[00:01] billion machines and we think that can have a profound impact on society. company. We put intelligence on machines, cars, trucks, tanks, drones.
[00:13] intelligent. >> Digital AI, of course, is building software and optimizing ads and creating videos. That's all interesting and good, but really where you talk about the global economy, that's physical AI. In
[00:25] this intelligence revolution, the companies that impact the physical world world. >> How many things are there where the idea are going to matter? >> There's no reason autonomy should be
[00:38] this obscure, difficult technology. Our vision for that is a high school kid that can make iPhone apps should be able to make autonomous systems. That platform for designing and developing is what we're launching. It's called Dana.
[00:51] Everything that we've built and developed over the past nearly a decade >> Which will we get first? A perfectly simulated real world environment for training autonomous devices or Grand Theft Auto 6. [laughter]
[01:10] >> Well, thanks for having us. Your name is [laughter] >> I feel like we've I think we've all each known each other for >> too long. more than I'd like to admit. >> Yeah. [laughter] Long time. We're lucky
[01:23] the first investor in the first round. Of course, different check sizes, but before then. >> Exactly. a lot to talk about today. We've, you know, the biggest launch in company
[01:35] why don't we just give an update or a [clears throat] status what does applied >> Yeah, for the for the people who don't know, applied intuition is a physical AI company. We put intelligence on machines. That's the the simple way of
[01:47] uh describing it. um and all types of machines. So cars, trucks, tanks, drones, you name it. It's a physical moving thing. We we make an intelligent originally started by making the tools that would make the int then we got into
[02:02] the actual intelligence itself. Um in a very like in some ways like a very uh boring AI company in the sense of you know 83% of the company is engineering. We win by making really great products. It's not like a good sales or something
[02:16] enough for for a for a sales enabled company but uh yeah over a thousand company but uh yeah over a thousand engineers um and based in Silicon Valley but we have offices globally 18 offices and uh you know our mission is to to put
[02:30] intelligence on a billion machines and uh we think that can have a profound impact on society both in the kind of pissy things everyone talks about safety who's been in a car accident or in a mining accident or you know uh in a
[02:45] mining accident or you know uh in a farming accident. Those are real gnarly situations. Beyond just fixing that, you just if you can unlock productivity, I think, you know, we've seen the unlock in the digital world and everyone's
[02:58] trillion dollar companies emerging. I'm a pretty strong believer that I think back at the internet now, you know, people actually, if you look at the original internet companies that are doing, you know, serving or they're
[03:11] are interesting, but really when you look back 25 years from now, the big monolithic companies are Amazon that delivers you stuff, you know, uh Apple, that come of age. And I think when we look back 25 years in this intelligence
[03:25] revolution, the companies that impact the physical world, you know, might that impact the digital world. >> I would love for you to talk about the on the company, I think, was oh well, it's it's like, you know, car it's
[03:38] Self-driving cars. But it's kind of like, okay, there's like whatever, more building their own self-driving cars, and then and then there's like six never get that big because there just
[03:50] >> So, What's the like how should people there that are things that move where the idea of physical AI physical intelligence are going to matter? >> Yeah, I mean even today even if you put
[04:03] that you know uh let's say uh uh view on us the automotive is like 30% of our business. So 70% already is non-automotive and I think if you fast forward another 10 20 years uh even the manufacturers themselves as a customer
[04:15] base will be a small amount. I think our that mission just keep thinking a uh you know a billion machines becoming intelligent and you think about all the is just an easy one. I think it sticks in people's heads because we all drive
[04:29] cars and it's a big market. Um but I think it's it'll be a minority of of the business. I think it'll be increasingly a minority of the business but that right? >> Automotive is still huge just as a part
[04:41] of the globe's GDP. Automotive is something like 3% of all GDP. Um I think the way we always think about it like as you try to get to your mission initially the manufacturers were the distribution to that intelligence to consumers but
[04:56] you start working in construction and mining and agriculture and suddenly the the mining operator is actually really important or the department of war is become customers and all of those are customers of ours as well.
[05:10] >> Yeah. I think if you split AI into digital AI and physical AI, right? software and optimizing ads and creating all interesting and good, but really
[05:22] that's physical AI. And then we're talking about manufacturing and uh and all of these things that >> supply chains. mean, let's build on that though for a second, which is like so things that
[05:36] things that move are things that have human beings at the wheel or at the controls in some form, right? Um, and so and you know, airplanes [snorts] have the cockpit. Uh, boats have had to, you know, get designed around a human, you
[05:48] know, steering things. >> Um, like in a world where in a world of autonomy like do we already know what the things are that there are a lot of new things that are going to get built uh when you don't
[06:00] seat? I think both. Uh be well the the the the thing that you have to remember is like you take like a a holage system that's on a in a in a port um like a
[06:12] catap kamasu the dirt mover uh in a mine those are made for 20 25 years. they're they might not have gotten their full cycle you know uh uh ROI on them. So they're not immediately going to buy
[06:26] something new no matter how much better it is. So one part of our strategy is intelligence because they're not going anywhere. The second is what you're talking about which is well that depends on a human in a cab. If you don't have a
[06:38] human in cab you can run the the machine can be smaller. It can be shaped in very underground, because the human needs to breathe and it's very dangerous and so you can build
[06:51] a very very different machine. We're doing both of those things. And um and about is we're all talking about intelligence almost like within a system but the system level intelligence is where the unlock is and we're already
[07:05] doing work like that where you say hey let's take an entire port let's take an entire mind let's take an entire query and this heterogeneous mix of machines they're all can talk to each other and they can optimize and be efficient when
[07:19] an issue the rest of the mind doesn't have to stop when it's human-driven. going to go down because there's no analysis the human is not plugged into the core uh systems of the machine. So like a simple thing like knowing when a
[07:35] break system is going to break >> is actually huge because you can start wear and tear is higher than in other mines. Just using an example but like the the other macro point is if you look at agriculture as an example you know
[07:48] average American farmers 58 years old the there's that the number something like under 35 it's like less than 10% of farmers are that young. So what's going to happen? the need for food growth is continuing to grow. The need for uh rare
[08:02] earth materials is contin so these these demands are only growing but the humans who are the bottleneck are decreasing. >> Trucking is the same way. Um and so you can you can really just unlock a lot more efficiency. So I mean one way to
[08:18] think maybe think about this is like imagine if the cost for food decreases What what's the downstream impact? the imagine for goods being transported let's say you know instead of a few dollars a mile it's 20 cents a mile and
[08:33] suddenly it's it's I think that the unlock is very very very big um and that need for all the machines to be redesigned from the ground up maybe just one one more question would be just give give it give us a sense of
[08:45] of a company today >> yeah uh uh north of a thousand engineers and those engineers are you know obviously the classic you know uh uh uh software and AI uh engineering teams, but we also have engineers who really
[09:00] engineers who really know hardware because the important thing that we we're kind of just uh tipping around stepping around is all this stuff is hard because it ultimately has to meet the real world and the real world is has
[09:13] lot more issues and we have engineering teams that can I mean we've deployed our our our models onto like 50ome platforms like even that sounds trivial because think about models you think about deploying them through a browser or on a
[09:28] phone and everything's abstracted away because you of iOS and you have Android and you have all these systems that have already taken care in the real world you engineering teams that can do that as well our kind of a you know claim to
[09:42] fame as we've uh raised over about a billion dollars in the company's history all that is sitting in the bank and uh I think uh that's I always say that with an astrog doesn't mean we're not going to spend it you know next uh next month
[09:55] >> yeah good news bad news uh it's uh but it's the the uh and I Like we're at that phase, you know, you you talk about scale, we're at that phase where these giant markets are around us and we can make the decision how aggressive do we
[10:10] want to pursue those because decade of frankly execution and deployment into production. I think the the hallmark of our engineering team is is putting you that really is is a big I don't know how do you think about scale? Yeah, I think
[10:25] of bringing intelligence to a billion machines that is how we think about it the types of machines that we'll have the most impact on and focusing on those areas first but uh we'll get there >> right good I uh let's go deeper into the
[10:38] differences between digital and physical AI and more so into where where are we today what progress has has made what are some of the main major bottlenecks that >> yeah I mean I think a lot of times
[10:51] people think about the progress in physical AI is limited to basically two they're obvious and interesting which is robo taxes and humanoids. Um they're excite you and they're kind of sci-fi. Um I think they're those are very
[11:07] interesting. Uh and they there is real work being done by us and other people in in those domains. I think all the other uh all the other domains I think mean, you just you just think about what happens on a port. That's there's a huge
[11:23] that's the that's the area we're we're we're really focused on. It's like all look at like we we've talked before about uh the rise of Cisco and how you
[11:35] know first individual machines and companies would get network and then entire countries are getting network. There's a similar thing happening with AI. AI's getting to that level of kind of sovereign AI is now a discussion.
[11:48] of sovereign AI is now a discussion. Sovereign AI really is about physical AI about AI in defense. You're talking about AI in the physical machines that are moving around. If you look just at the example of of uh Whimo from America
[12:02] and Pony from China trying to deploy in let's say the other countries, so not America, not Europe, not China. every one of those uh spaces, they're way more uh they're way more hesitant of saying, "Yeah, thumbs up. Your robo taxis can
[12:17] run unfettered on our in our uh country." And so, if you look back just at kind of this arc of the internet, you know, when the first internet companies sovereignty at all. It's like the browser goes everywhere, the internet
[12:31] goes everywhere. That's almost the power of it. Then when social media emerges there's a bit more of hey actually not every social media and then you have China not allowing Facebook to come in and you have some then you get into the
[12:45] stuff there's more resistance the Ubers the door dashes suddenly there's local players who are being favored very aggressively when we get to physical AI I think there's going to be huge and also there's like a larger ge you know
[12:58] geopolitical theme of of kind of uh more fracturing than globalization you're going to have this demand and for this AI should somehow be localized and I think that has to play into our strategy as well. We're a technology provider so
[13:13] the globe. Um and I think that's that's that's uh that's that's that's something conversation. >> Yeah. A few other things on on digital versus physical AI. So in digital AI, the state-of-the-art is you you can
[13:26] train models effectively on the entirety of the internet and then maybe augment that with additional data that's been collected and refined with some hired experts. Right? This is sort of a hot field right now. Uh but generally you're
[13:38] talking about a foundation model that's built on internet data. In physical AI, the internet data is is useful too. However, to actually build a foundation model in physical AI, there's also a lot of private data collection. you when
[13:52] we're talking about mines or or logistics or any of these other uh other fields, the data that's useful for training models there is not necessarily available. So we have to do a lot of work ourselves actually going going out
[14:04] and collecting that data. And then the other key factor is safety, right? Uh if smartphone app, you don't necessarily care about uh is is a safety critical application. But when you're talking
[14:16] about moving a a machine that weighs many tons, uh or think of a humanoid which could fall over on your children, uh you care a lot about safety and and the evaluation of that safety and and that is really sort of getting to the
[14:28] state of the art of physical AI and and really proving out uh the safety case >> Yeah. And I think like you know you talk about like humanoid data collection has been its own you know uh little uh area of interest but when you talk about
[14:41] collecting data like in places like Korea where they have north South Korea where you have North Korea they don't allow mapping companies let alone company to come in and data collect. We've figured out over the years whether
[14:54] it's the Middle East, whether it's Latim, how to get into these countries, work with the governments and get the thumbs up to collect proprietary data. And so in in the way that it's it is similar to you know other uh digital AI
[15:07] systems, your proprietary data sets, scaling laws, all that stuff is the same. It's just applied in a very very different way. And uh it's almost like the way to think about it is like the diffusion of these models is very
[15:21] different because you can't it's not everyone can just access them through a everyone can just access them through a phone. you're and so so that ironically is actually plays in our favor because once we have a massive
[15:34] building we already have hundreds of pabytes of data um and then we have our own tools which are like you know synthetic data tools uh neural sim we proprietary data and that allows us to build some of the best systems in the
[15:47] business is there's kind of a chicken and egg thing which is like in order to build an autonomous physical thing you need a lot of data physical autonomous things running around collecting the data. So, it's
[16:00] physical things running around, you have the data that makes them all work like is is there is there a flywheel aspect of that? And is there is there is there flywheel? >> Uh it's it's it's difficult, but it's
[16:13] we have one of the largest data collection fleets on the planet, frankly speaking. Um so that's how you bootstrap your way into it. That's just money and resources and technical knowledge, but it's not like there's probably more than
[16:27] knowledge. So, it's not extremely obscure. I think what is more difficult is then how do you actually have that model which is going to work on lots of different hardware and is, you know, is is tested appropriately because the you
[16:44] was this company that did amazing self-driving work and then one accident, super scared and they pull back. So, it's like just getting these things into production is actually more difficult than than uh than it seems. Um I think
[16:59] going to be important. So, we started our synthetic data team like 5 years ago now plus yeah more than that at this point. Uh and >> like and we're a strong believer that synthetic data can accelerate autonomy
[17:13] development. We've just seen that and then there are like lots of other technical innovations that happen. Obviously the transformer revolution hitting self-driving massive basically everything done in self-driving pre2122
[17:28] relevant but you're almost like that's kind of the starting point but it's also point like there those four five years are actually there has been a lot of work done you can see it most clearly with Tesla but there's other folks uh in
[17:42] that process the actual techniques historically and I'm just simplifying here imitation learning was the way the way of the which was collect a bunch of data and then the models would basically imitate what human drivers do.
[17:55] >> The real state-of-the-art right now is endtoend reinforcement learning in in a closed loop in in your tools. And so >> it's a little simplified to say the system learns itself. It identifies where the issues in the self-driving
[18:09] system are and essentially you then find data like that or you synthetically create data like that and then you you know close that loop and you see are you better and better. I think if you fast forward some years that will be a
[18:23] completely closed loop like with no humans intervening. Right now you still have like like what's the the fog error that we saw um we still see errors in the real world that impact self-driving. >> Oh yeah. So it's like well what what are
[18:35] bottlenecks. Uh there's plenty of them, but uh whenever you're dealing with physical systems, you inevitably you hit a lot of gnarly hardware problems. And uh it could be anything from overheating to uh sensor being slightly
[18:50] miscalibrated or a funny funny issue we saw yesterday was uh basically a fogging sensor like fog impacting a sensor. And uh but these are the things that you work very reliably in the real world. >> Yeah. So I want to ask you a thread a
[19:05] whether you guys want to engage on it or not. It might be an opportunity or might hate the question which is >> were you surprised? So Cruz was a super startup that was kind of running neck and neck with Tesla early on and so
[19:17] and then they famously got bought by General Motors and personally. So I >> There we go. Y Cominator Y Com Y Cominator company um >> and um and you know a top end team and
[19:30] making excellent progress. They got bought by General Motors. They became the GM autonomy program. GM got a lot of praise at least, you know, at least in for being like, okay, being like the the legacy automaker with the biggest
[19:43] was announced on that. And I said, hey, Cruz just got bought, you know, he's also GM family. We're both GM families. And uh Peter Guest was he said Nvidia? I said no. He said I said go fish is Apple. I said no. I said General
[19:57] explicitive motors. [laughter] >> Right. So, so that's surprising to >> that they were willing to buy that they did okay that they did it and then by like as far as I ever heard like they were making excellent progress uh and
[20:09] then they had this there was a there was an accident there was a was that was a >> uh it wasn't a fatality it was a serious injury somebody was [clears throat] >> yeah serious injury bad press and then and then they put a bullet they the GM
[20:22] CEO on board put a bullet in the cruise project and I know the that at least extremely upset you know [clears throat] the by by the aftermath of that was it that they did? >> Uh so you know full disclosure General
[20:35] General Motors Institute so we have a lot of love for the company. Uh but incidentally and ironically I'm reading uh coincidentally I should say I'm had actually never read before called on a clear day you can see General Motors
[20:49] >> As one does. As one does. >> Have you read that book? the great alltime book titles. And we should just pause to say John Delorean >> He was going to be the next president of General Motors
[21:02] >> of General Motors and then later on he started his own car company which like Back to the Future and then that whole thing collapsed for variety of reasons. was like one of the main principal
[21:14] drivers of innovation of the current Bob Lutz this category and you got to title of the book. >> and why why was that the title of the book? because uh there's a lot of
[21:27] [laughter] >> It's a very large complex. >> It's like it's like a nation state. >> Yeah. It I mean really I mean it is like I think we say that like sometimes almost like flippantly but these
[21:40] Hyundai is an extension of the state. Toyota is an extension of the state. Volkswagen is literally Volkswagen board members are members of the government. and almost every uh and there used to be an old saying what's good for General
[21:53] Motors is good for America and you cannot underate history of the American corporation Sloan's uh my years at General Motors and Adventures white collar man if you run a large engineering organization you
[22:06] should read that that is the this like you this corporation didn't just emerge Sloan and Ketaring create ketaring is the head of engineering created this this AR, you know, with levels and vice presidents
[22:20] organizations? It's there really is like the source code comes along John uh uh you know comes on Delorean and he says um he writes he's going to be president
[22:32] and he's so fed up with a company but what was controversial was GM was doing really well at the time. GM >> was like a when we say like GM was number one in the Fortune 100 it was like number one two and three. It was
[22:46] company in America. So somebody to openly criticize the company and so he has a whole he writes this book as he quits out of out of uh how annoyed he was general is being read led uh he writes this book and then after he like
[23:01] published [laughter] and so he fights for years for his co-author not to publish the book. The co-author still publishes it. So it's a real true insight into a large corporation. I'm incidentally just
[23:13] worked at GM 20 some years ago and had you know know a lot about the company and what's shocking is it's not only about GM most of the major manufacturers actually still operate that way uh on the inside and so the question isn't the
[23:28] away isn't that these people who run these companies are stupid they're not know when you're selling to the department of war and people say well the distribution defines the business >> that Like so like the distribution is
[23:43] this is a consumer product of of this this stat might be outdated but when I remember GM used to pound into your head of the top five consumer lawsuits in in American history three are automotive. We got the majority right so it's like
[23:57] had these like weird things like inside the company you couldn't it wasn't red, yellow, green. It was like purple or like you'd always have to decoder because you know why? Cuz when they go to lawsuits they're like you let a
[24:10] production. I was like no it was marked production. I was like no it was marked magenta. [laughter] like >> what does orange mean? Does this mean I
[24:23] have to like So fast forward to you're meeting that system. Well, for then the the Ford slogan for a very long time was that it was quality is job one, right? >> Yes. Yeah. Exactly. And and that's the one two punch of automotive. It's
[24:37] quality and safety. Quality and safety and quality really becomes because the Japanese really reset that that stage and and because that's a whole separate automotive history for for an hour. But the punch line is you have the Silicon
[24:50] object. >> There is a parallel universe that cruises out there right now. even as a part of General Motors. So, I think you of where the company is, where union negotiations are happening literally
[25:04] you're like, you can't make a billion dollars for us, but you're funding this thing that's killing people and it's sloppy. And so, I'm not saying precisely that's what happened to be very clear, but the it's a multivariate problem. My
[25:17] other hot take is, you know, I worked at both companies, right? Uh Google Google similar than they're different. >> Way way more similar. Literally, people don't need to know this. The Google leveling system is the same as the
[25:30] General Motors [laughter] leveling system. And I used to say I saying, you engineers I knew at General Motors are better than the engineers here. And people would look at me like I'm saying there's no God in church. It's like
[25:42] they're like, how dare you, you metal bending monkey from Detroit. [laughter] It's like, no, actually like making a modern combustion engine is extremely complex. It's not just like, you know, it's it's it's not simple stuff. And so
[25:57] the the the macro point I think is it's a bunch of things. I think safety is always at the top of their top of their list. I do think, you know, we've hired they dealt with that specific issue with a government, you you got to you got to
[26:12] and they just didn't dance exactly right and that just gives government bureaucrats >> more ammo to go after. and you're a big target like General Motors, you got to you know it it reminds you guys ever see
[26:25] that movie like uh uh Good Fellas uh you know uh there the one of the last scenes the house of the rising sun you know all the old bosses go in the back of the that's what happened. And they're like the the board was like, "What are we
[26:38] going to do about cruise?" Like, "What can we do?" [laughter] can we do?" [laughter] It's like, "Kyle's a good guy." But [laughter] and then it's like, "Que the rising sun."
[26:50] People running through a San Francisco office. Just kidding. [laughter] Don't Don't make that an AI video. [laughter] Gonna get a mean text from >> So, I think I think there is a universe that would have survived, but it's it's
[27:03] what applied intuition does is kind of as you said like that dance it's like these companies. >> Exactly. Bearing in mind their own re very real issues and constraints. I >> I think general also had the topic of
[27:16] business model right. So you have uh cruise was going after the robo taxi personal car ownership >> and those things can be a bit odd. So I >> Okay. >> Yeah. And I think it wasn't clear. I
[27:29] mean the by the way you know um you actually all people you spoke at YC at 20 in 2013. I was in the audience. I was a a partner at the time and um you said very like uh recursive here. We're we're feeding each other device. It's uh the
[27:45] key thing in in new techn in the new technology business actually everyone though that's still hard. It's still hard sometimes to build really complex them into the market. the when becomes really important. You're two years early
[27:59] late, there's too many competitors. You have to like hit it at the right spot. And I think >> it's like like I mean a controversial Cruz, you know, they were certainly moving at a much faster pace than Whimo.
[28:12] talking about neck andneck when you know ultimately the the the plug was pulled. So who knows what happens in the long term. Our hypothesis in that same term. Our hypothesis in that same equation is actually the distribution.
[28:25] we run self-driving trucks right now in Japan. They carry commercial loads. safety drivers there, but they're autonomously running. And but you won't >> Mhm. >> That's the customer. And and why it's so
[28:39] good for us to partner with Isuzu in that case is that company's been around for almost a hundred years, right? Uh if if I'm not mistaken, pre preWorld War II company. and they are uh you know they know the government they have test
[28:52] tracks they know safe they know their own trucks very well so when we go and the integration into their physical machinery that's a fantastic onetwo punch I think today the world is ready to consume AI in the real world and
[29:07] anthropic and all these you know everything that's happened so people are no longer like what's a self-driving car and it's because of whimo and Tesla And I think you just have to meet the
[29:20] possible. And our view of that has always been you go through some of the Whether it's a mining operator, whether it's a department of war, whether it's uh uh the manufacturers and we work with, you know, within each vertical uh
[29:33] with with the right partner. But that's a fundamentally different view than a vertical >> where we're really playing the horizont. we think about that our companies, we're kind of like a like a chipmaker,
[29:46] >> you know. We we we we actually look and talk and walk a lot like a silicon company except we obviously we don't make chips but you know we're are we large long-term relationships and then
[29:58] to you know take take us out. So you need deep trust our our partners have really a a lot of deep trust and we know their markets really really well. The customers. >> That's why Nvidia does well beyond the
[30:12] fact obviously they make a very complex technology. Mhm. So, how are these legacy uh car companies preparing for the future? Are they making or more building, you know, partnering with you? Are they how are they going to compete
[30:25] >> It's like saying like how are governments dealing with AI? It's such it's such a broad topic. Um and each manufacturer like even like you take Honda, Nissan, Toyota, three Japanese manufacturers with, you know, long
[30:37] differently. They're roughly in a spectrum of we're going to build to we're going to buy. Um and other both extremes more than ever we're going to buy is the common answer because they've been trying and we've been there the
[30:51] whole time. Uh for the folks that are going to build, we provide them tools uh and you we talk a little bit about our our our new uh product that we're we're announcing here. And then uh on the ones that just want to buy, we we sell them
[31:03] the actual intelligence that goes on the machines. And so we meet the customer wherever they're ready in their in their journey. Um the more nuanced version of that is you know uh the reality is like every every product is a different
[31:17] product and so the amount of silicon and amount of dollars you can put towards it uh towards sensors what the customer is willing to pay all that depends on what actually gets in the long horizon. All these things will be fully autonomous,
[31:32] but the intermittent steps are very much what we saw in the PC where you you have like now nobody really looks at laptop specs and even maybe frankly your phone specs but that's not the case from basically 85 to 2002 2005 where finally
[31:49] and and then they're really moving to laptops. uh but there's a similar kind of 20-y year I think uh horizon there >> broadly when we talk about machines and machines becoming intelligent right fundamentally a machine is it's a
[32:03] that are integrated right and and whoever does that final integration is often times the company that puts their their badge on it the brand name but technology that goes into that machines and so we now have a bunch of technology
[32:17] components and platforms that can go into these machines but we also sell the develop help them as well. And if you look, by the way, under the hood of a of a dirt mover or like combine or diesel truck, they'll have Cumins engines in
[32:30] them. But nobody says, "Well, because all these guys buy Cumins, this this whatever Caterpillar is not a good company." It's like, no, that's just a component that they buy. Uh they they they have a different role. So, if you
[32:43] when you look in any of these verticals, there's it's just a complex web of folks. That's why I always say like the the chip kind of analogy actually works quite effectively because some none of those companies make chips but they all
[32:55] buy chips. Uh and so I think that that's it's a good way to think about it. all been talking about self-driving cars for like I think the whole thing started the DARPA Grand Challenge originally and
[33:08] >> Yeah. Late double O's. Yeah. basically around a little less than 20 years maybe. Um, and there have been years of like self-driving cars are imminent at any moment. Um, so I guess
[33:21] today and most cars are not self-driving. The good news is there are >> And so the way most cars are driving all over, you know, in the places they're deployed, it's become, you know, like people in San Francisco, I think, treat
[33:33] >> And I think you can call I think Tesla, it's kind of like the AGI thing. It's years ago, everything we're seeing right now is like mind-blowingly AGI. The post You can look at a bunch of manufacturers. Blue Cruise, Super Cute
[33:48] Cruise, BMW, Volvo's Pilot, they're [clears throat] all quite impressive >> But yeah, >> well, it's it's full self-driving X, whatever remote [snorts] monitoring is [laughter] happening. Um, uh, the the
[34:00] Angeles, and you guys may recall there was a there was a large fire in Los Angeles. Then the power then and then the California power grid was buckling actually turns out among the things Cybertrs are good at is they're they're
[34:12] very good, uh, batteries. Yeah. Yeah. our backup battery for the house. And as of last year, whatever the FSD released, one where it like at least a lot of people thought it like really turned the
[34:24] to somebody yesterday uh talked to somebody yesterday who has a Model Y who the full route all the way up Highway One through Big Su. >> Yeah. I think mean disengage like uh the uh uh meanantime and uh like uh miles
[34:38] per disengagement are are really high. I think miles is like in the thousands. >> Yeah. The big for people who haven't driven the big highway one big su like that's a that's a that's a stressfilled drive. He said it was great the whole
[34:51] talking to him had it not been [laughter] So >> right right off the cliff. Um so um uh and then you know Tesla's rolling out their robo taxi you know is is starting
[35:06] so on the one hand those exist. On the other hand, you know, 99.9999999% And then I would say maybe just one other would be the self-driving trucks. panic in the press of like the trucks become self-driving and the employment,
[35:20] of a job. And sitting here today, I don't think I don't know. Is there are self-driving that don't have at least a safety driver in the truck? And I think >> Yeah, silver, if you so let's let's split let's split the there's multiple
[35:34] points that we brought up here. is on the let's say personally owned vehicles and why are they not more ubiquitous. Uh the part of that is the manufacturers are not good at deploying technology. Part of that is they're they're they
[35:47] want to be safety conscious but most of it is cost cost. Um the the what you're seeing in China which is a chi China is kind of a different EV ecosystem mainly when you're talking about business that doesn't care about profits, it changes
[36:02] the entire calculus of the entire industry that doesn't care. Uh but what you're seeing is you're seeing L2++ systems. So we can simplify the entire >> right? >> So this is a driver behind the steering
[36:16] Tesla drives everywhere. Uh they're like >> There's an aggressive that's that's chip, sensors, the package, the software, everything. uh we you know anticipate that there there's a very
[36:29] automotive OEMs will actually subsidize it for free they'll just give it to you remember nav systems used to be a big thing for you pay 4 grand 3,500 to get a free and it just became default I think that'll happen the there's a a weird
[36:44] thing which is like actually getting into a subset of your cars costs x dollars and to get into all the cars costs x plus just a small incremental amount because it's just a fixed cost and the way that how many vehicles and
[36:57] way you have homologation all these testing regimes all this stuff so I think you'll have wait and then a lot >> every single OEM without exception even FSD competitor >> so it it'll it'll come but it is just
[37:13] like uh you know with uh a good analogy to think about self-driving in the phones >> we had we had the the satellite phones then we had the Qualcomm you brick phones. Then we had the Motorola Razors
[37:27] and from you know the late '9s to the late two you know double zeros everybody was like when's mobile going to come there was a huge like and then it it comes and by by 07 from the iPhone launch like it's like four years when
[37:41] Snapchat, right? >> Those are the killer applications. So I think there's a very very similar kind of wait and then it's just basically ubiquitous in every vehicle. Um, if you had to ask me for what that number is,
[37:55] 28 SOP, 29, start of production, 29, 30, and then by the early 30s, it'll start becoming very cheap to free. >> Routinely by by the early 30s, you would self-driving. >> Exactly. Or it has the driver in seat
[38:09] L2++ system being very specific >> like Cybertruck or Tesla what Tesla will become common. >> Yeah. Will be default. So then the question then uh the the the you know the the other side of this is why don't
[38:24] the the other side of this is why don't we have a bunch of Whimos everywhere >> specifically Whimo has a different technology without getting into the technology without getting into the nuances uh here but Tesla and many of
[38:36] the Chinese and applied were very much in this uh endto-end model architecture. Um this is a new way of doing self-driving. Whimo for the lack of a doesn't mean they're not learned. It does it just there's not one endto-end
[38:51] system. It's not one monolithic model. Um, one of the proclivities of that their approach is it does depend on HD maps. Therefore, there is a geo fencing concept. I think Whimo is trying hard to remove that bottleneck so they can
[39:03] expand geographically faster. But the reality of today isn't there. The other thing is when you have researchers, which they which really was uh coming out of uh an alphabet research uh organization, they didn't put commercial
[39:16] constraints. So, the sensors are bespoke and expensive. The cars and the than the they're just not economically feasible. And they've tried a lot to get that easier to go from something that's really cheap and make it more uh, you
[39:31] know, more featureful than something that's overbuilt and then trying to trim that's the big debate. Who's going to get there first? Tesla with full self-driving or Whimo with cost and geographic ubiquity. Uh, but you know
[39:46] >> You know what we're not debating about? Like is is is there a big technical of those things. So now we're clearly in the engineering side of self-driving, which is just this grind down to like dollar per mile efficiency. And the
[40:03] the OEMs are smart. They'll just they just adopt it. It's not the OEMs are resistant because they don't think consumers want it or they don't understand the technology. It's because they want a price envelope which allows
[40:16] them to keep their thin razor thin margins and at a scale right >> which is deployed across 100 plus countries in V1 right >> and so if you're just doing a small deployment it's very different and I
[40:28] think and and that was the last thing I would say is the buyer of a Subaru >> or a buyer of a Suzuki have very different brand expectations that a buyer of a Tesla >> and so including the age of the consumer
[40:42] and what they think will happen and won't happen. Um, so that that also the reason. So if you're a Suzuki, you're like, well, my buyer is like not doesn't it into the car. It's not because they're not like technically competent.
[40:55] >> When do you think when do you think it'll be routine? Let's say the 200 biggest American cities like um would it be routine to [snorts] walk outside and you just you just take it for granted that a robot taxi can come pick you up?
[41:07] that a robot taxi can come pick you up? >> Uh it's 26 now. Um I mean certainly by 30. All right. Okay. >> Yeah. Certainly by 30. And I I would say like >> because you what Whimo will say is that
[41:21] the dollars and cents per city already work and it's like well a company that they not already in 200 cities? But then you see their launch schedule is pretty aggressive and you're like that that can that can get there. So
[41:33] >> Yeah. Okay. >> Like I would say I would say available in 30 but routine and maybe like 32. Yeah, 33. There's a volume. >> And also if you you you know you live in
[41:47] LA and so like five years ago I'd go to LA, people would be like, "What's applied intuition? I don't know what self-driving cars are." Uh in the last self-driving. And some of them even know applied intuition because they know from
[41:59] the other manufacturers. Uh I think you fast forward another two to four years, everybody knows it. Now >> does that mean everyone's taking Whimos >> That answer is no actually. Now there is a huge huge if you look at the numbers
[42:12] mean they're just eating into into ride sharing. >> Um >> yeah but to get 100% ubiquity I mean be extremely cheap. >> And what about long haul trucking?
[42:24] >> So long so that's what we that's the passenger side. The long haul trucking completely different economics completely different um business model. Um, there are many companies right now, I would say probably north of five that
[42:37] are running long haul trucks with drivers carrying loads between America probably getting into double digits. So, it's there, but the reason uh you don't know it and the reason it's not top of mind is it's not a consumer product. And
[42:50] unlike uh on the Whimo and Tesla side where investors are willing to where investors are willing to essentially give you uh you know some uh market cap uh you know uh adjustment for
[43:02] the potential of the they say the the trucking business is like you know made You buy a truck with a calculator. >> Yeah. It's a calculator [laughter] >> and they and so it's like pure dollars and cents and so I think
[43:18] >> um you as the provider of self-driving we're not >> you have to show every mile I'm going to and it's like for sure for sure for sure cuz the buyer's unsophisticated and
[43:32] they're just like well I already got a staff it can drive and it's like and they're like they're just not inclined now where we're playing in Japan it's not random that we're doing trucking demand. There's a massive labor shortage
[43:44] today and there's an imploding demographic uh uh you know situation and so there's a demand from almost every sector and that's why we've we've picked that market to to to really grow but I think like you can take like even more
[43:58] think like you can take like even more obscure like when will all uh queries know you you're moving cement you're moving you're moving dirt uh not queries u e a r u a r
[44:11] quaries. Quarries rock stone >> rock stone cement. Uh when when are those uh I can tell you the people who own those things and run those things >> So it's literally then you don't you don't have a which literally we can't
[44:24] >> right? >> Um the macro point though that people uh >> right? >> But that happened pretty soon >> and happened fairly soon. The macro point that in in legislation and and and
[44:37] kind of in in the in the kind of uh economics the political economy of of this conversation is AI is really you see you have this big push back in I don't know this is going to happen to my job and VCs I'm sure all of your
[44:52] associates are very scared but like in our univer they're debating whether they >> Yeah. Yeah. Yeah. In our in our universe it's the other way around. And it's like you literally I'll I'll I'll meet these you know operators and they're like
[45:05] can do this we'll give you everything. So then it's just up to us to like get there as as you know aggressively. >> Well, you know the fear for a long time triggers the at least the press's imagination on like you know sort of
[45:17] will there >> but that's it's so wrong. Go ahead. >> There's not there's not enough truck drivers and guess what? Nobody wants to >> Why is that? Explain that. >> Because it's a terrible job. It's like
[45:30] >> by the way I grew up the main feature of the town where I grew up was a truck truck why is truck driving now it's you know what this is this is like a you know talking to my kid who's like
[45:42] well why can't I put my hand on the stove? It's like [laughter] It's like but why? It's like after the third why it's like come on buddy let's do this [laughter]
[45:55] everybody knows I did not do that [laughter] with the first time. is being a truck driver a difficult job or why would kids not want to do it when they grow up? >> So what let me use a parallel analogy
[46:07] which is very clear and then you can why you know people will say like nobody wants to work anymore and they say well you know McDonald's has all these job is those people that used to work at McDonald's now Door Dash and Uber
[46:20] they can open they can start their hours and end their hours and they don't have to like stand on their feet and they can surf their phone in between, you know, orders and they don't like that's the reason it's not random. The market is
[46:35] efficient. And so in the truck driving example, why does somebody not want to example, why does somebody not want to be away from their family for 4 to 8 days in a row doing long haul trucking? The more sharp example is in Australia,
[46:49] why don't people want to go literally buy a plane to go to a mine and work on or you go offro uh offshore oil rigs. Those jobs exist. If you want a job that pays six figures, they exist. even with such lucrative pay packages, it's not
[47:04] what, >> I like kind of being around my family and I'm willing to take an incremental decrease in cost and and how much money >> And and then also like I think today more than ever things like
[47:18] >> back pain and like being exposed to the sun and cancer and people that care >> this is the thing. Tell me if I I have this right, but I long-term drivers die life expectancy 10 years less than their peers. And I think
[47:33] of several things. So one is some combination of nutrition and sleep. It's you know it's a basically you know >> it's yeah it's very difficult it's very >> what's your sleep score if you're a long haul trucker? Let me guess eight sleep
[47:46] >> Exactly. And so like obesity and then heart disease, hypertension so forth are all very high. Um one and then two is I think the vibration uh is very the third is you mentioned cancer, but I think it's the I think they uh truck
[47:58] melanoma on their left arm. >> Exactly. Yeah. There's photos of like a years, one half their face, the other half's face cuz they're exposed to the sun. Right. >> A more interesting or even more uh stark
[48:10] stat. Mining is 1% of the labor pool globally, 8% of work related fatalities. Do you think people are rushing to work in mines when they hear stats like this? Most major mines have a fatality regularly, which means once, twice a
[48:23] ever visit a mine, you'll see that everything is based around safety because once you experience one of your co-workers dying, then you're like, >> Yeah. >> Like there's other jobs I can take. And
[48:36] so it's uh I I I understand you're trying to enumerate for the audience >> Yeah. >> And and and and the best evidence is this is not a mining podcast. Isn't that a podcast about hey long haul trucking
[48:49] >> Yeah. And even trucker even truckers don't want their kids to become truckers you know they want their kids to be in a safe at the very least like safe safer safer line of work. >> But um do the do the notwithstanding all
[49:02] you think there will be safety drivers in long haul trucks that are that are even just somebody in the cab to deal with what happens when they arrive? driver goals right now. Okay. So like they're they are working to get drivers
[49:16] out right now. Um >> you know without going into our own >> to be honest it's not long. We're talking talking a few years and uh >> Yeah. On the long end. And the thing is it's it's a there there's a software
[49:29] technology thing which is one part of the problem. But the other part is it's hardware and the validation necessary for those redundancies. And in many cases that can actually be a long pull. It's like, oh, they're productionizing a
[49:42] fully redundant steering system, fully redundant braking system, that's that's production yet. And once you get that in high value production, now you got the >> near the price downs. Exactly.
[49:54] the you know these little delivery robots? Like is that do you see a world >> Yeah, I think so. I mean the uh the the the product that we're announcing I think it's probably come out around with this time is called Dana. So there's you
[50:07] applied intuition does into two buckets which is the we've been talking mostly about the models that go on the machines then this is we say onboard software or onboard AI then there's offboard AI this is the tools to design and develop these
[50:23] same systems the models that actually go on the machines our uh you know vision for that is and the the delivery robot is a great example is like a high school kid or a middle schooler they can make iPhone apps they should be able to
[50:36] just ask that's a very simple question. Why can't a >> ninth grader make a delivery robot in their in their home? Well, they don't have the the actual environment that they would first develop the scenarios
[50:50] in. They would define the requirements. Hey, I want this robot to go on my high four buildings. >> Uh then how okay now that you define the scenarios get made. where are all the scenarios that can that can uh that can
[51:05] be made by using let's say a satellite image of the high school. Uh then now you have to train the robot. So you need some data. Where do you get that data? There's maybe enough publicly available data that can actually train a fairly
[51:18] rudimentary robot. Okay. Now you got that data from online maybe YouTube robot's not doing now you need to deploy it onto the actual machine. So then you deploy it onto the machine and then the robot runs into the wall. Okay. What
[51:31] happened there? The loop closes. That platform for designing and developing is what we're launching. It's called Dana. Uh which is the street that applied intuition is [laughter] headquartered on. Uh and uh and our our
[51:44] our you know this comes from our tooling background. And if you look at like how background. And if you look at like how tooling has changed in the digital AI world, if you look at like what Claude did to all we alo remember like you know
[51:56] from mix panel to you know uh gitlab github all these now everything has moved into a very different almost IDE frankly speaking we think the same thing and so uh that that's yeah that's what we're that's what we're building that's
[52:10] launching and we already use it inhouse uh to develop our autonomy system which is you know and we're working on the most kind of scaled complex systems uh verticals. So we're pretty confident that it's actually quite useful. We've
[52:23] seen massive productivity gains. Uh but also uh you know we think like uh other own systems because it gets to that mission that building intelligent
[52:35] machines. Fundamentally or dana is our agentic platform for physical AI and and everything that we've built and developed over the past nearly a decade. every every tool, every technique that's available in Dana and it's very actually
[52:48] easy to use uh with the aenic interface and so workflows that used to maybe take days or weeks to run, >> you can now run those in in minutes in >> And uh and this just lowers the barrier to entry to building these systems and
[53:02] just lowering the bar of like >> you know what it means to develop an autonomous system. Autonomy is still actually quite in the scope of software is quite exotic. It's not because of the things that we've talked about and we've
[53:15] just brought that down very very aggressively and it's kind of like you know the old adage of like how do you make a great product in software? It's like you either increase safety, convenience or cost and we want to try
[53:27] to do all three of those things with with Dana. Um and our hope is just like you said like you know >> uh kids can develop robots for their uh to humanoids. So we're not just talking about like landbased systems or or you
[53:42] know ones where that are that are uh so you humanoids you can do drones. >> The fact that right now writing drone software and deploying it at the time it's quite obscure and almost hobbyist >> uh we want to just make that absolutely
[53:57] like you know maybe not child's play but like teenager play. So this points to a experimentation and entrepreneurship and like agriculture construction >> defense you just all of a sudden have a
[54:11] applying creativity and coming up with ideas and making things that move. it's like it's one thing just to make the engineer more efficient or bring when these agents really run you're
[54:26] getting into you it's just like the iPhone example of you couldn't imagine >> before >> like the iPhone it's like imagine 2005 >> 10 years there's going to be this this app.
[54:39] like well the phones don't have have cameras. like, yeah, but it's going to Like, so like Facebook, it's like, see, it's just hard to hard. And so, we think >> by lowering that barrier, you're going to get way way more creative uh autonomy
[54:54] >> I will definitely decide whether to include this or not. So, my kid is Factorio. >> Oh, nice. he's he's you know, he's had rolling because the the tool kit's not available
[55:07] >> Yeah. >> Uh he's gathering data in the game. Um like bots that he's developed that go. >> Yeah. So like explains of course it's a purely educational process and experience but
[55:23] you know >> like there's no reason autonomy should be this like >> uh you know uh obscure difficult you know alchemistic you know uh uh uh technology. And um and I think not only
[55:41] does that have a huge impact on on society, it also allows people to like >> you know >> It's like if I can develop a >> a Roomba for myself in my house on a
[55:56] >> right? >> Then why then it's not suddenly so that's important. >> And we can it can it can support it can Absolutely. >> Yeah. Exactly.
[56:08] folks with disabilities you know we always think about humanoids as like this very important task of folding laundry which seems to [laughter] be.
[56:20] >> So we focus on you know the important task but the when you allow these tools to exist. I mean I I you know we started a tooling company. I mean I feel so separates actually advanced civilizations from you know less
[56:33] advanced civil civilizations. And our our first uh uh mark for the company was a monkeykey's head and then we got a designer who said what this [laughter] is stupid. >> I was like I thought it was pretty good.
[56:47] >> So you were talking earlier about how when you know um the technology got got of these companies you know Uber, WhatsApp, Snap you know Airbnb etc. that
[56:59] emerged in quick succession. And so now that the technology is getting there for are some use cases or companies that you can obviously it's hard to predict the excited for? Like what what could we be talking about the equivalent here of in
[57:13] quick succession? >> I mean I think uh you know midterm we want Dana if not the short term to really you know make humanoids way more real. Uh there's I mean how many it's like a thousand core tasks in a home
[57:26] from uh from humanoids and these companies it's like such I mean I'm if companies it's everything is difficult every step of the way is difficult cleaning that data is difficult training those models or deploying the model is
[57:40] difficult and the bar being I want a high school kid to make a humanoid so think there there could be a lot there but that's like these the obvious stuff going to we will we'll look back will be will be way way more interesting
[57:55] ingredients that we're bringing together in data, right? We're making it way easier to to actually get imitation learning to work, way easier to make reinforcement learning work in combination with that. Um we're we have
[58:08] baseline for a lot of things. Um world models, advanced simulation tech, all of you're sort of limited by your c creativity like well what what do I want to do? And if you think about any kind of physical AI task as it's it's a you
[58:23] are understanding the world and you're manipulating something and and we can build that that can be built now much more easily in this in this tool and I a tooling company and like you take self-driving trucks we deploy
[58:35] self-driving trucking companies use our tools I I I think some sometimes people ask oh look you know with Dana are you going to like enable all these >> that's absolutely completely fine if If you look at Google and what Google did
[58:49] to web applications, there was a massive internet. Uh Google still succeeded through, you know, search and YouTube and and and other web apps and other products and then ultimately closed source products and ultimately
[59:02] ventureback products. And we think we think this the the same thing could happen here. I was at a robotics startup uh a while back that you you guys know you know they were doing go through a training process training their one of
[59:14] app that I thought was very appealing which was >> literally you know training over and >> and so you know I don't know why not right
[59:26] little robot follow you around when you walk the dog pick up the >> I know somebody who built I forget who lawn robot that would go around and individ pick up individual leaves.
[59:38] >> Because you got that problem right. Okay. You you rake you you rake your like two hours later there's like 14 leaves and you're like leaves. >> It's like if development costs are zero
[59:50] then people will do that. I mean you guys remember like the early iPhone apps the hits were like the beer one or the fart app. If you [laughter] imagine that in like Yeah. If you imagine that in 98 with a, you know, with the Symbian
[01:00:03] mobile, you know, whatever the OS from, I think it was Ericson or somebody, that'd be impossible. You need a team of like 50 people to to develop like the a think there's a similar type of thing that's happening. We're, you know, we
[01:00:16] we're going to enable that. And if it like makes making like I think it still be a while before like making a robo >> Yeah. >> But that'll happen. But there's I mean
[01:00:28] number of kinds of bots that could be deployed in healthare is almost healthare alone is is home care >> um and then um in um construction um you >> it's like us sitting in 2007 and saying let's uh we should have an app store
[01:00:43] what types of apps and we would come up with like a list of eight and then like there'll be a messaging one and then there'll be a camera one and it's like like you know there's an app for like the hotel you go to and it's like you
[01:00:57] >> Yeah. >> Yeah, makes sense. earlier about the the differences between digital AI and physical AI. We were sort of hinting at LLMs, but world models are, you know, in vogue right
[01:01:09] the the state of them as it relates to physical AI and how we should think >> So, so first off, world models means about 100 different things and uh we had a team at CVPR recently and and I was joking with them about just how many
[01:01:22] a world model is. But uh when we're we're typically thinking about it in the context of of a simulation, right? >> started as a sim company. Yeah. >> Yeah. Something that is like
[01:01:35] sufficiently able to represent the real world and and is reactive in a sense where you can actually have let's say a uh an autonomous agent that's acting in behaving appropriately in response to
[01:01:48] >> Maybe uh Peter, I think it's worth being super explicit here. We just go just one level lower. the the you know determinism in simulators kind of the rendering all the way to like this generated world.
[01:02:03] >> Where do we fit on it or or where you know Yeah. This describe the landscape I >> Yeah. Yeah. So, so this is like let's say simulation broadly, right? There's simulation and so the the more classical
[01:02:16] approaches of simulation very physics based and and you can decompose physics in all different ways and all different levels of abstraction and you can simulate with sensors or without sensors and and is it is just a body simulation
[01:02:28] example the light in in the environment or the almost think about like the way CGI is done. If we l literally had technical artists and we have technical would go in the simulator which would mimic real like road signs and have you
[01:02:42] properties that you would see in the real world but as you guys know through its own fundamental change and now you've generated uh techn the same well. >> Yeah. Yeah. So, so that's sort of on the
[01:02:56] that's at the far end of physics- based simulation and and then the opposite end is is purely neural simulation. But within that spectrum, there's there's are each useful in their own right. And so, one of those things is a
[01:03:09] Gaussianbased simulation, right? Where you have uh a a a effectively a representation of the real world that uh that has a a a 3D representation. um and
[01:03:21] that 3D representation is consistent meaning that if if you let's say have and that camera moves within that 3D world because the Gaussian uh is actually representing the 3D geometry of that world you'll actually get very high
[01:03:35] of value in that uh and and that's let's say one type of of world model but when you go further on that uh on that spectrum into really into neural simulation then you get into these uh where you're actually generating the
[01:03:49] video feeds you can think a neural network that's actually outputting uh a video as what's actually coming out of the neurons of that and and that can be interesting properties. >> Reactive is in the ego does something in
[01:04:03] the environment and the other agents respond to the ego. >> Exactly. Uh however, you're not guaranteed in that reactivity that it's it's accurate. Right. and and and now it's a question of well how can I align
[01:04:15] this this simulation this world model with the real world and the way that the you have perfect alignment between the real world and and the world model I think you've just sort of solved the universe roughly right that's impossibly
[01:04:29] difficult problem um but but as we make progress towards that uh it makes uh training physical AI models much easier because you can do more of that in simulation but the hardest part though is we're always talking about
[01:04:42] is we're always talking about performance, right? So the I like to say the the the labs they they have it easy because they they can they can make models that are trillions of parameters and those models can be super slow and
[01:04:54] luxury in physical AI, right? We deal we we deal in real time like the actual actual clock real time and so we we have so many milliseconds before we have to do something and and those performance constraints they actually constrain the
[01:05:08] very large models and we do have very large models that are used in the offboard environment but when once you go onboard all of those constraints are much smaller model that has these safety constraints these determinism
[01:05:21] constraints and uh and that's the hard part about physical it's also the moat right it's it's it is what makes our tooling and and our our competencies uh valuable uh because it's just really hard to to meet all of these constraints
[01:05:34] in in a physical system. When will you which will we get first a perfect a environment for training autonomy autonomous devices or Grand Theft Auto 6? [laughter] >> You know I as long as they keep putting
[01:05:47] out great trailers. I mean I I watch I feel like I'm getting entertained without paying a dollar and reintroduced to Tom Petty because of [laughter]
[01:06:00] >> Yeah. Go for it. [laughter] Go for it. Well, no, look, I mean, so the whole Theft Auto is the big innovation was open open world open world sandbox gaming. So, it's a it's a sim it's a simulated city and at least in theory
[01:06:13] many people out of the video game world on that spectrum. It's absolutely real. >> Well, tell tell us about that. Yeah. What's the spectum? So I here I this is this is speculation but I think Grand Theft Grand Theft Auto 6 will be perhaps
[01:06:25] Theft Grand Theft Auto 6 will be perhaps the last major uh realworld video game that's still really developed let's say in that legacy era of traditional >> technical artists and yeah >> like I think that the Grand Theft Auto 7
[01:06:39] will much more likely be like a world model based video game um and where you you could imagine as as AI tech evolves here you you have like this this concept there's there's like some sort of baseline let's say data store uh that
[01:06:53] represents the real world in somehow and then and then you have a some turning that data store into something that you can see and and run around in >> the game as a consequence could be the real world right as said you could have
[01:07:06] in the game this has kind of happened with flight simulators hasn't it isn't the most recent flight simulators are literally it's the entire planet at least from the air. Is that right? >> Yeah. Yeah. And I mean you're really
[01:07:18] that's where our our bread and butters when we started uh started the business we hired so many people out of the Microsoft flight sim organized it and uh whatever New Yorker or when you fly over to Duth in the flight simulator now it
[01:07:31] >> exactly but there there's some tricks that they play there and a lot of that is fidelity you know the real world the more you zoom in >> it stays a certain level of fidelity and uh and so the the tricks that you play
[01:07:43] there is you you basically are uh downsampling very very aggressively and then as you get closer you know then it becomes more more high fidelity where the real world isn't like that. the if you were to try to rebuild the world
[01:07:57] with this level of fidelity, it would, you know, would take the all the energy it's quite complex. And that's probably, by the way, the best argument against us being living in a simulation is among the But of course, and you would say,
[01:08:10] well, the simulator we're in doesn't follow the laws of physics that we're at simulator that we're in is rendering all the stuff that we can't see? happening outside this room doesn't even exist.
[01:08:23] >> Yeah. I mean you you know like Buddhism believes this is a different type of open your eyes the world is rendered and then you close your eyes the world that's literally religious. [laughter] >> I don't see why I don't see why it's
[01:08:36] I'm not there. [laughter] >> Buddhism from first principles. That'll get a lot of clicks. That's what you need to call this. [laughter] >> Yeah. Well, just go on the timeline topic. You know, we gave us timelines on
[01:08:49] self-driving cars. What timelines do you want to give us if any on sort of uh you worth tracking like perhaps when we'll get laundry folded or uh other you know >> and I think also maybe just touching a little bit on world models where we see
[01:09:03] world models going because I think it's it's fundamental to what the work we do. >> Yeah. Yeah. So, so to answer the the first question, um, so laundry folding, it's uh it's it's not terribly far from being solved to to be clear. Uh, and
[01:09:17] being >> and then humanity can rejoice. That's in >> and then humanity can rejoice. That's in Proverbs 4:16, [laughter] I think. >> Well, here I I I I do think I do think housekeeping is is a killer use case for
[01:09:31] about in the company. One is housekeeping and it's entertainment. >> Is it like what kind of entertainment I would I would 100% [laughter] agree killer ass. I don't think anybody knows. What do you
[01:09:45] >> I mean like [laughter] some of these Midwest white guys are really into >> I just want to know when I go west world. That's all I want. have a little I have a little a tiny little Chinese robot dog that's just
[01:09:58] it's just a little and it just like roams around and it just like does >> Would you pay to see Circus LA with robots? Yes. [laughter] Yes. I I want to see I want to see kung fu trapeze swinging
[01:10:12] >> spoken by like a compiler's guy here. [laughter] >> I want Westworld. I want Westworld. >> I mean the the funny thing is I was I the suburbs of Detroit actually that passes the test. I bet you people in
[01:10:27] see that. It's actually true. >> I stand corrected. I stand [laughter] >> But uh but back on laundry folding for a moment. Um It's it's actually not far from being folded if you remove the time constraint. And so the the trick that's
[01:10:40] research videos is they'll say like play it at 8x real or whatever, right? And and that's for you to make it watchable. >> So so the question is when can you actually reach human parody of performance? That's that's further off
[01:10:54] the hardware. The hardware can do it now. That used to be a constraint. So now which was actually are still being dealt with but it's it's not it's not terribly far off like these
[01:11:06] >> I mean it's far off from like you know when I was a mechie that was like >> What's the movie that has the most realistic future vision of robots >> is it okay >> yeah he's realistic [laughter] why that
[01:11:19] >> I like I like that scene I think it's iRoot when Will Smith jumps in the car and uh his you know whatever his like accomp's in the car and he's like puts the car in manual She's like, "What are you going to drive this thing yourself?"
[01:11:31] Like out of like, you know, she's Yeah. She's like, "Are you crazy? What are you Like, that's what we're That's applied to intuition's, you know, like goal. this movie in a long time. Probably since it came out. So, my recollection
[01:11:45] >> Don't worry, the internet will correct you. [laughter] >> But I I I think Bsentennial Man has uh fully self-driving cars. Uh and it also has the housekeeping robot, okay? which which is played by Robin Williams, and
[01:11:59] friendly guy that will uh the friendly robot that will clean up and also babysit your kids and stuff like that. And it seems like it's it's in the not >> I got a different answer. You guys ever see that movie uh Sam Rockwell Moon?
[01:12:13] >> Yeah. The the setup. I don't want to It's a great movie. Don't watch the trailer. Just watch the movie. Uh it's the the premise is the tagline of the movie is 250,000 miles from home. You find who you are. And it's one guy who
[01:12:25] works in an energy harvesting base run by applied intuition uh run by lunar by applied intuition uh run by lunar technologies. [laughter] to be you know that's from uh from the alien franchises and then tar
[01:12:40] corporation from bladeunner. No no I want to be lunar technologies in the moon franchise one. uh to this one guy who works on this and the base basically runs by itself and he's just there to kind of
[01:12:53] know error signal. >> Yeah. The the reason why it's it's I think so accurate is because the state-of-the-art for AI systems is like occasional grounding. They'll just go off and do something crazy and then you
[01:13:06] >> LMS are like that too. I mean that's that's what coding bots are like right >> 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 uncou now but Kevin Spy is the the AI
[01:13:18] you know smiley face and he's he's just there to kind of plate the human to to assist but to also like he's like oh you're you seem like you're sad Sam and like you know like that's the the the the but really it's the one running the
[01:13:32] base and uh hopefully I mean I shouldn't say we want to be lunar technologies a positive force in nature in that in that. But I think massive energy farm that's completely autonomous,
[01:13:44] >> that's going to be the future. And I and I think everyone like everyone reacts to it's like, >> guys, that's amazing. That means energy costs go way down. Like that's an incredible positive thing. I think I
[01:13:59] think the you know uh I just did this commencement speech at my uh my >> No. You know what? I I >> unlike Eric Schmidt. >> Yeah. Yeah. Yeah. Yeah. Listen, listen listen. My my This is the true story. My
[01:14:12] feel like you're yelling at me. I can't watch [laughter] this." >> 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 basically avoid it by like punting and saying I'm not going to talk about it. I
[01:14:25] I talk about this stuff. And partly it's the General Motors Institute. No one's don't I don't I don't want to throw judgment on you know the people we recruit out of out of MIT and Stanford but I say GMI people are a little
[01:14:38] they're like >> pragmatic people they under they you know you don't go to a place like GMI if you believe a superficial view of what corporations do. Corporations are just people working on projects together and
[01:14:51] together in government people working projects together in nonprofits they all screw up. It's and so it's it's too simple to say AI corporations are are other side which is like it'll all be great. So you have a role to play.
[01:15:05] That's basically what you know what my what my message is and it's that's that's the case. I think if you feel like if if it if the the of the the I think the obvious uh you know uh abundance that comes from self-driving
[01:15:19] truck self-driving cars and the fact that people don't die which is amazing but then you also get this efficiency of cheaper energy etc. If all those things don't still satisfy your your fear you you as a person it's up to your
[01:15:33] responsibility to really learn about that technology. You you you can't just say, "Well, I'm afraid of it and my reaction is shut it down." That's not that's simply it's the uh and I don't say this just to say that we're
[01:15:46] a confusion. [laughter] The saying by confusion is no hand can >> Mhm. [clears throat] >> And the sun is technological progress. And if we as a society don't embrace technological progress, we will be left
[01:15:59] >> Somebody else is going to do it. And it might it's not the Chinese. Who knows? Maybe it's a Usuzbck or you know it's another country that is is recognizing going to use this technology to remove
[01:16:12] them. It is honestly it's because we live in such a great society that we can have these like >> I would say stupid conversations like there still are people who don't can get food. Yeah.
[01:16:25] quip if they were debating me, they would say, "Well, there's plenty of that doesn't No, no, no. Let's let's be very specific. There's plenty of food, difficult." >> So, that means we should let robots get
[01:16:38] >> That's just that's just how it is. So, I think I'm I'm and I think like we we we we, you know, it's I think an inclination just to say leave these
[01:16:51] them along. You have to explain it to them. But we also have to treat folks like adults and say if you don't get it after I explain it a couple times then you just don't get it. So there's like a middle ground. It's not everyone's an
[01:17:03] idiot and every or we should just be technology will just be perfect perfect. conversation to a point and then we just move forward and we make society better and then the results show it. I mean there's people who still shockingly
[01:17:16] believe communism is the right answer. I mean I just want to say why and I'm I'm you know I I am a capitalist. I I cannot admit that. But there's 70 years of even a debate anymore. I mean that I think it could be a debate. If we're if
[01:17:31] we're sitting here in 1965 and having a debate, you say, "Okay, maybe centrally controlled systems work better." There's no debate anymore, folks. You know, systems where individuals make decisions on their own interest
[01:17:43] actually work better for society. And so that doesn't mean everything is perfect thing with, you know, AI. Doesn't mean everything is going to be perfect, but better. That's roughly what my commencement speech was without the
[01:17:56] They Mark and these guys were booing so they just cut it out. they just edited it out. >> You mentioned uh you the Japan market earlier. Why don't you talk briefly about sort of the global ambitions and
[01:18:11] interplay and and what we're doing here? So I think uh America particularly is still uh the most advanced in terms of when you take account the business like applied intuition, we're an extremely global company. We work with
[01:18:25] everybody uh uh uh uh minus we don't have an office in China but really everyone else on the globe. Uh and we're a horizontal company. We're a technology provider. And I think we I think more Silicon Valley companies I think can
[01:18:38] employ a little bit of what we do which is work very I would say collaboratively with the local economies as sovereign AI becomes more of a real thing. We have to that into account. By the way, we're not the first ones to do this. If you look
[01:18:53] at the history of America, you read the history of Standard Oil, you'll see that this is this is that was the history of companies. You'd work internationally. A RAMCO is not a random company, right? you you build based on the real
[01:19:06] uh so I think you know we've I think navigated it quite well. I' I've lived you know in Japan, I lived in Germany, I lived in Dubai. So also being Pakistani our company. Peter's only lived in Michigan [laughter] and here but he is
[01:19:22] Michigan [laughter] and here but he is uh a German. So, so but so I think innately we're more we think about the globe more and I think when I was at both at Google and at YC I was always surprised at how uh kind of almost
[01:19:34] myopic the companies are just always looking at the market that's just like and San Francisco it's like actually the market is really big I think physical AI the nature of it being physical I think we we have to be a very international
[01:19:47] success being a very very you know being international. Yeah. Cool. I think it's >> Okay. Peter Casser, thanks so much for coming on the podcast and congrats on >> Awesome. Great to see you. >> Great.