Host: Physical machines today are more akin to the state of the phone market before Android and iOS existed. Part of the reason that that Larry at Google decided to get into Android was they they wanted to run Google products on a bunch of phones and they they bought all of these phones from the industry and it turned out they had like 50 different operating systems on these phones and it was virtually impossible for for Google to to make their app run on all 50 devices equally well. And so the solution was well actually what if what if they created a really great operating system and and made it attractive to all of these phone makers. And that was sort of the genesis for what Android was and and why Android existed. It was a way for Google to get their products onto really wide diversity of devices. The state of of the physical uh industry right now. It's a little bit like that. So many different operating systems. It's so fragmented. And to actually get a modern AI application to run on these vehicles, you actually you first have to consolidate the operating system. And so that's that's why we've done that.

Host: Before we get into today's episode, I just have a small message for listeners. Thank you. We would not be able to bring you the AI engineering, science, and entertainment content that you so clearly want if you didn't choose to also click in and tune into our content. We've been approached by sponsors on an almost daily basis. But fortunately, enough of you actually subscribe to us to keep all this sustainable without ads. And we want to keep it that way. But I just have one favor to ask all of you. The single most powerful, completely free thing you can do is to click that subscribe button. It's the only thing I'll ever ask of you. And it means absolutely everything to me and my team that works so hard to bring the Inspace to you each and every week. If you do it, I promise you we'll never stop working to make the show even better. Now, let's get into it.

Host: Hey everyone, welcome to the L in Space podcast. This is Allesio, founder of Colonel Apps, and I'm joined by Swixs, editor of L in Space. We're very honored to have uh the founders of Applied Intuition, uh Kasar and Peter. Welcome.

Swixs: You guys really know how to turn it on to podcast mode. That was that was uh I you guys are real real pros at this. They were just joking around right before this and then they flipped it pretty quick.

Host: Yeah, it's great to have you guys. Maybe you just want to introduce yourself so people know the voice on the mic and

Peter Lewig: table. Yeah, I'm Peter Lewig. I'm the co-founder and CTO of Applied Intuition.

Kasar Younes: And uh my name is Casser Ununas. I am the CEO and co-founder with Peter.

Host: Nice. Can you guys give the high level overview of what applied intuition is and I I was reading through some of the Congress files uh when you went out there Peter and 18 of the top 20 global non-Chinese automakers use you guys. You have customers in agriculture, defense, construction. I think most people have heard of apply intuition tied to YC when it was first started and then you were kind of in stealth for a long time. So maybe just give people the high level overview of what it is today and then we'll dive into the different pieces.

Peter Lewig: Yeah. So applied intuition our mission is to build physical AI for a safer more prosperous world. And so we work on physical AI for all different types of moving systems everything from cars to trucks to construction and mining equipment uh to defense technologies. And uh and we're a true technology company. So we we build and sell the technology and we sell it to the companies that make the machines. We we sell it to to the government. Uh really anyone that wants to to buy a technology to make machines smart.

Kasar Younes: Yeah. And I think uh in the broader AI landscape, a lot of the focus uh rightfully so in the last uh 3 years has been on large language models and so everything that fits in a screen, you know, like uh whether it's code complete products or or or things like that. Um, and what's different about us is we're deploying intelligence onto a lot of things that don't have screens. You know, they're physical machines. There are sometimes screens within the cabin or for for example of a car or a truck or something like that. But, uh, most of the value we provide is putting intelligence that is in safety critical environments. So that those two words are really important because learned systems can make mistakes if you're asking for like you know some you know something like tell me about these podcast hosts that I'm about to go meet but uh you can't do that obviously when you're you know we run like as an example we run driverless trucks in Japan right now like as we speak you can't have errors that those are L4 trucks. Yeah.

Host: Yep.

Host: Was that always the mission? I remember initially I think people put you and Scale AI very similarly for some things about being kind of like on the data infrastructure side of things. What was the evolution of the company?

Peter Lewig: Well, from the very beginning we we always wanted to uh really be a technology company that uh that helped generally push forward the industrial sector. And so we started off working in autonomy. Our very first customers were robo taxi companies and and we started off doing a lot of work in simulation and and data infrastructure. And then over the years we've expanded our portfolios. Now we have over 30 products and it's a pretty broad technology play within the the landscape of physical AI.

Kasar Younes: Yeah, I think the the scale reason is because we're all YC universe companies, you know, and so uh but it was a very very different company, you know, Scale was is more of a services company, data labeling company fundamentally. We started and still are uh you know do a lot of tooling. So like you think you know developer tooling is now in vogue again thanks to thank thanks to you know thanks to the AI boom but honestly 10 years ago it was out of vogue like doing a tooling company in 2016 2017 was not like the thing to do because I don't know if you remember that the VCs generally their views was that tooling are just workflows and workflows ultimately are not really interesting uh and we've kind of come you know full full circle of that but when we started the company our our kind You know, it's kind of like in the periphery of what the company wants to be in was like from our earliest days like we want to deploy software on physical machines like on cars and on trucks and things like that. Now obviously we didn't know that the transformer boom was going to happen and we didn't know that autonomy systems would become end to end. Those things we didn't know and why that's important with autonomy system end it is just now you can those models can be generalized to you know multiple form factors. And so back 9 10 years ago tooling was a great way and still is a great way to you know build the technology and sell technology to our and customers a lot of them who want to build the stuff themselves. And so we just offer like a spectrum of solutions from you can just use like one part of of a development suite of tools all the way to buying the full thing. The way to think about the company or at least the way we think about the company is as Peter said a technology provider. It's kind of like uh you know what Nvidia does or what an AMD but we just don't do chips. We don't do silicon but we're a technology provider fundamentally and I think even you know we used to joke when we started the company like you know we're not the guys to build like Instagram like that was just not our that's just not us in a most fundamental way. I mean

Host: you have thoughts.

Peter Lewig: Yes. Well, it's it's I mean I think it's just like what and and and I mean we worked on maps and stuff Google maps consumer products are extremely difficult for a lot of different reasons. It just I think doesn't scratch the itch. I think we're like Michigan guys who are kind of more in that traditional engineering kind of a realm uh or lineage. I you said Joe I got to say that what was clear 10 years ago was that

Kasar Younes: there was so much more that was possible with software and AI and vehicles and that was generally the space that we started in 10 years ago and the precise path that we've taken over the years I think we've been strategic and we we've adjusted to make sure that we're actually building stuff that's valuable to the market and like the technology has changed so much like our own technology stack has has completely changed I would say roughly every two years and so now we've probably done let's say four complete evolutions of our own techn technology stack and I sort of see that cadence roughly keeping up and and so the way even we think about engineering is almost on this 2-year horizon we're preparing ourselves that hey like we want to invest the appropriate amount but then also be very dynamic as the research gets published and as our research team figures out new advancements and adapting to that.

Peter Lewig: Yeah, one thing that has been consistent is the type of people we've recruited. Frankly speaking it's engineers who are fall into the you know sometimes very traditional like you know Google gen suite um but way different from you know other companies we are hiring folks who really know the intersection of hardware and software who know systems obviously traditional ML researchers and folks who uh actually you know put ML systems into production that's been pretty consistent I I think that like you look at the mix of our engineering 83% of the company is engineering. So it's like a giantly a small lot of engineers

Host: which by the way a thousand engineers I mean that's on your website so I imagine it's up to date.

Peter Lewig: It is it is up to date. Yes.

Host: Okay. And then 40 plus founders.

Kasar Younes: Yeah. You would also uh this was more luck than than strategy. Um but we've recruited a lot of exfounders. It's been a great place for founders YC and non because obviously I know a lot of the YC folks. It's kind of like we recruit a lot of Google people for for them to exercise both their technical and non-technical skills because you know we're we're we're on the applied side. We have a research team that we do fundamental research. We publish and we we've had great traction there. But fundamentally the business wants to take this intelligence and deploy it into production and and there's like a certain type of person that's more interested in that.

Host: Yeah. You mentioned the tech stack, Peter. Uh so I just wanted to give you some rain to just go into it. I'm interested in where applied nutrition uh starts and ends in in in some sense. What won't you do? What uh do you do that's common among all the verticals that you cover?

Peter Lewig: There's a few buckets of of work that we do and we've been at this for almost 10 years now. So the technology is pretty broad. But uh we got started

Host: with a thousand engineers like you can work on lots of stuff. Yeah. Especially with AI tools now. Yeah.

Peter Lewig: So we got our start in in simulation and simulation tooling and infrastructure. And so generally if you're trying to build a very complex software system that involves moving machines, you need to test that. And the best way to test it is it's a combination of virtual developments. So simulation and then also obviously real world testing and then there's a very careful process of that correlation between the simulation results and the real world results and ensuring that the simulator is in fact accurate to that. Simulation is a a very deep topic. We have a whole whole suite of products in that and and we could talk for many many hours about that specifically. Um but that that is one part of what we do as a company.

Peter Lewig: Reinforcement learning as a sub part of that is also super critical. I think a lot of the a lot of the best advancements happening in in a lot of these AI systems right now in some way relate to reinforcement learning and with now we have lots of compute and you can do tons of interesting things reinforcement learning. The second bucket of work that we do is on operating systems technology. a true operating systems like think about uh schedulers and and memory management and uh and middleware and message passing and uh highly reliable networking and data links like the reality is if you want to deploy AI onto vehicles you need a really good operating system and when when we were getting deeper into that space there wasn't really anything that we were happy with like things existed absolutely and we were using what was available in the market and as an engineering organization, we roughly realized these things aren't great. We think we can do this better. And so let's let's build something. And that was then the that was the the moment of inspiration that started our operating systems business, which is now a very real business for us. And in order to write and run great AI, you need a great operating system. And so that that's what got us into that. And then the third bucket that we work on, it's it's true fundamental AI technology models. We do a lot of work in as mentioned the foundational research but then the also the uh the world models the actual autonomy models that are running on uh on these physical machines as that's across cars, trucks, mining, construction, agriculture and and defense and uh and so that's both land uh air and and sea

Kasar Younes: and also uh a smaller subsect of that third bucket is the interaction of humans with those machines. So, uh, that's a multimodal, uh, experience. Historically, if you're moving a dirt mover or any of these machines, there are like, you know, buttons you press, whether they're actual physical tactile buttons or or something like a touchcreen. That's just that fundamentally is changing to where you're just talking to the machine and the machine and you're teaming with the machine.

Host: Voice.

Kasar Younes: Yeah. Voice. Absolutely. Yeah. And also the machine just being aware of who's in the cabin, what their state is. Um, you can think from a safety systems perspective. The most simple version of this is like the driver is tired, right? They're there. You know, if you get those alerts when you're driving your cars and maybe take a coffee break, that take that times, you know, a couple of order of magnitudes up. But this concept of teaming man and machine is important. when you think about running agents or just running you know different instances of uh you know claude and doing work for you in the background you you can take that analogy I almost copy and paste and put it into like a farm where you have a farmer who's running a number of machines so where they interact with the machine is where there's maybe a critical decision or a disengagement or something but generally speaking the agent on the physical machine is running and making decisions on the behalf of the farmer until there's something maybe you critical and that that that's also what we work on. So that's not pure autonomy. It's a little bit of a mix, but it falls under uh autonomy in the automotive sense. That's typically defined in SAPE levels as an L2++ system. We have a human in the loop, but just take that idea, you know, to to other verticals.

Host: Yeah. You've not mentioned hardware at all like sensors or, you know, obviously we you mentioned you don't do chips. I think even in AV there's like a big uh you know cameras versus lightars like what what are like in your space maybe some of those design decisions that you made and are they driven by the OEM's ability to put things on the machinery like how much influence do you guys have on co-designing those?

Peter Lewig: Yeah. So so we don't make sensors like we're not a manufacturer obviously we use a lot of sensors in our autonomy products. Um, in terms of what actually goes on the vehicles, we have a preferred set of sensors that that we uh, let's say fully support and then our customers, they can sort of choose from those and obviously if there's a a very strong opinion on supporting something else, we we'll add that to the platform as well.

Kasar Younes: And the the LAR question is at this point sort of the age-old uh, topic in in autonomy. And the the state of the industry right now is LAR is hands down a useful sensor. Uh specifically for data collection and the R&D phase of autonomy development. Um if you see for example a a Tesla R&D vehicle, it actually has LAR on it to this day. Right in the Bay Area, we see these uh you'll see like Model Y's or Cyber Cav that have have lighters on them just driving around. So it's it's useful because it gives you per pixel depth information. So if you can pair a LAR with a camera and you can say that well this camera is looking this direction, this lighter is looking this direction and now for each each pixel of the camera I can see how far away is that pixel. Um you can actually then use that as a part of your model training and then the that depth information then becomes a learned uh a learned state of the camera data and then when when you're doing the production system you can now remove the LAR and and now you can actually get depth with just the camera. And so that that difference between like a highly sensored R&D vehicle and then the downcosted production vehicle, we use that across our whole portfolio of products. Um and of course the end goal is you want super low cost and super reliable and uh and then in certain use cases you have some more bespoke things like in in defense as an example you do things at night often times and so you care about sensors like infrared uh more so than and you don't you don't want to be putting energy out so you don't want to use LAR or radar but you still need to be able to see at night time. So yeah, we we work the whole gamut.

Host: Cool. So that's kind of like on the hardware level. Then on the OS level, how does that look like? What is like unique? I mean, my drive I drive a Tesla. Whenever I drive some other car that has a screen, it always sucks. It's some like cheap Android tablet is like it's laggy and all of that. What does the OS of like the autonomy future look like?

Peter Lewig: When most people uh it's sort of like what you just described. When you think about operating system in a vehicle, you're thinking about the HMI, right? The human machine interface. And absolutely that's a an important part of it but that's actually only one thin layer on top. Um, so when we talk about operating systems for AI in vehicles, there's many many layers that go deep into the safety critical realm and embedded systems and you're talking about the the real-time control of let's say the electric motors or the the engine and the actuators and you have different redundancies for uh for different let's say the steering actuation in the vehicle and all of these things uh need very core support in the in the operating system. And then of course for autonomy you have real-time sensor data that's streaming in and the latencies there are really important right if you try to imagine you try to run Microsoft Windows uh like streaming your sensor data in or controlling the vehicle like the latencies are going to be absurd like you can never do that and so what's special about what we do is we really have this system level thinking right so we're looking at we care about every performance characteristics of the entire system and then we also because we're doing a lot of the software or all of that software we can fine-tune and control all those things. So we can very very carefully tune in the latencies for every aspect of the system. We can carefully tune in the memory management. We can have the right uh fail safes and fallbacks uh for for different things because you have to account for what what if there is a critical failure. What if there's a cosmic ray that flips out a bit in the middle of the processor that causes some uh malfunction? Uh and you have to have a fail safe to all of that. And so the the core operating system is a part of that. And then the um the one last thing which is a lot less exciting but is actually a very big topic is reliability of updates,

Host: right?

Host: Uh so the I have a a Tesla and uh you get updates fairly frequently, right, once a month.

Peter Lewig: Most companies that are making vehicles, right, are basically never doing updates and they're and even if they are doing updates, they're usually only updating maybe one module. Maybe they're updating the the HMI module, but they're not able to update, let's say, the the safety critical parts of the system. you have to go into the dealer for that. And so with our operating system now, we can actually enable highly reliable updates of any system in the vehicle. And that's way easier said than done. Like there there's lots of technical technically deep stuff in the tech stack to to do that in a way that you're not going to accidentally brick a vehicle. And right if imagine you're read

Kasar Younes: Yeah. Bricking a car is a very expensive uh and honestly across the industry maybe one of the most just pure impactful things that we've done is we've just we're now enabling the industry to actually do software updates. Just to clarify as well who is the customer for this like uh I assume a lot of hardware manufacturers have their own firmware and I'm sure some of them would just have you write it for them because you're experts. Uh and others would have their own. It's like who pays for this? Who who invites you into the the house? Is it is it the end user or is it is it the manufacturer?

Peter Lewig: Yeah. Yeah. So let me make an analogy firstly on the on the fragmentation of software. So physical machines today are more akin to the state of the phone market before Android and iOS existed. Right? So uh I worked on Android at Google by the way many many years ago. And uh and part of the the reason that that Larry at Google decided to get into Android was they they wanted to run Google products on a bunch of phones and they they bought all of these phones from the industry. And it turned out they had like 50 different operating systems on these phones. And it was virtually impossible for for Google to to make their app run on all 50 devices equally well. And so the solution was well actually what if what if they created a really great operating system and and made it attractive to all of these phone makers. And that was sort of the genesis for what Android was and and why Android existed. It was a way for Google to get their products onto really wide diversity of devices. The state of of the physical uh industry right now, it's a little bit like that. Like there's yes, these companies have firmware, but they they have so many different operating systems. It's so fragmented. And to actually get a modern AI application to run on these vehicles, you actually you first have to consolidate the operating system. And so that's that's why we've done that.

Peter Lewig: And then uh your specific question was who are our customers? It's it's generally it's the companies that are making these machines. And you know, we're we're we're selling our technology to them to really simplify the architecture and then enable these applications to run on them. How much is reusable across like do you have like one OS that is just configured for everything or is there some more customization that it's needed?

Peter Lewig: Yeah, highly reusable. So the um the the fundamental technology is is quite universal, right? So things that we do have to think about though are like chipset support. And so, um, if you're if you're coding, let's say, uh, an LLM and you have start with an assumption that, hey, oh, I'm going to I'm going to use CUDA and I'm going to run this, uh, on an NVIDIA chip, then you don't really have to think about the hardware in that sense. Like you you're just, okay, I'm just I'm in the CUDA NVIDIA ecosystem and I'm I'm going to to use that. But the the hardware, especially in safety critical systems, it it's a lot more diverse. There's not one one or two players. There's a a bunch of different chipsets that we have to support. And so our operating system doesn't just run on like the equivalent of x86. It has it has to run on a number of different architectures from chips from a bunch of different companies. U but again we've been working on this for a long time now. So we have we have support for all of those those chipsets. And then when you want to then run the a applications we can then do that reliably across now a variety of providers. And I think that is like heavily inspired by Android right? Android has a huge suite of testing and uh and it's a reliable operating system that runs on thousands of devices and you know we think we can we can do the same in in all these physical moving machines with the difference that we're really in a safety critical realm. Android isn't. So on Android I don't need to use Gmail. I can use a superhuman like what about your machinery? Like can people bring somebody else's automation to it or is it kind of like allin-one?

Kasar Younes: You have to use I mean if uh Yeah. Yeah. It's totally open. Yeah.

Peter Lewig: Yeah. Yeah. Our our our philosophy is that we are a technology company and so we we license our technology to customers to use how they want and so if a customer wants to if they want to license our autonomy tech and our operating system then great we'll license those. If they just want to license the operating system and then use different autonomy tech that's fine also and we have great documentation to use developer tooling. Yeah exactly. Yeah, it's like a better together if obviously uh if you if they work together. Is it all C++? I I assume is with different compile targets.

Kasar Younes: We use a lot of C++. Uh I mean Rust is is sort of a hot the new hotkit on the block for for a bunch of things as well. Uh but yeah, when when you the lower level you get especially when you when you get to realtime constraints, you hit C++ at some point and at some point maybe you work your work work your way into assembly when needed.

Host: Oh damn.

Host: I'm curious about the um coding agent adoption just like since you're mentioning more esoteric languages like what's the adoption internally? What have you learned?

Kasar Younes: Yeah, we we use everything. Um I mean so Cursor was I think the the hottest tool in the company for a good while now. Claude code I think has has taken the the reign on that. We have a internal leader leaderboard that we use just to sort of encourage adoption uh with within the company and uh yeah they're phenomenally useful. I I mean it's uh honestly we we take inspiration from from some of those tools also and how we're adapting some of that mindset of thinking to the physical realm like oh if it's so easy to to build an app for this or that thing that lives just on a screen we can we we're taking out a lot of those same ideas and and applying that to okay well if you wanted a physical machine to do something how easy can we make that uh using our own tooling and platform as well. Are you changing any of like the OS architecture kind of like the way you expose services to like be more AI friendly or

Peter Lewig: Yeah, absolutely. The uh in the early days of our tools and infrastructure work, it was a lot about um you had engineers that were experts in certain topics, but the things that you're dealing with, they're often times more mathematical or more abstract where actually guey tools are very very useful for certain things. As an example, we have a a product we call Sensor Studio which is uh it helps you design the sensor suite for uh for your autonomous vehicle whether again it could be a car, it could be a a drone, could be a mining mining equipment, could be a robot and and you you place sensors in different places you there's different uh there's a library you can understand what are the trade-offs that you're making in in the design of that system and that was like a very a very gooey intensive uh thing because it's a little bit more like a CAD tool in that sense. if you've seen CAD tools. Nowadays though, uh we we expose all of the underlying APIs for that and and now using uh AI agents, you can actually configure a sensor suite with just text and and likely reach a better result than you could have through the guey in the past. And we're taking that thinking now through the whole product portfolio.

Host: The another thing I was thinking about is just in terms of like AI uh adoption, does that change your hiring at least a little bit or how do you how do you sort of manage engineers um differently?

Kasar Younes: Yeah, absolutely it does. Um we I think like every company in in the valley right now are evolving our our hiring practices um because the the skills required to be effective are changing so fast, right? I mean you used to really select for just wrote implementation ability and and now it it is more the AI engineer skill set right where it's like yeah you know how to implement but actually just banging out code is is no longer the core job right it's it's actually knowing what questions to ask knowing how tie how to tie together these different AI tools and so the the interviews that we give now I think are way harder than they've ever been but but we also allow right selective use of AI tools to solve problems. And I think in that you you start to see more of a biodal distribution of engineers, right? You you start to see like, wow, there's there's this subset of of people that they they really get it like they're they're all in and they they've clearly invested the the hours needed to learn these tools and and how to be effective.

Host: And then there's sort of the the group of people that haven't done that. And that the productivity gap is just enormous. And so we're trying to obviously select for the people that are are really really into this.

Host: I first wrote the my AI engineer piece 3 years ago and when I first wrote about it I was like actually not everyone should be an AI engineer because I think there's there's an extremist stance where well every software is an AI engineer is an AI engineer and my actual example of people who should not be adopting AI was embedded systems and operating systems and database people are they adopting AI? I think it's the classic bitter lesson uh topic which is the um six months ago I would have said the same thing but it's it's becoming super useful for every domain I'm sure right like

Peter Lewig: there was uh I think 6 months ago or maybe maybe a year ago if you tried to use let's say the latest cloud model for writing shaders uh GPU shaders the results were probably underwhelming and if you use the latest model now to do that kind of task you're a little bit blown away like, "Wow, that actually worked. That's amazing." And we see the same thing in in the embedded realm. The um no question though, especially when you get into safety critical systems, the the human validation is is 100% key. Um like I I you're not going to trust your life to uh AI written software that's that's not been very carefully uh checked by humans. And so I think now the um really the challenge is about that appropriate level of of human validation for for these safety critical systems.

Host: How do you think about Yeah. touching on the simulation side? I think verifiable reward and reinforcement learning is like the hottest thing. What have you done internally to build around that? And like what what gives you what makes you sleep at night? like if somebody's like you know just vip coding something or like wants to try something new you have like a good enough system because I think the opposite is also true is like if it's super easy to write anything then it puts a lot of work on like the verifiable yeah side of it like what does that look like for people

Peter Lewig: yeah yeah so so verifiability broader bucket of like evaluations right how do you evaluate the the results that you're you're getting I think this this is probably the hardest problem right now because the as the models get better, it can be harder and harder to find the faults in the system. And so like the problem of doing proper eval to find those faults, uh like that problem also keeps getting harder as as the models get better, but it's no less important than it's ever been, right? If you still there are still going to be edge cases that are not met and and whatnot. And so it's it's a big area of investment for us. the um on the reinforcement learning topic, I mean the key thing is there's all these new requirements that that come to be in in the latest generation of of these technologies. So for example, end to end is the big thing right now in in autonomy and physical AI, which is you can now train these models that can effectively take sensor data in and then put control signals out and get really good results out of that. But the way that you train and improve those models is really different from uh from the previous generations. And so to do reinforcement learning on an endto-end model, you now need to actually simulate all the sensor data. Right? So then this becomes we we call our our work in this neural simulation, but it's think of it like a a hybrid of Gaussian splatting and and diffusion methods and uh where you really care about performance. Like performance is is everything. If you can't do enough simulation fast enough and cheap enough, you actually can't get results that are are worthwhile in the end. It also gets gets to a lot of our work in embedded systems, which is like performance critical work. And that that performance optimization, performance criticality, it carries over to a lot of the the model training work um because the only way to make it affordable is it has to be really fast.

Kasar Younes: I think it's worth a few minutes talking about our own uh evolving thoughts on verification and validation within kind of uh traditional simulators which are you know uh you can think of like vehicle dynamics or something like that which are taking textbooks and taking those formulas and putting them into software to like now this neural sim/world model universe uh I think that's an interesting topic.

Peter Lewig: Yeah. Yeah. So, so in um in more traditional development, right, you you often times would have uh more black and white answers to questions. And so the uh in in Europe, as an example, there's a regulatory system. It's called Euro Encap. It's the European New Car Assessment Program. And as part of that, the vehicles have to pass a bunch of tests. And those those tests actually uh in include um safety systems. So, automatic emergency braking for a child that runs in front of a car uh or let's say an oluded child that runs out and and you hit it and and so you have you end up with sort of these binary answers of like well did did the car under test pass this specific test and there's a very very well-known set of of test cases that the vehicle has to pass and that was how the industry worked let's say uh until 10ish years ago but what's changed now is with these models everything is stat statist statistics, right? Like you no longer have a black and white answer, but it's like, well, how many orders of magnitude or how many nines of reliability can can I get in the system and how can I how can I prove that to be true? Um, and uh and and the big unlock honestly for physical AI as as an industry is that these models are just becoming much more reliable, right? Things like things actually work a lot better. like the number of nines you can get out of these systems are are now good enough that it actually becomes cost- effective to to really deploy these things. And so the the big shift in in so verification validation has been from a little bit more of a in the past it was strictly requirements and are you meeting or not and and now it's more of a statistical uh verification and validation case where it's all about how many nines are reliability and and meantime between failures that sort of thing.

Host: And is the target audience regulators or even the customers are few I imagine the customers are bought in and it's mostly regulators that need to be satisfied.

Kasar Younes: We do work with the US government. We do work of course with the European governments and and the government of Japan and um the government is not like an AI lab by any means. So

Peter Lewig: they just care about the outcome. they hear about the outcome and and so we we do education uh in in that regard and like s sort of teaching about hey this is how we think validation should be done and this is an approach that that we think is is reasonable and how to think about like when is a a driverless system actually safe enough to to to go on the roads and that that sort of thing. Um but uh I wouldn't say that the government is asking for it. It's like we're more teaching the government in that in that sense. It's honestly it's more so for our own our own comfort, right? Like we want to build very safe systems and then of course our customers care deeply about that as well. But in that context we're also typically educating our customers.

Kasar Younes: Yeah. Our first I mean uh uh our first core value is on round safety. So uh I think we can't underline enough that uh us also verifying and validating that the systems that we're deploying are safe to to us is probably as important as like some regulator or a customer saying you know.

Host: Of course. Okay. Yeah, you have to satisfy yourselves.

Peter Lewig: Yeah, as I say as as a whole across the world regulation often times it's like a almost lowest common denominator but like you really have to substantially exceed what the regulators are expecting to make good products.

Host: One thing I often talk talk about I think and I try to make this relatable to the audience also is Cruise where they had an accident that basically ended the company. I wonder if people overreact to a single incident because incidents are going to happen regardless, right? Because it's a statistical thing, but as long I don't know if regulators understand that uh you cannot extrapolate from a single incident, but we do because that's all we have to go on and your sample sizes are necessarily going to be lower than I consumer driving.

Kasar Younes: Yeah, I think the the the cruise example wasn't a technology failure. Um there was the the real uh compounding issue there was just how did the company talk to the regulators and and what was their kind of behavior and I think that became more of the issue if you look you know isn't it definitely was a technology failure but it was made much

Host: yeah yeah let me put it another way there is a version where crew still exists

Kasar Younes: right it was like the last straw in like a long chain of like ATG had that horrific accident or someone actually dying uh because you know uh that was a homeless person crossing the street. So yeah, I think I think uh we can't understate enough that ultimately like statistical validation of something that's one part of it but it's not the only part of it like consumer and let's say mainstream adoption of these technologies is also going to be part of that conversation. I think companies like Whimo are doing a lot of, you know, service positively to the industry in the sense of they're they're setting a high benchmark and they're showing, you know, kind of in a very responsible way how to how to deal with these. There have been way more incidences as well. They've just not been as significant as as the cruise one that you mentioned. But yeah, so I think I think you'll just continue to see that. I think probably the long-term question is really going to be again around like it is very clear humans are way worse drivers statistically. Yeah. Like there's no there's no debate. And so at what point we're emotional animals?

Host: Yeah. So my thing is like we have to get to a point as a society where we accept horrific accidents that would never happen by a human because statistically we understand that it is safer overall. In the same way that planes they're safer uh than I think they're the safest mode of transport that we have.

Peter Lewig: Yeah. I mean it's more dangerous to drive to the airport than it is to get the flight. So, so if you're ever if you're ever getting nervous about getting on a plane, just think I just got to get to the airport.

Host: If I get to the airport, I'll be good.

Kasar Younes: But then that's planes also concentrated terror risk and if if planes Yeah.

Peter Lewig: And I was I I I don't think we honestly have to worry about there ever being accidents from these systems that are much worse than what humans would cause because humans do do terrible things. People fall asleep at the wheel all the time.

Kasar Younes: I have I I've been a drowsy driver. kind of like drunk drivers and that's that's the extreme end of the example. But um I mean these AI systems you have redundancies, you have fallbacks like there's many many things have to go wrong for there to actually be something catastrophic because there's there's so many uh fallbacks that these systems have.

Host: Yeah.

Host: I mean your simulation is like so vast because there's so many use cases. What are like maybe things that worked in a simulation and then you put it out and it's like this just this just did not work at all. Yes, maybe a bit of a misconception uh about simulation there. So, let me go a little bit more more technical on this. So, um at first go, no simulation is is going to represent the real world. There's always a process of this uh sim to real matching where you actually you need the real world feedback to basically feed into the parameters that are being used in the simulator. And you have to do that. It's like this validation flow uh a number of times until you can get some confidence that oh I I think the simulator is now accurately representing what's going to happen in the real world. Now if if you have a situation where you've done that full validation and and you thought that it was accurate and then there's something different. Those are much trickier cases and that's that absolutely can happen. But really the validation process is a really important part. you can never skip the simulation validation process like where you're actually ensuring that hey actually my sim real gap here is is small enough that I can trust these uh these simulation results and and there's there's so many fun things that you can do when you get into it like I'll give one one fun example that came up recently is like in these uh these humanoid robotics uh systems overheating actuators is a real problem right so uh obviously phenomenal demos I the most amazing the most amazing I can get I I love watching robots do acrobatics like everybody. But these systems actually overheat, right? If if like and one of the ways you can use simulation though is you can actually have that the temperature of those actuators be one of the parameters that's represented in the simulation. And um and then if you're doing reinforcement learning over a certain task, then the robot can actually adjust its motions in the simulation to account for the fact that oh, it knows that as it's moving, it's actually beginning to overheat this this motor. But if you didn't have that parameter of let's say the heat of that motor represented in the simulation initially then your RL policy might it will disregard that and now you run that on the robot and the robot will overheat and and fail.

Host: I guess the question is like how do you have all of these parameters taken care of while also understanding the deployment environment like temperature is like a great example right well

Kasar Younes: why did you make my robot worse when it runs in like a freezer so it actually shouldn't worry about that you know it's like yeah how do you design these simulations

Peter Lewig: this is honestly the the this is what makes simulation so hard right uh it's because you simulation is is fundamentally about you're trying to optimize the development of a system right how can I build the system faster and better and cheaper and and what are all the levers that I have to actually accomplish that and and because simulation's just a software program you can you can change it a lot more easily than you can hardware systems and then what's particularly awesome about the let's say world models and using that as a part of simulation is now the simulation doesn't just scale with let's say adding new math equations in but we can actually scale the simulation environment now with uh with additional real world data and and that That also unlocks I would say a whole new field of robotics.

Kasar Younes: There is a meniscus line where you cross where still doing real world testing is better. There's a in this center wheel gap you can reproduce reality at exceedingly expensive costs and nothing is free. So really you you finding that line where you're getting great performance, you're getting great feedback, whether it's on the training side or on the eval side, but it's way cheaper than doing in in the real world. At some point that doesn't make sense. And so even, you know, from our earliest days in autonomy, our view was you're still going to do real world testing. There's there's not there's not this, you know, magical land where you're not going to do that. And uh maybe even like a more nuanced version of this in like traditional software development is you know most of your testing for software in a vehicle 95% of that can be like traditional CI/CD kind of flows that you'd have in traditional web development but once you have now you let's say you have a truck you can do like 4% of those in like a rig which has all the components the electrical electronics of a truck but doesn't have it doesn't have the the tires and it doesn't have the physics and then you have the 1% which is actually the vehicle. There's something sim there there's a similar analogy in terms of using simulation for intelligent systems. you do a lot in a simulator but um and and using world models uh but ultimately it's it's physical AI so you're going to deploy it on physical machines and the freezer example comes to comes to light

Host: the world model thing has been to me the hardest thing to wrap my head around like we have fill on on the

Kasar Younes: we've been doing a small series of like and other intuition companies general intuition as well uh yeah I mean lots of lots of coverage on nerfs and

Host: yes it it feels like like we talked about um the heliocentric system, right? It's like in a world model, if you just feed visual data, the model might learn that the sun spins around the earth, it makes sense, right? And it's like, well, not really. And I think what are like some of these other things that like like hydroplaning is one thing I think about. It's like, can a world model understand hydroplaning and like what amount of water like causes it to happen? And it's like, yeah, to me it's like I don't understand how you guys do it. I guess is like the the real thing is like when you're doing both cars and the highway in Japan versus the you know excavator and a line in uh wherever you're Arizona wherever you're deploying them how much of it are you relying on the world models to like generate the simulations for you and then try and close the gap after versus like giving the world models as a tool to your engineers to like curate the simulations if if that makes sense.

Peter Lewig: Yeah totally. So um I can say at a pure engineering level I think if if you're hoping to do real world deploys and you're purely relying on a world model approach you probably won't get to something that works uh before you go bankrupt. So there is just a very practical mindset of like hey world models are are amazing and they're extremely useful for a lot of use cases but there are a lot of other things that you need to do to actually get something started and something deployed and working. Uh most fundamentally role models are all about it's understanding the world but also understanding what's going to happen. It's like the cause effect relationship right and so like right if you have a take some sort of construction tool uh and that construction tool is going to be doing some work on the earth in in some way it's going to be move moving earth um the world model needs to understand that cause effect relationship like okay when I when I take this material from here and put it over there now I have things that are over here and and not over there anymore and that that cause effect relationship. Um, data obviously is a is a big problem. The hydroplane one is is actually a really great example because it's actually quite non-obvious sometimes, right? It's like, well, it's it's raining and uh and well, this this road uh has, let's say, the appropriate curvature to it, so the the water is running off the road and cars are driving faster here. And then you approach a road that's very flat and water is now puddling on that road and all of a sudden cars are driving slower because when they were driving faster they were starting to lose control. Um and uh there are a lot of visual nu uh very nuanced visual cues in the scene. And so I do think in in the world model concept there's a good chance the model actually would learn that you should just drive slower when these visual cues exist. And that's obviously the be the beauty of uh these kinds of models, right? just they learn these non-obvious things.

Host: It doesn't need to know about hydroplaning to know that it needs to drive slower. I guess it's Yeah. Yeah.

Host: want to ask questions about uh also the deploying models that presume like you you use a lot of these models for training data and simulation, but what about deploying it onto the systems in in production? Presumably, you have you have like GPUs on device, but they're I keep saying on device. What's what's the right term for machine?

Peter Lewig: We're embedded. Yeah.

Host: Yeah. Yeah. What what is the embedded world like? Because for for for people who are not used to that world, this is very alien.

Peter Lewig: Yeah. Yeah. So, so actually we we call it onboard and offboard. So, um onboard software and offboard software. And

Kasar Younes: the the great thing about offboard software is you don't have to care about time. Uh and you can run really large models, right? So, you can you can say, well, this this model, I don't care if it takes 1 second for it to give me a result or 10 seconds for it to give me a result. uh because we have time and the models can be really big and they can run uh in the data center or on a on a huge GPU and and you can obviously have distributed compute etc. But on board you don't have have any of those benefits. like well I need I have this many milliseconds where I need an answer from this model and so a lot more of the energy then is about think of it more like distillation and and it's like truly efficiency and like literally every every fraction of a millisecond counts and and uh and you you you can't have a situation where the model takes too long because then the vehicle can't actually function. Yeah. And so you can you can still use a lot of the same techniques uh and and the the models themselves you can think of as like a derivative of of larger models that you can run offline and then you're you're trying to just get a model that is still performs really well but it's it's a it's smaller small enough version that you can then run on this embedded system where you care about latency and power.

Host: Yeah.

Kasar Younes: Yeah. And I think like the the broader point I think uh which which uh maybe is not obvious but it's worth saying is in the physical AI world we're not really constrained right now by like the intelligence of the models. It's actually what Peter's talking about is actually deploying them in

Host: the hardware they give you.

Kasar Younes: Yeah. On the hardware you give you and and so and there's a reality is of safety critical systems. So those end up being your your limiting factors rather than let's say a limiting factor for you know a foundation model company is going to be just capital maybe you know or or or researchers. So we're we're in that way dealing with you know for us as people who kind of come in that realm of like a very interesting those constraints force creativity

Host: and I imagine you know nobody was deploying or giving you the hardware for transformers back in 2018 whatever but now they are. What's the evolution like? Uh just peel back the curtains a little bit.

Peter Lewig: Yeah, transformers first off. I think the paper was originally published in 2017. So there's no time

Host: and uh I

Host: but I'm just saying I guess I'm saying like you know embedded ML systems usually like a lot less parameters, a lot less compute and now like orders of magnitude more.

Peter Lewig: Yeah. Yeah. Absolutely. Um, what I was going to say though was I think in the uh in the original paper in 2017, maybe it's in the last paragraph somewhere in the paper, they talk about like by the way this this this technique might might be useful for like images and videos as well. And uh it took a few years for that impact to really hit. Uh but like now I we're seeing transformers are everywhere.

Kasar Younes: Transformers and uh the um yeah then the compute just keeps getting better and better. Um but you do have this fundamental trade-off, right? It's like you have power, you have cost uh and and performance and and like getting the right getting the right mix of those things in an embedded package that can also be like shaken and and baked and all the conditions that these things have to have to operate in. But yeah, I think that they're only going to keep getting better. And so we also try to plan our strategy understanding that uh we know the rate of improvements of these systems.

Host: Yeah. So like Google just released the Gemma 2B model like effective 2B model. Is that useful to you guys or is that too big? You can run that model on an embedded system definitely. Yeah. Um the um so so yes it's it's useful in in that regard. The bigger question is like what do you use for an embedded system? Like you actually need to customize it quite a bit to make it useful for for something. But uh but yeah you you could run a two billion parameter model definitely.

Host: Also interesting like what percent is a custom ML model that only does that thing versus a generalist LLM? Uh which probably is not that useful actually for your context. Like you can imagine different use cases, right? So the

Kasar Younes: the voice stuff, yes.

Peter Lewig: Totally. Yes. So for the um the actual uh autonomy elements, I mean that's 100% in-house, we do every bit of that. The data simulation, the model, everything. Um but when you get into the more generic use cases like voice or voice assistant kind of thing, that's where these these more generalist models like GMA actually can be quite quite useful.

Host: Yeah. And then there's also obviously a trade-off between like what percent must you do on machine uh versus just call home.

Kasar Younes: Yeah. It's all about latency. It's all the latency. Yeah.

Host: Yeah. Well, like you know I think actually in a lot of context especially in the US you can just have a connection to to the web.

Kasar Younes: Yeah. I think I I think though most of our universe is everything has to be fairly uh uh uh you know embedded and local because just the nature of

Host: even in the US there's a lot of like still have coverage right and if you look at like uh the old world of autonomy within mining which is like long before transformers and and and kind of a neural networks uh in the like CNN and kind of a universe they were really just handcoded, you know, systems. They were just like this machine is going to run to that place with this RG accurate GPS.

Peter Lewig: Yeah. And so that worked and that works for 20 years. So why would we actually need to use transformers or kind of more more modern end to end systems? Mainly because you can only really run a path and run backwards. That provided a lot of value, but not as much as you get when the machine is actually intelligent. It's it's seeing, it's perceiving, it's acting in a dynamic world.

Host: I looked up RTK, realtime kinematic, uh, one to two centimeter accuracy.

Peter Lewig: Yeah. Yeah. Fantastic. But, you know, and fantastic in far away lands where there's not going to be cell phone coverage.

Kasar Younes: Yeah. Same thing. It's widely used on the legacy mining and agriculture autonomy systems today. So, like, uh, for example, a combine that can be precise within one or two centimeters as it's driving down the the field. Uh, they use RTK. It's expensive.

Peter Lewig: Yeah. And it's it's autonomy, but it's not intelligent in the way that I think all of us if in 2026 would be talking about intelligence.

Host: In one of your blog posts, you mentioned research on large scale transformers that are similar to those doing modern generative AI. What are like the big differences other than you're absolutely right. I should steer the car. So I don't you probably want to remove that. We have a diversified bet strategy internally and and the reason we we've done that is because we operate in now a bunch of industries, a bunch of geographies and and each of the approaches has honestly a different risk to them. And so uh like we're not going to put all of our eggs in in a single in a single basket for a single approach because that approach may not work out.

Kasar Younes: Mhm.

Kasar Younes: Uh and so that's that's one of the bets that we have and it has certain advantages in in certain scenarios and then but the way that these things play out in practice is it has certain benefits. It also has certain drawbacks and and then and then the research team tries to then work on uh the situations where that's actually worse than than these other approaches and uh to ultimately arrive at at a really great solution for all these things.

Host: Is there a plan mode for physical autonomy like do you have a planning step and then action step or

Peter Lewig: so short answer is yes. Right. Right. So just like you can use uh cloud code to to plan out some complex coding task and and you get some almost specification written out. Those similar similar approaches absolutely can be applied to physical systems because imagine you're trying to accomplish some task. The easiest to think about is is robo taxi. But I think things get more interesting let's say in the defense context or in uh in the in the mining context. You actually do have to think about many steps in advance. It's it's not just this one thing, but to accomplish the goal, there's a hundred steps and then the this concept of the plan mode, it's yeah, very applicable in this.

Host: Yeah. I I was going to say to me, driving feels like a great next token prediction thing because you're kind of like on a path and like it doesn't really matter what you've done before, you know, you can always turn around. Yeah.

Peter Lewig: Yeah. Versus like mining, it's like, oh man, I took a, you know, I took a scoop out of this thing. It's like now we can't really, you know, I can't really go there anymore, you know? It's like is there like a huge different like how would you I guess like do you have like a tonomy of like these different types or there's kind of like driving excavating like flying

Kasar Younes: so the interesting thing is I think probably everything in the world can actually be boiled down to like a next token prediction problem um and uh any workflow anything uh can be thought of almost as like there's this the sequence of steps or the sequence of trajectories or whatever you want to call it and it can be boiled down actually to to that sort of thing and in the mining case you can imagine like taking that scoop okay that was that set of of tokens and and now that's the model is now understanding that okay that the state space is different and and now the next time I do token prediction it's going to going to be modified by that but yeah the remarkable thing about these techniques is just how how universally applicable they are right I mean it's it's truly is incredible

Host: what else is underrated about what you guys are building on the physical I I think there I mean we were talking about it before the episode. There's a lot of humanoid companies that do these great demos. Um and then I can't buy it so obviously it cannot be there. In your case, you're like in production on real streets with like a lot of customers. What what are like the things people are underestimating the same way the way demos seven years ago were great and then took seven years to actually get them on the street. Can you share about maybe like the last 1% that was really hard to to get done technically?

Peter Lewig: Yeah. So certainly productionizing stuff is really challenging no matter what. Um so I I maybe would I would split the answer maybe into research and then also in into production. First on the production side there's just so many problems that you find when you actually get the stuff to to go in the real world. And so I mean the classic problem in in humanoids right now is these systems are actually pretty brittle. Uh and so um I'm not talking about any one company but just as an industry these systems are are pretty brittle. I mean interestingly I saw this thing uh the other day that uh I think China is doing a a marathon with humanoids.

Kasar Younes: Yeah. Yeah. So so in in government and not China specifically but in any government there is a um there's a concept called uh prize policy which is so that there's there's different ways of of influencing an industry to go a certain direction like you can you can regulate it right you can do mandates or you can actually just do these competitions. So the US version of this was the Darper Grand Challenge.

Host: It really worked industry, but I think China is literally doing this marathon because they know that reliability uh of of humanoids is a problem. And so what cooler way to solve that than to have a competition where humanoids need to run 26 miles, right?

Peter Lewig: Are we there? Can can robots run a marathon?

Kasar Younes: I think it's happening any day now. So

Host: So we're there. By the way, also you know automotive, there's a version of this which is like 24 hours,

Kasar Younes: right? It's like Porsche wins 24 hours literally puts those, you know, products into production. I would actually break it down. You you talk about research and you talk about production. There's actually a step in the middle which is like advanced engineering and I think a lot of the industry is moving into advanced engineering where it's like it's not fundamental research like we're coming up with novel techniques. It really is advanced engineering for production. So what are the the subcomponents that are going to limit to getting into production once you're in production deal with another set of problems which is like the deployment maintenance uh of those machines that that exist. So I would say at least in our field we're mostly in advanced engineering in the like automotive parllets.

Peter Lewig: Honestly every every step is hard though.

Host: Well that's why you're worth $15 billion. So I think you you bleed every step.

Peter Lewig: Yeah. It's fun. I mean I think I mean it's like I don't know I I find it really enjoyable.

Kasar Younes: Yeah. But what was also fun is like so we've we've been in this now for almost 10 years and like we we've just seen we've seen so much bad time and so right now we we can look at any company in the space and get a demo and like I can I can write down a list of I know exactly the next 20 problems they're going to hit and like and I I can guess also what they're going to try to solve each of those and I can guess which one's going to actually work.

Host: Yeah. These were like particularly like geniuses. We've seen this stuff.

Peter Lewig: Yeah. We've seen enough of this stuff. We lived a little enough of this stuff. We, you know, our own kind of mental models of the world as as leads in the company.

Kasar Younes: We've tried so many things in many. We're talking about the wins here, right?

Peter Lewig: There's plenty of losses among that many people doing that many different things. And um that kind of like get baked into your like mental model of the world.

Kasar Younes: Yeah. But I would say in general like we're excited about robotics for sure and like the

Peter Lewig: massive opportunity

Kasar Younes: massive opportunity and what's what's happening in the industry is like none of these concepts are are new right what's new is like the stuff is actually working now

Peter Lewig: right like people have wanted to use uh neural net robotics for a long time uh but but now again we now have the data sets we have the simulation technologies where stuff is actually starting to really work and yeah we we want to be part we're going to be part of that for

Host: Do you have requests for startups or like advice against starting certain startups? There's a lot of like scale approval new companies. It's like what do you think are things

Kasar Younes: a lot of a lot of applied intuitions for other things? I think you you hit a you hit a certain uh what is it uh you know badge when Y right you become like a you know or or literally the same similar names like you know um I I mean I think my biggest advice you know in this like almost like commercialization of technology is I think often the that constraint so we talked about like hardware constraints and we talked about uh there's also like on the commercial side there's constraints which is we're going only do things that fit in this box. That is, I think, very good for founders. The reason I think it's not often focused on is because you have plenty of access to capital and the technical problems are so hard. You're like, I already have a constraint which is just getting this, you know, this technical problem solved. And I think the venture community generally speaking tends to be not very technical for them. If you just say if we solve this thing, it's going to be a lot of money. That's kind of enough for them. But you as a founder, I'm not giving you advice on how to pitch VCs. That'll work for VCs. You still got to run a sustainable business. And I think we're really in in in that question you asked earlier about kind of, you know, what's maybe not obvious about our company. It's like this is truly compounding technology. A lot of the work that we do just compounds it. We don't throw it away. It it gets better. The operating system work gets better. The de dev tooling gets better. The models get better. And so we're really going to get a hu I I think you see it in Whimo as an example like Whimo is a company that is I would say very interesting for a long time but not worth $126 billion right so what happens like is that the human brain just doesn't emotionally understand the compounding effects so that's going to happen in our universe so now if you're a founder you're at the beginning of that you know that long you know walk if you can put a little constraint on on commercials that has a small ability for you to more likely see the other end of that that walk because you get to the other end you will get the big return from compounding technology just a lot of people just don't make it. So yeah summarize like think a little bit about the equation of how you use money and where you use the limited resources and limited engineers that you have. I think sometimes in founders falsely kind of take very mature companies strategies and then apply to their like nent they're like oh well Steve Jobs says be completely vertical. Yeah, in 2007, Apple is very different than 1978 and 1982. Those companies were different. They they were literally just taking electronics from other manufacturers and putting it in enclosure. And so I just be a bit more like I don't know bit a bit more nuanced in your in your commercial approach as it informs your technical approach.

Host: Do you feel differently today? Like I mean you just joined X, right? You've been building this company. you've been building this company in stealth and now you're like well I should probably be talking about what I'm doing. I think a lot of founders are in a similar way where they want to raise a lot of money to signal they're strong and you raise a lot of money without

Kasar Younes: spending hire and to hire. Yeah.

Host: You obviously like that. Do you think that's still possible to like have a very narrow approach of like hey we're kind of like building a compounding thing without a grand vision right away versus

Kasar Younes: it's very difficult to answer very general questions that uh I I so maybe like maybe I reframe it as in is it possible to build a product that has a small let's say problem space and hope that the problem space will grow maybe that's like a different way of asking the same question but more answerable I think always yes that is the old YC like go really deep and then you know rather than very broad and shallow very broad and shallow unfortunately there's just too many tech especially in hard tech companies there's just too many problems and you can't you're going to do all of them in a very mediocre way and so the the the the full product is actually fairly mediocre so yeah I I I still in I'm still in the camp of find a small problem space the other question you're asking is a tangental is like should you like build in in stealth and anonymity. Well, yeah, if you're a YCCOO.

Host: Yeah, you can because we Yeah, we worked, you know, together at Google. We have a long history and we don't and which mean which is another way of saying we have big networks. I mean, our first

Kasar Younes: of 400 people, majority were Googlers. Like a majority of the company came from, you know, this giant company we worked at and that's just very different. you're a founder who is doesn't have that experience, you have to do these things. And I think it's that's so it's like just don't take my version of the world or whatever other founder Jensen's version of the world, they different time and space and most importantly their companies are in a different phase.

Host: No.

Kasar Younes: And so then if you want to take inspiration from other really young companies, that's also bad because most of them are going to fail, right? So the only the only solution you really have is use first principal thinking and say based on my skills, my co-founder skills, the skills of my early team members and the what I'm hearing from customers, what's the product space that I should I should build in.

Host: Yeah. Does that make sense?

Kasar Younes: Yeah, it does.

Host: Yeah. I I mean Sam Sam Alman, he said he regress a lot of the advice that he's given at Y. So I'm always curious to ask, you know, founders like you who've not been everyone who leaves YC like does the opposite. you, you know, we, you know, when Sam was president, I was COO, so and we'd have a CEO, so we worked together, you know, extremely closely would be an understatement because the firm was also small, but you know, YC wasn't wasn't as big as like an open AI is.

Peter Lewig: I I directionally agree with that, but I would say that's not more of a YC function. It's more of the market.

Kasar Younes: It is a different world. the AI industry is different is the the AI companies I should say more specifically and how they relate to the other YC companies and market just so fundamentally different the amount of money raised is different the amount of investors the sheer number of seed funds one of our early investors is floodgate um and they did some analysis in the late uh 2000 like double O's where they were like there's like singledigit number of funds that were like floodgate which were like writing sub$1 million checks first checks and they were not accelerating incubator and Ann who's who's one of the co-founders there uh with with Mike they they said that today they try to do or like today as in like 3 4 years ago they they they tried to do this analysis and they like lost count at like 350 funds or something like that. So we're just in a different environment. So the YC advice from 2014 just would not apply in 2026. But Sam is like way better at saying these things than me. He somehow makes up like

Host: he he he says it in a shorter most more interesting and than than me. I I can just give you like the like I like if you ask me like you know what are the purpose of a car like open the owner's manual I say number one look there's a steering wheel and you know instead of like it can change your life and

Kasar Younes: mo exactly yeah

Host: and then for Peter I was just kind of curious if there's any particular tech or research problem that you would call out as very meaningful for you guys if it was solved and unsolved and if anyone is working on it, they should get in touch with you.

Peter Lewig: Yeah, I think um generally the the making models very efficient, right? because we have to run on actual vehicles like physical AI is literally uh it's taking like very large AI and now making it very small and very efficient and uh and so we're constantly just at that boundary of these limitations of like well you can have a great model but now we need to make it faster and smaller and and so that that in general as a as a field and then I would say also um uh folks that are just really passionate about uh evaluating this technology as in like model eval um is is a hugely difficult topic especially in safety critical systems and um uh I mean we we have a a really great engineering team that works on this now and and and researchers but it's it's a big area of investment and so yeah folks that are passionate about um yeah performance I I say model performance both in terms of capability and and literally latency and then evaluation of models.

Host: Awesome. I guess any uh specific engineering roles that you're hiring for and especially like who are people that succeed at your company as engineers? I think that's always the most important thing.

Kasar Younes: Yeah, I mean fly.co/careers. I think there's literally hundreds of roles. Uh we're looking at all the topics we talked about from, you know, dev tooling and physical AI to operating systems to autonomy and and and and AI uh within physical machines. The types of engineers, that's a great question. actually more interesting than than the roles because we're you know we're a large company where we everything we are everything. Yeah.

Peter Lewig: I think you know we're a sunny veil company and um I think just from this conversation and kind of our backgrounds you can kind of predict a little bit of what that means. uh you know we tend to hire fairly serious people um who are who understand low-level systems not just just like a a superficial understanding of technology uh like engineers engineers almost we definitely hire folks who are uh like have some diverse skill sets we hire tons of specialists as well to be very very clear but they've seen production and I think that cuz that really informs how you how you build technology

Peter Lewig: Yeah, people that really appreciate the hardware software boundaries.

Kasar Younes: Yeah, exactly.

Kasar Younes: Definitely in the vibe coding era there there are a crop of engineers that they don't think about hardware at all and we don't have that luxury and so people that are a little more passionate about going a little bit deeper.

Peter Lewig: Yeah. If you're to contrast us versus like a you know AI lab or something that's where you're going to get the biggest contrast which is like we're just dealing with with reality. I mean, what other things all the classic stuff, you know, you want you want folks who work hard and who are uh who love the technology and like like a podcast like this or

Kasar Younes: like if you made it to this part of the podcast, you probably qualified for you're interested in this.

Host: Yeah. And and Peter said that he likes the podcast as well, which is like him. specifically on the hardware software boundary part is it's something I think about of our education system um in the states but also maybe just in gerally I feel like there is that retreat away from that classical computer science or e education

Kasar Younes: engineering or yeah

Host: yeah and like is there a point where you just do it yourself like you know because at this point you guys are the world experts on this and actually you shouldn't wait for some college system to spit them out for you

Peter Lewig: you mean in terms of education and upskilling kind of Yeah, just just grab like

Kasar Younes: General Motors already did it. Smart GMI University.

Peter Lewig: Yeah, that's where I went for undergrad. I went to the General Motors Institute. Uh,

Host: okay. I that did not come out. I saw HBS.

Peter Lewig: Yeah. Yeah. Everyone sees HBS the the Harvard brand at Lewis is is high.

Host: What was General Motors Institute like?

Kasar Younes: Uh, it started 100 years a to answer this exact question. Literally the question you just said, which is like not enough engineers in Michigan. uh you know you're talking about the early days of the modern corporation General Motors being there's a great book Alfred Peace Loans uh my years with General Motors uh that is highly recommended which basically talks about what becomes a modern corporation but a part of that is they're like we are we're basically buffering on engineers so they started a school and actually even Google as as as most as recent as probably 10 years ago was thinking of starting a university internally there was discussions on it so yeah it was absolutely we we definitely up I We definitely upscale folks as well. The amount of training we do in THR is actually surprising. Yeah. But it's a luxury you have when you're at our size. When you're like 25 engineers, you just got to survive.

Host: So again, take advice that's relevant for your company rather than like immediately start trying to take high schoolers and make good, you know, I I did go to a class that that you taught because like it sounds like you can teach a lot.

Peter Lewig: Yeah. So I think honestly the one of the most amazing use cases of these large models now is education right like I I've I've taken an engineer who uh very good engineer aerospace engineering background and in a relatively short time time span like he's doing very confident front end work very confident backend work like with the help of these models and like not only can you do implementation with them but you can also just learn right it's like you ask questions and you don't feel embarrassed because the model's not going to model's not going to call you out on Hey.

Host: Yeah. I think I think the thing you probably need more than an engineering degree though engineering degrees are like very important like I I I don't know if there's a way to shortcut like fluid dynamics or heat transfer the fundamental stuff.

Peter Lewig: The fundamental stuff uh at least on the mechanical side is you need an engineering mindset and that sometimes is actually not everybody actually has that. Some people are emotionally drawn towards the arts or something else and it's completely fine. There's no judgment there. But I think the engineering mindset maybe in a more usable way is like wanting to understand a lower level and a lower level and the lower like like how do photons move?

Host: Extreme curiosity

Peter Lewig: extreme curiosity like what is light? What is a radio wave? Like these really fundamental questions,

Host: right? If and if you get curious enough about software, you ultimately end up in hardware, right?

Kasar Younes: And so that's the Alen K. Yeah. Yeah. Exactly. So I'm trying to make analogies and then do all these things like you know you're kind of a blend between new General Motors and Tesla autonomy division for everyone else.

Peter Lewig: I mean uh you know we were in all these other fields. I think if you talk to our trucking customers they wouldn't even think perceive you know they like some sense like oh you guys did some automotive stuff but you're you're really helping us. So

Host: automotive is not trucking.

Peter Lewig: No no that's Yeah. Yeah. It's it's separate. There's different problems. The masses you have you have the general categories of onroad and off-road. I think that's what you're thinking. So there's onroad and off-road. Uh but within on-road there's all these sub subasses of machines. Especially when you talk about, you know, you know, you look at uh a delivery robot that doesn't have a human in it. That's actually very different because now you're not concerned with like the actual feeling that you have when you're in a self-driving system. You don't have to account for that. You can

Kasar Younes: you can you can break hard and you don't care about jerk and all of these metrics are become become insane. Uh the way to think about it honestly is a little bit like uh any system that you as an as a human would need special training to operate, you can think of a little bit differently. So like the license to operate a truck is different from the license to operate a car which is different from the license to fly a plane is different from you get it right.

Host: Awesome guys. Thank you for taking the time.

Kasar Younes: Yeah, thanks for having us. Thank you.