hey, if there’s a human back there and they’re using image eye, visible light spectrum sensing. Then why in the world do I need anything other than that with the car, especially if I have basically eyeballs all over the car viewing in the visible light spectrum.

[00:32:45] Cern Basher: Yeah. The other risk to Preston that Elon’s talked about is that what happens if you have the disagreement between the two sensors? How do you resolve that in real time?

[00:32:52] Preston Pysh: I don’t know if I buy that argument. I think that’s more him posturing for why it’s safe enough and like raising it as if it could potentially make it less safe. But yeah, I think if you trained it on enough data. I mean, AI is always better when you give it more information, right? So I don’t think that it’s going to have that rivalry if it’s true in theory.

[00:33:15] Cern Basher: I’ve seen some examples where that makes sense to me again. Yeah, I’m not an expert in the mechanics of this area. Certainly the more parameters, the more inputs you give into the AI model, the better off it should be. But certainly you want to be careful about confusing it and saying, one sensors saying that’s a wall. Another sensors saying that’s a plastic bag. And how do you resolve that? You resolve it in the wrong way, suddenly you could have a problem.

[00:33:37] Preston Pysh: Yeah. Well, that’s a good argument because the, yeah, the plastic bag. Imagine a plastic bag like being over top of the LiDAR sensor. And yeah, no, that’s a great point.

[00:33:46] Cern Basher: Well, that too, I mean, in that case, the car would just have to slow down. I mean, just if something came on the windscreen of your vehicle, you just try to slow down as safely and as quickly as you can pull off to the side of the road. The same thing would’ve to happen with an autonomous vehicle if it’s cameras or sensors were degraded in some way.

[00:34:01] Cern Basher: I think too, like for example, during rain. A lot of people say, well, these vehicles are not going to be able to operate when it’s raining and it’s not true. They can but to a point and they stop operating when they think it’s unsafe.

[00:34:13] Cern Basher: Most humans actually continue to drive actually, even in those unsafe conditions. So it’s actually going to make travel a lot more safer on all kinds of different levels, even if the vehicles don’t operate during periods of time.

[00:34:25] Preston Pysh: So where are we at? They’re already doing test runs in Austin. How much longer until we’re going to start seeing these things like show up? I was really surprised with the Tesla truck. It was just like you started seeing them everywhere. Like outta nowhere. Is that kind of what we’re going to see with the Robotaxis where they just show up and like they seem to be everywhere within months? Or is this going to be a slow rollout? City by city?

[00:34:48] Cern Basher: Yeah, they’re operating right now, kinda a pilot service in Austin. They’re operating in pilot service in the Silicon Valley area. Those are the two areas we’re seeing testing in a bunch of other places. Elon certainly has designs on rolling out robot taxii in multiple locations as quickly as possible, but the reality is I think they need to make sure that it’s as safe as it can possibly be.

[00:35:08] Cern Basher: The last thing they want to do is rush this. And if they are six months a year later than everybody’s expectations so big, that’s fine. Yeah. You really have to be careful with this. At the same time, they’re improving the underlying full self-driving model. We’re all anxiously awaiting for version 14, and hopefully we’ll see that soon.

[00:35:26] Cern Basher: Anything expected with that version versus, well, Elon’s talked about how it’s going to 10 x the number of parameters that the model considers, and so it, it should make the system that much better. Again, to your point, the more information that you give ai, the better the outcome should be. And Elon has made sort of comments that the car seems sentient, so we’re all anxiously awaiting, experience this for ourselves and see what it’s like.

[00:35:51] Preston Pysh: Because you basically have AI nested itself into a body, which is a car . he’s said like, what examples does he have?

[00:35:59] Cern Basher: We were short on examples at this point as just comments from him. And he’s seeing things months before we get to see it. So we’re all anxiously awaiting what this new version of FSD is really like.

[00:36:09] Preston Pysh: Well, yeah. What in the world does that even mean?

[00:36:12] Cern Basher: Yeah. I mean, the goal obviously is for the car to drive better and safer than a human. And I think in many respects it already does.

[00:36:19] Preston Pysh: Yeah. What are those stats? What are you hearing from that regard.

[00:36:22] Cern Basher: Well, that’s mixed. I mean, a lot of it is the personal stories that people post on x. and how the car behaves in certain conditions. I had one experience. For example, I was on a highway in Tennessee and I was doing probably 75 miles an hour, whatever the speed limit was, and all of a sudden I felt my, I drive a cyber truck.

[00:36:40] Cern Basher: I felt my cyber truck accelerate. And before I could even realize what was going on, another car passed me probably going 140 miles an hour. It was coming up right behind me in the right lane and then suddenly turned into the left as it passed me. But the truck thought that car might actually rear end me.

[00:36:57] Cern Basher: So it correctly calculated that it should accelerate so that impact would be left no way. And then as soon as that car passed me, the truck slowed back down again to the speed. So that kind of behavior that humans. We just can’t see. We can’t react to it. I didn’t see that car until it passed me.

[00:37:14] Cern Basher: So there’s stories like that and just other things where it sees kids that walk onto the street. The human driver never saw, because they were blocked by a van or something. It saw the kid approaching the van from the sidewalk, recognized that there’s a child behind there that might pop out.

[00:37:30] Cern Basher: Therefore, the car slowed down. There’s things like that, but also Preston, there’s also examples of the car kind of behaving weird in certain situations. Of course. Yeah. So, not everything is perfect yet.

[00:37:41] Preston Pysh: I think I read somewhere that the car is demonstrating per mile way lower crash rates than a human driver. Is that accurate? Is that true?

[00:37:52] Cern Basher: I think that’s fair to say. Again, we don’t have our hands on the actual underlying data based on what Tesla shares with us. We believe that to be true and in quite a significant way too, by the way, based on the data that Tesla shares.

[00:38:03] Preston Pysh: Interesting. Okay. So those were two examples. Do you have any others that you think are close to market or something that’s going to be an S-curve in the evaluation?

[00:38:14] Cern Basher: Well, the energy storage business. So Tesla makes these mega packs. They are the size, it’s a box, basically, the size that fits on a flatbed trailer. Okay. And they’re designed to be that big because batteries are big and heavy. You want them to be as big as possible, but not so big that you have problems moving the system.

[00:38:32] Cern Basher: These mega pack systems basically allow us to store energy and use it whenever needed, so it’s perfect. For example, if you have wind and solar that only generate certain times of the day, you can store the energy and use it later. That’s great.

[00:38:45] Cern Basher: Elon has said that if we applied enough battery storage, we could actually double the generating capacity of the United States without adding any more generation, because we have a lot of sort of capacity in places that’s excess. That’s only used in certain times of the year. Certain times of the day, for example.

[00:39:02] Cern Basher: But with battery storage systems, you could run those at capacity and store that energy all the time. So that’s one quick way to accelerate the energy generation infrastructure that we have already in place without building anymore. Now, of course, we have to build more, but an easy win would just be to add battery storage to everything. And so I think that Tesla energy, their energy business should see accelerated growth. It already has, but I think even further from here.

[00:39:29] Preston Pysh: Do you think that this is one of the main reasons why Elon just like doesn’t talk about Bitcoin, is this is a competitor to, in a way, this is a competitor to Bitcoin mining.

[00:39:40] Cern Basher: Yeah, it puzzles me a little bit when I think about Bitcoin and Tesla. I mean, certainly Tesla bought Bitcoin, I think it was in February of 2021. You know that time Bitcoin’s market value was like a trillion dollars or approaching a trillion. They sold some, and one of the Elon’s comments about it was, well, tell me when the Bitcoin network has crossed 50% renewable energy sources. That was just his beef.

[00:40:03] Cern Basher: And since then, there’s not really been much discussion from him about it. Yeah. Right now, I’ve been agitating a little bit that Tesla should start buying more Bitcoin, not necessarily become a Bitcoin treasury company, although that could be interesting too. But that may be taking a little bit too far.

[00:40:17] Cern Basher: But even just acquiring more Bitcoin. Because my stance on this is that as we approach this world of a GI and A SI That is going to be a very uncertain time in many ways. I would want any company to go into that with as much capital as possible. And not to mention that as we approach that world where we get disruption from humanoid labor or even just AI in general. The value of fee up money is going to potentially be compromised that much more.

[00:40:45] Preston Pysh: They’re going to have to offset all of the technological deflation that’s happening with printing.

[00:40:51] Cern Basher: Yeah. 100%. So the only place when the store capital is going to be in Bitcoin. That’s going to be safe from that. Now Tesla should reinvest in their business as much as possible. That’s what’s limiting them. Now that they’re not buying back stock, they’re reinvesting in their business now they have a cash forward of about 37 billion I think it is. and a billion in Bitcoin. So I’d like to see them over time, build up their Bitcoin position.

[00:41:13] Cern Basher: Certainly companies like NVIDIA that are aggressively buying back stock, I think are making a big mistake, and I think we’re seeing already that the amount of money that needs to go into AI infrastructure is so large and growing Every quarter we get the estimates are higher and higher that for NVIDIA to give away their capital right now, I think is actually a big mistake on their part. I think they’ll be fine. I don’t think it’s putting the company at risk, but they may be missing out on future opportunities.

[00:41:36] Preston Pysh: Yeah, I would agree with that, obviously. as a hardcore Bitcoiner, right? One last topic that I want to get into this with you on is the Optimus robot. I find this mind-blowing, I find, from an engineering standpoint. Just watching videos on here’s how we designed, and not even from Tesla, but just like other people that are operating in this space.

[00:42:00] Preston Pysh: Here’s how we designed the ankle for our humanoid robot in the crazy amount of engineering. Into just some of the joints in the hands. I think Elon recently said that designing a humanoid robot’s hand is crazy difficult.

[00:42:16] Preston Pysh: Insanely difficult to get all the joints and like you just don’t understand how much engineering goes into something like that. Yeah. Talk to us about where you see this. Start off with like the timeline. How far out are we from this thing really going to market? What would that market be? And then its use in utility.

[00:42:36] Cern Basher: First of all, just to add onto to what you said, I think Elon, when I asked about was it harder than making Starship, and he said, no, that’s a harder problem. So to put it in context, yeah. Okay. Yeah. So it’s the second hardest thing that Elon’s working on, I suppose, right now. Maybe AI’s up there too. I’m not sure.

[00:42:51] Cern Basher: The humanoid robot thing is really interesting. In my view. For the first time in human history, we’re developing something that can outcompete human labor. Eventually at all levels. We’re going to be developing a system that can do absolutely everything better than a human can. It can be a craftsman at all times.

[00:43:09] Cern Basher: There’ll be no sloppy work. It’ll be doing things perfection every single time. Not only that, it can work probably 22 to 23 hours a day. Yeah. But right there you can compete against humans. Even if it was operating at one third, the speed one robot could outcompete by human over a 24 hour period.

[00:43:26] Cern Basher: Because humans only work, let’s say eight hours a day or something like that. But over time, not only will it be as capable of a human, it’ll probably become more capable. So the timeline’s interesting because the robots that they have today actually are useful. They are already showing that they can do useful work.

[00:43:43] Cern Basher: Even just picking up a box or something and moving it from point A to point B is useful work. There’s a lot of people that do that all day and batteries and warehouses and stuff. Sorting packages, all that kind of stuff. Yeah. The robots today can already do that. Now, maybe not as fast as a human, but again, they don’t have to be as quick.

[00:44:01] Cern Basher: Humans have all kinds of problems, right? We take breaks, we get tired, we make mistakes. Some people don’t show up for work. Some people cause problems at work. They show up drunk or drugged. All those issues that you won’t have with humanoid robots. So the timeline is one of those things is at what point does Elon want to bring a robot to market?

[00:44:18] Cern Basher: How productive or how capable does it need to be? Now, we were expecting earlier this year that by the end of this year, they would’ve made five to 10,000 robots and that they’ll be deployed within Tesla’s own factories and maybe their suppliers, they’ve stepped back from that because they’ve been redesigning the hand and so on.

[00:44:34] Cern Basher: So the timeline’s been pushed back a little bit. I think ultimately we’re going to have a far more capable robot. And so perhaps we start seeing some production in earnest in 2026. Elon has always said, it typically takes three versions before you get a commercial ready robot. To me, this is a bit like iPhone, right?

[00:44:51] Cern Basher: You come out with the first iPhone and the next one’s that much better, and then iPhone three, and it just keeps getting better and better So I think there’ll be actually pretty good demand for these robots as soon as they can really begin making them. And then an interesting thing happens is as they become more capable, they become more valuable.

[00:45:07] Cern Basher: So the amount of money that they can actually earn per robot will go up initially could be as much as $150,000 a year per bot in terms of revenue. And I’ve always said that they should do a robot as a service because if you sell a robot. You might be giving up a lot of upside as that bot becomes more capable.

[00:45:24] Cern Basher: Now, I suppose that could always charge for the updates, right? If they sell a bot that does, $5 an hour work, fine, you pay a certain price for that. It does $5 an hour work. But if you want the $10 an hour work, you’re going to have to pay for the software update. To me, an easier method is just to provide robots as a service and you just, pay for the capability of the bot on a per hour basis, kinda like companies do today on for workers on a per hour basis.

[00:45:48] Cern Basher: Let me just touch on Preston, something that’s really interesting that’s happening here. If you think about companies that have all these workers, yes, they’re assets of the company, but they’re not on the company’s balance sheet anywhere. There’s no value of the employees as a workforce that shows up on the company’s balance sheet, with the exception perhaps, of a soccer or football club, where you pay for these players and you get to trade them and you get the money for the players.

[00:46:13] Cern Basher: That doesn’t work that way for employees with companies. What happens is when you have a robot, you get to capitalize that labor for the first time. So think about what happens if you have a robot that makes $25,000 a year in profit, and you use a PE multiple of 40, that’s a million dollars in value for every million robots that Tesla makes that’s a trillion dollars in new market cap potentially for whoever owns it. For Tesla. And then also for the companies that are using it.

[00:46:40] Preston Pysh: You’re saying, that they would if they would rent it out.

[00:46:42] Cern Basher: Yeah. So what we’re seeing for the first time in human history is potentially the capitalization of labor. And I have said that Optimus will become the vast majority, maybe 80% of Tesla’s market value at some point in the future, let’s say by 2040, maybe even earlier.

[00:47:00] Preston Pysh: Yeah. It’s my understanding, Elon is in complete agreement with that. He has statements.

[00:47:04] Cern Basher: Yeah. Now, not to toot my own horn here, but I was saying this, a year ago, and Elon said it recently, now again, Elon’s way ahead, me, he’s not listening to me. But this is a very important point because market for labor in the world is about 50% of world GDP.

[00:47:21] Cern Basher: If we’re going to turn a good portion of that into robotic labor, it will be capitalized, and we’re going to create market value out of thin air. This is why I think that Tesla has such an incredible opportunity because if they can successfully make millions of robots for every million that they make is a trillion dollars in market value.

[00:47:38] Cern Basher: Elon, by the way, has talked about making billions of robots. And when you think about too, what he’s doing with SpaceX and going to the moon and going to Mars and potentially mining asteroids and all the other things, I think of space as kinda like a new continent. And so you’re not going to send people, you’re going to send the bots. So the off planet uses for robots are just enormous.

[00:47:58] Preston Pysh: Yeah, totally. I want to tell people just some numbers on this so that they can wrap their head around it. So this year they were shooting for 5,000 of these robots to be built. It looks like they hit a snag in the production line back in June.

[00:48:13] Preston Pysh: They were assembling a thousand units. And I guess they went back for a redesign on that. And so the numbers that are being tossed around now are maybe between two to 5,000 of these things made by the end of this year, perhaps next year. By the end of 2026, they’re shooting for 20 to 50,000 units produced, and then by 2027.

[00:48:35] Preston Pysh: Over a hundred thousand units. By 2030 they’re looking at a million units a year being produced. Do you know how many off the top of your head, how many cars they produce per year right now?

[00:48:47] Cern Basher: Yeah, it’s a little bit less than 2 million. And by the way, a robot, an Optimus robot, weighs about 3% of a Tesla model three.

[00:48:54] Cern Basher: So let’s say for example, that for every car they made, they can make 10 robots, right? So even though it’s 3% of the weight, let’s say it’s 10% of their production capacity. So yeah, for every car, 10 robots. So if they make a million robots, that’s like making a hundred thousand cars. That’s not difficult for Tesla to make a hundred thousand vehicles just to put in context like a million of something sounds like a lot.

[00:49:15] Cern Basher: But that’s equivalent to making another a hundred thousand cars. Or if they can make 20 robots for every car. That’s like making 50,000 cars. They made that many cyber trucks in the first year and that’s a very complex product to make. They’re designing Optimus to be made very efficiently, very quickly, and maybe even by Optimus itself.

[00:49:32] Cern Basher: The robots building the bots. And that’ll be very interesting if they can do that. But the one thing that they’re going to have a challenge with, at least in the early years, is scaling up the supply chain for these things.

[00:49:42] Cern Basher: Building a new product from scratch, it could be a challenge. So in the early years, I think we should keep our expectation pretty low. But 10 years from now, they’re going to be pumping out potentially a hundred million of these things a year.

[00:49:53] Preston Pysh: And is the use case going to the factories right out of the get, is that the primary user from the beginning? I’m assuming it’s not going into homes.

[00:50:02] Cern Basher: Well, there are some other robot companies that are focused on the home first, and I think that’s smart for them because they probably can’t compete with Tesla on the factory side. But eventually we’re going to see them everywhere. Everywhere where human labor is needed, where there’s a shortage. There’s a shortage in manufacturing right now. Also, we have a lot of humans that do jobs that are dirty, dangerous, really boring humans don’t really want these jobs.

[00:50:24] Cern Basher: Look at the turnover that Walmart has. It’s like 40% a year. Yeah, Stocking shelves and unloading trucks and moving stuff around. It’s perfect work for robots. Yeah, right? And so you don’t have to fire anybody. It’s just the natural attrition of these jobs. So I think these big companies will. Adopt these robots very quickly. But ultimately, there are a lot of people excited to have robots in the homes as caregivers for elderly, as cures for kids.

[00:50:50] Cern Basher: Like my kids love robots. They would love to have one in the house that they could talk to and get the robot to pick up their toys and that kind of stuff, right? So I think there’ll be a big demand from all different corners. But the economic win is batteries that run three ships. Yeah, because you can use these things, 22, 23 hours a day.

[00:51:08] Preston Pysh: Good lord.

[00:51:10] Cern Basher: And so a factory may be willing to pay a couple hundred thousand dollars a year for a robot. There are not many consumers that would be willing to pay that much. Yeah. Initially. Yeah. Over time, as they scale up production, the cost of these things will come way down and it’ll become another consumer product.

[00:51:26] Preston Pysh: Cern, I’ve thoroughly enjoyed this. We have to do this again in the future, maybe quarterly or something, but wow, you’re just a breadth of knowledge on all of these different topics. I can’t thank you enough for making time to come on and talk about this.

[00:51:41] Preston Pysh: If you want to give people a handoff to where they can follow you or learn more, what do you got?

[00:51:46] Cern Basher: Yeah, just find me on X, at Cern Basher, my first name and last name at Cern Basher. And that is my real name by the way. Some people think that I’m a particle accelerator but no, that’s my real name.

[00:51:57] Preston Pysh: It’s perfect for being in the tech space, Cern.

[00:51:59] Cern Basher: I suppose it is.

[00:52:01] Preston Pysh: Alright, we’ll have links to your X account and also your company, Brilliant Advice.

[00:52:07] Preston Pysh: And thank you, Cern, for coming on the show. This was a really pleasant conversation.

[00:52:12] Cern Basher: It was my pleasure, Preston. Thank you very much.

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