Why Physical AI Is the Next Frontier | The a16z Show

Our mission is to put intelligence on a Our mission is to put intelligence on a billion machines and we think that can billion machines and we think that can billion machines and we think that can have a profound impact on society. have a profound impact on society. have a profound impact on society. Applied intuition is a physical AI Applied intuition is a physical AI Applied intuition is a physical AI company. We put intelligence on company. We put intelligence on company. We put intelligence on machines, cars, trucks, tanks, drones. machines, cars, trucks, tanks, drones. machines, cars, trucks, tanks, drones. It's a physical moving thing. We make it It's a physical moving thing. We make it It's a physical moving thing. We make it intelligent. intelligent. intelligent. >> Digital AI, of course, is building >> Digital AI, of course, is building >> Digital AI, of course, is building software and optimizing ads and creating software and optimizing ads and creating software and optimizing ads and creating videos. That's all interesting and good, videos. That's all interesting and good, videos. That's all interesting and good, but really where you talk about the but really where you talk about the but really where you talk about the global economy, that's physical AI. In global economy, that's physical AI. In global economy, that's physical AI. In this intelligence revolution, the this intelligence revolution, the this intelligence revolution, the companies that impact the physical world companies that impact the physical world companies that impact the physical world might actually be bigger [music] than might actually be bigger [music] than might actually be bigger [music] than the companies that impact the digital the companies that impact the digital the companies that impact the digital world. world. world. >> How many things are there where the idea >> How many things are there where the idea >> How many things are there where the idea of physical AI, physical intelligence of physical AI, physical intelligence of physical AI, physical intelligence are going to matter? are going to matter? are going to matter? >> There's no reason autonomy should be >> There's no reason autonomy should be >> There's no reason autonomy should be this obscure, difficult technology. Our this obscure, difficult technology. Our this obscure, difficult technology. Our vision for that is a high school kid vision for that is a high school kid vision for that is a high school kid that can make iPhone apps should be able that can make iPhone apps should be able that can make iPhone apps should be able to make autonomous systems. That to make autonomous systems. That to make autonomous systems. That platform for designing and developing is platform for designing and developing is platform for designing and developing is what we're launching. It's called Dana. what we're launching. It's called Dana. what we're launching. It's called Dana. Everything that we've built and Everything that we've built and Everything that we've built and developed over the past nearly a decade developed over the past nearly a decade developed over the past nearly a decade that's available in Dana. that's available in Dana. that's available in Dana. >> Which will we get first? A perfectly >> Which will we get first? A perfectly >> Which will we get first? A perfectly simulated real world environment for simulated real world environment for simulated real world environment for training autonomous devices or Grand training autonomous devices or Grand training autonomous devices or Grand Theft Auto 6. [laughter] >> Kazer Peter welcome to the Z podcast. >> Kazer Peter welcome to the Z podcast. >> Well, thanks for having us. Your name is >> Well, thanks for having us. Your name is >> Well, thanks for having us. Your name is [laughter] [laughter] [laughter] >> just one of many. >> just one of many. >> just one of many. >> I feel like we've I think we've all each >> I feel like we've I think we've all each >> I feel like we've I think we've all each known each other for known each other for known each other for >> too long. more than I'd like to admit. >> too long. more than I'd like to admit. >> too long. more than I'd like to admit. >> Yeah. [laughter] Long time. We're lucky >> Yeah. [laughter] Long time. We're lucky >> Yeah. [laughter] Long time. We're lucky to both be the first investors or among to both be the first investors or among to both be the first investors or among the first investor in the first round. the first investor in the first round. the first investor in the first round. Of course, different check sizes, but Of course, different check sizes, but Of course, different check sizes, but >> and I was an investor for you even >> and I was an investor for you even >> and I was an investor for you even before then. before then. before then. >> Exactly. >> Exactly. >> Exactly. >> Um, so let's do that as a segue. We have >> Um, so let's do that as a segue. We have >> Um, so let's do that as a segue. We have a lot to talk about today. We've, you a lot to talk about today. We've, you a lot to talk about today. We've, you know, the biggest launch in company know, the biggest launch in company know, the biggest launch in company history to talk about today, but first, history to talk about today, but first, history to talk about today, but first, why don't we just give an update or a why don't we just give an update or a why don't we just give an update or a [clears throat] status what does applied [clears throat] status what does applied [clears throat] status what does applied intuition do for those who intuition do for those who intuition do for those who >> Yeah, for the for the people who don't >> Yeah, for the for the people who don't >> Yeah, for the for the people who don't know, applied intuition is a physical AI know, applied intuition is a physical AI know, applied intuition is a physical AI company. We put intelligence on company. We put intelligence on company. We put intelligence on machines. That's the the simple way of machines. That's the the simple way of machines. That's the the simple way of uh describing it. um and all types of uh describing it. um and all types of uh describing it. um and all types of machines. So cars, trucks, tanks, machines. So cars, trucks, tanks, machines. So cars, trucks, tanks, drones, you name it. It's a physical drones, you name it. It's a physical drones, you name it. It's a physical moving thing. We we make an intelligent moving thing. We we make an intelligent moving thing. We we make an intelligent and uh the history of the company is we and uh the history of the company is we and uh the history of the company is we originally started by making the tools originally started by making the tools originally started by making the tools that would make the int then we got into that would make the int then we got into that would make the int then we got into the actual intelligence itself. Um in a the actual intelligence itself. Um in a the actual intelligence itself. Um in a very like in some ways like a very uh very like in some ways like a very uh very like in some ways like a very uh boring AI company in the sense of you boring AI company in the sense of you boring AI company in the sense of you know 83% of the company is engineering. know 83% of the company is engineering. know 83% of the company is engineering. We win by making really great products. We win by making really great products. We win by making really great products. It's not like a good sales or something It's not like a good sales or something It's not like a good sales or something like that. I don't think we're good like that. I don't think we're good like that. I don't think we're good enough for for a for a sales enabled enough for for a for a sales enabled enough for for a for a sales enabled company but uh yeah over a thousand company but uh yeah over a thousand company but uh yeah over a thousand engineers um and based in Silicon Valley engineers um and based in Silicon Valley engineers um and based in Silicon Valley but we have offices globally 18 offices but we have offices globally 18 offices but we have offices globally 18 offices and uh you know our mission is to to put and uh you know our mission is to to put and uh you know our mission is to to put intelligence on a billion machines and intelligence on a billion machines and intelligence on a billion machines and uh we think that can have a profound uh we think that can have a profound uh we think that can have a profound impact on society both in the kind of impact on society both in the kind of impact on society both in the kind of pissy things everyone talks about safety pissy things everyone talks about safety pissy things everyone talks about safety you know if you really talk to somebody you know if you really talk to somebody you know if you really talk to somebody who's been in a car accident or in a who's been in a car accident or in a who's been in a car accident or in a mining accident or you know uh in a mining accident or you know uh in a mining accident or you know uh in a farming accident. Those are real gnarly farming accident. Those are real gnarly farming accident. Those are real gnarly situations. Beyond just fixing that, you situations. Beyond just fixing that, you situations. Beyond just fixing that, you just if you can unlock productivity, I just if you can unlock productivity, I just if you can unlock productivity, I think, you know, we've seen the unlock think, you know, we've seen the unlock think, you know, we've seen the unlock in the digital world and everyone's in the digital world and everyone's in the digital world and everyone's super excited about it and you have super excited about it and you have super excited about it and you have trillion dollar companies emerging. I'm trillion dollar companies emerging. I'm trillion dollar companies emerging. I'm a pretty strong believer that I think a pretty strong believer that I think a pretty strong believer that I think when we look back 25 years, if you look when we look back 25 years, if you look when we look back 25 years, if you look back at the internet now, you know, back at the internet now, you know, back at the internet now, you know, people actually, if you look at the people actually, if you look at the people actually, if you look at the original internet companies that are original internet companies that are original internet companies that are doing, you know, serving or they're doing, you know, serving or they're doing, you know, serving or they're they're doing some analytics and those they're doing some analytics and those they're doing some analytics and those are interesting, but really when you are interesting, but really when you are interesting, but really when you look back 25 years from now, the big look back 25 years from now, the big look back 25 years from now, the big monolithic companies are Amazon that monolithic companies are Amazon that monolithic companies are Amazon that delivers you stuff, you know, uh Apple, delivers you stuff, you know, uh Apple, delivers you stuff, you know, uh Apple, these are the true kind of companies these are the true kind of companies these are the true kind of companies that come of age. And I think when we that come of age. And I think when we that come of age. And I think when we look back 25 years in this intelligence look back 25 years in this intelligence look back 25 years in this intelligence revolution, the companies that impact revolution, the companies that impact revolution, the companies that impact the physical world, you know, might the physical world, you know, might the physical world, you know, might actually be bigger than the companies actually be bigger than the companies actually be bigger than the companies that impact the digital world. that impact the digital world. that impact the digital world. >> I would love for you to talk about the >> I would love for you to talk about the >> I would love for you to talk about the following, which is when you first following, which is when you first following, which is when you first started the company, you know, the knock started the company, you know, the knock started the company, you know, the knock on the company, I think, was oh well, on the company, I think, was oh well, on the company, I think, was oh well, it's it's like, you know, car it's it's it's like, you know, car it's it's it's like, you know, car it's making making cars autonomous, right? making making cars autonomous, right? making making cars autonomous, right? Self-driving cars. But it's kind of Self-driving cars. But it's kind of Self-driving cars. But it's kind of like, okay, there's like whatever, like, okay, there's like whatever, like, okay, there's like whatever, >> you know, there's there Tesla and way >> you know, there's there Tesla and way >> you know, there's there Tesla and way more building their own self-driving more building their own self-driving more building their own self-driving cars, and then and then there's like six cars, and then and then there's like six cars, and then and then there's like six or eight other car companies that or eight other car companies that or eight other car companies that matter, and then the company just could matter, and then the company just could matter, and then the company just could never get that big because there just never get that big because there just never get that big because there just aren't that many customers. aren't that many customers. aren't that many customers. >> Yeah. >> Yeah. >> Yeah. >> So, What's the like how should people >> So, What's the like how should people >> So, What's the like how should people think about like how many things are think about like how many things are think about like how many things are there that are things that move where there that are things that move where there that are things that move where the idea of physical AI physical the idea of physical AI physical the idea of physical AI physical intelligence are going to matter? intelligence are going to matter? intelligence are going to matter? >> Yeah, I mean even today even if you put >> Yeah, I mean even today even if you put >> Yeah, I mean even today even if you put that you know uh let's say uh uh view on that you know uh let's say uh uh view on that you know uh let's say uh uh view on us the automotive is like 30% of our us the automotive is like 30% of our us the automotive is like 30% of our business. So 70% already is business. So 70% already is business. So 70% already is non-automotive and I think if you fast non-automotive and I think if you fast non-automotive and I think if you fast forward another 10 20 years uh even the forward another 10 20 years uh even the forward another 10 20 years uh even the manufacturers themselves as a customer manufacturers themselves as a customer manufacturers themselves as a customer base will be a small amount. I think our base will be a small amount. I think our base will be a small amount. I think our that mission just keep thinking a uh you that mission just keep thinking a uh you that mission just keep thinking a uh you know a billion machines becoming know a billion machines becoming know a billion machines becoming intelligent and you think about all the intelligent and you think about all the intelligent and you think about all the types of machines that exist. Automotive types of machines that exist. Automotive types of machines that exist. Automotive is just an easy one. I think it sticks is just an easy one. I think it sticks is just an easy one. I think it sticks in people's heads because we all drive in people's heads because we all drive in people's heads because we all drive cars and it's a big market. Um but I cars and it's a big market. Um but I cars and it's a big market. Um but I think it's it'll be a minority of of the think it's it'll be a minority of of the think it's it'll be a minority of of the business. I mean it is a minority business. I mean it is a minority business. I mean it is a minority business. I think it'll be increasingly business. I think it'll be increasingly business. I think it'll be increasingly a minority of the business but that a minority of the business but that a minority of the business but that doesn't necessarily mean it'll be small, doesn't necessarily mean it'll be small, doesn't necessarily mean it'll be small, right? right? right? >> Automotive is still huge just as a part >> Automotive is still huge just as a part >> Automotive is still huge just as a part of the globe's GDP. Automotive is of the globe's GDP. Automotive is of the globe's GDP. Automotive is something like 3% of all GDP. Um I think something like 3% of all GDP. Um I think something like 3% of all GDP. Um I think the way we always think about it like as the way we always think about it like as the way we always think about it like as you try to get to your mission initially you try to get to your mission initially you try to get to your mission initially the manufacturers were the distribution the manufacturers were the distribution the manufacturers were the distribution to that intelligence to consumers but to that intelligence to consumers but to that intelligence to consumers but then you start working in defense and then you start working in defense and then you start working in defense and you start working in construction and you start working in construction and you start working in construction and mining and agriculture and suddenly the mining and agriculture and suddenly the mining and agriculture and suddenly the manufacturers are important but maybe manufacturers are important but maybe manufacturers are important but maybe the mining operator is actually really the mining operator is actually really the mining operator is actually really important or the department of war is important or the department of war is important or the department of war is really important and suddenly they really important and suddenly they really important and suddenly they become customers and all of those are become customers and all of those are become customers and all of those are customers of ours as well. customers of ours as well. customers of ours as well. >> Yeah. I think if you split AI into >> Yeah. I think if you split AI into >> Yeah. I think if you split AI into digital AI and physical AI, right? digital AI and physical AI, right? digital AI and physical AI, right? Digital AI of course is building Digital AI of course is building Digital AI of course is building software and optimizing ads and creating software and optimizing ads and creating software and optimizing ads and creating videos, that sort of thing. Uh that's videos, that sort of thing. Uh that's videos, that sort of thing. Uh that's all interesting and good, but really all interesting and good, but really all interesting and good, but really where you talk about the global economy, where you talk about the global economy, where you talk about the global economy, that's physical AI. And then we're that's physical AI. And then we're that's physical AI. And then we're talking about manufacturing and uh and talking about manufacturing and uh and talking about manufacturing and uh and mining and logistics and transportation, mining and logistics and transportation, mining and logistics and transportation, all of these things that all of these things that all of these things that >> supply chains. >> supply chains. >> supply chains. >> Yeah. Supply chain. Exactly. Well, I >> Yeah. Supply chain. Exactly. Well, I >> Yeah. Supply chain. Exactly. Well, I mean, let's build on that though for a mean, let's build on that though for a mean, let's build on that though for a second, which is like so things that second, which is like so things that second, which is like so things that move today or you know, historically move today or you know, historically move today or you know, historically things that move are things that have things that move are things that have things that move are things that have human beings at the wheel or at the human beings at the wheel or at the human beings at the wheel or at the controls in some form, right? Um, and so controls in some form, right? Um, and so controls in some form, right? Um, and so and you know, airplanes [snorts] have and you know, airplanes [snorts] have and you know, airplanes [snorts] have had to get designed around a human in had to get designed around a human in had to get designed around a human in the cockpit. Uh, boats have had to, you the cockpit. Uh, boats have had to, you the cockpit. Uh, boats have had to, you know, get designed around a human, you know, get designed around a human, you know, get designed around a human, you know, steering things. know, steering things. know, steering things. >> Um, like in a world where in a world of >> Um, like in a world where in a world of >> Um, like in a world where in a world of autonomy like autonomy like autonomy like do we already know what the things are do we already know what the things are do we already know what the things are that move or are we going to discover that move or are we going to discover that move or are we going to discover that there are a lot of new things that that there are a lot of new things that that there are a lot of new things that are going to get built uh when you don't are going to get built uh when you don't are going to get built uh when you don't need a human in the in the driver's need a human in the in the driver's need a human in the in the driver's seat? I think both. Uh be well the the seat? I think both. Uh be well the the seat? I think both. Uh be well the the the the thing that you have to remember the the thing that you have to remember the the thing that you have to remember is like you take like a a holage system is like you take like a a holage system is like you take like a a holage system that's on a in a in a port um like a that's on a in a in a port um like a that's on a in a in a port um like a catap kamasu the dirt mover uh in a mine catap kamasu the dirt mover uh in a mine catap kamasu the dirt mover uh in a mine those are made for 20 25 years. those are made for 20 25 years. those are made for 20 25 years. >> So the buyers of that of those products >> So the buyers of that of those products >> So the buyers of that of those products they're they might not have gotten their they're they might not have gotten their they're they might not have gotten their full cycle you know uh uh ROI on them. full cycle you know uh uh ROI on them. full cycle you know uh uh ROI on them. So they're not immediately going to buy So they're not immediately going to buy So they're not immediately going to buy something new no matter how much better something new no matter how much better something new no matter how much better it is. So one part of our strategy is it is. So one part of our strategy is it is. So one part of our strategy is you got to make those things you got to make those things you got to make those things intelligence because they're not going intelligence because they're not going intelligence because they're not going anywhere. The second is what you're anywhere. The second is what you're anywhere. The second is what you're talking about which is well that depends talking about which is well that depends talking about which is well that depends on a human in a cab. If you don't have a on a human in a cab. If you don't have a on a human in a cab. If you don't have a human in cab you can run the the machine human in cab you can run the the machine human in cab you can run the the machine can be smaller. It can be shaped in very can be smaller. It can be shaped in very can be smaller. It can be shaped in very different ways. You talk about mining different ways. You talk about mining different ways. You talk about mining underground, underground, underground, >> right? >> right? >> right? >> The constraint actually is the human >> The constraint actually is the human >> The constraint actually is the human because the human needs to breathe and because the human needs to breathe and because the human needs to breathe and it's very dangerous and so you can build it's very dangerous and so you can build it's very dangerous and so you can build a very very different machine. We're a very very different machine. We're a very very different machine. We're doing both of those things. And um and doing both of those things. And um and doing both of those things. And um and then the thing that we're not talking then the thing that we're not talking then the thing that we're not talking about is we're all talking about about is we're all talking about about is we're all talking about intelligence almost like within a system intelligence almost like within a system intelligence almost like within a system but the system level intelligence is but the system level intelligence is but the system level intelligence is where the unlock is and we're already where the unlock is and we're already where the unlock is and we're already doing work like that where you say hey doing work like that where you say hey doing work like that where you say hey let's take an entire port let's take an let's take an entire port let's take an let's take an entire port let's take an entire mind let's take an entire query entire mind let's take an entire query entire mind let's take an entire query and this heterogeneous mix of machines and this heterogeneous mix of machines and this heterogeneous mix of machines they're all can talk to each other and they're all can talk to each other and they're all can talk to each other and they can optimize and be efficient when they can optimize and be efficient when they can optimize and be efficient when one machine goes down or one machine has one machine goes down or one machine has one machine goes down or one machine has an issue the rest of the mind doesn't an issue the rest of the mind doesn't an issue the rest of the mind doesn't have to stop when it's human-driven. have to stop when it's human-driven. have to stop when it's human-driven. event we don't even know the machine's event we don't even know the machine's event we don't even know the machine's going to go down because there's no going to go down because there's no going to go down because there's no analysis the human is not plugged into analysis the human is not plugged into analysis the human is not plugged into the core uh systems of the machine. So the core uh systems of the machine. So the core uh systems of the machine. So like a simple thing like knowing when a like a simple thing like knowing when a like a simple thing like knowing when a break system is going to break break system is going to break break system is going to break >> is actually huge because you can start >> is actually huge because you can start >> is actually huge because you can start preparing for it in advance. Oh this preparing for it in advance. Oh this preparing for it in advance. Oh this wear and tear is higher than in other wear and tear is higher than in other wear and tear is higher than in other mines. Just using an example but like mines. Just using an example but like mines. Just using an example but like the the other macro point is if you look the the other macro point is if you look the the other macro point is if you look at agriculture as an example you know at agriculture as an example you know at agriculture as an example you know average American farmers 58 years old average American farmers 58 years old average American farmers 58 years old the there's that the number something the there's that the number something the there's that the number something like under 35 it's like less than 10% of like under 35 it's like less than 10% of like under 35 it's like less than 10% of farmers are that young. So what's going farmers are that young. So what's going farmers are that young. So what's going to happen? the need for food growth is to happen? the need for food growth is to happen? the need for food growth is continuing to grow. The need for uh rare continuing to grow. The need for uh rare continuing to grow. The need for uh rare earth materials is contin so these these earth materials is contin so these these earth materials is contin so these these demands are only growing but the humans demands are only growing but the humans demands are only growing but the humans who are the bottleneck are decreasing. who are the bottleneck are decreasing. who are the bottleneck are decreasing. >> Trucking is the same way. Um and so >> Trucking is the same way. Um and so >> Trucking is the same way. Um and so you can you can really just unlock a lot you can you can really just unlock a lot you can you can really just unlock a lot more efficiency. So I mean one way to more efficiency. So I mean one way to more efficiency. So I mean one way to think maybe think about this is like think maybe think about this is like think maybe think about this is like imagine if the cost for food decreases imagine if the cost for food decreases imagine if the cost for food decreases because it's way way more efficient. because it's way way more efficient. because it's way way more efficient. What what's the downstream impact? the What what's the downstream impact? the What what's the downstream impact? the imagine for goods being transported imagine for goods being transported imagine for goods being transported let's say you know instead of a few let's say you know instead of a few let's say you know instead of a few dollars a mile it's 20 cents a mile and dollars a mile it's 20 cents a mile and dollars a mile it's 20 cents a mile and suddenly it's it's I think that the suddenly it's it's I think that the suddenly it's it's I think that the unlock is very very very big um and that unlock is very very very big um and that unlock is very very very big um and that isn't ne I think doesn't necessarily isn't ne I think doesn't necessarily isn't ne I think doesn't necessarily need for all the machines to be need for all the machines to be need for all the machines to be redesigned from the ground up redesigned from the ground up redesigned from the ground up >> right right got it makes sense and then >> right right got it makes sense and then >> right right got it makes sense and then maybe just one one more question would maybe just one one more question would maybe just one one more question would be just give give it give us a sense of be just give give it give us a sense of be just give give it give us a sense of parameterized like the scope and scale parameterized like the scope and scale parameterized like the scope and scale of a company today of a company today of a company today >> yeah uh uh north of a thousand engineers >> yeah uh uh north of a thousand engineers >> yeah uh uh north of a thousand engineers and those engineers are you know and those engineers are you know and those engineers are you know obviously the classic you know uh uh uh obviously the classic you know uh uh uh obviously the classic you know uh uh uh software and AI uh engineering teams, software and AI uh engineering teams, software and AI uh engineering teams, but we also have engineers who really but we also have engineers who really but we also have engineers who really know safety systems. Uh we also have know safety systems. Uh we also have know safety systems. Uh we also have engineers who really know hardware engineers who really know hardware engineers who really know hardware because the important thing that we because the important thing that we because the important thing that we we're kind of just uh tipping around we're kind of just uh tipping around we're kind of just uh tipping around stepping around is all this stuff is stepping around is all this stuff is stepping around is all this stuff is hard because it ultimately has to meet hard because it ultimately has to meet hard because it ultimately has to meet the real world and the real world is has the real world and the real world is has the real world and the real world is has way more complexity and and and has a way more complexity and and and has a way more complexity and and and has a lot more issues and we have engineering lot more issues and we have engineering lot more issues and we have engineering teams that can I mean we've deployed our teams that can I mean we've deployed our teams that can I mean we've deployed our our our models onto like 50ome platforms our our models onto like 50ome platforms our our models onto like 50ome platforms like even that sounds trivial because like even that sounds trivial because like even that sounds trivial because when you think about mostly when you when you think about mostly when you when you think about mostly when you think about models you think about think about models you think about think about models you think about deploying them through a browser or on a deploying them through a browser or on a deploying them through a browser or on a phone and everything's abstracted away phone and everything's abstracted away phone and everything's abstracted away because you of iOS and you have Android because you of iOS and you have Android because you of iOS and you have Android and you have Windows and you have Linux and you have Windows and you have Linux and you have Windows and you have Linux and you have all these systems that have and you have all these systems that have and you have all these systems that have already taken care in the real world you already taken care in the real world you already taken care in the real world you don't have that and so we've we have don't have that and so we've we have don't have that and so we've we have engineering teams that can do that as engineering teams that can do that as engineering teams that can do that as well our kind of a you know claim to well our kind of a you know claim to well our kind of a you know claim to fame as we've uh raised over about a fame as we've uh raised over about a fame as we've uh raised over about a billion dollars in the company's history billion dollars in the company's history billion dollars in the company's history all that is sitting in the bank and uh I all that is sitting in the bank and uh I all that is sitting in the bank and uh I think uh that's I always say that with think uh that's I always say that with think uh that's I always say that with an astrog doesn't mean we're not going an astrog doesn't mean we're not going an astrog doesn't mean we're not going to spend it you know next uh next month to spend it you know next uh next month to spend it you know next uh next month >> good news bad news >> good news bad news >> good news bad news >> yeah good news bad news uh it's uh but >> yeah good news bad news uh it's uh but >> yeah good news bad news uh it's uh but it's the the uh and I Like we're at that it's the the uh and I Like we're at that it's the the uh and I Like we're at that phase, you know, you you talk about phase, you know, you you talk about phase, you know, you you talk about scale, we're at that phase where these scale, we're at that phase where these scale, we're at that phase where these giant markets are around us and we can giant markets are around us and we can giant markets are around us and we can make the decision how aggressive do we make the decision how aggressive do we make the decision how aggressive do we want to pursue those because decade of want to pursue those because decade of want to pursue those because decade of frankly execution and deployment into frankly execution and deployment into frankly execution and deployment into production. I think the the hallmark of production. I think the the hallmark of production. I think the the hallmark of our engineering team is is putting you our engineering team is is putting you our engineering team is is putting you know products into production that that know products into production that that know products into production that that that really is is a big I don't know how that really is is a big I don't know how that really is is a big I don't know how do you think about scale? Yeah, I think do you think about scale? Yeah, I think do you think about scale? Yeah, I think that that's roughly I mean the mission that that's roughly I mean the mission that that's roughly I mean the mission of bringing intelligence to a billion of bringing intelligence to a billion of bringing intelligence to a billion machines that is how we think about it machines that is how we think about it machines that is how we think about it and then thinking about well what are and then thinking about well what are and then thinking about well what are the types of machines that we'll have the types of machines that we'll have the types of machines that we'll have the most impact on and focusing on those the most impact on and focusing on those the most impact on and focusing on those areas first but uh we'll get there areas first but uh we'll get there areas first but uh we'll get there >> right good I uh let's go deeper into the >> right good I uh let's go deeper into the >> right good I uh let's go deeper into the differences between digital and physical differences between digital and physical differences between digital and physical AI and more so into where where are we AI and more so into where where are we AI and more so into where where are we today what progress has has made what today what progress has has made what today what progress has has made what are some of the main major bottlenecks are some of the main major bottlenecks are some of the main major bottlenecks in physical AI when you unpack some of in physical AI when you unpack some of in physical AI when you unpack some of that that that >> yeah I mean I think a lot of times >> yeah I mean I think a lot of times >> yeah I mean I think a lot of times people think about the progress in people think about the progress in people think about the progress in physical AI is limited to basically two physical AI is limited to basically two physical AI is limited to basically two case use cases and they're just cuz case use cases and they're just cuz case use cases and they're just cuz they're obvious and interesting which is they're obvious and interesting which is they're obvious and interesting which is robo taxes and humanoids. Um they're robo taxes and humanoids. Um they're robo taxes and humanoids. Um they're very visceral. They're they're they very visceral. They're they're they very visceral. They're they're they excite you and they're kind of sci-fi. excite you and they're kind of sci-fi. excite you and they're kind of sci-fi. Um I think they're those are very Um I think they're those are very Um I think they're those are very interesting. Uh and they there is real interesting. Uh and they there is real interesting. Uh and they there is real work being done by us and other people work being done by us and other people work being done by us and other people in in those domains. I think all the in in those domains. I think all the in in those domains. I think all the other uh all the other domains I think other uh all the other domains I think other uh all the other domains I think are going to be just as important. I are going to be just as important. I are going to be just as important. I mean, you just you just think about what mean, you just you just think about what mean, you just you just think about what happens on a port. That's there's a huge happens on a port. That's there's a huge happens on a port. That's there's a huge unlock there. Um, and that's I think unlock there. Um, and that's I think unlock there. Um, and that's I think that's the that's the area we're we're that's the that's the area we're we're that's the that's the area we're we're we're really focused on. It's like all we're really focused on. It's like all we're really focused on. It's like all the other nooks and crannies. If you the other nooks and crannies. If you the other nooks and crannies. If you look at like we we've talked before look at like we we've talked before look at like we we've talked before about uh the rise of Cisco and how you about uh the rise of Cisco and how you about uh the rise of Cisco and how you know networking kind of went from you know networking kind of went from you know networking kind of went from you know first individual machines and know first individual machines and know first individual machines and companies would get network and then companies would get network and then companies would get network and then entire countries are getting network. entire countries are getting network. entire countries are getting network. There's a similar thing happening with There's a similar thing happening with There's a similar thing happening with AI. AI's getting to that level of kind AI. AI's getting to that level of kind AI. AI's getting to that level of kind of sovereign AI is now a discussion. of sovereign AI is now a discussion. of sovereign AI is now a discussion. Sovereign AI really is about physical AI Sovereign AI really is about physical AI Sovereign AI really is about physical AI because that's where you're talking because that's where you're talking because that's where you're talking about AI in defense. You're talking about AI in defense. You're talking about AI in defense. You're talking about AI in the physical machines that about AI in the physical machines that about AI in the physical machines that are moving around. If you look just at are moving around. If you look just at are moving around. If you look just at the example of of uh Whimo from America the example of of uh Whimo from America the example of of uh Whimo from America and Pony from China trying to deploy in and Pony from China trying to deploy in and Pony from China trying to deploy in let's say the other countries, so not let's say the other countries, so not let's say the other countries, so not America, not Europe, not China. every America, not Europe, not China. every America, not Europe, not China. every one of those uh spaces, they're way more one of those uh spaces, they're way more one of those uh spaces, they're way more uh they're way more hesitant of saying, uh they're way more hesitant of saying, uh they're way more hesitant of saying, "Yeah, thumbs up. Your robo taxis can "Yeah, thumbs up. Your robo taxis can "Yeah, thumbs up. Your robo taxis can run unfettered on our in our uh run unfettered on our in our uh run unfettered on our in our uh country." And so, if you look back just country." And so, if you look back just country." And so, if you look back just at kind of this arc of the internet, you at kind of this arc of the internet, you at kind of this arc of the internet, you know, when the first internet companies know, when the first internet companies know, when the first internet companies come, nobody's really think about come, nobody's really think about come, nobody's really think about sovereignty at all. It's like the sovereignty at all. It's like the sovereignty at all. It's like the browser goes everywhere, the internet browser goes everywhere, the internet browser goes everywhere, the internet goes everywhere. That's almost the power goes everywhere. That's almost the power goes everywhere. That's almost the power of it. Then when social media emerges of it. Then when social media emerges of it. Then when social media emerges there's a bit more of hey actually not there's a bit more of hey actually not there's a bit more of hey actually not every social media and then you have every social media and then you have every social media and then you have China not allowing Facebook to come in China not allowing Facebook to come in China not allowing Facebook to come in and you have some then you get into the and you have some then you get into the and you have some then you get into the next level of like the online offline next level of like the online offline next level of like the online offline stuff there's more resistance the Ubers stuff there's more resistance the Ubers stuff there's more resistance the Ubers the door dashes suddenly there's local the door dashes suddenly there's local the door dashes suddenly there's local players who are being favored very players who are being favored very players who are being favored very aggressively when we get to physical AI aggressively when we get to physical AI aggressively when we get to physical AI I think there's going to be huge and I think there's going to be huge and I think there's going to be huge and also there's like a larger ge you know also there's like a larger ge you know also there's like a larger ge you know geopolitical theme of of kind of uh more geopolitical theme of of kind of uh more geopolitical theme of of kind of uh more fracturing than globalization you're fracturing than globalization you're fracturing than globalization you're going to have this demand and for this going to have this demand and for this going to have this demand and for this AI should somehow be localized and I AI should somehow be localized and I AI should somehow be localized and I think that has to play into our strategy think that has to play into our strategy think that has to play into our strategy as well. We're a technology provider so as well. We're a technology provider so as well. We're a technology provider so we can provide that technology across we can provide that technology across we can provide that technology across the globe. Um and I think that's that's the globe. Um and I think that's that's the globe. Um and I think that's that's that's uh that's that's that's something that's uh that's that's that's something that's uh that's that's that's something that's understated in this in this uh that's understated in this in this uh that's understated in this in this uh conversation. conversation. conversation. >> Yeah. A few other things on on digital >> Yeah. A few other things on on digital >> Yeah. A few other things on on digital versus physical AI. So in digital AI, versus physical AI. So in digital AI, versus physical AI. So in digital AI, the state-of-the-art is you you can the state-of-the-art is you you can the state-of-the-art is you you can train models effectively on the entirety train models effectively on the entirety train models effectively on the entirety of the internet and then maybe augment of the internet and then maybe augment of the internet and then maybe augment that with additional data that's been that with additional data that's been that with additional data that's been collected and refined with some hired collected and refined with some hired collected and refined with some hired experts. Right? This is sort of a hot experts. Right? This is sort of a hot experts. Right? This is sort of a hot field right now. Uh but generally you're field right now. Uh but generally you're field right now. Uh but generally you're talking about a foundation model that's talking about a foundation model that's talking about a foundation model that's built on internet data. In physical AI, built on internet data. In physical AI, built on internet data. In physical AI, the internet data is is useful too. the internet data is is useful too. the internet data is is useful too. However, to actually build a foundation However, to actually build a foundation However, to actually build a foundation model in physical AI, there's also a lot model in physical AI, there's also a lot model in physical AI, there's also a lot of private data collection. you when of private data collection. you when of private data collection. you when we're talking about mines or or we're talking about mines or or we're talking about mines or or logistics or any of these other uh other logistics or any of these other uh other logistics or any of these other uh other fields, the data that's useful for fields, the data that's useful for fields, the data that's useful for training models there is not necessarily training models there is not necessarily training models there is not necessarily available. So we have to do a lot of available. So we have to do a lot of available. So we have to do a lot of work ourselves actually going going out work ourselves actually going going out work ourselves actually going going out and collecting that data. And then the and collecting that data. And then the and collecting that data. And then the other key factor is safety, right? Uh if other key factor is safety, right? Uh if other key factor is safety, right? Uh if you're talking about uh building a you're talking about uh building a you're talking about uh building a smartphone app, you don't necessarily smartphone app, you don't necessarily smartphone app, you don't necessarily care about uh is is a safety critical care about uh is is a safety critical care about uh is is a safety critical application. But when you're talking application. But when you're talking application. But when you're talking about moving a a machine that weighs about moving a a machine that weighs about moving a a machine that weighs many tons, uh or think of a humanoid many tons, uh or think of a humanoid many tons, uh or think of a humanoid which could fall over on your children, which could fall over on your children, which could fall over on your children, uh you care a lot about safety and and uh you care a lot about safety and and uh you care a lot about safety and and the evaluation of that safety and and the evaluation of that safety and and the evaluation of that safety and and that is really sort of getting to the that is really sort of getting to the that is really sort of getting to the state of the art of physical AI and and state of the art of physical AI and and state of the art of physical AI and and really proving out uh the safety case really proving out uh the safety case really proving out uh the safety case around some of these state-of-the-art around some of these state-of-the-art around some of these state-of-the-art models. models. models. >> Yeah. And I think like you know you talk >> Yeah. And I think like you know you talk >> Yeah. And I think like you know you talk about like humanoid data collection has about like humanoid data collection has about like humanoid data collection has been its own you know uh little uh area been its own you know uh little uh area been its own you know uh little uh area of interest but when you talk about of interest but when you talk about of interest but when you talk about collecting data like in places like collecting data like in places like collecting data like in places like Korea where they have north South Korea Korea where they have north South Korea Korea where they have north South Korea where you have North Korea they don't where you have North Korea they don't where you have North Korea they don't allow mapping companies let alone allow mapping companies let alone allow mapping companies let alone allowing a you know a an American allowing a you know a an American allowing a you know a an American company to come in and data collect. company to come in and data collect. company to come in and data collect. We've figured out over the years whether We've figured out over the years whether We've figured out over the years whether it's the Middle East, whether it's it's the Middle East, whether it's it's the Middle East, whether it's Latim, how to get into these countries, Latim, how to get into these countries, Latim, how to get into these countries, work with the governments and get the work with the governments and get the work with the governments and get the thumbs up to collect proprietary data. thumbs up to collect proprietary data. thumbs up to collect proprietary data. And so in in the way that it's it is And so in in the way that it's it is And so in in the way that it's it is similar to you know other uh digital AI similar to you know other uh digital AI similar to you know other uh digital AI systems, your proprietary data sets, systems, your proprietary data sets, systems, your proprietary data sets, scaling laws, all that stuff is the scaling laws, all that stuff is the scaling laws, all that stuff is the same. It's just applied in a very very same. It's just applied in a very very same. It's just applied in a very very different way. And uh it's almost like different way. And uh it's almost like different way. And uh it's almost like the way to think about it is like the the way to think about it is like the the way to think about it is like the diffusion of these models is very diffusion of these models is very diffusion of these models is very different because you can't it's not different because you can't it's not different because you can't it's not everyone can just access them through a everyone can just access them through a everyone can just access them through a phone. you're and so so that phone. you're and so so that phone. you're and so so that ironically is actually plays in our ironically is actually plays in our ironically is actually plays in our favor because once we have a massive favor because once we have a massive favor because once we have a massive proprietary data set where we've been proprietary data set where we've been proprietary data set where we've been building we already have hundreds of building we already have hundreds of building we already have hundreds of pabytes of data um and then we have our pabytes of data um and then we have our pabytes of data um and then we have our own tools which are like you know own tools which are like you know own tools which are like you know synthetic data tools uh neural sim we synthetic data tools uh neural sim we synthetic data tools uh neural sim we can use our own tools with our own can use our own tools with our own can use our own tools with our own proprietary data and that allows us to proprietary data and that allows us to proprietary data and that allows us to build some of the best systems in the build some of the best systems in the build some of the best systems in the business is there's kind of a chicken business is there's kind of a chicken business is there's kind of a chicken and egg thing which is like in order to and egg thing which is like in order to and egg thing which is like in order to build an autonomous physical thing you build an autonomous physical thing you build an autonomous physical thing you need a lot of data need a lot of data need a lot of data to gather that data, you need a lot of to gather that data, you need a lot of to gather that data, you need a lot of physical autonomous things running physical autonomous things running physical autonomous things running around collecting the data. So, it's around collecting the data. So, it's around collecting the data. So, it's like once you have a giant network of like once you have a giant network of like once you have a giant network of physical things running around, you have physical things running around, you have physical things running around, you have the data that makes them all work like the data that makes them all work like the data that makes them all work like is is there is there a flywheel aspect is is there is there a flywheel aspect is is there is there a flywheel aspect of that? And is there is there is there of that? And is there is there is there of that? And is there is there is there like what what's the level of difficulty like what what's the level of difficulty like what what's the level of difficulty involved in kind of booting up that involved in kind of booting up that involved in kind of booting up that flywheel? flywheel? flywheel? >> Uh it's it's it's difficult, but it's >> Uh it's it's it's difficult, but it's >> Uh it's it's it's difficult, but it's also not difficult. I I mean I think we also not difficult. I I mean I think we also not difficult. I I mean I think we we have one of the largest data we have one of the largest data we have one of the largest data collection fleets on the planet, frankly collection fleets on the planet, frankly collection fleets on the planet, frankly speaking. Um so that's how you bootstrap speaking. Um so that's how you bootstrap speaking. Um so that's how you bootstrap your way into it. That's just money and your way into it. That's just money and your way into it. That's just money and resources and technical knowledge, but resources and technical knowledge, but resources and technical knowledge, but it's not like there's probably more than it's not like there's probably more than it's not like there's probably more than five companies that have that technical five companies that have that technical five companies that have that technical knowledge. So, it's not extremely knowledge. So, it's not extremely knowledge. So, it's not extremely obscure. I think what is more difficult obscure. I think what is more difficult obscure. I think what is more difficult is then how do you actually have that is then how do you actually have that is then how do you actually have that model which is going to work on lots of model which is going to work on lots of model which is going to work on lots of different hardware and is, you know, is different hardware and is, you know, is different hardware and is, you know, is is tested appropriately because the you is tested appropriately because the you is tested appropriately because the you saw it, you know, in Cruz, right? Cruz saw it, you know, in Cruz, right? Cruz saw it, you know, in Cruz, right? Cruz was this company that did amazing was this company that did amazing was this company that did amazing self-driving work and then one accident, self-driving work and then one accident, self-driving work and then one accident, General Motors owns them and they get General Motors owns them and they get General Motors owns them and they get super scared and they pull back. So, super scared and they pull back. So, super scared and they pull back. So, it's like just getting these things into it's like just getting these things into it's like just getting these things into production is actually more difficult production is actually more difficult production is actually more difficult than than uh than it seems. Um I think than than uh than it seems. Um I think than than uh than it seems. Um I think like we believed synthetic data was like we believed synthetic data was like we believed synthetic data was going to be important. So, we started going to be important. So, we started going to be important. So, we started our synthetic data team like 5 years ago our synthetic data team like 5 years ago our synthetic data team like 5 years ago now plus yeah more than that at this now plus yeah more than that at this now plus yeah more than that at this point. Uh and point. Uh and point. Uh and >> like and we're a strong believer that >> like and we're a strong believer that >> like and we're a strong believer that synthetic data can accelerate autonomy synthetic data can accelerate autonomy synthetic data can accelerate autonomy development. We've just seen that and development. We've just seen that and development. We've just seen that and then there are like lots of other then there are like lots of other then there are like lots of other secondary and tertiary like kind of secondary and tertiary like kind of secondary and tertiary like kind of technical innovations that happen. technical innovations that happen. technical innovations that happen. Obviously the transformer revolution Obviously the transformer revolution Obviously the transformer revolution hitting self-driving massive basically hitting self-driving massive basically hitting self-driving massive basically everything done in self-driving pre2122 everything done in self-driving pre2122 everything done in self-driving pre2122 relevant but you're almost like that's relevant but you're almost like that's relevant but you're almost like that's kind of the starting point but it's also kind of the starting point but it's also kind of the starting point but it's also different than today being the starting different than today being the starting different than today being the starting point like there those four five years point like there those four five years point like there those four five years are actually there has been a lot of are actually there has been a lot of are actually there has been a lot of work done you can see it most clearly work done you can see it most clearly work done you can see it most clearly with Tesla but there's other folks uh in with Tesla but there's other folks uh in with Tesla but there's other folks uh in that process the actual techniques that process the actual techniques that process the actual techniques historically and I'm just simplifying historically and I'm just simplifying historically and I'm just simplifying here imitation learning was the way the here imitation learning was the way the here imitation learning was the way the way of the which was collect a bunch of way of the which was collect a bunch of way of the which was collect a bunch of data and then the models would basically data and then the models would basically data and then the models would basically imitate what human drivers do. imitate what human drivers do. imitate what human drivers do. >> The real state-of-the-art right now is >> The real state-of-the-art right now is >> The real state-of-the-art right now is endtoend reinforcement learning in in a endtoend reinforcement learning in in a endtoend reinforcement learning in in a closed loop in in your tools. And so closed loop in in your tools. And so closed loop in in your tools. And so >> it's a little simplified to say the >> it's a little simplified to say the >> it's a little simplified to say the system learns itself. It identifies system learns itself. It identifies system learns itself. It identifies where the issues in the self-driving where the issues in the self-driving where the issues in the self-driving system are and essentially you then find system are and essentially you then find system are and essentially you then find data like that or you synthetically data like that or you synthetically data like that or you synthetically create data like that and then you you create data like that and then you you create data like that and then you you know close that loop and you see are you know close that loop and you see are you know close that loop and you see are you performing in those same scenarios performing in those same scenarios performing in those same scenarios better and better. I think if you fast better and better. I think if you fast better and better. I think if you fast forward some years that will be a forward some years that will be a forward some years that will be a completely closed loop like with no completely closed loop like with no completely closed loop like with no humans intervening. Right now you still humans intervening. Right now you still humans intervening. Right now you still have like like what's the the fog error have like like what's the the fog error have like like what's the the fog error that we saw um we still see errors in that we saw um we still see errors in that we saw um we still see errors in the real world that impact self-driving. the real world that impact self-driving. the real world that impact self-driving. >> Oh yeah. So it's like well what what are >> Oh yeah. So it's like well what what are >> Oh yeah. So it's like well what what are the bottlenecks right? and and the the the bottlenecks right? and and the the the bottlenecks right? and and the the bottlenecks. Uh there's plenty of them, bottlenecks. Uh there's plenty of them, bottlenecks. Uh there's plenty of them, but uh whenever you're dealing with but uh whenever you're dealing with but uh whenever you're dealing with physical systems, you inevitably you hit physical systems, you inevitably you hit physical systems, you inevitably you hit a lot of gnarly hardware problems. And a lot of gnarly hardware problems. And a lot of gnarly hardware problems. And uh it could be anything from overheating uh it could be anything from overheating uh it could be anything from overheating to uh sensor being slightly to uh sensor being slightly to uh sensor being slightly miscalibrated or a funny funny issue we miscalibrated or a funny funny issue we miscalibrated or a funny funny issue we saw yesterday was uh basically a fogging saw yesterday was uh basically a fogging saw yesterday was uh basically a fogging sensor like fog impacting a sensor. And sensor like fog impacting a sensor. And sensor like fog impacting a sensor. And uh but these are the things that you uh but these are the things that you uh but these are the things that you actually have to solve for this stuff to actually have to solve for this stuff to actually have to solve for this stuff to work very reliably in the real world. work very reliably in the real world. work very reliably in the real world. >> Yeah. So I want to ask you a thread a >> Yeah. So I want to ask you a thread a >> Yeah. So I want to ask you a thread a question and you can we can decide question and you can we can decide question and you can we can decide whether you guys want to engage on it or whether you guys want to engage on it or whether you guys want to engage on it or not. It might be an opportunity or might not. It might be an opportunity or might not. It might be an opportunity or might hate the question which is hate the question which is hate the question which is >> were you surprised? So Cruz was a super >> were you surprised? So Cruz was a super >> were you surprised? So Cruz was a super high-flying Silicon Valley autonomy high-flying Silicon Valley autonomy high-flying Silicon Valley autonomy startup that was kind of running neck startup that was kind of running neck startup that was kind of running neck and neck with Tesla early on and so and neck with Tesla early on and so and neck with Tesla early on and so forth and you know very top- end team forth and you know very top- end team forth and you know very top- end team and then they famously got bought by and then they famously got bought by and then they famously got bought by General Motors and General Motors and General Motors and >> one of my first distributions >> one of my first distributions >> one of my first distributions personally. So I personally. So I personally. So I >> There we go. Y Cominator Y Com Y >> There we go. Y Cominator Y Com Y >> There we go. Y Cominator Y Com Y Cominator company um Cominator company um Cominator company um >> and um and you know a top end team and >> and um and you know a top end team and >> and um and you know a top end team and they they were you know by all accounts they they were you know by all accounts they they were you know by all accounts making excellent progress. They got making excellent progress. They got making excellent progress. They got bought by General Motors. They became bought by General Motors. They became bought by General Motors. They became the GM autonomy program. GM got a lot of the GM autonomy program. GM got a lot of the GM autonomy program. GM got a lot of praise at least, you know, at least in praise at least, you know, at least in praise at least, you know, at least in the in the in the tech in tech circles the in the in the tech in tech circles the in the in the tech in tech circles for being like, okay, being like the the for being like, okay, being like the the for being like, okay, being like the the legacy automaker with the biggest legacy automaker with the biggest legacy automaker with the biggest investment. investment. investment. >> I I called Peter when before before it >> I I called Peter when before before it >> I I called Peter when before before it was announced on that. And I said, hey, was announced on that. And I said, hey, was announced on that. And I said, hey, Cruz just got bought, you know, he's Cruz just got bought, you know, he's Cruz just got bought, you know, he's also GM family. We're both GM families. also GM family. We're both GM families. also GM family. We're both GM families. And uh Peter Guest was he said Nvidia? I And uh Peter Guest was he said Nvidia? I And uh Peter Guest was he said Nvidia? I said no. He said I said go fish is said no. He said I said go fish is said no. He said I said go fish is Apple. I said no. I said General Apple. I said no. I said General Apple. I said no. I said General explicitive motors. [laughter] explicitive motors. [laughter] explicitive motors. [laughter] >> Right. So, so that's surprising to >> Right. So, so that's surprising to >> Right. So, so that's surprising to people who are from GM people who are from GM people who are from GM >> that they were willing to buy that they >> that they were willing to buy that they >> that they were willing to buy that they did okay that they did it and then by did okay that they did it and then by did okay that they did it and then by all accounts they were I mean as far as all accounts they were I mean as far as all accounts they were I mean as far as like as far as I ever heard like they like as far as I ever heard like they like as far as I ever heard like they were making excellent progress uh and were making excellent progress uh and were making excellent progress uh and then they had this there was a there was then they had this there was a there was then they had this there was a there was an accident there was a was that was a an accident there was a was that was a an accident there was a was that was a injury or fatality or injury or fatality or injury or fatality or >> uh it wasn't a fatality it was a serious >> uh it wasn't a fatality it was a serious >> uh it wasn't a fatality it was a serious injury somebody was [clears throat] injury somebody was [clears throat] injury somebody was [clears throat] dragged for 20 ft dragged for 20 ft dragged for 20 ft >> yeah serious injury bad press and then >> yeah serious injury bad press and then >> yeah serious injury bad press and then and then they put a bullet they the GM and then they put a bullet they the GM and then they put a bullet they the GM CEO on board put a bullet in the cruise CEO on board put a bullet in the cruise CEO on board put a bullet in the cruise project and I know the that at least project and I know the that at least project and I know the that at least some of the senior cruise people were some of the senior cruise people were some of the senior cruise people were extremely upset you know [clears throat] extremely upset you know [clears throat] extremely upset you know [clears throat] the by by the aftermath of that was it the by by the aftermath of that was it the by by the aftermath of that was it surprising that they reacted the way surprising that they reacted the way surprising that they reacted the way that they did? that they did? that they did? >> Uh so you know full disclosure General >> Uh so you know full disclosure General >> Uh so you know full disclosure General Motors is a customer and I went to the Motors is a customer and I went to the Motors is a customer and I went to the General Motors Institute so we have a General Motors Institute so we have a General Motors Institute so we have a lot of love for the company. Uh but lot of love for the company. Uh but lot of love for the company. Uh but incidentally and ironically I'm reading incidentally and ironically I'm reading incidentally and ironically I'm reading uh coincidentally I should say I'm uh coincidentally I should say I'm uh coincidentally I should say I'm reading this very famous book which I reading this very famous book which I reading this very famous book which I had actually never read before called on had actually never read before called on had actually never read before called on a clear day you can see General Motors a clear day you can see General Motors a clear day you can see General Motors right [laughter] right [laughter] right [laughter] >> and Delorean's book. Have you read it? >> and Delorean's book. Have you read it? >> and Delorean's book. Have you read it? >> As one does. As one does. >> As one does. As one does. >> As one does. As one does. >> Have you read that book? >> Have you read that book? >> Have you read that book? >> So I have years ago I have. It's one of >> So I have years ago I have. It's one of >> So I have years ago I have. It's one of the great alltime book titles. And we the great alltime book titles. And we the great alltime book titles. And we should just pause to say John Delorean should just pause to say John Delorean should just pause to say John Delorean was like what he was like the super was like what he was like the super was like what he was like the super genius of the car industry. genius of the car industry. genius of the car industry. >> He was going to be the next president of >> He was going to be the next president of >> He was going to be the next president of General Motors General Motors General Motors >> of General Motors and then later on he >> of General Motors and then later on he >> of General Motors and then later on he started his own car company which like started his own car company which like started his own car company which like Back to the Future and then that whole Back to the Future and then that whole Back to the Future and then that whole thing collapsed for variety of reasons. thing collapsed for variety of reasons. thing collapsed for variety of reasons. >> Uh but yeah he he was like a legend. He >> Uh but yeah he he was like a legend. He >> Uh but yeah he he was like a legend. He was like one of the main principal was like one of the main principal was like one of the main principal drivers of innovation of the current drivers of innovation of the current drivers of innovation of the current Bob Lutz this category and you got to Bob Lutz this category and you got to Bob Lutz this category and you got to remember this is this right remember this is this right remember this is this right >> but sorry repeat the title. Repeat the >> but sorry repeat the title. Repeat the >> but sorry repeat the title. Repeat the title of the book. title of the book. title of the book. >> On a clear day you can see General >> On a clear day you can see General >> On a clear day you can see General Motors Motors Motors >> and why why was that the title of the >> and why why was that the title of the >> and why why was that the title of the book? because uh there's a lot of book? because uh there's a lot of book? because uh there's a lot of [laughter] [laughter] [laughter] >> It's a very large complex. >> It's a very large complex. >> It's a very large complex. >> Yeah. Complex. Yeah. >> Yeah. Complex. Yeah. >> Yeah. Complex. Yeah. >> It's like it's like a nation state. >> It's like it's like a nation state. >> It's like it's like a nation state. >> Yeah. It I mean really I mean it is like >> Yeah. It I mean really I mean it is like >> Yeah. It I mean really I mean it is like I think we say that like sometimes I think we say that like sometimes I think we say that like sometimes almost like flippantly but these almost like flippantly but these almost like flippantly but these companies are like extension like companies are like extension like companies are like extension like Hyundai is an extension of the state. Hyundai is an extension of the state. Hyundai is an extension of the state. Toyota is an extension of the state. Toyota is an extension of the state. Toyota is an extension of the state. Volkswagen is literally Volkswagen board Volkswagen is literally Volkswagen board Volkswagen is literally Volkswagen board members are members of the government. members are members of the government. members are members of the government. So these are extensions of the state and So these are extensions of the state and So these are extensions of the state and and almost every uh and there used to be and almost every uh and there used to be and almost every uh and there used to be an old saying what's good for General an old saying what's good for General an old saying what's good for General Motors is good for America and you Motors is good for America and you Motors is good for America and you cannot underate cannot underate cannot underate how important General Motors is the how important General Motors is the how important General Motors is the history of the American corporation history of the American corporation history of the American corporation Sloan's uh my years at General Motors Sloan's uh my years at General Motors Sloan's uh my years at General Motors and Adventures white collar man if you and Adventures white collar man if you and Adventures white collar man if you run a large engineering organization you run a large engineering organization you run a large engineering organization you should read that that is the this like should read that that is the this like should read that that is the this like you this you this you this >> this thing that that we talk as a modern >> this thing that that we talk as a modern >> this thing that that we talk as a modern corporation didn't just emerge Sloan and corporation didn't just emerge Sloan and corporation didn't just emerge Sloan and Ketaring create ketaring is the head of Ketaring create ketaring is the head of Ketaring create ketaring is the head of engineering created this this AR, you engineering created this this AR, you engineering created this this AR, you know, with levels and vice presidents know, with levels and vice presidents know, with levels and vice presidents and how do you do functional and matrix and how do you do functional and matrix and how do you do functional and matrix organizations? It's there really is like organizations? It's there really is like organizations? It's there really is like the source code comes along John uh uh the source code comes along John uh uh the source code comes along John uh uh you know comes on Delorean and he says you know comes on Delorean and he says you know comes on Delorean and he says um he writes he's going to be president um he writes he's going to be president um he writes he's going to be president and he's so fed up with a company but and he's so fed up with a company but and he's so fed up with a company but what was controversial was GM was doing what was controversial was GM was doing what was controversial was GM was doing really well at the time. GM really well at the time. GM really well at the time. GM >> was like a when we say like GM was >> was like a when we say like GM was >> was like a when we say like GM was number one in the Fortune 100 it was number one in the Fortune 100 it was number one in the Fortune 100 it was like number one two and three. It was like number one two and three. It was like number one two and three. It was everything and it was seen as the best everything and it was seen as the best everything and it was seen as the best company in America. So somebody to company in America. So somebody to company in America. So somebody to openly criticize the company and so he openly criticize the company and so he openly criticize the company and so he has a whole he writes this book as he has a whole he writes this book as he has a whole he writes this book as he quits out of out of uh how annoyed he quits out of out of uh how annoyed he quits out of out of uh how annoyed he was general is being read led uh he was general is being read led uh he was general is being read led uh he writes this book and then after he like writes this book and then after he like writes this book and then after he like so up he's like I don't want that book so up he's like I don't want that book so up he's like I don't want that book published [laughter] published [laughter] published [laughter] and so he fights for years for his and so he fights for years for his and so he fights for years for his co-author not to publish the book. The co-author not to publish the book. The co-author not to publish the book. The co-author still publishes it. So it's a co-author still publishes it. So it's a co-author still publishes it. So it's a real true insight into a large real true insight into a large real true insight into a large corporation. I'm incidentally just corporation. I'm incidentally just corporation. I'm incidentally just reading it out even though I've you know reading it out even though I've you know reading it out even though I've you know worked at GM 20 some years ago and had worked at GM 20 some years ago and had worked at GM 20 some years ago and had you know know a lot about the company you know know a lot about the company you know know a lot about the company and what's shocking is it's not only and what's shocking is it's not only and what's shocking is it's not only about GM most of the major manufacturers about GM most of the major manufacturers about GM most of the major manufacturers actually still operate that way uh on actually still operate that way uh on actually still operate that way uh on the inside and so the question isn't the the inside and so the question isn't the the inside and so the question isn't the the point I think for everyone to take the point I think for everyone to take the point I think for everyone to take away isn't that these people who run away isn't that these people who run away isn't that these people who run these companies are stupid they're not these companies are stupid they're not these companies are stupid they're not stupid they're it's kind of like you stupid they're it's kind of like you stupid they're it's kind of like you know when you're selling to the know when you're selling to the know when you're selling to the department of war and people say well department of war and people say well department of war and people say well why are you doing that it's like well why are you doing that it's like well why are you doing that it's like well the distribution defines the business the distribution defines the business the distribution defines the business >> that Like so like the distribution is >> that Like so like the distribution is >> that Like so like the distribution is this is a consumer product of of this this is a consumer product of of this this is a consumer product of of this this stat might be outdated but when I this stat might be outdated but when I this stat might be outdated but when I worked in safety systems 20 years ago I worked in safety systems 20 years ago I worked in safety systems 20 years ago I remember GM used to pound into your head remember GM used to pound into your head remember GM used to pound into your head of the top five consumer lawsuits in in of the top five consumer lawsuits in in of the top five consumer lawsuits in in American history three are automotive. American history three are automotive. American history three are automotive. We got the majority right so it's like We got the majority right so it's like We got the majority right so it's like you have to be extremely careful. But we you have to be extremely careful. But we you have to be extremely careful. But we had these like weird things like inside had these like weird things like inside had these like weird things like inside the company you couldn't it wasn't red, the company you couldn't it wasn't red, the company you couldn't it wasn't red, yellow, green. It was like purple or yellow, green. It was like purple or yellow, green. It was like purple or like you'd always have to decoder like you'd always have to decoder like you'd always have to decoder because you know why? Cuz when they go because you know why? Cuz when they go because you know why? Cuz when they go to lawsuits they're like you let a to lawsuits they're like you let a to lawsuits they're like you let a safety system that was marked red go to safety system that was marked red go to safety system that was marked red go to production. I was like no it was marked production. I was like no it was marked production. I was like no it was marked magenta. [laughter] Like so like can you imagine how Like so like can you imagine how infuriating that is? Every time you're infuriating that is? Every time you're infuriating that is? Every time you're like like like >> what does orange mean? Does this mean I >> what does orange mean? Does this mean I >> what does orange mean? Does this mean I have to like So fast forward to you're have to like So fast forward to you're have to like So fast forward to you're meeting that system. Well, for then the meeting that system. Well, for then the meeting that system. Well, for then the the Ford slogan for a very long time was the Ford slogan for a very long time was the Ford slogan for a very long time was that it was quality is job one, right? that it was quality is job one, right? that it was quality is job one, right? Safety is job. Safety is job. Safety is job. >> Yes. Yeah. Exactly. And and that's the >> Yes. Yeah. Exactly. And and that's the >> Yes. Yeah. Exactly. And and that's the one two punch of automotive. It's one two punch of automotive. It's one two punch of automotive. It's quality and safety. Quality and safety quality and safety. Quality and safety quality and safety. Quality and safety and quality really becomes because the and quality really becomes because the and quality really becomes because the Japanese really reset that that stage Japanese really reset that that stage Japanese really reset that that stage and and because that's a whole separate and and because that's a whole separate and and because that's a whole separate automotive history. We could talk about automotive history. We could talk about automotive history. We could talk about automotive history for for an hour. But automotive history for for an hour. But automotive history for for an hour. But the punch line is you have the Silicon the punch line is you have the Silicon the punch line is you have the Silicon Valley company meeting this immovable Valley company meeting this immovable Valley company meeting this immovable object. object. object. >> There is a parallel universe that >> There is a parallel universe that >> There is a parallel universe that cruises out there right now. even as a cruises out there right now. even as a cruises out there right now. even as a part of General Motors. So, I think you part of General Motors. So, I think you part of General Motors. So, I think you always have to take it into the context always have to take it into the context always have to take it into the context of where the company is, where union of where the company is, where union of where the company is, where union negotiations are happening literally negotiations are happening literally negotiations are happening literally that year. And if you're the union, that year. And if you're the union, that year. And if you're the union, you're like, you can't make a billion you're like, you can't make a billion you're like, you can't make a billion dollars for us, but you're funding this dollars for us, but you're funding this dollars for us, but you're funding this thing that's killing people and it's thing that's killing people and it's thing that's killing people and it's sloppy. And so, I'm not saying precisely sloppy. And so, I'm not saying precisely sloppy. And so, I'm not saying precisely that's what happened to be very clear, that's what happened to be very clear, that's what happened to be very clear, but the it's a multivariate problem. My but the it's a multivariate problem. My but the it's a multivariate problem. My other hot take is, you know, I worked at other hot take is, you know, I worked at other hot take is, you know, I worked at both companies, right? Uh Google Google both companies, right? Uh Google Google both companies, right? Uh Google Google Motors. Those companies are way more Motors. Those companies are way more Motors. Those companies are way more similar than they're different. similar than they're different. similar than they're different. >> Way way more similar. Literally, people >> Way way more similar. Literally, people >> Way way more similar. Literally, people don't need to know this. The Google don't need to know this. The Google don't need to know this. The Google leveling system is the same as the leveling system is the same as the leveling system is the same as the General Motors [laughter] leveling General Motors [laughter] leveling General Motors [laughter] leveling system. And I used to say I saying, you system. And I used to say I saying, you system. And I used to say I saying, you know, this inside of Google meetings is know, this inside of Google meetings is know, this inside of Google meetings is like, hey, actually some of the like, hey, actually some of the like, hey, actually some of the engineers I knew at General Motors are engineers I knew at General Motors are engineers I knew at General Motors are better than the engineers here. And better than the engineers here. And better than the engineers here. And people would look at me like I'm saying people would look at me like I'm saying people would look at me like I'm saying there's no God in church. It's like there's no God in church. It's like there's no God in church. It's like they're like, how dare you, you metal they're like, how dare you, you metal they're like, how dare you, you metal bending monkey from Detroit. [laughter] bending monkey from Detroit. [laughter] bending monkey from Detroit. [laughter] It's like, no, actually like making a It's like, no, actually like making a It's like, no, actually like making a modern combustion engine is extremely modern combustion engine is extremely modern combustion engine is extremely complex. It's not just like, you know, complex. It's not just like, you know, complex. It's not just like, you know, it's it's it's not simple stuff. And so it's it's it's not simple stuff. And so it's it's it's not simple stuff. And so the the the macro point I think is it's the the the macro point I think is it's the the the macro point I think is it's a bunch of things. I think safety is a bunch of things. I think safety is a bunch of things. I think safety is always at the top of their top of their always at the top of their top of their always at the top of their top of their list. I do think, you know, we've hired list. I do think, you know, we've hired list. I do think, you know, we've hired lots of cruise people. I think the way lots of cruise people. I think the way lots of cruise people. I think the way they dealt with that specific issue with they dealt with that specific issue with they dealt with that specific issue with a government, you you got to you got to a government, you you got to you got to a government, you you got to you got to dance a particular way when that happens dance a particular way when that happens dance a particular way when that happens and they just didn't dance exactly right and they just didn't dance exactly right and they just didn't dance exactly right and that just gives government and that just gives government and that just gives government bureaucrats bureaucrats bureaucrats >> more ammo to go after. and you're a big >> more ammo to go after. and you're a big >> more ammo to go after. and you're a big target like General Motors, you got to target like General Motors, you got to target like General Motors, you got to you know it it reminds you guys ever see you know it it reminds you guys ever see you know it it reminds you guys ever see that movie like uh uh Good Fellas uh you that movie like uh uh Good Fellas uh you that movie like uh uh Good Fellas uh you know uh there the one of the last scenes know uh there the one of the last scenes know uh there the one of the last scenes the house of the rising sun you know all the house of the rising sun you know all the house of the rising sun you know all the old bosses go in the back of the the old bosses go in the back of the the old bosses go in the back of the courtroom and they're like and you know courtroom and they're like and you know courtroom and they're like and you know that's what happened. And they're like that's what happened. And they're like that's what happened. And they're like the the board was like, "What are we the the board was like, "What are we the the board was like, "What are we going to do about cruise?" Like, "What going to do about cruise?" Like, "What going to do about cruise?" Like, "What can we do?" [laughter] can we do?" [laughter] can we do?" [laughter] It's like, "Kyle's a good guy." But It's like, "Kyle's a good guy." But It's like, "Kyle's a good guy." But [laughter] and then it's like, "Que the [laughter] and then it's like, "Que the [laughter] and then it's like, "Que the rising sun." rising sun." rising sun." People running through a San Francisco People running through a San Francisco People running through a San Francisco office. Just kidding. [laughter] office. Just kidding. [laughter] office. Just kidding. [laughter] Don't Don't make that an AI video. Don't Don't make that an AI video. Don't Don't make that an AI video. [laughter] Gonna get a mean text from [laughter] Gonna get a mean text from [laughter] Gonna get a mean text from Kyle. Kyle. Kyle. >> So, I think I think there is a universe >> So, I think I think there is a universe >> So, I think I think there is a universe that would have survived, but it's it's that would have survived, but it's it's that would have survived, but it's it's it's it's tough. So then a lot a lot of it's it's tough. So then a lot a lot of it's it's tough. So then a lot a lot of what applied intuition does is kind of what applied intuition does is kind of what applied intuition does is kind of as you said like that dance it's like as you said like that dance it's like as you said like that dance it's like how to how to be a great partner to how to how to be a great partner to how to how to be a great partner to these companies. these companies. these companies. >> Exactly. Bearing in mind their own re >> Exactly. Bearing in mind their own re >> Exactly. Bearing in mind their own re very real issues and constraints. I very real issues and constraints. I very real issues and constraints. I >> I think general also had the topic of >> I think general also had the topic of >> I think general also had the topic of business model right. So you have uh business model right. So you have uh business model right. So you have uh cruise was going after the robo taxi cruise was going after the robo taxi cruise was going after the robo taxi concept but GM makes its profits from concept but GM makes its profits from concept but GM makes its profits from personal car ownership personal car ownership personal car ownership >> and those things can be a bit odd. So I >> and those things can be a bit odd. So I >> and those things can be a bit odd. So I think that was also a bit of the bit of think that was also a bit of the bit of think that was also a bit of the bit of the equation. the equation. the equation. >> Okay. >> Okay. >> Okay. >> Yeah. And I think it wasn't clear. I >> Yeah. And I think it wasn't clear. I >> Yeah. And I think it wasn't clear. I mean the by the way you know um you mean the by the way you know um you mean the by the way you know um you actually all people you spoke at YC at actually all people you spoke at YC at actually all people you spoke at YC at 20 in 2013. I was in the audience. I was 20 in 2013. I was in the audience. I was 20 in 2013. I was in the audience. I was a a partner at the time and um you said a a partner at the time and um you said a a partner at the time and um you said something which I think is it's it's something which I think is it's it's something which I think is it's it's very like uh recursive here. We're we're very like uh recursive here. We're we're very like uh recursive here. We're we're feeding each other device. It's uh the feeding each other device. It's uh the feeding each other device. It's uh the key thing in in new techn in the new key thing in in new techn in the new key thing in in new techn in the new technology business actually everyone technology business actually everyone technology business actually everyone kind of figures out the technology kind of figures out the technology kind of figures out the technology though that's still hard. It's still though that's still hard. It's still though that's still hard. It's still hard sometimes to build really complex hard sometimes to build really complex hard sometimes to build really complex things. It's when and how you deploy things. It's when and how you deploy things. It's when and how you deploy them into the market. the when becomes them into the market. the when becomes them into the market. the when becomes really important. You're two years early really important. You're two years early really important. You're two years early and you're doomed. You're two years and you're doomed. You're two years and you're doomed. You're two years late, there's too many competitors. You late, there's too many competitors. You late, there's too many competitors. You have to like hit it at the right spot. have to like hit it at the right spot. have to like hit it at the right spot. And I think And I think And I think >> it's like like I mean a controversial >> it's like like I mean a controversial >> it's like like I mean a controversial thing to say is like I actually think thing to say is like I actually think thing to say is like I actually think Cruz, you know, they were certainly Cruz, you know, they were certainly Cruz, you know, they were certainly moving at a much faster pace than Whimo. moving at a much faster pace than Whimo. moving at a much faster pace than Whimo. They started way behind and you're They started way behind and you're They started way behind and you're talking about neck andneck when you know talking about neck andneck when you know talking about neck andneck when you know ultimately the the the plug was pulled. ultimately the the the plug was pulled. ultimately the the the plug was pulled. So who knows what happens in the long So who knows what happens in the long So who knows what happens in the long term. Our hypothesis in that same term. Our hypothesis in that same term. Our hypothesis in that same equation is actually the distribution. equation is actually the distribution. equation is actually the distribution. You let the manufacturers do that. Like You let the manufacturers do that. Like You let the manufacturers do that. Like we run self-driving trucks right now in we run self-driving trucks right now in we run self-driving trucks right now in Japan. They carry commercial loads. Japan. They carry commercial loads. Japan. They carry commercial loads. They're they're, you know, they're They're they're, you know, they're They're they're, you know, they're safety drivers there, but they're safety drivers there, but they're safety drivers there, but they're autonomously running. And but you won't autonomously running. And but you won't autonomously running. And but you won't know that because the brand is Isuzu. know that because the brand is Isuzu. know that because the brand is Isuzu. >> Mhm. >> Mhm. >> Mhm. >> That's the customer. And and why it's so >> That's the customer. And and why it's so >> That's the customer. And and why it's so good for us to partner with Isuzu in good for us to partner with Isuzu in good for us to partner with Isuzu in that case is that company's been around that case is that company's been around that case is that company's been around for almost a hundred years, right? Uh if for almost a hundred years, right? Uh if for almost a hundred years, right? Uh if if I'm not mistaken, pre preWorld War II if I'm not mistaken, pre preWorld War II if I'm not mistaken, pre preWorld War II company. and they are uh you know they company. and they are uh you know they company. and they are uh you know they know the government they have test know the government they have test know the government they have test tracks they know safe they know their tracks they know safe they know their tracks they know safe they know their own trucks very well so when we go and own trucks very well so when we go and own trucks very well so when we go and provide them with the intelligence and provide them with the intelligence and provide them with the intelligence and the integration into their physical the integration into their physical the integration into their physical machinery that's a fantastic onetwo machinery that's a fantastic onetwo machinery that's a fantastic onetwo punch I think today the world is ready punch I think today the world is ready punch I think today the world is ready to consume AI in the real world and to consume AI in the real world and to consume AI in the real world and that's a lot because of chat GPT and that's a lot because of chat GPT and that's a lot because of chat GPT and anthropic and all these you know anthropic and all these you know anthropic and all these you know everything that's happened so people are everything that's happened so people are everything that's happened so people are no longer like what's a self-driving car no longer like what's a self-driving car no longer like what's a self-driving car and it's because of whimo and Tesla and it's because of whimo and Tesla and it's because of whimo and Tesla >> so the market is ready to consume and >> so the market is ready to consume and >> so the market is ready to consume and And I think you just have to meet the And I think you just have to meet the And I think you just have to meet the market in the way that the best way market in the way that the best way market in the way that the best way possible. And our view of that has possible. And our view of that has possible. And our view of that has always been you go through some of the always been you go through some of the always been you go through some of the people who run the economy right now. people who run the economy right now. people who run the economy right now. Whether it's a mining operator, whether Whether it's a mining operator, whether Whether it's a mining operator, whether it's a department of war, whether it's it's a department of war, whether it's it's a department of war, whether it's uh uh the manufacturers and we work uh uh the manufacturers and we work uh uh the manufacturers and we work with, you know, within each vertical uh with, you know, within each vertical uh with, you know, within each vertical uh with with the right partner. But that's with with the right partner. But that's with with the right partner. But that's a fundamentally different view than a a fundamentally different view than a a fundamentally different view than a Tesla or a Whimo which are going to be Tesla or a Whimo which are going to be Tesla or a Whimo which are going to be vertical vertical vertical >> where we're really playing the horizont. >> where we're really playing the horizont. >> where we're really playing the horizont. And I think the way we can always think And I think the way we can always think And I think the way we can always think we think about that our companies, we're we think about that our companies, we're we think about that our companies, we're kind of like a like a chipmaker, kind of like a like a chipmaker, kind of like a like a chipmaker, >> you know. We we we we actually look and >> you know. We we we we actually look and >> you know. We we we we actually look and talk and walk a lot like a silicon talk and walk a lot like a silicon talk and walk a lot like a silicon company except we obviously we don't company except we obviously we don't company except we obviously we don't make chips but you know we're are we make chips but you know we're are we make chips but you know we're are we have design wins and then we have really have design wins and then we have really have design wins and then we have really large long-term relationships and then large long-term relationships and then large long-term relationships and then once we're in we're in it's really hard once we're in we're in it's really hard once we're in we're in it's really hard to you know take take us out. So you to you know take take us out. So you to you know take take us out. So you need deep trust our our partners have need deep trust our our partners have need deep trust our our partners have really a a lot of deep trust and we know really a a lot of deep trust and we know really a a lot of deep trust and we know their markets really really well. The their markets really really well. The their markets really really well. The things that Jensen knows is he knows his things that Jensen knows is he knows his things that Jensen knows is he knows his customers. customers. customers. >> That's why Nvidia does well beyond the >> That's why Nvidia does well beyond the >> That's why Nvidia does well beyond the fact obviously they make a very complex fact obviously they make a very complex fact obviously they make a very complex technology. Mhm. So, how are these technology. Mhm. So, how are these technology. Mhm. So, how are these legacy uh car companies preparing for legacy uh car companies preparing for legacy uh car companies preparing for the future? Are they making or more the future? Are they making or more the future? Are they making or more acquisitions? They're going to are they acquisitions? They're going to are they acquisitions? They're going to are they building, you know, partnering with you? building, you know, partnering with you? building, you know, partnering with you? Are they how are they going to compete Are they how are they going to compete Are they how are they going to compete with, you know, tech technative with, you know, tech technative with, you know, tech technative companies? companies? companies? >> It's like saying like how are >> It's like saying like how are >> It's like saying like how are governments dealing with AI? It's such governments dealing with AI? It's such governments dealing with AI? It's such it's such a broad topic. Um and each it's such a broad topic. Um and each it's such a broad topic. Um and each manufacturer like even like you take manufacturer like even like you take manufacturer like even like you take Honda, Nissan, Toyota, three Japanese Honda, Nissan, Toyota, three Japanese Honda, Nissan, Toyota, three Japanese manufacturers with, you know, long manufacturers with, you know, long manufacturers with, you know, long legacies, they all approach it very legacies, they all approach it very legacies, they all approach it very differently. They're roughly in a differently. They're roughly in a differently. They're roughly in a spectrum of we're going to build to spectrum of we're going to build to spectrum of we're going to build to we're going to buy. Um and other both we're going to buy. Um and other both we're going to buy. Um and other both extremes more than ever we're going to extremes more than ever we're going to extremes more than ever we're going to buy is the common answer because they've buy is the common answer because they've buy is the common answer because they've been trying and we've been there the been trying and we've been there the been trying and we've been there the whole time. Uh for the folks that are whole time. Uh for the folks that are whole time. Uh for the folks that are going to build, we provide them tools uh going to build, we provide them tools uh going to build, we provide them tools uh and you we talk a little bit about our and you we talk a little bit about our and you we talk a little bit about our our our new uh product that we're we're our our new uh product that we're we're our our new uh product that we're we're announcing here. And then uh on the ones announcing here. And then uh on the ones announcing here. And then uh on the ones that just want to buy, we we sell them that just want to buy, we we sell them that just want to buy, we we sell them the actual intelligence that goes on the the actual intelligence that goes on the the actual intelligence that goes on the machines. And so we meet the customer machines. And so we meet the customer machines. And so we meet the customer wherever they're ready in their in their wherever they're ready in their in their wherever they're ready in their in their journey. Um the more nuanced version of journey. Um the more nuanced version of journey. Um the more nuanced version of that is you know uh the reality is like that is you know uh the reality is like that is you know uh the reality is like every every product is a different every every product is a different every every product is a different product and so the amount of silicon and product and so the amount of silicon and product and so the amount of silicon and amount of dollars you can put towards it amount of dollars you can put towards it amount of dollars you can put towards it uh towards sensors what the customer is uh towards sensors what the customer is uh towards sensors what the customer is willing to pay all that depends on what willing to pay all that depends on what willing to pay all that depends on what actually gets in the long horizon. All actually gets in the long horizon. All actually gets in the long horizon. All these things will be fully autonomous, these things will be fully autonomous, these things will be fully autonomous, but the intermittent steps are very much but the intermittent steps are very much but the intermittent steps are very much what we saw in the PC where you you have what we saw in the PC where you you have what we saw in the PC where you you have this slow step up to one day that'll be this slow step up to one day that'll be this slow step up to one day that'll be like now nobody really looks at laptop like now nobody really looks at laptop like now nobody really looks at laptop specs and even maybe frankly your phone specs and even maybe frankly your phone specs and even maybe frankly your phone specs but that's not the case from specs but that's not the case from specs but that's not the case from basically 85 to 2002 2005 where finally basically 85 to 2002 2005 where finally basically 85 to 2002 2005 where finally people stop actually specking at all and people stop actually specking at all and people stop actually specking at all and and and then they're really moving to and and then they're really moving to and and then they're really moving to laptops. uh but there's a similar kind laptops. uh but there's a similar kind laptops. uh but there's a similar kind of 20-y year I think uh horizon there of 20-y year I think uh horizon there of 20-y year I think uh horizon there >> broadly when we talk about machines and >> broadly when we talk about machines and >> broadly when we talk about machines and machines becoming intelligent right machines becoming intelligent right machines becoming intelligent right fundamentally a machine is it's a fundamentally a machine is it's a fundamentally a machine is it's a collection of these different components collection of these different components collection of these different components that are integrated right and and that are integrated right and and that are integrated right and and whoever does that final integration is whoever does that final integration is whoever does that final integration is often times the company that puts their often times the company that puts their often times the company that puts their their badge on it the brand name but their badge on it the brand name but their badge on it the brand name but many many companies are building many many companies are building many many companies are building technology that goes into that machines technology that goes into that machines technology that goes into that machines and so we now have a bunch of technology and so we now have a bunch of technology and so we now have a bunch of technology components and platforms that can go components and platforms that can go components and platforms that can go into these machines but we also sell the into these machines but we also sell the into these machines but we also sell the core technology that can be used to core technology that can be used to core technology that can be used to develop help them as well. And if you develop help them as well. And if you develop help them as well. And if you look, by the way, under the hood of a of look, by the way, under the hood of a of look, by the way, under the hood of a of a dirt mover or like combine or diesel a dirt mover or like combine or diesel a dirt mover or like combine or diesel truck, they'll have Cumins engines in truck, they'll have Cumins engines in truck, they'll have Cumins engines in them. But nobody says, "Well, because them. But nobody says, "Well, because them. But nobody says, "Well, because all these guys buy Cumins, this this all these guys buy Cumins, this this all these guys buy Cumins, this this means that they're, you know, uh means that they're, you know, uh means that they're, you know, uh whatever Caterpillar is not a good whatever Caterpillar is not a good whatever Caterpillar is not a good company." It's like, no, that's just a company." It's like, no, that's just a company." It's like, no, that's just a component that they buy. Uh they they component that they buy. Uh they they component that they buy. Uh they they they have a different role. So, if you they have a different role. So, if you they have a different role. So, if you when you look in any of these verticals, when you look in any of these verticals, when you look in any of these verticals, there's it's just a complex web of there's it's just a complex web of there's it's just a complex web of folks. That's why I always say like the folks. That's why I always say like the folks. That's why I always say like the the chip kind of analogy actually works the chip kind of analogy actually works the chip kind of analogy actually works quite effectively because some none of quite effectively because some none of quite effectively because some none of those companies make chips but they all those companies make chips but they all those companies make chips but they all buy chips. Uh and so I think that that's buy chips. Uh and so I think that that's buy chips. Uh and so I think that that's it's a good way to think about it. it's a good way to think about it. it's a good way to think about it. >> So self-driving cars. So you know we've >> So self-driving cars. So you know we've >> So self-driving cars. So you know we've all been talking about self-driving cars all been talking about self-driving cars all been talking about self-driving cars for like I think the whole thing started for like I think the whole thing started for like I think the whole thing started like around what 2005 or something with like around what 2005 or something with like around what 2005 or something with the DARPA Grand Challenge originally and the DARPA Grand Challenge originally and the DARPA Grand Challenge originally and so and then Google engaged in the so and then Google engaged in the so and then Google engaged in the program shortly after that. program shortly after that. program shortly after that. >> Yeah. Late double O's. Yeah. >> Yeah. Late double O's. Yeah. >> Yeah. Late double O's. Yeah. >> Late double O's. Um uh so almost >> Late double O's. Um uh so almost >> Late double O's. Um uh so almost basically around a little less than 20 basically around a little less than 20 basically around a little less than 20 years maybe. Um, and there have been years maybe. Um, and there have been years maybe. Um, and there have been lots of predictions over the last 20 lots of predictions over the last 20 lots of predictions over the last 20 years of like self-driving cars are years of like self-driving cars are years of like self-driving cars are imminent at any moment. Um, so I guess imminent at any moment. Um, so I guess imminent at any moment. Um, so I guess the the bad news is we're sitting here the the bad news is we're sitting here the the bad news is we're sitting here today and most cars are not today and most cars are not today and most cars are not self-driving. The good news is there are self-driving. The good news is there are self-driving. The good news is there are now self-driving cars. now self-driving cars. now self-driving cars. >> Yeah. >> Yeah. >> Yeah. >> And so the way most cars are driving all >> And so the way most cars are driving all >> And so the way most cars are driving all over, you know, in the places they're over, you know, in the places they're over, you know, in the places they're deployed, it's become, you know, like deployed, it's become, you know, like deployed, it's become, you know, like people in San Francisco, I think, treat people in San Francisco, I think, treat people in San Francisco, I think, treat it now as routine that they get into. it now as routine that they get into. it now as routine that they get into. >> And I think you can call I think Tesla, >> And I think you can call I think Tesla, >> And I think you can call I think Tesla, it's kind of like the AGI thing. It's it's kind of like the AGI thing. It's it's kind of like the AGI thing. It's like, you know, if we're talking 20 like, you know, if we're talking 20 like, you know, if we're talking 20 years ago, everything we're seeing right years ago, everything we're seeing right years ago, everything we're seeing right now is like mind-blowingly AGI. The post now is like mind-blowingly AGI. The post now is like mind-blowingly AGI. The post keeps moving. The Tesla stuff's amazing. keeps moving. The Tesla stuff's amazing. keeps moving. The Tesla stuff's amazing. You can look at a bunch of You can look at a bunch of You can look at a bunch of manufacturers. Blue Cruise, Super Cute manufacturers. Blue Cruise, Super Cute manufacturers. Blue Cruise, Super Cute Cruise, BMW, Volvo's Pilot, they're Cruise, BMW, Volvo's Pilot, they're Cruise, BMW, Volvo's Pilot, they're [clears throat] all quite impressive [clears throat] all quite impressive [clears throat] all quite impressive systems. They're not full self-driving, systems. They're not full self-driving, systems. They're not full self-driving, right? right? right? >> But yeah, >> But yeah, >> But yeah, >> well, it's it's full self-driving X, >> well, it's it's full self-driving X, >> well, it's it's full self-driving X, whatever remote [snorts] monitoring is whatever remote [snorts] monitoring is whatever remote [snorts] monitoring is [laughter] happening. Um, uh, the the [laughter] happening. Um, uh, the the [laughter] happening. Um, uh, the the Tesla we have we have a home in in Los Tesla we have we have a home in in Los Tesla we have we have a home in in Los Angeles, and you guys may recall there Angeles, and you guys may recall there Angeles, and you guys may recall there was a there was a large fire in Los was a there was a large fire in Los was a there was a large fire in Los Angeles. Then the power then and then Angeles. Then the power then and then Angeles. Then the power then and then the California power grid was buckling the California power grid was buckling the California power grid was buckling even before that. And so, um, it even before that. And so, um, it even before that. And so, um, it actually turns out among the things actually turns out among the things actually turns out among the things Cybertrs are good at is they're they're Cybertrs are good at is they're they're Cybertrs are good at is they're they're very good, uh, batteries. Yeah. Yeah. very good, uh, batteries. Yeah. Yeah. very good, uh, batteries. Yeah. Yeah. >> And so literally we have Cyber Trucks as >> And so literally we have Cyber Trucks as >> And so literally we have Cyber Trucks as our backup battery for the house. And as our backup battery for the house. And as our backup battery for the house. And as of last year, whatever the FSD released, of last year, whatever the FSD released, of last year, whatever the FSD released, I forget the exact one, but there was I forget the exact one, but there was I forget the exact one, but there was one where it like at least a lot of one where it like at least a lot of one where it like at least a lot of people thought it like really turned the people thought it like really turned the people thought it like really turned the corner corner corner >> 14. Yeah. >> 14. Yeah. >> 14. Yeah. >> And like that thing drives you. I talked >> And like that thing drives you. I talked >> And like that thing drives you. I talked to somebody yesterday uh talked to to somebody yesterday uh talked to to somebody yesterday uh talked to somebody yesterday who has a Model Y who somebody yesterday who has a Model Y who somebody yesterday who has a Model Y who let the let the thing let the thing do let the let the thing let the thing do let the let the thing let the thing do the full route all the way up Highway the full route all the way up Highway the full route all the way up Highway One through Big Su. One through Big Su. One through Big Su. >> Yeah. I think mean disengage like uh the >> Yeah. I think mean disengage like uh the >> Yeah. I think mean disengage like uh the uh uh meanantime and uh like uh miles uh uh meanantime and uh like uh miles uh uh meanantime and uh like uh miles per disengagement are are really high. I per disengagement are are really high. I per disengagement are are really high. I think miles is like in the thousands. think miles is like in the thousands. think miles is like in the thousands. >> Yeah. which is like very impressive. >> Yeah. which is like very impressive. >> Yeah. which is like very impressive. >> Yeah. The big for people who haven't >> Yeah. The big for people who haven't >> Yeah. The big for people who haven't driven the big highway one big su like driven the big highway one big su like driven the big highway one big su like that's a that's a that's a stressfilled that's a that's a that's a stressfilled that's a that's a that's a stressfilled drive. He said it was great the whole drive. He said it was great the whole drive. He said it was great the whole Anyway, so and I wouldn't have been Anyway, so and I wouldn't have been Anyway, so and I wouldn't have been talking to him had it not been talking to him had it not been talking to him had it not been [laughter] [laughter] [laughter] would have gone right off right off would have gone right off right off would have gone right off right off >> because he unbolted the steering wheel. >> because he unbolted the steering wheel. >> because he unbolted the steering wheel. So So So >> right right off the cliff. Um so um uh >> right right off the cliff. Um so um uh >> right right off the cliff. Um so um uh and then you know Tesla's rolling out and then you know Tesla's rolling out and then you know Tesla's rolling out their robo taxi you know is is starting their robo taxi you know is is starting their robo taxi you know is is starting starting to show up in in the wild. So, starting to show up in in the wild. So, starting to show up in in the wild. So, so on the one hand those exist. On the so on the one hand those exist. On the so on the one hand those exist. On the other hand, you know, 99.9999999% other hand, you know, 99.9999999% other hand, you know, 99.9999999% of cars are are still not self-driving. of cars are are still not self-driving. of cars are are still not self-driving. And then I would say maybe just one And then I would say maybe just one And then I would say maybe just one other would be the self-driving trucks. other would be the self-driving trucks. other would be the self-driving trucks. There's been this recurring kind of There's been this recurring kind of There's been this recurring kind of panic in the press of like the trucks panic in the press of like the trucks panic in the press of like the trucks become self-driving and the employment, become self-driving and the employment, become self-driving and the employment, you know, all these truck drivers be out you know, all these truck drivers be out you know, all these truck drivers be out of a job. And sitting here today, I of a job. And sitting here today, I of a job. And sitting here today, I don't think I don't know. Is there are don't think I don't know. Is there are don't think I don't know. Is there are there any trucks on the road that are there any trucks on the road that are there any trucks on the road that are self-driving that don't have at least a self-driving that don't have at least a self-driving that don't have at least a safety driver in the truck? And I think safety driver in the truck? And I think safety driver in the truck? And I think the answer is probably still the answer is probably still the answer is probably still >> Yeah, silver, if you so let's let's >> Yeah, silver, if you so let's let's >> Yeah, silver, if you so let's let's split let's split the there's multiple split let's split the there's multiple split let's split the there's multiple points that we brought up here. is on points that we brought up here. is on points that we brought up here. is on the let's say personally owned vehicles the let's say personally owned vehicles the let's say personally owned vehicles and why are they not more ubiquitous. Uh and why are they not more ubiquitous. Uh and why are they not more ubiquitous. Uh the part of that is the manufacturers the part of that is the manufacturers the part of that is the manufacturers are not good at deploying technology. are not good at deploying technology. are not good at deploying technology. Part of that is they're they're they Part of that is they're they're they Part of that is they're they're they want to be safety conscious but most of want to be safety conscious but most of want to be safety conscious but most of it is cost cost. Um the the what you're it is cost cost. Um the the what you're it is cost cost. Um the the what you're seeing in China which is a chi China is seeing in China which is a chi China is seeing in China which is a chi China is kind of a different EV ecosystem mainly kind of a different EV ecosystem mainly kind of a different EV ecosystem mainly because they don't care about profits. because they don't care about profits. because they don't care about profits. when you're talking about business that when you're talking about business that when you're talking about business that doesn't care about profits, it changes doesn't care about profits, it changes doesn't care about profits, it changes the entire calculus of the entire the entire calculus of the entire the entire calculus of the entire industry that doesn't care. Uh but what industry that doesn't care. Uh but what industry that doesn't care. Uh but what you're seeing is you're seeing L2++ you're seeing is you're seeing L2++ you're seeing is you're seeing L2++ systems. So we can simplify the entire systems. So we can simplify the entire systems. So we can simplify the entire self-driving conversation to is there a self-driving conversation to is there a self-driving conversation to is there a driver behind the steering wheel, driver behind the steering wheel, driver behind the steering wheel, >> right? >> right? >> right? >> So this is a driver behind the steering >> So this is a driver behind the steering >> So this is a driver behind the steering wheel still there, but generally like wheel still there, but generally like wheel still there, but generally like Tesla drives everywhere. Uh they're like Tesla drives everywhere. Uh they're like Tesla drives everywhere. Uh they're like sub $1,000, right? sub $1,000, right? sub $1,000, right? >> There's an aggressive that's that's >> There's an aggressive that's that's >> There's an aggressive that's that's chip, sensors, the package, the chip, sensors, the package, the chip, sensors, the package, the software, everything. uh we you know software, everything. uh we you know software, everything. uh we you know anticipate that there there's a very anticipate that there there's a very anticipate that there there's a very aggress once you get to like 500 the aggress once you get to like 500 the aggress once you get to like 500 the automotive OEMs will actually subsidize automotive OEMs will actually subsidize automotive OEMs will actually subsidize it for free they'll just give it to you it for free they'll just give it to you it for free they'll just give it to you this happened in nav systems if you guys this happened in nav systems if you guys this happened in nav systems if you guys remember nav systems used to be a big remember nav systems used to be a big remember nav systems used to be a big thing for you pay 4 grand 3,500 to get a thing for you pay 4 grand 3,500 to get a thing for you pay 4 grand 3,500 to get a nav system and then suddenly it became nav system and then suddenly it became nav system and then suddenly it became free and it just became default I think free and it just became default I think free and it just became default I think that'll happen the there's a a weird that'll happen the there's a a weird that'll happen the there's a a weird thing which is like actually getting thing which is like actually getting thing which is like actually getting into a subset of your cars costs x into a subset of your cars costs x into a subset of your cars costs x dollars and to get into all the cars dollars and to get into all the cars dollars and to get into all the cars costs x plus just a small incremental costs x plus just a small incremental costs x plus just a small incremental amount because it's just a fixed cost amount because it's just a fixed cost amount because it's just a fixed cost and the way that how many vehicles and and the way that how many vehicles and and the way that how many vehicles and the way the assembly line comes and the the way the assembly line comes and the the way the assembly line comes and the way you have homologation all these way you have homologation all these way you have homologation all these testing regimes all this stuff so I testing regimes all this stuff so I testing regimes all this stuff so I think you'll have wait and then a lot think you'll have wait and then a lot think you'll have wait and then a lot >> every single OEM without exception even >> every single OEM without exception even >> every single OEM without exception even the lowest dollar OEMs are working on an the lowest dollar OEMs are working on an the lowest dollar OEMs are working on an FSD competitor FSD competitor FSD competitor >> so it it'll it'll come but it is just >> so it it'll it'll come but it is just >> so it it'll it'll come but it is just like uh you know with uh a good analogy like uh you know with uh a good analogy like uh you know with uh a good analogy to think about self-driving in the to think about self-driving in the to think about self-driving in the personally owned ecosystem is mobile personally owned ecosystem is mobile personally owned ecosystem is mobile phones phones phones >> we had we had the the satellite phones >> we had we had the the satellite phones >> we had we had the the satellite phones then we had the Qualcomm you brick then we had the Qualcomm you brick then we had the Qualcomm you brick phones. Then we had the Motorola Razors phones. Then we had the Motorola Razors phones. Then we had the Motorola Razors and from you know the late '9s to the and from you know the late '9s to the and from you know the late '9s to the late two you know double zeros everybody late two you know double zeros everybody late two you know double zeros everybody was like when's mobile going to come was like when's mobile going to come was like when's mobile going to come there was a huge like and then it it there was a huge like and then it it there was a huge like and then it it comes and by by 07 from the iPhone comes and by by 07 from the iPhone comes and by by 07 from the iPhone launch like it's like four years when launch like it's like four years when launch like it's like four years when you get Uber, Instagram, WhatsApp, you get Uber, Instagram, WhatsApp, you get Uber, Instagram, WhatsApp, Snapchat, right? Snapchat, right? Snapchat, right? >> Those are the killer applications. So I >> Those are the killer applications. So I >> Those are the killer applications. So I think there's a very very similar kind think there's a very very similar kind think there's a very very similar kind of wait and then it's just basically of wait and then it's just basically of wait and then it's just basically ubiquitous in every vehicle. Um, if you ubiquitous in every vehicle. Um, if you ubiquitous in every vehicle. Um, if you had to ask me for what that number is, had to ask me for what that number is, had to ask me for what that number is, 28 SOP, 29, start of production, 29, 30, 28 SOP, 29, start of production, 29, 30, 28 SOP, 29, start of production, 29, 30, and then by the early 30s, it'll start and then by the early 30s, it'll start and then by the early 30s, it'll start becoming very cheap to free. becoming very cheap to free. becoming very cheap to free. >> Routinely by by the early 30s, you would >> Routinely by by the early 30s, you would >> Routinely by by the early 30s, you would just you buy a car and you just assume just you buy a car and you just assume just you buy a car and you just assume self-driving. self-driving. self-driving. >> Exactly. Or it has the driver in seat >> Exactly. Or it has the driver in seat >> Exactly. Or it has the driver in seat L2++ system being very specific L2++ system being very specific L2++ system being very specific >> like Cybertruck or Tesla what Tesla >> like Cybertruck or Tesla what Tesla >> like Cybertruck or Tesla what Tesla people have what Tesla owners have today people have what Tesla owners have today people have what Tesla owners have today will become common. will become common. will become common. >> Yeah. Will be default. So then the >> Yeah. Will be default. So then the >> Yeah. Will be default. So then the question then uh the the the you know question then uh the the the you know question then uh the the the you know the the other side of this is why don't the the other side of this is why don't the the other side of this is why don't we have a bunch of Whimos everywhere we have a bunch of Whimos everywhere we have a bunch of Whimos everywhere >> specifically Whimo has a different >> specifically Whimo has a different >> specifically Whimo has a different technology without getting into the technology without getting into the technology without getting into the nuances uh here but Tesla and many of nuances uh here but Tesla and many of nuances uh here but Tesla and many of the Chinese and applied were very much the Chinese and applied were very much the Chinese and applied were very much in this uh endto-end model architecture. in this uh endto-end model architecture. in this uh endto-end model architecture. Um this is a new way of doing Um this is a new way of doing Um this is a new way of doing self-driving. Whimo for the lack of a self-driving. Whimo for the lack of a self-driving. Whimo for the lack of a better word is not that. It's not better word is not that. It's not better word is not that. It's not doesn't mean they're not learned. It doesn't mean they're not learned. It doesn't mean they're not learned. It does it just there's not one endto-end does it just there's not one endto-end does it just there's not one endto-end system. It's not one monolithic model. system. It's not one monolithic model. system. It's not one monolithic model. Um, one of the proclivities of that Um, one of the proclivities of that Um, one of the proclivities of that their approach is it does depend on HD their approach is it does depend on HD their approach is it does depend on HD maps. Therefore, there is a geo fencing maps. Therefore, there is a geo fencing maps. Therefore, there is a geo fencing concept. I think Whimo is trying hard to concept. I think Whimo is trying hard to concept. I think Whimo is trying hard to remove that bottleneck so they can remove that bottleneck so they can remove that bottleneck so they can expand geographically faster. But the expand geographically faster. But the expand geographically faster. But the reality of today isn't there. The other reality of today isn't there. The other reality of today isn't there. The other thing is when you have researchers, thing is when you have researchers, thing is when you have researchers, which they which really was uh coming which they which really was uh coming which they which really was uh coming out of uh an alphabet research uh out of uh an alphabet research uh out of uh an alphabet research uh organization, they didn't put commercial organization, they didn't put commercial organization, they didn't put commercial constraints. So, the sensors are bespoke constraints. So, the sensors are bespoke constraints. So, the sensors are bespoke and expensive. The cars and the than the and expensive. The cars and the than the and expensive. The cars and the than the compute that are in there, they're compute that are in there, they're compute that are in there, they're they're just not economically feasible. they're just not economically feasible. they're just not economically feasible. And they've tried a lot to get that And they've tried a lot to get that And they've tried a lot to get that down. But, it's kind of like it's a lot down. But, it's kind of like it's a lot down. But, it's kind of like it's a lot easier to go from something that's easier to go from something that's easier to go from something that's really cheap and make it more uh, you really cheap and make it more uh, you really cheap and make it more uh, you know, more featureful than something know, more featureful than something know, more featureful than something that's overbuilt and then trying to trim that's overbuilt and then trying to trim that's overbuilt and then trying to trim and make it really, really cheap. And and make it really, really cheap. And and make it really, really cheap. And that's the big debate. Who's going to that's the big debate. Who's going to that's the big debate. Who's going to get there first? Tesla with full get there first? Tesla with full get there first? Tesla with full self-driving or Whimo with cost and self-driving or Whimo with cost and self-driving or Whimo with cost and geographic ubiquity. Uh, but you know geographic ubiquity. Uh, but you know geographic ubiquity. Uh, but you know what we're not debating about? Is it what we're not debating about? Is it what we're not debating about? Is it gonna happen, gonna happen, gonna happen, >> right? >> right? >> right? >> You know what we're not debating about? >> You know what we're not debating about? >> You know what we're not debating about? Like is is is there a big technical Like is is is there a big technical Like is is is there a big technical breakthrough that needs to happen? None breakthrough that needs to happen? None breakthrough that needs to happen? None of those things. So now we're clearly in of those things. So now we're clearly in of those things. So now we're clearly in the engineering side of self-driving, the engineering side of self-driving, the engineering side of self-driving, which is just this grind down to like which is just this grind down to like which is just this grind down to like dollar per mile efficiency. And the dollar per mile efficiency. And the dollar per mile efficiency. And the moment that it's cheap, guess what? All moment that it's cheap, guess what? All moment that it's cheap, guess what? All the OEMs are smart. They'll just they the OEMs are smart. They'll just they the OEMs are smart. They'll just they just adopt it. It's not the OEMs are just adopt it. It's not the OEMs are just adopt it. It's not the OEMs are resistant because they don't think resistant because they don't think resistant because they don't think consumers want it or they don't consumers want it or they don't consumers want it or they don't understand the technology. It's because understand the technology. It's because understand the technology. It's because they want a price envelope which allows they want a price envelope which allows they want a price envelope which allows them to keep their thin razor thin them to keep their thin razor thin them to keep their thin razor thin margins and at a scale right margins and at a scale right margins and at a scale right >> which is deployed across 100 plus >> which is deployed across 100 plus >> which is deployed across 100 plus countries in V1 right countries in V1 right countries in V1 right >> and so if you're just doing a small >> and so if you're just doing a small >> and so if you're just doing a small deployment it's very different and I deployment it's very different and I deployment it's very different and I think and and that was the last thing I think and and that was the last thing I think and and that was the last thing I would say is the buyer of a Subaru would say is the buyer of a Subaru would say is the buyer of a Subaru >> or a buyer of a Suzuki have very >> or a buyer of a Suzuki have very >> or a buyer of a Suzuki have very different brand expectations that a different brand expectations that a different brand expectations that a buyer of a Tesla buyer of a Tesla buyer of a Tesla >> and so including the age of the consumer >> and so including the age of the consumer >> and so including the age of the consumer and what they think will happen and and what they think will happen and and what they think will happen and won't happen. Um, so that that also the won't happen. Um, so that that also the won't happen. Um, so that that also the reason. So if you're a Suzuki, you're reason. So if you're a Suzuki, you're reason. So if you're a Suzuki, you're like, well, my buyer is like not doesn't like, well, my buyer is like not doesn't like, well, my buyer is like not doesn't want this stuff, so I'm not going to jam want this stuff, so I'm not going to jam want this stuff, so I'm not going to jam it into the car. It's not because it into the car. It's not because it into the car. It's not because they're not like technically competent. they're not like technically competent. they're not like technically competent. It's just a different area. It's just a different area. It's just a different area. >> When do you think when do you think >> When do you think when do you think >> When do you think when do you think it'll be routine? Let's say the 200 it'll be routine? Let's say the 200 it'll be routine? Let's say the 200 biggest American cities like um would it biggest American cities like um would it biggest American cities like um would it be routine to [snorts] walk outside and be routine to [snorts] walk outside and be routine to [snorts] walk outside and you just you just take it for granted you just you just take it for granted you just you just take it for granted that a robot taxi can come pick you up? that a robot taxi can come pick you up? that a robot taxi can come pick you up? >> Uh it's 26 now. Um I mean certainly by >> Uh it's 26 now. Um I mean certainly by >> Uh it's 26 now. Um I mean certainly by 30. All right. Okay. 30. All right. Okay. 30. All right. Okay. >> Yeah. Certainly by 30. And I I would say >> Yeah. Certainly by 30. And I I would say >> Yeah. Certainly by 30. And I I would say the big like variable there really is the big like variable there really is the big like variable there really is like like like >> because you what Whimo will say is that >> because you what Whimo will say is that >> because you what Whimo will say is that the dollars and cents per city already the dollars and cents per city already the dollars and cents per city already work and it's like well a company that work and it's like well a company that work and it's like well a company that has basically unlimited capital. Why are has basically unlimited capital. Why are has basically unlimited capital. Why are they not already in 200 cities? But then they not already in 200 cities? But then they not already in 200 cities? But then you see their launch schedule is pretty you see their launch schedule is pretty you see their launch schedule is pretty aggressive and you're like that that can aggressive and you're like that that can aggressive and you're like that that can that can get there. So that can get there. So that can get there. So >> maybe if I was being aggressive I would >> maybe if I was being aggressive I would >> maybe if I was being aggressive I would say 28. say 28. say 28. >> Yeah. Okay. >> Yeah. Okay. >> Yeah. Okay. >> Like I would say I would say available >> Like I would say I would say available >> Like I would say I would say available in 30 but routine and maybe like 32. in 30 but routine and maybe like 32. in 30 but routine and maybe like 32. Yeah, 33. Yeah, 33. Yeah, 33. >> Sure. Because there's a scale up. >> Sure. Because there's a scale up. >> Sure. Because there's a scale up. There's a volume. There's a volume. There's a volume. >> And also if you you you know you live in >> And also if you you you know you live in >> And also if you you you know you live in LA and so like five years ago I'd go to LA and so like five years ago I'd go to LA and so like five years ago I'd go to LA, people would be like, "What's LA, people would be like, "What's LA, people would be like, "What's applied intuition? I don't know what applied intuition? I don't know what applied intuition? I don't know what self-driving cars are." Uh in the last self-driving cars are." Uh in the last self-driving cars are." Uh in the last couple years now, they all know couple years now, they all know couple years now, they all know self-driving. And some of them even know self-driving. And some of them even know self-driving. And some of them even know applied intuition because they know from applied intuition because they know from applied intuition because they know from the other manufacturers. Uh I think you the other manufacturers. Uh I think you the other manufacturers. Uh I think you fast forward another two to four years, fast forward another two to four years, fast forward another two to four years, everybody knows it. Now everybody knows it. Now everybody knows it. Now >> does that mean everyone's taking Whimos >> does that mean everyone's taking Whimos >> does that mean everyone's taking Whimos exclusively? exclusively? exclusively? >> Right? >> Right? >> Right? >> That answer is no actually. Now there is >> That answer is no actually. Now there is >> That answer is no actually. Now there is a huge huge if you look at the numbers a huge huge if you look at the numbers a huge huge if you look at the numbers if you're Uber you got to be scared. I if you're Uber you got to be scared. I if you're Uber you got to be scared. I mean they're just eating into into ride mean they're just eating into into ride mean they're just eating into into ride sharing. sharing. sharing. >> Um >> Um >> Um >> yeah but to get 100% ubiquity I mean >> yeah but to get 100% ubiquity I mean >> yeah but to get 100% ubiquity I mean that's that's that's another it has to that's that's that's another it has to that's that's that's another it has to be extremely cheap. be extremely cheap. be extremely cheap. >> And what about long haul trucking? >> And what about long haul trucking? >> And what about long haul trucking? >> So long so that's what we that's the >> So long so that's what we that's the >> So long so that's what we that's the passenger side. The long haul trucking passenger side. The long haul trucking passenger side. The long haul trucking completely different economics completely different economics completely different economics completely different um business model. completely different um business model. completely different um business model. Um, there are many companies right now, Um, there are many companies right now, Um, there are many companies right now, I would say probably north of five that I would say probably north of five that I would say probably north of five that are running long haul trucks with are running long haul trucks with are running long haul trucks with drivers carrying loads between America drivers carrying loads between America drivers carrying loads between America and China. If you had China, it's and China. If you had China, it's and China. If you had China, it's probably getting into double digits. So, probably getting into double digits. So, probably getting into double digits. So, it's there, but the reason uh you don't it's there, but the reason uh you don't it's there, but the reason uh you don't know it and the reason it's not top of know it and the reason it's not top of know it and the reason it's not top of mind is it's not a consumer product. And mind is it's not a consumer product. And mind is it's not a consumer product. And unlike uh on the Whimo and Tesla side unlike uh on the Whimo and Tesla side unlike uh on the Whimo and Tesla side where investors are willing to where investors are willing to where investors are willing to essentially give you uh you know some uh essentially give you uh you know some uh essentially give you uh you know some uh market cap uh you know uh adjustment for market cap uh you know uh adjustment for market cap uh you know uh adjustment for the potential of the they say the the the potential of the they say the the the potential of the they say the the trucking business is like you know made trucking business is like you know made trucking business is like you know made you buy a car with your heartstrings. you buy a car with your heartstrings. you buy a car with your heartstrings. You buy a truck with a calculator. You buy a truck with a calculator. You buy a truck with a calculator. >> Yeah. It's a calculator [laughter] >> Yeah. It's a calculator [laughter] >> Yeah. It's a calculator [laughter] business business business >> and they and so it's like pure dollars >> and they and so it's like pure dollars >> and they and so it's like pure dollars and cents and so I think and cents and so I think and cents and so I think >> um you as the provider of self-driving >> um you as the provider of self-driving >> um you as the provider of self-driving trucks if you're doing the whole thing trucks if you're doing the whole thing trucks if you're doing the whole thing like some of the companies are which like some of the companies are which like some of the companies are which we're not we're not we're not >> you have to show every mile I'm going to >> you have to show every mile I'm going to >> you have to show every mile I'm going to I'm going to save you this many dollars I'm going to save you this many dollars I'm going to save you this many dollars and it's like for sure for sure for sure and it's like for sure for sure for sure and it's like for sure for sure for sure cuz the buyer's unsophisticated and cuz the buyer's unsophisticated and cuz the buyer's unsophisticated and they're just like well I already got a they're just like well I already got a they're just like well I already got a staff it can drive and it's like and staff it can drive and it's like and staff it can drive and it's like and they're like they're just not inclined they're like they're just not inclined they're like they're just not inclined now where we're playing in Japan it's now where we're playing in Japan it's now where we're playing in Japan it's not random that we're doing trucking not random that we're doing trucking not random that we're doing trucking demand. There's a massive labor shortage demand. There's a massive labor shortage demand. There's a massive labor shortage today and there's an imploding today and there's an imploding today and there's an imploding demographic uh uh you know situation and demographic uh uh you know situation and demographic uh uh you know situation and so there's a demand from almost every so there's a demand from almost every so there's a demand from almost every sector and that's why we've we've picked sector and that's why we've we've picked sector and that's why we've we've picked that market to to to really grow but I that market to to to really grow but I that market to to to really grow but I think like you can take like even more think like you can take like even more think like you can take like even more obscure like when will all uh queries obscure like when will all uh queries obscure like when will all uh queries you know literally like uh where you you you know literally like uh where you you you know literally like uh where you you know you you're moving cement you're know you you're moving cement you're know you you're moving cement you're moving you're moving dirt uh not queries moving you're moving dirt uh not queries moving you're moving dirt uh not queries u e a r u a r u e a r u a r u e a r u a r quaries. Quarries rock stone quaries. Quarries rock stone quaries. Quarries rock stone >> rock stone cement. Uh when when are >> rock stone cement. Uh when when are >> rock stone cement. Uh when when are those uh I can tell you the people who those uh I can tell you the people who those uh I can tell you the people who own those things and run those things own those things and run those things own those things and run those things want it today, want it today, want it today, >> right? >> right? >> right? >> So it's literally then you don't you >> So it's literally then you don't you >> So it's literally then you don't you don't have a which literally we can't don't have a which literally we can't don't have a which literally we can't make the stuff fast enough, make the stuff fast enough, make the stuff fast enough, >> right? >> right? >> right? >> Um the macro point though that people uh >> Um the macro point though that people uh >> Um the macro point though that people uh don't talk about I I think all the don't talk about I I think all the don't talk about I I think all the stuff's going to happen, stuff's going to happen, stuff's going to happen, >> right? >> right? >> right? >> But that happened pretty soon >> But that happened pretty soon >> But that happened pretty soon >> and happened fairly soon. The macro >> and happened fairly soon. The macro >> and happened fairly soon. The macro point that in in legislation and and and point that in in legislation and and and point that in in legislation and and and kind of in in the in the kind of uh kind of in in the in the kind of uh kind of in in the in the kind of uh economics the political economy of of economics the political economy of of economics the political economy of of this conversation is AI is really you this conversation is AI is really you this conversation is AI is really you see you have this big push back in see you have this big push back in see you have this big push back in digital AI because accountants are like digital AI because accountants are like digital AI because accountants are like I don't know this is going to happen to I don't know this is going to happen to I don't know this is going to happen to my job and VCs I'm sure all of your my job and VCs I'm sure all of your my job and VCs I'm sure all of your associates are very scared but like in associates are very scared but like in associates are very scared but like in our univer they're debating whether they our univer they're debating whether they our univer they're debating whether they need us. need us. need us. >> Yeah. Yeah. Yeah. In our in our universe >> Yeah. Yeah. Yeah. In our in our universe >> Yeah. Yeah. Yeah. In our in our universe it's the other way around. And it's like it's the other way around. And it's like it's the other way around. And it's like you literally I'll I'll I'll meet these you literally I'll I'll I'll meet these you literally I'll I'll I'll meet these you know operators and they're like you know operators and they're like you know operators and they're like we'll give you everything. Like if you we'll give you everything. Like if you we'll give you everything. Like if you can do this we'll give you everything. can do this we'll give you everything. can do this we'll give you everything. So then it's just up to us to like get So then it's just up to us to like get So then it's just up to us to like get there as as you know aggressively. there as as you know aggressively. there as as you know aggressively. >> Well, you know the fear for a long time >> Well, you know the fear for a long time >> Well, you know the fear for a long time has been for trucking for some reason has been for trucking for some reason has been for trucking for some reason triggers the at least the press's triggers the at least the press's triggers the at least the press's imagination on like you know sort of imagination on like you know sort of imagination on like you know sort of apocalyptic levels of job loss. Like apocalyptic levels of job loss. Like apocalyptic levels of job loss. Like will there will there will there >> but that's it's so wrong. Go ahead. >> but that's it's so wrong. Go ahead. >> but that's it's so wrong. Go ahead. >> There's not there's not enough truck >> There's not there's not enough truck >> There's not there's not enough truck drivers and guess what? Nobody wants to drivers and guess what? Nobody wants to drivers and guess what? Nobody wants to freaking be a truck driver. freaking be a truck driver. freaking be a truck driver. >> Why is that? Explain that. >> Why is that? Explain that. >> Why is that? Explain that. >> Because it's a terrible job. It's like >> Because it's a terrible job. It's like >> Because it's a terrible job. It's like you're you're like you're like you're you're like you're like you're you're like you're like >> by the way I grew up the main feature of >> by the way I grew up the main feature of >> by the way I grew up the main feature of the town where I grew up was a truck the town where I grew up was a truck the town where I grew up was a truck stop. So I know the answer but why is stop. So I know the answer but why is stop. So I know the answer but why is truck why is truck driving now truck why is truck driving now truck why is truck driving now >> it's like it's like you're asking me >> it's like it's like you're asking me >> it's like it's like you're asking me it's you know what this is this is like it's you know what this is this is like it's you know what this is this is like a you know talking to my kid who's like a you know talking to my kid who's like a you know talking to my kid who's like well why can't I put my hand on the well why can't I put my hand on the well why can't I put my hand on the stove? It's like stove? It's like stove? It's like because it's going to burn your hand. because it's going to burn your hand. because it's going to burn your hand. [laughter] It's like but why? It's like [laughter] It's like but why? It's like [laughter] It's like but why? It's like after the third why it's like come on after the third why it's like come on after the third why it's like come on buddy let's do this buddy let's do this buddy let's do this [laughter] [laughter] [laughter] >> the hard way. >> the hard way. >> the hard way. >> Yeah let's [clears throat] >> Yeah let's [clears throat] >> Yeah let's [clears throat] So what's hard? So what's hard? So what's hard? >> I'm kidding. Just wanted to make sure >> I'm kidding. Just wanted to make sure >> I'm kidding. Just wanted to make sure everybody knows I did not do that everybody knows I did not do that everybody knows I did not do that [laughter] with the first time. [laughter] with the first time. [laughter] with the first time. >> Yes. Yes. So what's hard? What why why >> Yes. Yes. So what's hard? What why why >> Yes. Yes. So what's hard? What why why is being a truck driver a difficult job is being a truck driver a difficult job is being a truck driver a difficult job or why would kids not want to do it when or why would kids not want to do it when or why would kids not want to do it when they grow up? they grow up? they grow up? >> So what let me use a parallel analogy >> So what let me use a parallel analogy >> So what let me use a parallel analogy which is very clear and then you can why which is very clear and then you can why which is very clear and then you can why you know people will say like nobody you know people will say like nobody you know people will say like nobody wants to work anymore and they say well wants to work anymore and they say well wants to work anymore and they say well you know McDonald's has all these job you know McDonald's has all these job you know McDonald's has all these job openings. No, no. Actually, what it is openings. No, no. Actually, what it is openings. No, no. Actually, what it is is those people that used to work at is those people that used to work at is those people that used to work at McDonald's now Door Dash and Uber McDonald's now Door Dash and Uber McDonald's now Door Dash and Uber >> because it's better for them because >> because it's better for them because >> because it's better for them because they can open they can start their hours they can open they can start their hours they can open they can start their hours and end their hours and they don't have and end their hours and they don't have and end their hours and they don't have to there's no boss and they don't have to there's no boss and they don't have to there's no boss and they don't have to like stand on their feet and they can to like stand on their feet and they can to like stand on their feet and they can surf their phone in between, you know, surf their phone in between, you know, surf their phone in between, you know, orders and they don't like that's the orders and they don't like that's the orders and they don't like that's the reason it's not random. The market is reason it's not random. The market is reason it's not random. The market is efficient. And so in the truck driving efficient. And so in the truck driving efficient. And so in the truck driving example, why does somebody not want to example, why does somebody not want to example, why does somebody not want to be away from their family for 4 to 8 be away from their family for 4 to 8 be away from their family for 4 to 8 days in a row doing long haul trucking? days in a row doing long haul trucking? days in a row doing long haul trucking? The more sharp example is in Australia, The more sharp example is in Australia, The more sharp example is in Australia, why don't people want to go literally why don't people want to go literally why don't people want to go literally buy a plane to go to a mine and work on buy a plane to go to a mine and work on buy a plane to go to a mine and work on or you go offro uh offshore oil rigs. or you go offro uh offshore oil rigs. or you go offro uh offshore oil rigs. Those jobs exist. If you want a job that Those jobs exist. If you want a job that Those jobs exist. If you want a job that pays six figures, they exist. even with pays six figures, they exist. even with pays six figures, they exist. even with such lucrative pay packages, it's not such lucrative pay packages, it's not such lucrative pay packages, it's not enough because people are like, you know enough because people are like, you know enough because people are like, you know what, what, what, >> I like kind of being around my family >> I like kind of being around my family >> I like kind of being around my family and I'm willing to take an incremental and I'm willing to take an incremental and I'm willing to take an incremental decrease in cost and and how much money decrease in cost and and how much money decrease in cost and and how much money I make. I make. I make. >> And and then also like I think today >> And and then also like I think today >> And and then also like I think today more than ever things like more than ever things like more than ever things like >> back pain and like being exposed to the >> back pain and like being exposed to the >> back pain and like being exposed to the sun and cancer and people that care sun and cancer and people that care sun and cancer and people that care about that's now a part of the about that's now a part of the about that's now a part of the >> this is the thing. >> this is the thing. >> this is the thing. Tell me if I I have this right, but I Tell me if I I have this right, but I Tell me if I I have this right, but I believe it's because I think commercial believe it's because I think commercial believe it's because I think commercial long-term drivers die life expectancy 10 long-term drivers die life expectancy 10 long-term drivers die life expectancy 10 years less than their peers. And I think years less than their peers. And I think years less than their peers. And I think it's a con people say it's a consequence it's a con people say it's a consequence it's a con people say it's a consequence of several things. So one is some of several things. So one is some of several things. So one is some combination of nutrition and sleep. It's combination of nutrition and sleep. It's combination of nutrition and sleep. It's you know it's a basically you know you know it's a basically you know you know it's a basically you know >> it's yeah it's very difficult it's very >> it's yeah it's very difficult it's very >> it's yeah it's very difficult it's very difficult to eat well and exercise and difficult to eat well and exercise and difficult to eat well and exercise and >> what's your sleep score if you're a long >> what's your sleep score if you're a long >> what's your sleep score if you're a long haul trucker? Let me guess eight sleep haul trucker? Let me guess eight sleep haul trucker? Let me guess eight sleep on that. on that. on that. >> Exactly. And so like obesity and then >> Exactly. And so like obesity and then >> Exactly. And so like obesity and then heart disease, hypertension so forth are heart disease, hypertension so forth are heart disease, hypertension so forth are all very high. Um one and then two is I all very high. Um one and then two is I all very high. Um one and then two is I think the vibration uh is very think the vibration uh is very think the vibration uh is very difficult. stress in the body. And then difficult. stress in the body. And then difficult. stress in the body. And then the third is you mentioned cancer, but I the third is you mentioned cancer, but I the third is you mentioned cancer, but I think it's the I think they uh truck think it's the I think they uh truck think it's the I think they uh truck drivers have like a much higher rate of drivers have like a much higher rate of drivers have like a much higher rate of melanoma on their left arm. melanoma on their left arm. melanoma on their left arm. >> Exactly. Yeah. There's photos of like a >> Exactly. Yeah. There's photos of like a >> Exactly. Yeah. There's photos of like a truck driver who's been driving for 30 truck driver who's been driving for 30 truck driver who's been driving for 30 years, one half their face, the other years, one half their face, the other years, one half their face, the other half's face cuz they're exposed to the half's face cuz they're exposed to the half's face cuz they're exposed to the sun. Right. sun. Right. sun. Right. >> A more interesting or even more uh stark >> A more interesting or even more uh stark >> A more interesting or even more uh stark stat. Mining is 1% of the labor pool stat. Mining is 1% of the labor pool stat. Mining is 1% of the labor pool globally, 8% of work related fatalities. globally, 8% of work related fatalities. globally, 8% of work related fatalities. Do you think people are rushing to work Do you think people are rushing to work Do you think people are rushing to work in mines when they hear stats like this? in mines when they hear stats like this? in mines when they hear stats like this? Most major mines have a fatality Most major mines have a fatality Most major mines have a fatality regularly, which means once, twice a regularly, which means once, twice a regularly, which means once, twice a year, three times a year. And if you year, three times a year. And if you year, three times a year. And if you ever visit a mine, you'll see that ever visit a mine, you'll see that ever visit a mine, you'll see that everything is based around safety everything is based around safety everything is based around safety because once you experience one of your because once you experience one of your because once you experience one of your co-workers dying, then you're like, co-workers dying, then you're like, co-workers dying, then you're like, >> "What am I doing here?" >> "What am I doing here?" >> "What am I doing here?" >> Yeah. >> Yeah. >> Yeah. >> Like there's other jobs I can take. And >> Like there's other jobs I can take. And >> Like there's other jobs I can take. And so it's uh I I I understand you're so it's uh I I I understand you're so it's uh I I I understand you're trying to enumerate for the audience trying to enumerate for the audience trying to enumerate for the audience like, "But these are not good jobs." like, "But these are not good jobs." like, "But these are not good jobs." >> Yeah. >> Yeah. >> Yeah. >> And and and and the best evidence is >> And and and and the best evidence is >> And and and and the best evidence is this is not a mining podcast. Isn't that this is not a mining podcast. Isn't that this is not a mining podcast. Isn't that a podcast about hey long haul trucking a podcast about hey long haul trucking a podcast about hey long haul trucking is so great? They're just not attractive is so great? They're just not attractive is so great? They're just not attractive jobs. jobs. jobs. >> Yeah. And even trucker even truckers >> Yeah. And even trucker even truckers >> Yeah. And even trucker even truckers don't want their kids to become truckers don't want their kids to become truckers don't want their kids to become truckers like it's it's for that reason why they like it's it's for that reason why they like it's it's for that reason why they you know they want their kids to be in a you know they want their kids to be in a you know they want their kids to be in a safe at the very least like safe safer safe at the very least like safe safer safe at the very least like safe safer safer line of work. safer line of work. safer line of work. >> But um do the do the notwithstanding all >> But um do the do the notwithstanding all >> But um do the do the notwithstanding all that do they how long will there be do that do they how long will there be do that do they how long will there be do you think there will be safety drivers you think there will be safety drivers you think there will be safety drivers in long haul trucks that are that are in long haul trucks that are that are in long haul trucks that are that are self-driving or or or let's say other self-driving or or or let's say other self-driving or or or let's say other even just somebody in the cab to deal even just somebody in the cab to deal even just somebody in the cab to deal with what happens when they arrive? with what happens when they arrive? with what happens when they arrive? >> We we know multiple companies that have >> We we know multiple companies that have >> We we know multiple companies that have driver goals right now. Okay. So like driver goals right now. Okay. So like driver goals right now. Okay. So like they're they are working to get drivers they're they are working to get drivers they're they are working to get drivers out right now. Um out right now. Um out right now. Um >> you know without going into our own >> you know without going into our own >> you know without going into our own details [laughter] details [laughter] details [laughter] >> to be honest it's not long. We're >> to be honest it's not long. We're >> to be honest it's not long. We're talking talking a few years and uh talking talking a few years and uh talking talking a few years and uh >> I think on the long end. >> I think on the long end. >> I think on the long end. >> Yeah. On the long end. And the thing is >> Yeah. On the long end. And the thing is >> Yeah. On the long end. And the thing is it's it's a there there's a software it's it's a there there's a software it's it's a there there's a software technology thing which is one part of technology thing which is one part of technology thing which is one part of the problem. But the other part is it's the problem. But the other part is it's the problem. But the other part is it's the redundancies that you need in the redundancies that you need in the redundancies that you need in hardware and the validation necessary hardware and the validation necessary hardware and the validation necessary for those redundancies. And in many for those redundancies. And in many for those redundancies. And in many cases that can actually be a long pull. cases that can actually be a long pull. cases that can actually be a long pull. It's like, oh, they're productionizing a It's like, oh, they're productionizing a It's like, oh, they're productionizing a fully redundant steering system, fully fully redundant steering system, fully fully redundant steering system, fully redundant braking system, that's that's redundant braking system, that's that's redundant braking system, that's that's not in high that's not in high value not in high that's not in high value not in high that's not in high value production yet. And once you get that in production yet. And once you get that in production yet. And once you get that in high value production, now you got the high value production, now you got the high value production, now you got the quality up and then that's validated and quality up and then that's validated and quality up and then that's validated and now you can actually do these now you can actually do these now you can actually do these >> near the price downs. Exactly. >> near the price downs. Exactly. >> near the price downs. Exactly. >> Do do you guys do do you look like it's >> Do do you guys do do you look like it's >> Do do you guys do do you look like it's the you know these little delivery the you know these little delivery the you know these little delivery robots? Like is that do you see a world robots? Like is that do you see a world robots? Like is that do you see a world where there's a billion of those running where there's a billion of those running where there's a billion of those running around? around? around? >> Yeah, I think so. I mean the uh the the >> Yeah, I think so. I mean the uh the the >> Yeah, I think so. I mean the uh the the the product that we're announcing I the product that we're announcing I the product that we're announcing I think it's probably come out around with think it's probably come out around with think it's probably come out around with this time is called Dana. So there's you this time is called Dana. So there's you this time is called Dana. So there's you can just simplify everything that can just simplify everything that can just simplify everything that applied intuition does into two buckets applied intuition does into two buckets applied intuition does into two buckets which is the we've been talking mostly which is the we've been talking mostly which is the we've been talking mostly about the models that go on the machines about the models that go on the machines about the models that go on the machines then this is we say onboard software or then this is we say onboard software or then this is we say onboard software or onboard AI then there's offboard AI this onboard AI then there's offboard AI this onboard AI then there's offboard AI this is the tools to design and develop these is the tools to design and develop these is the tools to design and develop these same systems the models that actually go same systems the models that actually go same systems the models that actually go on the machines our uh you know vision on the machines our uh you know vision on the machines our uh you know vision for that is and the the delivery robot for that is and the the delivery robot for that is and the the delivery robot is a great example is like a high school is a great example is like a high school is a great example is like a high school kid or a middle schooler they can make kid or a middle schooler they can make kid or a middle schooler they can make iPhone apps they should be able to iPhone apps they should be able to iPhone apps they should be able to autonomous systems. So why can't they autonomous systems. So why can't they autonomous systems. So why can't they just ask that's a very simple question. just ask that's a very simple question. just ask that's a very simple question. Why can't a Why can't a Why can't a >> ninth grader make a delivery robot in >> ninth grader make a delivery robot in >> ninth grader make a delivery robot in their in their home? Well, they don't their in their home? Well, they don't their in their home? Well, they don't have the the actual environment that have the the actual environment that have the the actual environment that they would first develop the scenarios they would first develop the scenarios they would first develop the scenarios in. They would define the requirements. in. They would define the requirements. in. They would define the requirements. Hey, I want this robot to go on my high Hey, I want this robot to go on my high Hey, I want this robot to go on my high school campus around these let's say school campus around these let's say school campus around these let's say four buildings. four buildings. four buildings. >> Uh then how okay now that you define the >> Uh then how okay now that you define the >> Uh then how okay now that you define the the requirements then you have the the requirements then you have the the requirements then you have the scenarios get made. where are all the scenarios get made. where are all the scenarios get made. where are all the scenarios that can that can uh that can scenarios that can that can uh that can scenarios that can that can uh that can be made by using let's say a satellite be made by using let's say a satellite be made by using let's say a satellite image of the high school. Uh then now image of the high school. Uh then now image of the high school. Uh then now you have to train the robot. So you need you have to train the robot. So you need you have to train the robot. So you need some data. Where do you get that data? some data. Where do you get that data? some data. Where do you get that data? There's maybe enough publicly available There's maybe enough publicly available There's maybe enough publicly available data that can actually train a fairly data that can actually train a fairly data that can actually train a fairly rudimentary robot. Okay. Now you got rudimentary robot. Okay. Now you got rudimentary robot. Okay. Now you got that data from online maybe YouTube that data from online maybe YouTube that data from online maybe YouTube videos couple other places. Suddenly the videos couple other places. Suddenly the videos couple other places. Suddenly the robot's not doing now you need to deploy robot's not doing now you need to deploy robot's not doing now you need to deploy it onto the actual machine. So then you it onto the actual machine. So then you it onto the actual machine. So then you deploy it onto the machine and then the deploy it onto the machine and then the deploy it onto the machine and then the robot runs into the wall. Okay. What robot runs into the wall. Okay. What robot runs into the wall. Okay. What happened there? The loop closes. That happened there? The loop closes. That happened there? The loop closes. That platform for designing and developing is platform for designing and developing is platform for designing and developing is what we're launching. It's called Dana. what we're launching. It's called Dana. what we're launching. It's called Dana. Uh which is the street that applied Uh which is the street that applied Uh which is the street that applied intuition is [laughter] intuition is [laughter] intuition is [laughter] headquartered on. Uh and uh and our our headquartered on. Uh and uh and our our headquartered on. Uh and uh and our our our you know this comes from our tooling our you know this comes from our tooling our you know this comes from our tooling background. And if you look at like how background. And if you look at like how background. And if you look at like how tooling has changed in the digital AI tooling has changed in the digital AI tooling has changed in the digital AI world, if you look at like what Claude world, if you look at like what Claude world, if you look at like what Claude did to all we alo remember like you know did to all we alo remember like you know did to all we alo remember like you know from mix panel to you know uh gitlab from mix panel to you know uh gitlab from mix panel to you know uh gitlab github all these now everything has github all these now everything has github all these now everything has moved into a very different almost IDE moved into a very different almost IDE moved into a very different almost IDE frankly speaking we think the same thing frankly speaking we think the same thing frankly speaking we think the same thing is going to happen in the physical world is going to happen in the physical world is going to happen in the physical world and so uh that that's yeah that's what and so uh that that's yeah that's what and so uh that that's yeah that's what we're that's what we're building that's we're that's what we're building that's we're that's what we're building that's what we built and that's what we're what we built and that's what we're what we built and that's what we're launching and we already use it inhouse launching and we already use it inhouse launching and we already use it inhouse uh to develop our autonomy system which uh to develop our autonomy system which uh to develop our autonomy system which is you know and we're working on the is you know and we're working on the is you know and we're working on the most kind of scaled complex systems uh most kind of scaled complex systems uh most kind of scaled complex systems uh on the planet in all these different on the planet in all these different on the planet in all these different verticals. So we're pretty confident verticals. So we're pretty confident verticals. So we're pretty confident that it's actually quite useful. We've that it's actually quite useful. We've that it's actually quite useful. We've seen massive productivity gains. Uh but seen massive productivity gains. Uh but seen massive productivity gains. Uh but also uh you know we think like uh other also uh you know we think like uh other also uh you know we think like uh other companies will use this to build their companies will use this to build their companies will use this to build their own systems because it gets to that own systems because it gets to that own systems because it gets to that mission that building intelligent mission that building intelligent mission that building intelligent machines. Fundamentally or dana is our machines. Fundamentally or dana is our machines. Fundamentally or dana is our agentic platform for physical AI and and agentic platform for physical AI and and agentic platform for physical AI and and everything that we've built and everything that we've built and everything that we've built and developed over the past nearly a decade. developed over the past nearly a decade. developed over the past nearly a decade. every every tool, every technique that's every every tool, every technique that's every every tool, every technique that's available in Dana and it's very actually available in Dana and it's very actually available in Dana and it's very actually easy to use uh with the aenic interface easy to use uh with the aenic interface easy to use uh with the aenic interface and so workflows that used to maybe take and so workflows that used to maybe take and so workflows that used to maybe take days or weeks to run, days or weeks to run, days or weeks to run, >> you can now run those in in minutes in >> you can now run those in in minutes in >> you can now run those in in minutes in many cases, right? many cases, right? many cases, right? >> And uh and this just lowers the barrier >> And uh and this just lowers the barrier >> And uh and this just lowers the barrier to entry to building these systems and to entry to building these systems and to entry to building these systems and just lowering the bar of like just lowering the bar of like just lowering the bar of like >> you know what it means to develop an >> you know what it means to develop an >> you know what it means to develop an autonomous system. Autonomy is still autonomous system. Autonomy is still autonomous system. Autonomy is still actually quite in the scope of software actually quite in the scope of software actually quite in the scope of software is quite exotic. It's not because of the is quite exotic. It's not because of the is quite exotic. It's not because of the things that we've talked about and we've things that we've talked about and we've things that we've talked about and we've just brought that down very very just brought that down very very just brought that down very very aggressively and it's kind of like you aggressively and it's kind of like you aggressively and it's kind of like you know the old adage of like how do you know the old adage of like how do you know the old adage of like how do you make a great product in software? It's make a great product in software? It's make a great product in software? It's like you either increase safety, like you either increase safety, like you either increase safety, convenience or cost and we want to try convenience or cost and we want to try convenience or cost and we want to try to do all three of those things with to do all three of those things with to do all three of those things with with Dana. Um and our hope is just like with Dana. Um and our hope is just like with Dana. Um and our hope is just like you said like you know you said like you know you said like you know >> uh kids can develop robots for their uh >> uh kids can develop robots for their uh >> uh kids can develop robots for their uh for their own use and and that extends for their own use and and that extends for their own use and and that extends to humanoids. So we're not just talking to humanoids. So we're not just talking to humanoids. So we're not just talking about like landbased systems or or you about like landbased systems or or you about like landbased systems or or you know ones where that are that are uh so know ones where that are that are uh so know ones where that are that are uh so you humanoids you can do drones. you humanoids you can do drones. you humanoids you can do drones. >> The fact that right now writing drone >> The fact that right now writing drone >> The fact that right now writing drone software and deploying it at the time software and deploying it at the time software and deploying it at the time it's quite obscure and almost hobbyist it's quite obscure and almost hobbyist it's quite obscure and almost hobbyist >> uh we want to just make that absolutely >> uh we want to just make that absolutely >> uh we want to just make that absolutely like you know maybe not child's play but like you know maybe not child's play but like you know maybe not child's play but like teenager play. So this points to a like teenager play. So this points to a like teenager play. So this points to a world of like just like a lot more world of like just like a lot more world of like just like a lot more experimentation and entrepreneurship and experimentation and entrepreneurship and experimentation and entrepreneurship and like agriculture like agriculture like agriculture bots and like basically every domain bots and like basically every domain bots and like basically every domain construction construction construction >> defense you just all of a sudden have a >> defense you just all of a sudden have a >> defense you just all of a sudden have a much larger number of people who are much larger number of people who are much larger number of people who are applying creativity and coming up with applying creativity and coming up with applying creativity and coming up with ideas and making things that move. ideas and making things that move. ideas and making things that move. >> Yeah. And have you seen like with Claude >> Yeah. And have you seen like with Claude >> Yeah. And have you seen like with Claude it's like it's one thing just to make it's like it's one thing just to make it's like it's one thing just to make the engineer more efficient or bring the engineer more efficient or bring the engineer more efficient or bring more people into engineering but then more people into engineering but then more people into engineering but then when these agents really run you're when these agents really run you're when these agents really run you're getting into you it's just like the getting into you it's just like the getting into you it's just like the iPhone example of you couldn't imagine iPhone example of you couldn't imagine iPhone example of you couldn't imagine Instagram Instagram Instagram >> Yeah. >> Yeah. >> Yeah. >> before >> before >> before >> like the iPhone it's like imagine 2005 >> like the iPhone it's like imagine 2005 >> like the iPhone it's like imagine 2005 on laptops you're like in on laptops you're like in on laptops you're like in >> Yeah. >> Yeah. >> Yeah. >> 10 years there's going to be this this >> 10 years there's going to be this this >> 10 years there's going to be this this app. app. app. >> Yeah. >> Yeah. >> Yeah. >> Where and you can put photos. They're >> Where and you can put photos. They're >> Where and you can put photos. They're like well the phones don't have have like well the phones don't have have like well the phones don't have have cameras. like, yeah, but it's going to cameras. like, yeah, but it's going to cameras. like, yeah, but it's going to be like social, like what the hell? be like social, like what the hell? be like social, like what the hell? Like, so like Facebook, it's like, see, Like, so like Facebook, it's like, see, Like, so like Facebook, it's like, see, it's just hard to hard. And so, we think it's just hard to hard. And so, we think it's just hard to hard. And so, we think >> by lowering that barrier, you're going >> by lowering that barrier, you're going >> by lowering that barrier, you're going to get way way more creative uh autonomy to get way way more creative uh autonomy to get way way more creative uh autonomy products, products, products, >> right? >> right? >> right? >> Yeah. >> Yeah. >> Yeah. >> I will definitely decide whether to >> I will definitely decide whether to >> I will definitely decide whether to include this or not. So, my kid is include this or not. So, my kid is include this or not. So, my kid is building autonomous bots in um uh building autonomous bots in um uh building autonomous bots in um uh Factorio. Factorio. Factorio. >> Oh, nice. >> Oh, nice. >> Oh, nice. >> Is one of his projects. And so, and but >> Is one of his projects. And so, and but >> Is one of his projects. And so, and but he's he's you know, he's had rolling he's he's you know, he's had rolling he's he's you know, he's had rolling because the the tool kit's not available because the the tool kit's not available because the the tool kit's not available yet. So, he's actually training and he's yet. So, he's actually training and he's yet. So, he's actually training and he's actually he's actually training models. actually he's actually training models. actually he's actually training models. >> Yeah. >> Yeah. >> Yeah. >> Uh he's gathering data in the game. Um >> Uh he's gathering data in the game. Um >> Uh he's gathering data in the game. Um and uh actually has like a whole army of and uh actually has like a whole army of and uh actually has like a whole army of like bots that he's developed that go. like bots that he's developed that go. like bots that he's developed that go. >> Yeah. So like >> Yeah. So like >> Yeah. So like >> and then his mother is like why are you >> and then his mother is like why are you >> and then his mother is like why are you playing that game so much? And he playing that game so much? And he playing that game so much? And he explains of course it's a purely explains of course it's a purely explains of course it's a purely educational process and experience but educational process and experience but educational process and experience but but it's you know it's the kind of but it's you know it's the kind of but it's you know it's the kind of thing. It's like yeah it's like there thing. It's like yeah it's like there thing. It's like yeah it's like there you know you know you know >> like there's no reason autonomy should >> like there's no reason autonomy should >> like there's no reason autonomy should be this like be this like be this like >> uh you know uh obscure difficult you >> uh you know uh obscure difficult you >> uh you know uh obscure difficult you know alchemistic you know uh uh uh know alchemistic you know uh uh uh know alchemistic you know uh uh uh technology. And um and I think not only technology. And um and I think not only technology. And um and I think not only does that have a huge impact on on does that have a huge impact on on does that have a huge impact on on society, it also allows people to society, it also allows people to society, it also allows people to understand that these systems are not understand that these systems are not understand that these systems are not like like like >> you know >> you know >> you know magic. magic. magic. >> It's like if I can develop a >> It's like if I can develop a >> It's like if I can develop a >> a Roomba for myself in my house on a >> a Roomba for myself in my house on a >> a Roomba for myself in my house on a weekend using Dana, weekend using Dana, weekend using Dana, >> right? >> right? >> right? >> Then why then it's not suddenly so >> Then why then it's not suddenly so >> Then why then it's not suddenly so scary. And I think that's like that's scary. And I think that's like that's scary. And I think that's like that's that's important. that's important. that's important. >> And we can it can it can support it can >> And we can it can it can support it can >> And we can it can it can support it can support people in all kinds of ways that support people in all kinds of ways that support people in all kinds of ways that we haven't even imagined yet cuz Yeah. we haven't even imagined yet cuz Yeah. we haven't even imagined yet cuz Yeah. Absolutely. Absolutely. Absolutely. >> Yeah. Exactly. >> Yeah. Exactly. >> Yeah. Exactly. >> I mean you think about like uh you know >> I mean you think about like uh you know >> I mean you think about like uh you know folks with disabilities you know we folks with disabilities you know we folks with disabilities you know we always think about humanoids as like always think about humanoids as like always think about humanoids as like this very important task of folding this very important task of folding this very important task of folding laundry which seems to [laughter] be. laundry which seems to [laughter] be. laundry which seems to [laughter] be. >> So we focus on you know the important >> So we focus on you know the important >> So we focus on you know the important task but the when you allow these tools task but the when you allow these tools task but the when you allow these tools to exist. I mean I I you know we started to exist. I mean I I you know we started to exist. I mean I I you know we started a tooling company. I mean I feel so a tooling company. I mean I feel so a tooling company. I mean I feel so importantly that tools are like what importantly that tools are like what importantly that tools are like what separates actually advanced separates actually advanced separates actually advanced civilizations from you know less civilizations from you know less civilizations from you know less advanced civil civilizations. And our advanced civil civilizations. And our advanced civil civilizations. And our our first uh uh mark for the company was our first uh uh mark for the company was our first uh uh mark for the company was a monkeykey's head and then we got a a monkeykey's head and then we got a a monkeykey's head and then we got a designer who said what this [laughter] designer who said what this [laughter] designer who said what this [laughter] is stupid. is stupid. is stupid. >> I was like I thought it was pretty good. >> I was like I thought it was pretty good. >> I was like I thought it was pretty good. >> So you were talking earlier about how >> So you were talking earlier about how >> So you were talking earlier about how when you know um the technology got got when you know um the technology got got when you know um the technology got got so good in mobile that there was a wave so good in mobile that there was a wave so good in mobile that there was a wave of these companies you know Uber, of these companies you know Uber, of these companies you know Uber, WhatsApp, Snap you know Airbnb etc. that WhatsApp, Snap you know Airbnb etc. that WhatsApp, Snap you know Airbnb etc. that emerged in quick succession. And so now emerged in quick succession. And so now emerged in quick succession. And so now that the technology is getting there for that the technology is getting there for that the technology is getting there for the infrastructure for physical AI, what the infrastructure for physical AI, what the infrastructure for physical AI, what are some use cases or companies that you are some use cases or companies that you are some use cases or companies that you can obviously it's hard to predict the can obviously it's hard to predict the can obviously it's hard to predict the future, but where where are you most future, but where where are you most future, but where where are you most excited for? Like what what could we be excited for? Like what what could we be excited for? Like what what could we be talking about the equivalent here of in talking about the equivalent here of in talking about the equivalent here of in quick succession? quick succession? quick succession? >> I mean I think uh you know midterm we >> I mean I think uh you know midterm we >> I mean I think uh you know midterm we want Dana if not the short term to want Dana if not the short term to want Dana if not the short term to really you know make humanoids way more really you know make humanoids way more really you know make humanoids way more real. Uh there's I mean how many it's real. Uh there's I mean how many it's real. Uh there's I mean how many it's like a thousand core tasks in a home like a thousand core tasks in a home like a thousand core tasks in a home from uh from humanoids and these from uh from humanoids and these from uh from humanoids and these companies it's like such I mean I'm if companies it's like such I mean I'm if companies it's like such I mean I'm if you talk to people who work in these you talk to people who work in these you talk to people who work in these companies it's everything is difficult companies it's everything is difficult companies it's everything is difficult every step of the way is difficult every step of the way is difficult every step of the way is difficult collecting data is difficult uh you know collecting data is difficult uh you know collecting data is difficult uh you know cleaning that data is difficult training cleaning that data is difficult training cleaning that data is difficult training those models or deploying the model is those models or deploying the model is those models or deploying the model is difficult and the bar being I want a difficult and the bar being I want a difficult and the bar being I want a high school kid to make a humanoid so high school kid to make a humanoid so high school kid to make a humanoid so that that's our our our path and we that that's our our our path and we that that's our our our path and we think there there could be a lot there think there there could be a lot there think there there could be a lot there but that's like these the obvious stuff but that's like these the obvious stuff but that's like these the obvious stuff I think the true nonobvious stuff is I think the true nonobvious stuff is I think the true nonobvious stuff is going to we will we'll look back will be going to we will we'll look back will be going to we will we'll look back will be will be way way more interesting will be way way more interesting will be way way more interesting >> and and there's there's some core >> and and there's there's some core >> and and there's there's some core ingredients that we're bringing together ingredients that we're bringing together ingredients that we're bringing together in data, right? We're making it way in data, right? We're making it way in data, right? We're making it way easier to to actually get imitation easier to to actually get imitation easier to to actually get imitation learning to work, way easier to make learning to work, way easier to make learning to work, way easier to make reinforcement learning work in reinforcement learning work in reinforcement learning work in combination with that. Um we're we have combination with that. Um we're we have combination with that. Um we're we have pre-trained models that can be used as a pre-trained models that can be used as a pre-trained models that can be used as a baseline for a lot of things. Um world baseline for a lot of things. Um world baseline for a lot of things. Um world models, advanced simulation tech, all of models, advanced simulation tech, all of models, advanced simulation tech, all of these things come together and then these things come together and then these things come together and then you're sort of limited by your c you're sort of limited by your c you're sort of limited by your c creativity like well what what do I want creativity like well what what do I want creativity like well what what do I want to do? And if you think about any kind to do? And if you think about any kind to do? And if you think about any kind of physical AI task as it's it's a you of physical AI task as it's it's a you of physical AI task as it's it's a you are understanding the world and you're are understanding the world and you're are understanding the world and you're manipulating something and and we can manipulating something and and we can manipulating something and and we can build that that can be built now much build that that can be built now much build that that can be built now much more easily in this in this tool and I more easily in this in this tool and I more easily in this in this tool and I think sometimes people ask like us being think sometimes people ask like us being think sometimes people ask like us being a tooling company and like you take a tooling company and like you take a tooling company and like you take self-driving trucks we deploy self-driving trucks we deploy self-driving trucks we deploy self-driving trucks and many of the self-driving trucks and many of the self-driving trucks and many of the self-driving trucking companies use our self-driving trucking companies use our self-driving trucking companies use our tools I I I think some sometimes people tools I I I think some sometimes people tools I I I think some sometimes people ask oh look you know with Dana are you ask oh look you know with Dana are you ask oh look you know with Dana are you going to like enable all these going to like enable all these going to like enable all these competitors that's great competitors that's great competitors that's great >> right >> right >> right >> that's absolutely completely fine if If >> that's absolutely completely fine if If >> that's absolutely completely fine if If you look at Google and what Google did you look at Google and what Google did you look at Google and what Google did to web applications, there was a massive to web applications, there was a massive to web applications, there was a massive internet. Uh Google still succeeded internet. Uh Google still succeeded internet. Uh Google still succeeded through, you know, search and YouTube through, you know, search and YouTube through, you know, search and YouTube and and and other web apps and other and and and other web apps and other and and and other web apps and other folks learned and used open source folks learned and used open source folks learned and used open source products and then ultimately closed products and then ultimately closed products and then ultimately closed source products and ultimately source products and ultimately source products and ultimately ventureback products. And we think we ventureback products. And we think we ventureback products. And we think we think this the the same thing could think this the the same thing could think this the the same thing could happen here. I was at a robotics startup happen here. I was at a robotics startup happen here. I was at a robotics startup uh a while back that you you guys know uh a while back that you you guys know uh a while back that you you guys know well. Um and they had they were training well. Um and they had they were training well. Um and they had they were training you know they were doing go through a you know they were doing go through a you know they were doing go through a training process training their one of training process training their one of training process training their one of their arms to do particularly a killer their arms to do particularly a killer their arms to do particularly a killer app that I thought was very appealing app that I thought was very appealing app that I thought was very appealing which was which was which was >> picking up dog poop [laughter] >> picking up dog poop [laughter] >> picking up dog poop [laughter] >> literally you know training over and >> literally you know training over and >> literally you know training over and over again the difference was for the over again the difference was for the over again the difference was for the >> and so you know I don't know why not >> and so you know I don't know why not >> and so you know I don't know why not right right right >> why not have the little why not have the >> why not have the little why not have the >> why not have the little why not have the little robot follow you around when you little robot follow you around when you little robot follow you around when you walk the dog pick up the walk the dog pick up the walk the dog pick up the >> poop. Yeah. And I think like like you >> poop. Yeah. And I think like like you >> poop. Yeah. And I think like like you >> I know somebody who built I forget who >> I know somebody who built I forget who >> I know somebody who built I forget who it was. Somebody who built a a little it was. Somebody who built a a little it was. Somebody who built a a little lawn robot that would go around and lawn robot that would go around and lawn robot that would go around and individ pick up individual leaves. individ pick up individual leaves. individ pick up individual leaves. >> Yeah. >> Yeah. >> Yeah. >> Because you got that problem right. >> Because you got that problem right. >> Because you got that problem right. Okay. You you rake you you rake your Okay. You you rake you you rake your Okay. You you rake you you rake your yard. It's completely clean and then yard. It's completely clean and then yard. It's completely clean and then like two hours later there's like 14 like two hours later there's like 14 like two hours later there's like 14 leaves and you're like leaves and you're like leaves and you're like >> Yeah. Yeah. >> Yeah. Yeah. >> Yeah. Yeah. >> Send up the little bot to pick up the >> Send up the little bot to pick up the >> Send up the little bot to pick up the leaves. leaves. leaves. >> It's like if development costs are zero >> It's like if development costs are zero >> It's like if development costs are zero then people will do that. I mean you then people will do that. I mean you then people will do that. I mean you guys remember like the early iPhone apps guys remember like the early iPhone apps guys remember like the early iPhone apps the hits were like the beer one or the the hits were like the beer one or the the hits were like the beer one or the fart app. If you [laughter] imagine that fart app. If you [laughter] imagine that fart app. If you [laughter] imagine that in like Yeah. If you imagine that in 98 in like Yeah. If you imagine that in 98 in like Yeah. If you imagine that in 98 with a, you know, with the Symbian with a, you know, with the Symbian with a, you know, with the Symbian mobile, you know, whatever the OS from, mobile, you know, whatever the OS from, mobile, you know, whatever the OS from, I think it was Ericson or somebody, I think it was Ericson or somebody, I think it was Ericson or somebody, that'd be impossible. You need a team of that'd be impossible. You need a team of that'd be impossible. You need a team of like 50 people to to develop like the a like 50 people to to develop like the a like 50 people to to develop like the a beer thing for the Blackberry. So, I beer thing for the Blackberry. So, I beer thing for the Blackberry. So, I think there's a similar type of thing think there's a similar type of thing think there's a similar type of thing that's happening. We're, you know, we that's happening. We're, you know, we that's happening. We're, you know, we really want to be a part of that and really want to be a part of that and really want to be a part of that and we're going to enable that. And if it we're going to enable that. And if it we're going to enable that. And if it like makes making like I think it still like makes making like I think it still like makes making like I think it still be a while before like making a robo be a while before like making a robo be a while before like making a robo taxi is like super super easy. taxi is like super super easy. taxi is like super super easy. >> Yeah. >> Yeah. >> Yeah. >> But that'll happen. But there's I mean >> But that'll happen. But there's I mean >> But that'll happen. But there's I mean the number of bots that could be the the number of bots that could be the the number of bots that could be the number of kinds of bots that could be number of kinds of bots that could be number of kinds of bots that could be deployed in healthare is almost deployed in healthare is almost deployed in healthare is almost healthare alone is is home care healthare alone is is home care healthare alone is is home care >> um and then um in um construction um you >> um and then um in um construction um you >> um and then um in um construction um you know all the physical traits know all the physical traits know all the physical traits >> it's like us sitting in 2007 and saying >> it's like us sitting in 2007 and saying >> it's like us sitting in 2007 and saying let's uh we should have an app store let's uh we should have an app store let's uh we should have an app store what types of apps and we would come up what types of apps and we would come up what types of apps and we would come up with like a list of eight and then like with like a list of eight and then like with like a list of eight and then like there'll be a messaging one and then there'll be a messaging one and then there'll be a messaging one and then there'll be a camera one and it's like there'll be a camera one and it's like there'll be a camera one and it's like now you look at the app store and it's now you look at the app store and it's now you look at the app store and it's like you know there's an app for like like you know there's an app for like like you know there's an app for like the hotel you go to and it's like you the hotel you go to and it's like you the hotel you go to and it's like you know to to order, you know, food off the know to to order, you know, food off the know to to order, you know, food off the menu, menu, menu, >> right? >> right? >> right? >> Yeah. >> Yeah. >> Yeah. >> Yeah, makes sense. >> Yeah, makes sense. >> Yeah, makes sense. >> Yeah. The we're talking, you know, >> Yeah. The we're talking, you know, >> Yeah. The we're talking, you know, earlier about the the differences earlier about the the differences earlier about the the differences between digital AI and physical AI. We between digital AI and physical AI. We between digital AI and physical AI. We were sort of hinting at LLMs, but world were sort of hinting at LLMs, but world were sort of hinting at LLMs, but world models are, you know, in vogue right models are, you know, in vogue right models are, you know, in vogue right now. Why don't you talk about sort of now. Why don't you talk about sort of now. Why don't you talk about sort of the the state of them as it relates to the the state of them as it relates to the the state of them as it relates to physical AI and how we should think physical AI and how we should think physical AI and how we should think about them? about them? about them? >> So, so first off, world models means >> So, so first off, world models means >> So, so first off, world models means about 100 different things and uh we had about 100 different things and uh we had about 100 different things and uh we had a team at CVPR recently and and I was a team at CVPR recently and and I was a team at CVPR recently and and I was joking with them about just how many joking with them about just how many joking with them about just how many different ways you can define what what different ways you can define what what different ways you can define what what a world model is. But uh when we're a world model is. But uh when we're a world model is. But uh when we're thinking about a world model way, we're thinking about a world model way, we're thinking about a world model way, we're we're typically thinking about it in the we're typically thinking about it in the we're typically thinking about it in the context of of a simulation, right? context of of a simulation, right? context of of a simulation, right? Something that is effectively Something that is effectively Something that is effectively >> started as a sim company. Yeah. >> started as a sim company. Yeah. >> started as a sim company. Yeah. >> Yeah. Something that is like >> Yeah. Something that is like >> Yeah. Something that is like sufficiently able to represent the real sufficiently able to represent the real sufficiently able to represent the real world and and is reactive in a sense world and and is reactive in a sense world and and is reactive in a sense where you can actually have let's say a where you can actually have let's say a where you can actually have let's say a uh an autonomous agent that's acting in uh an autonomous agent that's acting in uh an autonomous agent that's acting in this world and the world model is is this world and the world model is is this world and the world model is is behaving appropriately in response to behaving appropriately in response to behaving appropriately in response to that autonomous agent. that autonomous agent. that autonomous agent. >> Maybe uh Peter, I think it's worth being >> Maybe uh Peter, I think it's worth being >> Maybe uh Peter, I think it's worth being super explicit here. We just go just one super explicit here. We just go just one super explicit here. We just go just one level lower. the the you know level lower. the the you know level lower. the the you know determinism in simulators kind of the determinism in simulators kind of the determinism in simulators kind of the sim tore gap physics based you know sim tore gap physics based you know sim tore gap physics based you know rendering all the way to like this rendering all the way to like this rendering all the way to like this generated world. generated world. generated world. >> Yeah. >> Yeah. >> Yeah. >> Where do we fit on it or or where you >> Where do we fit on it or or where you >> Where do we fit on it or or where you know Yeah. This describe the landscape I know Yeah. This describe the landscape I know Yeah. This describe the landscape I think maybe. think maybe. think maybe. >> Yeah. Yeah. So, so this is like let's >> Yeah. Yeah. So, so this is like let's >> Yeah. Yeah. So, so this is like let's say simulation broadly, right? There's say simulation broadly, right? There's say simulation broadly, right? There's there's so many different ways of doing there's so many different ways of doing there's so many different ways of doing simulation and so the the more classical simulation and so the the more classical simulation and so the the more classical approaches of simulation very physics approaches of simulation very physics approaches of simulation very physics based and and you can decompose physics based and and you can decompose physics based and and you can decompose physics in all different ways and all different in all different ways and all different in all different ways and all different levels of abstraction and you can levels of abstraction and you can levels of abstraction and you can simulate with sensors or without sensors simulate with sensors or without sensors simulate with sensors or without sensors and and is it is just a body simulation and and is it is just a body simulation and and is it is just a body simulation or are we actually simulating for or are we actually simulating for or are we actually simulating for example the light in in the environment example the light in in the environment example the light in in the environment or the almost think about like the way or the almost think about like the way or the almost think about like the way CGI is done. If we l literally had CGI is done. If we l literally had CGI is done. If we l literally had technical artists and we have technical technical artists and we have technical technical artists and we have technical artists who would create assets which artists who would create assets which artists who would create assets which would go in the simulator which would would go in the simulator which would would go in the simulator which would mimic real like road signs and have you mimic real like road signs and have you mimic real like road signs and have you know reflectivity and material know reflectivity and material know reflectivity and material properties that you would see in the properties that you would see in the properties that you would see in the real world but as you guys know real world but as you guys know real world but as you guys know Hollywood [clears throat] is going Hollywood [clears throat] is going Hollywood [clears throat] is going through its own fundamental change and through its own fundamental change and through its own fundamental change and now you've generated uh techn the same now you've generated uh techn the same now you've generated uh techn the same thing is happening in our universe as thing is happening in our universe as thing is happening in our universe as well. well. well. >> Yeah. Yeah. So, so that's sort of on the >> Yeah. Yeah. So, so that's sort of on the >> Yeah. Yeah. So, so that's sort of on the that's at the far end of physics- based that's at the far end of physics- based that's at the far end of physics- based simulation and and then the opposite end simulation and and then the opposite end simulation and and then the opposite end is is purely neural simulation. But is is purely neural simulation. But is is purely neural simulation. But within that spectrum, there's there's within that spectrum, there's there's within that spectrum, there's there's many different things you can do that many different things you can do that many different things you can do that are each useful in their own right. And are each useful in their own right. And are each useful in their own right. And so, one of those things is a so, one of those things is a so, one of those things is a Gaussianbased simulation, right? Where Gaussianbased simulation, right? Where Gaussianbased simulation, right? Where you have uh a a a effectively a you have uh a a a effectively a you have uh a a a effectively a representation of the real world that uh representation of the real world that uh representation of the real world that uh that has a a a 3D representation. um and that has a a a 3D representation. um and that has a a a 3D representation. um and that 3D representation is consistent that 3D representation is consistent that 3D representation is consistent meaning that if if you let's say have meaning that if if you let's say have meaning that if if you let's say have some reference point let's say a camera some reference point let's say a camera some reference point let's say a camera and that camera moves within that 3D and that camera moves within that 3D and that camera moves within that 3D world because the Gaussian uh is world because the Gaussian uh is world because the Gaussian uh is actually representing the 3D geometry of actually representing the 3D geometry of actually representing the 3D geometry of that world you'll actually get very high that world you'll actually get very high that world you'll actually get very high quality output from that there's a lot quality output from that there's a lot quality output from that there's a lot of value in that uh and and that's let's of value in that uh and and that's let's of value in that uh and and that's let's say one type of of world model but when say one type of of world model but when say one type of of world model but when you go further on that uh on that you go further on that uh on that you go further on that uh on that spectrum into really into neural spectrum into really into neural spectrum into really into neural simulation then you get into these uh simulation then you get into these uh simulation then you get into these uh where you're actually generating the where you're actually generating the where you're actually generating the video feeds you can think a neural video feeds you can think a neural video feeds you can think a neural network that's actually outputting uh a network that's actually outputting uh a network that's actually outputting uh a video as what's actually coming out of video as what's actually coming out of video as what's actually coming out of the neurons of that and and that can be the neurons of that and and that can be the neurons of that and and that can be reactive uh which gives you some very reactive uh which gives you some very reactive uh which gives you some very interesting properties. interesting properties. interesting properties. >> Reactive is in the ego does something in >> Reactive is in the ego does something in >> Reactive is in the ego does something in the environment and the other agents the environment and the other agents the environment and the other agents respond to the ego. respond to the ego. respond to the ego. >> Exactly. Uh however, you're not >> Exactly. Uh however, you're not >> Exactly. Uh however, you're not guaranteed in that reactivity that it's guaranteed in that reactivity that it's guaranteed in that reactivity that it's it's accurate. Right. and and and now it's accurate. Right. and and and now it's accurate. Right. and and and now it's a question of well how can I align it's a question of well how can I align it's a question of well how can I align this this simulation this world model this this simulation this world model this this simulation this world model with the real world and the way that the with the real world and the way that the with the real world and the way that the real world would actually react and if real world would actually react and if real world would actually react and if you have perfect alignment between the you have perfect alignment between the you have perfect alignment between the real world and and the world model I real world and and the world model I real world and and the world model I think you've just sort of solved the think you've just sort of solved the think you've just sort of solved the universe roughly right that's impossibly universe roughly right that's impossibly universe roughly right that's impossibly difficult problem um but but as we make difficult problem um but but as we make difficult problem um but but as we make progress towards that uh it makes uh progress towards that uh it makes uh progress towards that uh it makes uh training physical AI models much easier training physical AI models much easier training physical AI models much easier because you can do more of that in because you can do more of that in because you can do more of that in simulation but the hardest part though simulation but the hardest part though simulation but the hardest part though is we're always talking about is we're always talking about is we're always talking about performance, right? So the I like to say performance, right? So the I like to say performance, right? So the I like to say the the the labs they they have it easy the the the labs they they have it easy the the the labs they they have it easy because they they can they can make because they they can they can make because they they can they can make models that are trillions of parameters models that are trillions of parameters models that are trillions of parameters and those models can be super slow and and those models can be super slow and and those models can be super slow and that's fine but we don't have that that's fine but we don't have that that's fine but we don't have that luxury in physical AI, right? We deal we luxury in physical AI, right? We deal we luxury in physical AI, right? We deal we we deal in real time like the actual we deal in real time like the actual we deal in real time like the actual actual clock real time and so we we have actual clock real time and so we we have actual clock real time and so we we have so many milliseconds before we have to so many milliseconds before we have to so many milliseconds before we have to do something and and those performance do something and and those performance do something and and those performance constraints they actually constrain the constraints they actually constrain the constraints they actually constrain the problem in a lot of ways. So we can have problem in a lot of ways. So we can have problem in a lot of ways. So we can have very large models and we do have very very large models and we do have very very large models and we do have very large models that are used in the large models that are used in the large models that are used in the offboard environment but when once you offboard environment but when once you offboard environment but when once you go onboard all of those constraints are go onboard all of those constraints are go onboard all of those constraints are very real and now we need to train a very real and now we need to train a very real and now we need to train a much smaller model that has these safety much smaller model that has these safety much smaller model that has these safety constraints these determinism constraints these determinism constraints these determinism constraints and uh and that's the hard constraints and uh and that's the hard constraints and uh and that's the hard part about physical it's also the moat part about physical it's also the moat part about physical it's also the moat right it's it's it is what makes our right it's it's it is what makes our right it's it's it is what makes our tooling and and our our competencies uh tooling and and our our competencies uh tooling and and our our competencies uh valuable uh because it's just really valuable uh because it's just really valuable uh because it's just really hard to to meet all of these constraints hard to to meet all of these constraints hard to to meet all of these constraints in in a physical system. When will you in in a physical system. When will you in in a physical system. When will you which will we get first a perfect a which will we get first a perfect a which will we get first a perfect a perfectly simulated real world perfectly simulated real world perfectly simulated real world environment for training autonomy environment for training autonomy environment for training autonomy autonomous devices or Grand Theft Auto autonomous devices or Grand Theft Auto autonomous devices or Grand Theft Auto 6? [laughter] 6? [laughter] 6? [laughter] >> You know I as long as they keep putting >> You know I as long as they keep putting >> You know I as long as they keep putting out great trailers. I mean I I watch I out great trailers. I mean I I watch I out great trailers. I mean I I watch I feel like I'm getting entertained feel like I'm getting entertained feel like I'm getting entertained without paying a dollar and reintroduced without paying a dollar and reintroduced without paying a dollar and reintroduced to Tom Petty because of [laughter] >> Will you give us some timelines? >> Will you give us some timelines? >> So this for a second. >> So this for a second. >> So this for a second. >> Yeah. Go for it. [laughter] Go for it. >> Yeah. Go for it. [laughter] Go for it. >> Yeah. Go for it. [laughter] Go for it. Well, no, look, I mean, so the whole Well, no, look, I mean, so the whole Well, no, look, I mean, so the whole thing was the whole thing with Grand thing was the whole thing with Grand thing was the whole thing with Grand Theft Auto is the big innovation was Theft Auto is the big innovation was Theft Auto is the big innovation was open open world open world sandbox open open world open world sandbox open open world open world sandbox gaming. So, it's a it's a sim it's a gaming. So, it's a it's a sim it's a gaming. So, it's a it's a sim it's a simulated city and at least in theory simulated city and at least in theory simulated city and at least in theory >> on that spectrum. I mean, we hire so >> on that spectrum. I mean, we hire so >> on that spectrum. I mean, we hire so many people out of the video game world many people out of the video game world many people out of the video game world on that spectrum. It's absolutely real. on that spectrum. It's absolutely real. on that spectrum. It's absolutely real. >> Well, tell tell us about that. Yeah. >> Well, tell tell us about that. Yeah. >> Well, tell tell us about that. Yeah. What's the spectum? So I here I this is What's the spectum? So I here I this is What's the spectum? So I here I this is this is speculation but I think Grand this is speculation but I think Grand this is speculation but I think Grand Theft Grand Theft Auto 6 will be perhaps Theft Grand Theft Auto 6 will be perhaps Theft Grand Theft Auto 6 will be perhaps the last major uh realworld video game the last major uh realworld video game the last major uh realworld video game that's still really developed let's say that's still really developed let's say that's still really developed let's say in that legacy era of traditional in that legacy era of traditional in that legacy era of traditional computer graphics tooling computer graphics tooling computer graphics tooling >> technical artists and yeah >> technical artists and yeah >> technical artists and yeah >> like I think that the Grand Theft Auto 7 >> like I think that the Grand Theft Auto 7 >> like I think that the Grand Theft Auto 7 will much more likely be like a world will much more likely be like a world will much more likely be like a world model based video game um and where you model based video game um and where you model based video game um and where you you could imagine as as AI tech evolves you could imagine as as AI tech evolves you could imagine as as AI tech evolves here you you have like this this concept here you you have like this this concept here you you have like this this concept of this video game world model And of this video game world model And of this video game world model And there's there's like some sort of there's there's like some sort of there's there's like some sort of baseline let's say data store uh that baseline let's say data store uh that baseline let's say data store uh that represents the real world in somehow and represents the real world in somehow and represents the real world in somehow and then and then you have a some then and then you have a some then and then you have a some translation layer that's actually translation layer that's actually translation layer that's actually turning that data store into something turning that data store into something turning that data store into something that you can see and and run around in that you can see and and run around in that you can see and and run around in like it's like it's like it's >> the game as a consequence could be the >> the game as a consequence could be the >> the game as a consequence could be the real world right as said you could have real world right as said you could have real world right as said you could have a complete recreation of the real world a complete recreation of the real world a complete recreation of the real world in the game this has kind of happened in the game this has kind of happened in the game this has kind of happened with flight simulators hasn't it isn't with flight simulators hasn't it isn't with flight simulators hasn't it isn't the most recent flight simulators are the most recent flight simulators are the most recent flight simulators are literally it's the entire planet literally it's the entire planet literally it's the entire planet rendered accurately is my understanding rendered accurately is my understanding rendered accurately is my understanding at least from the air. Is that right? at least from the air. Is that right? at least from the air. Is that right? >> Yeah. Yeah. And I mean you're really >> Yeah. Yeah. And I mean you're really >> Yeah. Yeah. And I mean you're really that's where our our bread and butters that's where our our bread and butters that's where our our bread and butters when we started uh started the business when we started uh started the business when we started uh started the business we hired so many people out of the we hired so many people out of the we hired so many people out of the Microsoft flight sim organized it and uh Microsoft flight sim organized it and uh Microsoft flight sim organized it and uh you know you know you know >> but when you fly over you you know >> but when you fly over you you know >> but when you fly over you you know whatever New Yorker or when you fly over whatever New Yorker or when you fly over whatever New Yorker or when you fly over to Duth in the flight simulator now it to Duth in the flight simulator now it to Duth in the flight simulator now it is the real city right is the real city right is the real city right >> exactly but there there's some tricks >> exactly but there there's some tricks >> exactly but there there's some tricks that they play there and a lot of that that they play there and a lot of that that they play there and a lot of that is fidelity you know the real world the is fidelity you know the real world the is fidelity you know the real world the more you zoom in more you zoom in more you zoom in >> it stays a certain level of fidelity and >> it stays a certain level of fidelity and >> it stays a certain level of fidelity and uh and so the the tricks that you play uh and so the the tricks that you play uh and so the the tricks that you play there is you you basically are uh there is you you basically are uh there is you you basically are uh downsampling very very aggressively and downsampling very very aggressively and downsampling very very aggressively and then as you get closer you know then it then as you get closer you know then it then as you get closer you know then it becomes more more high fidelity where becomes more more high fidelity where becomes more more high fidelity where the real world isn't like that. the if the real world isn't like that. the if the real world isn't like that. the if you were to try to rebuild the world you were to try to rebuild the world you were to try to rebuild the world with this level of fidelity, it would, with this level of fidelity, it would, with this level of fidelity, it would, you know, would take the all the energy you know, would take the all the energy you know, would take the all the energy of the universe, right? It's it's it's of the universe, right? It's it's it's of the universe, right? It's it's it's it's quite complex. And that's probably, it's quite complex. And that's probably, it's quite complex. And that's probably, by the way, the best argument against us by the way, the best argument against us by the way, the best argument against us being living in a simulation is among being living in a simulation is among being living in a simulation is among the But of course, and you would say, the But of course, and you would say, the But of course, and you would say, well, the simulator we're in doesn't well, the simulator we're in doesn't well, the simulator we're in doesn't follow the laws of physics that we're at follow the laws of physics that we're at follow the laws of physics that we're at that point, how how do we know that the that point, how how do we know that the that point, how how do we know that the simulator that we're in is rendering all simulator that we're in is rendering all simulator that we're in is rendering all the stuff that we can't see? the stuff that we can't see? the stuff that we can't see? >> Yeah. Yeah. Yeah. [laughter] >> Yeah. Yeah. Yeah. [laughter] >> Yeah. Yeah. Yeah. [laughter] >> As far as far as I know, everything >> As far as far as I know, everything >> As far as far as I know, everything happening outside this room doesn't even happening outside this room doesn't even happening outside this room doesn't even exist. exist. exist. >> Yeah. I mean you you know like Buddhism >> Yeah. I mean you you know like Buddhism >> Yeah. I mean you you know like Buddhism believes this is a different type of believes this is a different type of believes this is a different type of podcast. It's like you know when you podcast. It's like you know when you podcast. It's like you know when you open your eyes the world is rendered and open your eyes the world is rendered and open your eyes the world is rendered and then you close your eyes the world then you close your eyes the world then you close your eyes the world that's literally religious. [laughter] that's literally religious. [laughter] that's literally religious. [laughter] >> I don't see why I don't see why it's >> I don't see why I don't see why it's >> I don't see why I don't see why it's necessary for it to keep rendering if necessary for it to keep rendering if necessary for it to keep rendering if I'm not there. [laughter] I'm not there. [laughter] I'm not there. [laughter] >> Buddhism from first principles. >> Buddhism from first principles. >> Buddhism from first principles. >> Yeah. Exactly. That's what you should >> Yeah. Exactly. That's what you should >> Yeah. Exactly. That's what you should That'll get a lot of clicks. That's what That'll get a lot of clicks. That's what That'll get a lot of clicks. That's what you need to call this. [laughter] you need to call this. [laughter] you need to call this. [laughter] >> Yeah. Well, just go on the timeline >> Yeah. Well, just go on the timeline >> Yeah. Well, just go on the timeline topic. You know, we gave us timelines on topic. You know, we gave us timelines on topic. You know, we gave us timelines on self-driving cars. What timelines do you self-driving cars. What timelines do you self-driving cars. What timelines do you want to give us if any on sort of uh you want to give us if any on sort of uh you want to give us if any on sort of uh you know other interesting things that were know other interesting things that were know other interesting things that were worth tracking like perhaps when we'll worth tracking like perhaps when we'll worth tracking like perhaps when we'll get laundry folded or uh other you know get laundry folded or uh other you know get laundry folded or uh other you know things that emerge because of humanoid things that emerge because of humanoid things that emerge because of humanoid >> and I think also maybe just touching a >> and I think also maybe just touching a >> and I think also maybe just touching a little bit on world models where we see little bit on world models where we see little bit on world models where we see world models going because I think it's world models going because I think it's world models going because I think it's it's fundamental to what the work we do. it's fundamental to what the work we do. it's fundamental to what the work we do. >> True. >> True. >> True. >> Yeah. Yeah. So, so to answer the the >> Yeah. Yeah. So, so to answer the the >> Yeah. Yeah. So, so to answer the the first question, um, so laundry folding, first question, um, so laundry folding, first question, um, so laundry folding, it's uh it's it's not terribly far from it's uh it's it's not terribly far from it's uh it's it's not terribly far from being solved to to be clear. Uh, and being solved to to be clear. Uh, and being solved to to be clear. Uh, and there is a lot of interesting research there is a lot of interesting research there is a lot of interesting research being being being >> and then humanity can rejoice. That's in >> and then humanity can rejoice. That's in >> and then humanity can rejoice. That's in Proverbs 4:16, [laughter] I think. Proverbs 4:16, [laughter] I think. Proverbs 4:16, [laughter] I think. >> Well, here I I I I do think I do think >> Well, here I I I I do think I do think >> Well, here I I I I do think I do think housekeeping is is a killer use case for housekeeping is is a killer use case for housekeeping is is a killer use case for physically eye, right? physically eye, right? physically eye, right? >> Peter thinks two that he always talks >> Peter thinks two that he always talks >> Peter thinks two that he always talks about in the company. One is about in the company. One is about in the company. One is housekeeping and it's entertainment. housekeeping and it's entertainment. housekeeping and it's entertainment. Peter's long on humanoid entertainment. Peter's long on humanoid entertainment. Peter's long on humanoid entertainment. >> Is it like what kind of entertainment I >> Is it like what kind of entertainment I >> Is it like what kind of entertainment I would I would 100% [laughter] agree would I would 100% [laughter] agree would I would 100% [laughter] agree with. I think entertainment is robotics with. I think entertainment is robotics with. I think entertainment is robotics killer ass. I don't think anybody knows. killer ass. I don't think anybody knows. killer ass. I don't think anybody knows. What do you What do you What do you >> I mean like [laughter] some of these >> I mean like [laughter] some of these >> I mean like [laughter] some of these Midwest white guys are really into Midwest white guys are really into Midwest white guys are really into >> I'm just saying what I think I think >> I'm just saying what I think I think >> I'm just saying what I think I think >> I just want to know when I go west >> I just want to know when I go west >> I just want to know when I go west world. That's all I want. world. That's all I want. world. That's all I want. >> No, I actually have an entertaining I >> No, I actually have an entertaining I >> No, I actually have an entertaining I have a little I have a little a tiny have a little I have a little a tiny have a little I have a little a tiny little Chinese robot dog that's just little Chinese robot dog that's just little Chinese robot dog that's just like literally it's just like a little like literally it's just like a little like literally it's just like a little it's just a little and it just like it's just a little and it just like it's just a little and it just like roams around and it just like does roams around and it just like does roams around and it just like does >> Would you pay to see Circus LA with >> Would you pay to see Circus LA with >> Would you pay to see Circus LA with robots? Yes. [laughter] Yes. I I want to robots? Yes. [laughter] Yes. I I want to robots? Yes. [laughter] Yes. I I want to see I want to see kung fu trapeze see I want to see kung fu trapeze see I want to see kung fu trapeze swinging swinging swinging >> spoken by like a compiler's guy here. >> spoken by like a compiler's guy here. >> spoken by like a compiler's guy here. [laughter] >> I know. But >> I know. But >> I want Westworld. I want Westworld. >> I want Westworld. I want Westworld. >> I want Westworld. I want Westworld. >> I mean the the funny thing is I was I >> I mean the the funny thing is I was I >> I mean the the funny thing is I was I was just saying like well people in like was just saying like well people in like was just saying like well people in like the suburbs of Detroit actually that the suburbs of Detroit actually that the suburbs of Detroit actually that passes the test. I bet you people in passes the test. I bet you people in passes the test. I bet you people in Sterling Heights would actually pay to Sterling Heights would actually pay to Sterling Heights would actually pay to see that. It's actually true. see that. It's actually true. see that. It's actually true. >> I stand corrected. I stand [laughter] >> I stand corrected. I stand [laughter] >> I stand corrected. I stand [laughter] corrected. corrected. corrected. >> But uh but back on laundry folding for a >> But uh but back on laundry folding for a >> But uh but back on laundry folding for a moment. Um It's it's actually not far moment. Um It's it's actually not far moment. Um It's it's actually not far from being folded if you remove the time from being folded if you remove the time from being folded if you remove the time constraint. And so the the trick that's constraint. And so the the trick that's constraint. And so the the trick that's played and if you look at the latest played and if you look at the latest played and if you look at the latest research videos is they'll say like play research videos is they'll say like play research videos is they'll say like play it at 8x real or whatever, right? And it at 8x real or whatever, right? And it at 8x real or whatever, right? And and that's for you to make it watchable. and that's for you to make it watchable. and that's for you to make it watchable. >> So so the question is when can you >> So so the question is when can you >> So so the question is when can you actually reach human parody of actually reach human parody of actually reach human parody of performance? That's that's further off performance? That's that's further off performance? That's that's further off >> when you decouple uh models from just >> when you decouple uh models from just >> when you decouple uh models from just the hardware. The hardware can do it the hardware. The hardware can do it the hardware. The hardware can do it now. That used to be a constraint. So now. That used to be a constraint. So now. That used to be a constraint. So the hardware is very fast and accurate the hardware is very fast and accurate the hardware is very fast and accurate now which was actually now which was actually now which was actually >> there's still overheating issues that >> there's still overheating issues that >> there's still overheating issues that are still being dealt with but it's it's are still being dealt with but it's it's are still being dealt with but it's it's not it's not terribly far off like these not it's not terribly far off like these not it's not terribly far off like these are sol are sol are sol >> I mean it's far off from like you know >> I mean it's far off from like you know >> I mean it's far off from like you know when I was a mechie that was like when I was a mechie that was like when I was a mechie that was like fantasy like there's like nothing can fantasy like there's like nothing can fantasy like there's like nothing can >> What's the movie that has the most >> What's the movie that has the most >> What's the movie that has the most realistic future vision of robots realistic future vision of robots realistic future vision of robots >> oh man bysentennial man >> oh man bysentennial man >> oh man bysentennial man >> is it okay >> is it okay >> is it okay >> yeah he's realistic [laughter] why that >> yeah he's realistic [laughter] why that >> yeah he's realistic [laughter] why that one I actually haven't seen one I actually haven't seen one I actually haven't seen >> I like I like that scene I think it's >> I like I like that scene I think it's >> I like I like that scene I think it's iRoot when Will Smith jumps in the car iRoot when Will Smith jumps in the car iRoot when Will Smith jumps in the car and uh his you know whatever his like and uh his you know whatever his like and uh his you know whatever his like accomp's in the car and he's like puts accomp's in the car and he's like puts accomp's in the car and he's like puts the car in manual She's like, "What are the car in manual She's like, "What are the car in manual She's like, "What are you going to drive this thing yourself?" you going to drive this thing yourself?" you going to drive this thing yourself?" Like out of like, you know, she's Yeah. Like out of like, you know, she's Yeah. Like out of like, you know, she's Yeah. She's like, "Are you crazy? What are you She's like, "Are you crazy? What are you She's like, "Are you crazy? What are you going to drive this thing yourself?" going to drive this thing yourself?" going to drive this thing yourself?" Like, that's what we're That's applied Like, that's what we're That's applied Like, that's what we're That's applied to intuition's, you know, like goal. to intuition's, you know, like goal. to intuition's, you know, like goal. Yeah. Yeah. Yeah. >> Well, get by the way, I haven't seen >> Well, get by the way, I haven't seen >> Well, get by the way, I haven't seen this movie in a long time. Probably this movie in a long time. Probably this movie in a long time. Probably since it came out. So, my recollection since it came out. So, my recollection since it came out. So, my recollection of probably a bit incorrect. of probably a bit incorrect. of probably a bit incorrect. >> Don't worry, the internet will correct >> Don't worry, the internet will correct >> Don't worry, the internet will correct you. [laughter] you. [laughter] you. [laughter] >> But I I I think Bsentennial Man has uh >> But I I I think Bsentennial Man has uh >> But I I I think Bsentennial Man has uh fully self-driving cars. Uh and it also fully self-driving cars. Uh and it also fully self-driving cars. Uh and it also has the housekeeping robot, okay? which has the housekeeping robot, okay? which has the housekeeping robot, okay? which which is played by Robin Williams, and which is played by Robin Williams, and which is played by Robin Williams, and that's it's sort of like the the that's it's sort of like the the that's it's sort of like the the friendly guy that will uh the friendly friendly guy that will uh the friendly friendly guy that will uh the friendly robot that will clean up and also robot that will clean up and also robot that will clean up and also babysit your kids and stuff like that. babysit your kids and stuff like that. babysit your kids and stuff like that. And it seems like it's it's in the not And it seems like it's it's in the not And it seems like it's it's in the not terribly distant future. terribly distant future. terribly distant future. >> I got a different answer. You guys ever >> I got a different answer. You guys ever >> I got a different answer. You guys ever see that movie uh Sam Rockwell Moon? see that movie uh Sam Rockwell Moon? see that movie uh Sam Rockwell Moon? >> Oh, yeah. >> Oh, yeah. >> Oh, yeah. >> Yeah. The the setup. I don't want to >> Yeah. The the setup. I don't want to >> Yeah. The the setup. I don't want to It's a great movie. Don't watch the It's a great movie. Don't watch the It's a great movie. Don't watch the trailer. Just watch the movie. Uh it's trailer. Just watch the movie. Uh it's trailer. Just watch the movie. Uh it's the the premise is the tagline of the the the premise is the tagline of the the the premise is the tagline of the movie is 250,000 miles from home. You movie is 250,000 miles from home. You movie is 250,000 miles from home. You find who you are. And it's one guy who find who you are. And it's one guy who find who you are. And it's one guy who works in an energy harvesting base run works in an energy harvesting base run works in an energy harvesting base run by applied intuition uh run by lunar by applied intuition uh run by lunar by applied intuition uh run by lunar technologies. [laughter] I I I don't want to be whale I don't I I I don't want to be whale I don't want to be whailing utani. I don't want want to be whailing utani. I don't want want to be whailing utani. I don't want to be you know that's from uh from the to be you know that's from uh from the to be you know that's from uh from the alien franchises and then tar alien franchises and then tar alien franchises and then tar corporation from bladeunner. No no I corporation from bladeunner. No no I corporation from bladeunner. No no I want to be lunar technologies in the want to be lunar technologies in the want to be lunar technologies in the moon franchise moon franchise moon franchise one. uh to this one guy who works on one. uh to this one guy who works on one. uh to this one guy who works on this and the base basically runs by this and the base basically runs by this and the base basically runs by itself and he's just there to kind of itself and he's just there to kind of itself and he's just there to kind of mind it when things kind of some you mind it when things kind of some you mind it when things kind of some you know error signal. know error signal. know error signal. >> Yeah. The the reason why it's it's I >> Yeah. The the reason why it's it's I >> Yeah. The the reason why it's it's I think so accurate is because the think so accurate is because the think so accurate is because the state-of-the-art for AI systems is like state-of-the-art for AI systems is like state-of-the-art for AI systems is like these systems they just need the these systems they just need the these systems they just need the occasional grounding. They'll just go occasional grounding. They'll just go occasional grounding. They'll just go off and do something crazy and then you off and do something crazy and then you off and do something crazy and then you say no no stop doing that. say no no stop doing that. say no no stop doing that. >> LMS are like that too. I mean that's >> LMS are like that too. I mean that's >> LMS are like that too. I mean that's that's what coding bots are like right that's what coding bots are like right that's what coding bots are like right >> and and the reason other reasons I think >> and and the reason other reasons I think >> and and the reason other reasons I think it's quite accurate they it's uh maybe it's quite accurate they it's uh maybe it's quite accurate they it's uh maybe uncou now but Kevin Spy is the the AI uncou now but Kevin Spy is the the AI uncou now but Kevin Spy is the the AI you know smiley face and he's he's just you know smiley face and he's he's just you know smiley face and he's he's just there to kind of plate the human to to there to kind of plate the human to to there to kind of plate the human to to assist but to also like he's like oh assist but to also like he's like oh assist but to also like he's like oh you're you seem like you're sad Sam and you're you seem like you're sad Sam and you're you seem like you're sad Sam and like you know like that's the the the like you know like that's the the the like you know like that's the the the the but really it's the one running the the but really it's the one running the the but really it's the one running the base and uh hopefully I mean I shouldn't base and uh hopefully I mean I shouldn't base and uh hopefully I mean I shouldn't say we want to be lunar technologies say we want to be lunar technologies say we want to be lunar technologies because I don't know that they're quite because I don't know that they're quite because I don't know that they're quite a positive force in nature in that in a positive force in nature in that in a positive force in nature in that in that. But I think massive energy farm that. But I think massive energy farm that. But I think massive energy farm that's completely autonomous, that's completely autonomous, that's completely autonomous, >> that's going to be the future. And I and >> that's going to be the future. And I and >> that's going to be the future. And I and I think everyone like everyone reacts to I think everyone like everyone reacts to I think everyone like everyone reacts to things like that with like fear. And things like that with like fear. And things like that with like fear. And it's like, it's like, it's like, >> guys, that's amazing. That means energy >> guys, that's amazing. That means energy >> guys, that's amazing. That means energy costs go way down. Like that's an costs go way down. Like that's an costs go way down. Like that's an incredible positive thing. I think I incredible positive thing. I think I incredible positive thing. I think I think the you know uh I just did this think the you know uh I just did this think the you know uh I just did this commencement speech at my uh my commencement speech at my uh my commencement speech at my uh my undergrad. undergrad. undergrad. >> Did you get uh destroyed? >> Did you get uh destroyed? >> Did you get uh destroyed? >> No. You know what? I I >> No. You know what? I I >> No. You know what? I I >> unlike Eric Schmidt. >> unlike Eric Schmidt. >> unlike Eric Schmidt. >> Yeah. Yeah. Yeah. Yeah. Listen, listen >> Yeah. Yeah. Yeah. Yeah. Listen, listen >> Yeah. Yeah. Yeah. Yeah. Listen, listen listen. My my This is the true story. My listen. My my This is the true story. My listen. My my This is the true story. My My wife started watching. She said, "I My wife started watching. She said, "I My wife started watching. She said, "I feel like you're yelling at me. I can't feel like you're yelling at me. I can't feel like you're yelling at me. I can't watch [laughter] this." watch [laughter] this." watch [laughter] this." >> So, I I I basically I mean I I don't I'm >> So, I I I basically I mean I I don't I'm >> So, I I I basically I mean I I don't I'm not like I don't I don't I'm not going not like I don't I don't I'm not going not like I don't I don't I'm not going to say which tech leaders who just to say which tech leaders who just to say which tech leaders who just basically avoid it by like punting and basically avoid it by like punting and basically avoid it by like punting and saying I'm not going to talk about it. I saying I'm not going to talk about it. I saying I'm not going to talk about it. I I talk about this stuff. And partly it's I talk about this stuff. And partly it's I talk about this stuff. And partly it's the General Motors Institute. No one's the General Motors Institute. No one's the General Motors Institute. No one's like these these are I I don't want to I like these these are I I don't want to I like these these are I I don't want to I don't I don't I don't want to throw don't I don't I don't want to throw don't I don't I don't want to throw judgment on you know the people we judgment on you know the people we judgment on you know the people we recruit out of out of MIT and Stanford recruit out of out of MIT and Stanford recruit out of out of MIT and Stanford but I say GMI people are a little but I say GMI people are a little but I say GMI people are a little different and they're like you know different and they're like you know different and they're like you know they're like they're like they're like >> pragmatic people they under they you >> pragmatic people they under they you >> pragmatic people they under they you know you don't go to a place like GMI if know you don't go to a place like GMI if know you don't go to a place like GMI if you believe a superficial view of what you believe a superficial view of what you believe a superficial view of what corporations do. Corporations are just corporations do. Corporations are just corporations do. Corporations are just people working on projects together and people working on projects together and people working on projects together and by the way people working on projects by the way people working on projects by the way people working on projects together in government people working together in government people working together in government people working projects together in nonprofits they all projects together in nonprofits they all projects together in nonprofits they all screw up. It's and so it's it's too screw up. It's and so it's it's too screw up. It's and so it's it's too simple to say AI corporations are are simple to say AI corporations are are simple to say AI corporations are are terrible. This also you can't say the terrible. This also you can't say the terrible. This also you can't say the other side which is like it'll all be other side which is like it'll all be other side which is like it'll all be great. So you have a role to play. great. So you have a role to play. great. So you have a role to play. That's basically what you know what my That's basically what you know what my That's basically what you know what my what my message is and it's that's what my message is and it's that's what my message is and it's that's that's the case. I think if you feel that's the case. I think if you feel that's the case. I think if you feel like if if it if the the of the the I like if if it if the the of the the I like if if it if the the of the the I think the obvious uh you know uh think the obvious uh you know uh think the obvious uh you know uh abundance that comes from self-driving abundance that comes from self-driving abundance that comes from self-driving truck self-driving cars and the fact truck self-driving cars and the fact truck self-driving cars and the fact that people don't die which is amazing that people don't die which is amazing that people don't die which is amazing but then you also get this efficiency of but then you also get this efficiency of but then you also get this efficiency of cheaper energy etc. If all those things cheaper energy etc. If all those things cheaper energy etc. If all those things don't still satisfy your your fear you don't still satisfy your your fear you don't still satisfy your your fear you you as a person it's up to your you as a person it's up to your you as a person it's up to your responsibility to really learn about responsibility to really learn about responsibility to really learn about that technology. You you you can't just that technology. You you you can't just that technology. You you you can't just say, "Well, I'm afraid of it and my say, "Well, I'm afraid of it and my say, "Well, I'm afraid of it and my reaction is shut it down." That's not reaction is shut it down." That's not reaction is shut it down." That's not that's simply it's the uh and I don't that's simply it's the uh and I don't that's simply it's the uh and I don't say this just to say that we're say this just to say that we're say this just to say that we're competing with the Chinese, but there's competing with the Chinese, but there's competing with the Chinese, but there's a confusion. [laughter] a confusion. [laughter] a confusion. [laughter] The saying by confusion is no hand can The saying by confusion is no hand can The saying by confusion is no hand can block the sun. block the sun. block the sun. >> Mhm. [clears throat] >> Mhm. [clears throat] >> Mhm. [clears throat] >> And the sun is technological progress. >> And the sun is technological progress. >> And the sun is technological progress. And if we as a society don't embrace And if we as a society don't embrace And if we as a society don't embrace technological progress, we will be left technological progress, we will be left technological progress, we will be left behind. behind. behind. >> Somebody else is going to do it. >> Somebody else is going to do it. >> Somebody else is going to do it. >> Somebody else is going to do it. And it >> Somebody else is going to do it. And it >> Somebody else is going to do it. And it might it's not the Chinese. Who knows? might it's not the Chinese. Who knows? might it's not the Chinese. Who knows? Maybe it's a Usuzbck or you know it's Maybe it's a Usuzbck or you know it's Maybe it's a Usuzbck or you know it's another country that is is recognizing another country that is is recognizing another country that is is recognizing hey my citizens are suffering and I'm hey my citizens are suffering and I'm hey my citizens are suffering and I'm going to use this technology to remove going to use this technology to remove going to use this technology to remove them. It is honestly it's because we them. It is honestly it's because we them. It is honestly it's because we live in such a great society that we can live in such a great society that we can live in such a great society that we can have these like have these like have these like >> I would say stupid conversations like >> I would say stupid conversations like >> I would say stupid conversations like there still are people who don't can get there still are people who don't can get there still are people who don't can get food. Yeah. food. Yeah. food. Yeah. >> And and and and someone will immediately >> And and and and someone will immediately >> And and and and someone will immediately quip if they were debating me, they quip if they were debating me, they quip if they were debating me, they would say, "Well, there's plenty of would say, "Well, there's plenty of would say, "Well, there's plenty of food. It's it's the capitalist system food. It's it's the capitalist system food. It's it's the capitalist system that doesn't No, no, no. Let's let's be that doesn't No, no, no. Let's let's be that doesn't No, no, no. Let's let's be very specific. There's plenty of food, very specific. There's plenty of food, very specific. There's plenty of food, but getting that food to those people is but getting that food to those people is but getting that food to those people is difficult." difficult." difficult." >> So, that means we should let robots get >> So, that means we should let robots get >> So, that means we should let robots get that food to them faster. that food to them faster. that food to them faster. >> That's just that's just how it is. So, I >> That's just that's just how it is. So, I >> That's just that's just how it is. So, I think I'm I'm and I think like we we we think I'm I'm and I think like we we we think I'm I'm and I think like we we we as like technologists, I think sometimes as like technologists, I think sometimes as like technologists, I think sometimes we, you know, it's I think an we, you know, it's I think an we, you know, it's I think an inclination just to say leave these inclination just to say leave these inclination just to say leave these people behind. I think you have to bring people behind. I think you have to bring people behind. I think you have to bring them along. You have to explain it to them along. You have to explain it to them along. You have to explain it to them. But we also have to treat folks them. But we also have to treat folks them. But we also have to treat folks like adults and say if you don't get it like adults and say if you don't get it like adults and say if you don't get it after I explain it a couple times then after I explain it a couple times then after I explain it a couple times then you just don't get it. So there's like a you just don't get it. So there's like a you just don't get it. So there's like a middle ground. It's not everyone's an middle ground. It's not everyone's an middle ground. It's not everyone's an idiot and every or we should just be idiot and every or we should just be idiot and every or we should just be technology will just be perfect perfect. technology will just be perfect perfect. technology will just be perfect perfect. There's a middle ground. Let's have that There's a middle ground. Let's have that There's a middle ground. Let's have that conversation to a point and then we just conversation to a point and then we just conversation to a point and then we just move forward and we make society better move forward and we make society better move forward and we make society better and then the results show it. I mean and then the results show it. I mean and then the results show it. I mean there's people who still shockingly there's people who still shockingly there's people who still shockingly believe communism is the right answer. I believe communism is the right answer. I believe communism is the right answer. I mean I just want to say why and I'm I'm mean I just want to say why and I'm I'm mean I just want to say why and I'm I'm you know I I am a capitalist. I I cannot you know I I am a capitalist. I I cannot you know I I am a capitalist. I I cannot admit that. But there's 70 years of admit that. But there's 70 years of admit that. But there's 70 years of history there. Like we're that's not history there. Like we're that's not history there. Like we're that's not even a debate anymore. I mean that I even a debate anymore. I mean that I even a debate anymore. I mean that I think it could be a debate. If we're if think it could be a debate. If we're if think it could be a debate. If we're if we're sitting here in 1965 and having a we're sitting here in 1965 and having a we're sitting here in 1965 and having a debate, you say, "Okay, maybe centrally debate, you say, "Okay, maybe centrally debate, you say, "Okay, maybe centrally controlled systems work better." There's controlled systems work better." There's controlled systems work better." There's no debate anymore, folks. You know, no debate anymore, folks. You know, no debate anymore, folks. You know, systems where individuals systems where individuals systems where individuals make decisions on their own interest make decisions on their own interest make decisions on their own interest actually work better for society. And so actually work better for society. And so actually work better for society. And so that doesn't mean everything is perfect that doesn't mean everything is perfect that doesn't mean everything is perfect and you can't extrapolate that same and you can't extrapolate that same and you can't extrapolate that same thing with, you know, AI. Doesn't mean thing with, you know, AI. Doesn't mean thing with, you know, AI. Doesn't mean everything is going to be perfect, but everything is going to be perfect, but everything is going to be perfect, but net net it's definitely going to be net net it's definitely going to be net net it's definitely going to be better. That's roughly what my better. That's roughly what my better. That's roughly what my commencement speech was without the commencement speech was without the commencement speech was without the booze. [laughter] booze. [laughter] booze. [laughter] They Mark and these guys were booing so They Mark and these guys were booing so They Mark and these guys were booing so they just cut it out. they just cut it out. they just cut it out. Eric was booing. He's throwing stuff and Eric was booing. He's throwing stuff and Eric was booing. He's throwing stuff and they just edited it out. they just edited it out. they just edited it out. >> You mentioned uh you the Japan market >> You mentioned uh you the Japan market >> You mentioned uh you the Japan market earlier. Why don't you talk briefly earlier. Why don't you talk briefly earlier. Why don't you talk briefly about sort of the global ambitions and about sort of the global ambitions and about sort of the global ambitions and how these technologies you know how these technologies you know how these technologies you know interplay and and what we're doing here? interplay and and what we're doing here? interplay and and what we're doing here? So I think uh America particularly is So I think uh America particularly is So I think uh America particularly is still uh the most advanced in terms of still uh the most advanced in terms of still uh the most advanced in terms of when you take account the business when you take account the business when you take account the business model. The second thing for a company model. The second thing for a company model. The second thing for a company like applied intuition, we're an like applied intuition, we're an like applied intuition, we're an extremely global company. We work with extremely global company. We work with extremely global company. We work with everybody uh uh uh uh minus we don't everybody uh uh uh uh minus we don't everybody uh uh uh uh minus we don't have an office in China but really have an office in China but really have an office in China but really everyone else on the globe. Uh and we're everyone else on the globe. Uh and we're everyone else on the globe. Uh and we're a horizontal company. We're a technology a horizontal company. We're a technology a horizontal company. We're a technology provider. And I think we I think more provider. And I think we I think more provider. And I think we I think more Silicon Valley companies I think can Silicon Valley companies I think can Silicon Valley companies I think can employ a little bit of what we do which employ a little bit of what we do which employ a little bit of what we do which is work very I would say collaboratively is work very I would say collaboratively is work very I would say collaboratively with the local economies as sovereign AI with the local economies as sovereign AI with the local economies as sovereign AI becomes more of a real thing. We have to becomes more of a real thing. We have to becomes more of a real thing. We have to uh you know build businesses that take uh you know build businesses that take uh you know build businesses that take that into account. By the way, we're not that into account. By the way, we're not that into account. By the way, we're not the first ones to do this. If you look the first ones to do this. If you look the first ones to do this. If you look at the history of America, you read the at the history of America, you read the at the history of America, you read the history of Standard Oil, you'll see that history of Standard Oil, you'll see that history of Standard Oil, you'll see that this is this is that was the history of this is this is that was the history of this is this is that was the history of companies. You'd work internationally. A companies. You'd work internationally. A companies. You'd work internationally. A RAMCO is not a random company, right? RAMCO is not a random company, right? RAMCO is not a random company, right? you you build based on the real you you build based on the real you you build based on the real geopolitical realities of the time. And geopolitical realities of the time. And geopolitical realities of the time. And uh so I think you know we've I think uh so I think you know we've I think uh so I think you know we've I think navigated it quite well. I' I've lived navigated it quite well. I' I've lived navigated it quite well. I' I've lived you know in Japan, I lived in Germany, I you know in Japan, I lived in Germany, I you know in Japan, I lived in Germany, I lived in Dubai. So also being Pakistani lived in Dubai. So also being Pakistani lived in Dubai. So also being Pakistani by birth, I think that's also influenced by birth, I think that's also influenced by birth, I think that's also influenced our company. Peter's only lived in our company. Peter's only lived in our company. Peter's only lived in Michigan [laughter] and here but he is Michigan [laughter] and here but he is Michigan [laughter] and here but he is uh a German. So, so but so I think uh a German. So, so but so I think uh a German. So, so but so I think innately we're more we think about the innately we're more we think about the innately we're more we think about the globe more and I think when I was at globe more and I think when I was at globe more and I think when I was at both at Google and at YC I was always both at Google and at YC I was always both at Google and at YC I was always surprised at how uh kind of almost surprised at how uh kind of almost surprised at how uh kind of almost myopic the companies are just always myopic the companies are just always myopic the companies are just always looking at the market that's just like looking at the market that's just like looking at the market that's just like within the 30 you know between San Jose within the 30 you know between San Jose within the 30 you know between San Jose and San Francisco it's like actually the and San Francisco it's like actually the and San Francisco it's like actually the market is really big I think physical AI market is really big I think physical AI market is really big I think physical AI the nature of it being physical I think the nature of it being physical I think the nature of it being physical I think we we have to be a very international we we have to be a very international we we have to be a very international company and I think we've had a lot of company and I think we've had a lot of company and I think we've had a lot of success being a very very you know being success being a very very you know being success being a very very you know being international. Yeah. Cool. I think it's international. Yeah. Cool. I think it's international. Yeah. Cool. I think it's a good place to wrap. a good place to wrap. a good place to wrap. >> Okay. Peter Casser, thanks so much for >> Okay. Peter Casser, thanks so much for >> Okay. Peter Casser, thanks so much for coming on the podcast and congrats on coming on the podcast and congrats on coming on the podcast and congrats on big launch with Dana. big launch with Dana. big launch with Dana. >> Yeah, thanks for having us. >> Yeah, thanks for having us. >> Yeah, thanks for having us. >> Awesome. Great to see you. >> Awesome. Great to see you. >> Awesome. Great to see you. >> Great.