Podcast appearances and mentions of qasar younis

From building Applied Intuition from YC-era autonomy tooling into a $15B physical AI company, Qasar Younis and Peter Ludwig have spent the last decade living through the full arc of autonomy: from simulation and data infrastructure for robotaxi companies, to operating systems for safety-critical machines, to deploying AI onto cars, trucks, mining equipment, construction vehicles, agriculture, defense systems, and driverless L4 trucks running in Japan today. They join us to explain why “physical AI” is not just LLMs on wheels, why the real bottleneck is no longer model intelligence but deployment onto constrained hardware, and why the future of autonomy may look less like one-off demos and more like Android for every moving machine.We discuss:* Applied Intuition's mission: building physical AI for a safer, more prosperous world, powering cars, trucks, construction and mining equipment, agriculture, defense, and other moving machines* Why physical AI is different from screen-based AI: learned systems can make mistakes in chat or coding, but safety-critical machines like driverless trucks, autonomous vehicles, and robots need much higher reliability* The evolution from autonomy tooling to a broad physical AI platform: starting with simulation and data infrastructure for robotaxi companies, then expanding into 30+ products across simulation, operating systems, autonomy, and AI models* Why tooling companies came back into fashion: Qasar on why developer tooling looked unfashionable in 2016, why Applied Intuition still bet on it, and how the AI boom made workflows and tools central again* The three core buckets of Applied Intuition's technology: simulation and RL infrastructure, true operating systems for vehicles and machines, and fundamental AI models for autonomy and world understanding* Why vehicles need a real AI operating system: real-time control, sensor streaming, latency, memory management, fail-safes, reliable updates, and why “bricking a car” is much worse than bricking an iPad* Physical machines as “phones before Android and iOS”: Peter explains why today's vehicle and machine software stack is fragmented across many operating systems, and why Applied Intuition wants to consolidate the platform layer* Coding agents inside Applied Intuition: Cursor, Claude Code, internal adoption leaderboards, and how AI tools are changing engineering workflows even in embedded systems and safety-critical software* Verification and validation for physical AI: why evals get harder as models improve, how end-to-end autonomy changes simulation requirements, and why neural simulation has to be fast and cheap enough to make RL practical* From deterministic tests to statistical safety: why autonomy validation is shifting from binary pass/fail requirements toward “how many nines” of reliability and mean time between failures* Cruise, Waymo, and public trust: Qasar and Peter discuss why autonomy failures are not just technical issues, how companies interact with regulators, and why Waymo is setting a high bar for the industry* Simulation vs. reality: why no simulator perfectly represents the real world, how sim-to-real validation works, and why real-world testing will never disappear* World models for physical AI: hydroplaning, construction equipment, visual cues, cause-and-effect learning, and where world models help versus where they are not enough* Onboard vs. offboard AI: why data-center models can be huge and slow, but onboard vehicle models need millisecond-level latency, low power, small size, and distillation-like efficiency* Why physical AI is not constrained by model intelligence alone: the hard part is deploying models onto real hardware, under safety, latency, power, cost, and reliability constraints* Legacy autonomy vs. intelligent autonomy: RTK GPS in mining and agriculture, why hand-coded path-following worked for decades, and why modern systems need perception and dynamic intelligence* Planning for physical systems: how “plan mode” applies to robotaxis, mining, defense, and multi-step physical tasks where actions change the state of the world* Why robotics demos are not production: the brittle last 1%, humanoid reliability, DARPA Grand Challenge-style prize policy, and the advanced engineering gap between research and deployment* Applied Intuition's hard-earned lessons: after nearly a decade, Peter says they can look at a robotics demo and predict the next 20 problems the company will hit* Qasar's advice to founders: constrain the commercial problem, avoid copying mature-company strategies too early, and remember that compounding technology only matters if you survive long enough to see it compound* Why 2014 YC advice may not apply in 2026: capital markets, AI company dynamics, and the difference between building in stealth with a deep network versus building as a new founder today* What Applied is hiring for: operating systems, autonomy, dev tooling, model performance, evals, safety-critical systems, hardware/software boundaries, and engineers with deep curiosity about how things workApplied Intuition:* YouTube: https://www.youtube.com/@AppliedIntuitionInc* X: https://x.com/AppliedInt* LinkedIn: https://www.linkedin.com/company/applied-intuition-incQasar Younis:* X: https://x.com/qasar* LinkedIn: https://www.linkedin.com/in/qasar/Peter Ludwig:* LinkedIn: https://www.linkedin.com/in/peterwludwig/Timestamps00:00:00 Introduction: Applied Intuition, Physical AI, and 10 Years of Building00:01:37 Physical AI vs. Screen AI: Why Safety-Critical Changes Everything00:02:51 The Origin Story: Tooling, YC, and the Scale AI Comparison00:05:41 The Three Buckets: Simulation, Operating Systems, and Autonomy Models00:11:10 Hardware, Sensors, and the LiDAR Question00:14:26 The Operating System Layer: Why Vehicles Are Like Pre-Android Phones00:19:13 Customers, Licensing, and the Better-Together Stack00:21:19 AI Coding Adoption: Cursor, Claude Code, and the Bimodal Engineer00:26:41 Verifiable Rewards, Evals, and Neural Simulation00:31:04 Statistical Validation, Regulators, and the Cruise Lesson00:40:25 World Models, Hydroplaning, and Cause-Effect Learning00:43:34 Onboard vs. Offboard: Latency, Embedded ML, and Distillation00:50:57 Plan Mode for Physical Systems and Next-Token Prediction Universally00:53:04 Productionization: The 20 Problems Every Robotics Demo Will Hit00:58:00 Founder Advice: Constraints, Compounding Tech, and Mature-Company Mimicry01:05:41 Hiring Philosophy: Hardware/Software Boundary and Engineering Mindset01:08:50 General Motors Institute, Education, and the Curiosity MindsetTranscriptIntroduction: Applied Intuition, Physical AI, and 10 Years of BuildingAlessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: And today we're very honored to have the founders of Applied Intuition, Qasar and Peter. Welcome.Qasar [00:00:17]: You guys really know how to turn it on to podcast mode. That was, you guys are real pros at this.Qasar [00:00:23]: They were just joking around right before this, and then they flipped it pretty quick.Alessio [00:00:29]: Oh, yeah, it's good to have you guys. Maybe you just wanna introduce yourself so people know the voice on the mic and they'll know what they're hearing.Peter [00:00:33]: Oh, sure. Yeah, I'm Peter Ludwig. I'm the co-founder and CTO of Applied Intuition.Qasar [00:00:38]: And my name is Qasar Younis. I am the CEO and co-founder with Peter.Alessio [00:00:42]: Nice. Can you guys give the high-level overview of what Applied Intuition is? And I was reading through some of the Congress files, when you went out there, Peter, and eighteen of the top twenty global non-Chinese automakers, you two guys, you have customers in agriculture, defense, construction. I think most people have heard of Applied Intuition tied to YC when it was first started, and then you were kinda 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 [00:01:10]: Yeah. So at 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, to defense technologies. And we're a true technology company, so we build and sell the technology, and we sell it to the companies that make the machines. We sell it to the government, really anyone that wants to buy a technology to make machines smart.Physical AI vs. Screen AI: Why Safety-Critical Changes EverythingQasar [00:01:38]: Yeah. And I think in the broader AI landscape, a lot of the focus, rightfully so in the last, three years has been on large language models, and so everything fits in a screen. Like, whether it's code complete products or things like that. And what's different about us is we're deploying intelligence onto a lot of things that don't have screens. they're physical machines. There are sometimes screens within the cabin or for example of a car or a truck or something like that, but most of the value we provide is putting intelligence that is in safety critical environments. So that those two words are really important because learn systems can make mistakes if you're asking for, like, some, so something like, “Tell me about these podcast hostsQasar [00:02:28]: that I'm about to go meet.” But you can't do that obviously when you run, like, as an example, we run driverless trucks in Japan right now, as we speak. We can't have errors. Those are L4 trucks. Yeah.Alessio [00:02:40]: Yeah. Was that always the mission? I remember initially, I think people put you and Scale AI very similarly for some things about being kinda like on the data infrastructure side of things. What was the evolution of the company?The Origin Story: Tooling, YC, and the Scale AI ComparisonPeter [00:02:51]: Well, from the very beginning, we always wanted to, really be a technology company that helped generally push forward the industrial sector. And so we started off working in autonomy. Our very first customers were robotaxi companies. And we started off doing a lot of work in simulation and data infrastructure. And then over the years, we've expanded our portfolios. Now we have, over thirty products, and it's a pretty broad technology play within the landscape of physical AI.Qasar [00:03:19]: Yeah, I think the Scale reason is because we're all YC Universe companies. But it was a very different company. Scale, was, is more of a services company, data labeling company fundamentally. We started and still are, do a lot of tooling. So like, you think developer tooling is now in vogue again, thanks to the AI boom. But honestly, ten years ago, it was out of vogue. It w Like, doing a tooling company in 2016, 2017 was not, like, the thing to do because, I don't know if you remember, the VCs generally, their views was that toolings are They're just workflows, and workflows ultimately are not really interesting. And we've gone and come, full circle with that. But when we started the company, our kind of it's kinda like in the periphery of what the company wants to be. It was like, from our earliest days, like, we wanna deploy software on physical machines, like on cars and on trucks and things like that. And obviously, we didn't know that the transformer boom was gonna happen. We didn't know that autonomy systems would become end-to-end. Those things we didn't know. And why that's important when autonomy systems become end-to-end, it is just now those models can be generalized to, multiple form factors. And so back nine, ten years ago, tooling was a great way, and still is a great way to, build the technology and sell technology to our end customers, a lot of them who wanna build this stuff themselves. And so we just offer like a spectrum of solutions from you can just use like one part 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 kinda like, what NVIDIA does or what an AMD, but we just don't do chips.Qasar [00:05:06]: We don't do silicon. But we're a technology provider fundamentally. And I think even, we used to joke when we started the company, like, we're not the guys to build, like, Instagram. Like that was just towards That's not our That's just not us in a most fundamental way. IAlessio [00:05:20]: You have thoughts.Qasar [00:05:21]: Yes.Qasar [00:05:22]: Well, it's, it's I mean, I think it's just like what 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 of that traditional engineering kind of a realm, or lineage. we used to jokeThe Three Buckets: Simulation, Operating Systems, and Autonomy ModelsPeter [00:05:41]: I gotta say, though, what was clear ten years ago was that there was so much more that was possible with software and AI in vehiclesPeter [00:05:47]: and that was generally the space that we started in ten years ago.Peter [00:05:51]: And the precise path that we've taken over the years, I think we've been strategic, and 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 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 technology stack. And I sort of see that cadence roughly keeping up.Peter [00:06:13]: And so the way even we think about engineering is almost on this two-year horizon, we're preparing ourselves that, hey, like, we wanna 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.Qasar [00:06:27]: Yeah. One thing that has been consistent is the type of people we've, we've recruited. It's engineers who are fall into the sometimes very traditional, like, GoogleQasar [00:06:38]: -gen suite, but way different from, other companies. We are hiring folks who really know the intersection of hardware and software, who know really low-level systems. Obviously, traditional ML researchers and folks who've, actually, put ML systems into production. That's been pretty consistent. I think that, like, you look at the mix of our engineering, eighty-three percent of the company is engineering, so it's, like, a giant list.Qasar [00:07:05]: A lot of engineers.Alessio [00:07:06]: Which, by the way, a thousand engineersQasar [00:07:07]: Yeah. A thousand engineers.Alessio [00:07:08]: that's on your website, so I imagine it's up to date.Qasar [00:07:11]: It is, it is up to date, yes. Yes.Alessio [00:07:12]: okay. And then forty-plus founders.Qasar [00:07:15]: Yeah. We would tend to also, This was more luck than strategy. But we've recruited a lot of ex-founders. It's been a great place for founders, YC and non, ‘cause obviously I know a lot of the YC folks. It's kind of like we recruit a lot of Google people.Qasar [00:07:33]: For them to exercise both their technical and non-technical skills because, we're, we're, we're on the applied side. We have a research team that we do fundamental research, we publish, and we've, we've had great traction there. But fundamentally, the business wants to take this intelligence and deploy it into production and there's, like, a certain type of person that's more interested in that.Alessio [00:07:54]: Yeah. You mentioned the tech stack, Peter, so I just wanted to give you some rein to just go into it. I'm interested in where Wayve Nutrition, starts and ends in some sense, what won't you do? What, do you do that's common among all the verticals that you cover?Peter [00:08:10]: There's a few buckets of work that we do, and we've been at this for almost ten years now, so the technology's pretty broad. But we got startedQasar [00:08:17]: Yeah, with a thousand engineers, like, you could work on lots of things.Peter [00:08:19]: There's lots of stuff, yeah, espe-especially with AI tools to help.Peter [00:08:22]: So we got our start 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, a simulation, and then also obviously real world testing.Peter [00:08:39]: 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's a very deep topic.Peter [00:08:49]: We have a whole suite of products in that, and we could talk for many hours about that specifically. But that is one part of what we do as a company. Reinforcement learning as a subpart of that is also super critical. I think a lot of the a lot of the best advancements happening 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 for reinforcement learning. The second bucket of work that we do is on operating systems technology. true operating systems. Like, think about, schedulers and memory management and middleware and message passing and 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 we were getting deeper into that space, there wasn't really anything that we were happy with.Peter [00:09:39]: 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 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 world models and the actual autonomy models that are running on these physical machines, and that's across cars, trucks, mining, construction, agriculture, and defense, and so that's both land, air, and sea.Qasar [00:10:31]: And also, a smaller subsector of that third bucket is the interaction of humans with those machines.Qasar [00:10:38]: So that's a multimodal, experience. Historically, if you're moving a dirt mover or any of these machines, there are, like, buttons you press, whether they're actual physical tactile buttons or something like a touch screen. 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.Alessio [00:10:58]: Voice?Qasar [00:10:59]: Yeah, voice, absolutely, yeah.Alessio [00:11:00]: Oh.Qasar [00:11:00]: And also the machine just being aware of who is in the cabin, what their state is. you can think from a safety systems perspective, the most simple version of this is, like, the driver is tired, right? They're, they're if you get those alerts when you're driving your car and saysHardware, Sensors, and the LiDAR QuestionQasar [00:11:15]: -maybe take a coffee break, that take that times, 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, different instances of, Claude and doing work for you in the background, you can take that analogy out, 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 like that, 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 critical. And 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, autonomy. In the automotive sense, that's typically defined in SAE levels as an L2++ systemQasar [00:12:05]: -with a human in the loop. But just take that idea, to other verticals.Alessio [00:12:09]: Yeah. You've not mentioned hardware at all, like sensors or obviously we you mentioned you don't do chips. I think even in AV there's, like, a big, cameras versus lidars. Like, 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? And like, how much influence do you guys have on co-designing those?Peter [00:12:32]: Yeah. So we don't make sensors. Like, we're, we're not a manufacturer. Obviously, we use a lot of sensors in our autonomy products. in terms of what actually goes on the vehicles, we have a preferred set of sensors that we, let's say fully support, and then our customers, they can sort of choose from those. And obviously if there's a very strong opinion on supporting something else, we'll add that to the platform as well. And the lidar question is at this point sort of the age-old,Peter [00:12:59]: topic in autonomy, and the state of the industry right now is lidar is hands down a useful sensor, specifically for data collection and the R&D phase of autonomy development. if you see, for example, a Tesla R&D vehicle, it actually has lidar on itPeter [00:13:17]: to this day, right? In the Bay Area we see these. you'll see, like, Model Ys or Cybercab that have lidars 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 lidar with a camerand you can say that, well, this camera's looking this direction, this lidar's looking this direction, and now for each pixel of the camera I can see how far away is that pixel. you can actually then use that as a part of your model training, and then the that depth information then becomes a learned, a learned state of the camera data. And then when you're doing the production system, you can now remove the lidarPeter [00:13:52]: and now you can actually get depth with just the camera. And so that difference between, like, a highly sensored R&D vehicle and then the down-costed production vehicle, we use that across our whole portfolio of products. And of course the end goal is you want super low cost and super reliable.Peter [00:14:08]: And then in certain use cases you have some more, bespoke things. Like in defense as an example, you do things at night oftentimes, and so you care about sensors like infrared, more so than And you don't, you don't wanna be putting energy out, so you don't wanna use lidar or radar.Peter [00:14:23]: but you still need to be able to see at nighttime. So yeah, we work the whole gamut.The Operating System Layer: Why Vehicles Are Like Pre-Android PhonesAlessio [00:14:27]: Cool. So that's kinda like on the hardware level. Then on the OS level, how does that look like? What is, like, unique? my drive- I drive a Tesla. Whenever I drive some other car that has a screen, it always sucks.Alessio [00:14:38]: It's on, like, cheap Android tablet. It's like, it's laggy and all of that. What does the OS of, like, the autonomy future look like?Peter [00:14:46]: When most people, it's really 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. So when we talk about operating systems for, like, AI in vehicles, there's many layers that go deep into the CPU critical realm and embedded systems, and you're talking about the real time control ofPeter [00:15:13]: let's say the electric motors or the engine and the actuators, and you have different redundancies for different, let's say, the steering actuation in the vehicle. And all of these things, 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 WindowsPeter [00:15:35]: like streaming your sensor data in or controlling the vehicle. Like, the latencies are gonna 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 of those things. So we can 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, fail-safes and fallbacks, for different things. ‘Cause you have to account for what if, what if there is a critical failure? What if there's a cosmic ray that flipsPeter [00:16:14]: a bit in the middle of the processor that causes some, malfunction? And you have to have a fail-safe to all of that, and so the core operating system is a part of that. And then the one last thing, which is a lot less exciting but is, actually a very big topic, is reliability of updates.Peter [00:16:30]: so the I have a Tesla and you get updates fairly frequently, right?Peter [00:16:36]: Once a month. Most companies that are making vehiclesPeter [00:16:40]: 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 HMI module. But they're not able to update, let's say, the CPU critical parts of the system.Peter [00:16:51]: 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's lots of technical, technically deep stuff, in the tech stack to do that in a way that you're not going to accidentally brick a vehicle.Peter [00:17:08]: And right? If, imagine yourAlessio [00:17:10]: That would be bad.Alessio [00:17:11]: Bad.Peter [00:17:11]: Bricking a car is a very expensivePeter [00:17:13]: and honestly, like across the industry maybe one of the most just pure impactful things that we've done is we've just, we're, we're now enabling the industry to actually do software updates.Alessio [00:17:22]: Just to clarify as well, who is the customer for this? Like, 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. And others would have their own. Like, who pays for this? Who invites you into the house? Is it, is it the end user, or is it, is it the manufacturer?Peter [00:17:41]: 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 I worked on Android at Google by the way many years ago, and part of the reason that Larry at Google decided to get into Android was they wanted to run Google products on a bunch of phones, and 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 impossiblePeter [00:18:17]: for Google 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 made it attractive to all of these phone makers, and that was sort of the genesis for what Android was and why Android existed. It was a way for Google to get their products onto really wide diversity of devices. The state of the physical, industry right now, it's a little bit like that. Like, there's yes, these companies have firmware, but 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. And then, your specific question was who are our customers? It's, it's, generally it's the companies that are making these machines.Peter [00:19:06]: And we're, we're, we're selling our technology to them to really simplify the architecture and then enable these AI applications to run on them.Customers, Licensing, and the Better-Together StackSwyx [00:19:13]: 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 is needed?Peter [00:19:22]: Yeah, highly reusable. So the fundamental technology is quite universal, right? So things that we do have to think about though are, like, chipset support. And so if you're, if you're coding, let's say, an LLM and you have start with an assumption that, “Hey, oh, I'm gonna, I'm gonna use CUDA, and I'm gonna run this, on an NVIDIA chip,” then you don't really have to think about the hardware in that sense. Like, you're just, “Okay, I'm just I'm in the CUDA/NVIDIA ecosystem, and I'm, I'm going to use that.” But the hardware, especially in safety critical systems, it's a lot more diverse. There's not one or one or two players. There's 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 to, it has to run on a number of different architectures from chips from a bunch of different companies. But again, we've been working on this for a long time now, so we have, we have support for all of those chipsets. And then when you want to then run the AI applications, we can then do that reliably across now a variety of providers.Qasar [00:20:19]: And I think that is, like, heavily inspired by Android, right? Android has a huge suite of testing and it's a reliable operating system that runs on thousands of devices. And we think we can, we can do the same in all these physical moving machines, with the difference that we're really in a safety critical realm. Android isn't.Alessio [00:20:40]: So on Android, I don't need to use Gmail, I can use Superhuman. Like, what about your machinery? Like, can people bring somebody else's automation to it, or is it kinda like all-in-one?Qasar [00:20:50]: You have to use us. No. Yeah. we're If, Yeah. Yeah, it's totally open. Yeah.Peter [00:20:56]: Yeah. our philosophy is that we are a technology company, and so we license our technology to customers to use how they want. And so if a customer wants to If they wanna license our autonomy tech and our operating system, then great, we'll license those. If they just wanna license the operating system and then use different autonomy tech, that's fine also, and we have great documentation andSwyx [00:21:17]: Or if they wanna use developer tooling.Peter [00:21:18]: Yeah, exactly.AI Coding Adoption: Cursor, Claude Code, and the Bimodal EngineerSwyx [00:21:19]: It's, like, a better together if, obviously, if you, if they work together. Is it all C++ I assume is with different compile targets?Peter [00:21:27]: We use a lot of C++.Peter [00:21:28]: Rust is sort of a hot, the new hot kid on the blockPeter [00:21:32]: for a bunch of things as well. But yeah, the lower level you get, especially when you get to real-time constraints, you hit C++ at some point, and at some point maybe you work your way into assembly when needed.Swyx [00:21:44]: Oh, damn.Alessio [00:21:46]: I'm curious about the coding agent adoption, just, like, since you're mentioning more esoteric languages. Like, what's the adoption internally? What have you learned?Peter [00:21:55]: Yeah. We use everything. So Cursor was, I think the hottest tool in the company for a good while. Now Claude Code, I think has taken the reign on that. We have a internal leader, leaderboard that we use just to sort of encourage adoptionPeter [00:22:09]: with-within the company. And yeah, it's, they're phenomenally useful. it's, Honestly, we take inspiration from some of those tools also in how we're adapting some of that mindset of thinking to the physical realm. Like if it's so easy to build an app for this or that thing that lives just on a screen, we can We're taking now a lot of the same ideas and applying that to, “Okay, well, if you wanted a physical machine to do something, how easy can we make that, using our own tooling and platform as well?”Alessio [00:22:40]: Are you changing any of, like, the OS architecture, kinda like the way you expose services to, like, be more AI friendly or?Peter [00:22:48]: Yeah, absolutely. The in the early days of our tools infrastructure work, it was a lot about, You had engineers that were experts in certain topics, but the things that you're dealing with, they're oftentimes more mathematical or more abstract, where actually GUI tools are very useful for certain things. Like as an example, we have a product we call Sensor Studio, which is, it helps you design the sensor suite for your autonomous vehicle, whether, again, it could be a car, it could be a drone, could be a mining equipment, could be a robot. And you place sensors in different places. You There's different, There's a library. You can understand what are the trade-offs that you're making in the design of that system, and that was, like, a very, a very GUI intensive, thing ‘cause it's a little more like a CAD tool in that senseSwyx [00:23:37]: YepPeter [00:23:37]: if you've seen CAD tools. Nowadays, though, right, we expose all of the underlying APIs for that and now using, AI agents, you can actually configure a sensor suite with just text and likely reach a better result than you could've through the GUI in the past, and we're taking that thinking now through the whole product portfolio.Swyx [00:23:57]: Another thing I was thinking about is just in terms of, like, AI, adoption, does it change your hiring at least a little bit, or how do you, how do you sort of manage engineers, differently?Peter [00:24:08]: Yeah. absolutely, it does. we, I think like every company in the Valley right now, are evolving our hiring practicesPeter [00:24:16]: because the skills required to be effective are changing so fast, right? you used to really select for just rote implementation ability and now it is more the AI engineer skill set, right? Where it's like, yeah, how to implement, but actually-Just banging out code is no longer the core job, right? It's, it's actually knowing what questions to ask, knowing how to tie, how to tie together these different AI tools. And so the interviews that we give now I think are way harder than they've ever been.Peter [00:24:46]: But we also allow, right, selective use of AI tools to solve the problems. And I think in that you start to see more of a bimodal distribution of engineers, right? You start to see like wow, there's, there's this subset of people that they really get it. Like they're, they're all in and they've, they've clearly invested the hours needed to learn these tools and how to be effective.Peter [00:25:09]: And then there's sort of the group of people that haven't done that, and that the productivity gap is just enormous. And so we're, we're trying to obviously select for the people that are really into this.Qasar [00:25:20]: I first wrote the my AI engineer piece three years ago, and when I first wrote about it, I was like, “Actually, not everyone should be an AI engineer,” ‘cause I think there's a there's an extremist stance where well, every software is an 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?Peter [00:25:41]: I think it's the classic bitter lesson, topic, which is the Six months ago I would've said the same thing, but it's, it's becoming super useful for every domain.Qasar [00:25:53]: I'm sure.Peter [00:25:54]: Right? Like,Peter [00:25:56]: there was, I think six months ago, or maybe a year ago, if you tried to use, let's say the latest Claude model for writing shaders, 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 the embedded realm. No question though, especially when you get into safety critical systems, the human validation isPeter [00:26:25]: is 100% key. Like I You're not gonna trust your life to a an AI written software that's, that's not been very carefully, checked by humans. And so I think now the really the challenge is about that appropriate level of human validation for these safety critical systems.Verifiable Rewards, Evals, and Neural SimulationAlessio [00:26:41]: 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 gives you What makes you sleep at night? Like, if somebody's like, just web coding something or likeAlessio [00:26:57]: 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 anythingAlessio [00:27:04]: then it puts a lot of work on like the verifiableAlessio [00:27:07]: side of it. Like, what does that look like for people?Peter [00:27:10]: Yeah. So verifiability, a broader bucket of like evaluations, right? Like how do you evaluate the results that you're, you're getting? I think 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 on the system.Peter [00:27:29]: And so like the problem of doing proper eval to find those faults, like that problem also keeps getting harder as the models get better. But it's no less important than it's ever been, right? You still there are still going to be edge cases that are not met and whatnot. And so it's, it's a big area of investment for us. On the reinforcement learning topic, the key thing is there's all these new requirements that come to be in the latest generation of these technologies. So for example, end-to-end is the big thing right now 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 the previous generations. And so to do reinforcement learning on an end-to-end model, you now need to actually simulate all the sensor data, right? So then this becomes a we call our, work in this neural simulation, but it'sPeter [00:28:26]: think of it like a hybrid of Gaussian, splatting and diffusion methods, and where you really care about performance. Like performance is everything. If you can't do enough simulation fast enough and cheap enough, you actually can't get results that are worthwhile, in the end. It also gets to a lot of our work in embedded systems, which is like performance critical work, and that performance optimization, performance criticality, it carries over to a lot of the model training work. because, like, the only way to make it affordable is it has to be really fast.Qasar [00:28:58]: I think it's worth a few minutes talking about our own, evolving thoughts on verification and validation withinQasar [00:29:05]: kind of, traditional simulators, which are, you can think of like vehicle dynamics or something like that, which you're just taking textbooks and taking those formulasQasar [00:29:13]: and putting them into software, to like now this neural sim/world model universe. I think that's an interesting topic.Peter [00:29:20]: Yeah. So in more traditional development, right, you oftentimes would have, more black-and-white answers to questions.Peter [00:29:28]: And so the in Europe as an example, there's, a regulatory, system, it's called Euro NCAP. It's the European New Car Assessment Program, and as part of that, the vehicles have to pass a bunch of tests, and those tests actually, include, safety systems. So automatic emergency braking for a child that runs in front of a carPeter [00:29:51]: or let's say an occluded child that runs out and you hit it. And so you have You end up with sort of these binary answers of like, well, did the car under test pass this specific test? And there's a very well-known set of test casesPeter [00:30:05]: that the vehicle has to pass. And that was how the industry worked, let's say, until 10-ish years ago. But what's changed now is with these models, everything is 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 I get in the system, and how can I, how can I prove that to be true? And the big unlock honestly for physical AI as an industry is that these models are just becoming much more reliable. Right? Things like things actually work a lot better. It's like the number of nines you can get out of these systems are now good enough that it actually becomes cost effective to really deploy these things. And so the big shift in, so verification and validation has been from a little bit more of a Again the past it was strictly requirements, and are you meeting or not? And now it's more of a statistical, verification and validation case where it's all about how many nines of reliability and meantime between failures, that sort of thing.Statistical Validation, Regulators, and the Cruise LessonSwyx [00:31:04]: And is the target audience regulators or even the customers are yeah, if you I imagine the customers are bought in, and it's mostly regulators that need to be satisfied.Peter [00:31:15]: We do work with the US government, we do work of course with the European governments and the government of Japan, and the government is not like an AI lab by any means.Peter [00:31:25]: So Swyx [00:31:26]: They just care about the outcome.Peter [00:31:27]: They care about the outcome.Peter [00:31:28]: And so we do education, in that regard, and like so sort of teaching about, “Hey, this is how we think validation should be done, and this is an approach that we think is reasonable,” and how to think about like when is a driverless system actually safe enough to go on the roads and that sort of thing. But 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.Qasar [00:32:01]: Yeah. Our first, our first core value is on round safety. So I think we can't underline enough that, us also verifying and validating that the systems that we're deploying are safe to us is probably as important as, like, some regulator or a customer saying,Swyx [00:32:19]: Of course. Okay. Yeah.Swyx [00:32:20]: You have to satisfy yourselves.Peter [00:32:22]: As I say, as a whole across the world, regulation oftentimes 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.Swyx [00:32:33]: Yeah. One thing I often 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 single incidents, because incidents are going to happen regardless, right? ‘Cause it's a statistical thing, but as long I don't know if regulators understand that, 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 gonna be lower than, I don't knowSwyx [00:33:00]: consumer driving.Qasar [00:33:01]: Yeah. I think the Cruise example wasn't a technology failure. there was The real, compounding issue there was just how did the company talk to the regulators and what was their kind of behavior, and I think that became more of the issue. If you look,Peter [00:33:19]: It isn't It definitely was a technology failure, but it was made much worse by theSwyx [00:33:23]: Put the car back on the woman.Qasar [00:33:25]: Yeah. And let me put it another way. There is a version where Cruise still exists.Swyx [00:33:29]: right. Right.Qasar [00:33:30]: Right. It'sSwyx [00:33:30]: It was like the last strawQasar [00:33:31]: ItSwyx [00:33:31]: in like a long chain ofSwyx [00:33:33]: like issues.Qasar [00:33:33]: So do you feel like ATG had that horrific accident or someone actually dying, because, that was a homeless person crossing the street? So yeah, I think 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 gonna be part of that conversation. I think companies like Waymo are doing a lot of service positively to the industry in the sense of they're, they're setting a high benchmark and they're showing, kind of in a very responsible way how to, how to deal with these. There have been Waymo incidences as well. They've just not been as significant as the Cruise one that you mentioned. But yeah, so I think you'll just continue to see that. I think probably the long term question is really gonna be, again, around Like it is very clear humans are way worse drivers statistically.Qasar [00:34:29]: Like, there's no, there's no debate. And so at what point But we're emotional animals.Swyx [00:34:34]: 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, than I think they're the safest mode of transport that we have.Qasar [00:34:50]: Yeah. it's more dangerous to drive to the airport than it is to get on a flight.Qasar [00:34:53]: So if you're everQasar [00:34:54]: if you're ever getting nervous about getting on a plane, just think “I just gotta get to the airport.”Swyx [00:34:58]: Yes, we're flying.Qasar [00:34:59]: If I get to the airportQasar [00:35:00]: I'll be good.Swyx [00:35:00]: But then it's, planes also concentrate the tail risk if planesQasar [00:35:03]: Yeah. AndPeter [00:35:04]: And I was, I don't think we honestly have to worry about there ever being, accidents from these systems that are like much worse than what humans would cause, ‘cause humans do terrible things.Peter [00:35:14]: Like, people fall asleep at the wheel all the time.Swyx [00:35:16]: I have.Swyx [00:35:17]: Like, I'll call, I've been a drowsy driver.Peter [00:35:19]: Kinda drunk drivers, and that'sPeter [00:35:20]: that's the extreme end of the example. But these AI systems, you have redundancies, you have fallbacks. Like, there's many things have to go wrong for there to actually be a something catastrophic because there's, there's so many, fallbacks that these systems have.Alessio [00:35:36]: 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, “F**k, this isAlessio [00:35:45]: this just did not work at all?”Peter [00:35:47]: Yes.Alessio [00:35:47]: IsPeter [00:35:47]: That's maybe a bit of a misconception, about simulation there. So let me go a little bit, more technical on this. So at first go, no simulation is going to represent the real world. There's always a process of this, sim to real matchingPeter [00:36:02]: 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, a number of times until you can get some confidence that, like I think the simulator is now accurately representingPeter [00:36:19]: what's gonna happen in the real world. Now, if you have a situation where you've done that full validation 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 I think the validation process is a really important part. You can never skip the simulation validation process, like where you're actually ensuring that, hey, the actual, my sim to real gap here is small enough that I can trust these simulation results. And there's, there's so many fun things that you can do when you get into it. Like, I'll, I'll give one fun example that came up recently is like in these humanoid robotics, systemsOverheating actuators is a real problem, right? So obviously phenomenal demos. IPeter [00:37:01]: The most amazingAlessio [00:37:02]: For 10 minutes.Peter [00:37:03]: The most amazing I can get. I love, I love watching robots do acrobatics like everybody but the these systems actually overheat, right? 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 representedPeter [00:37:18]: in the simulation. And 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 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 fail.Alessio [00:37:43]: 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? WellAlessio [00:37:53]: why did you make my robot worse when it runs in like a freezer?Alessio [00:37:57]: So it actually shouldn't worry about that. it's like, yeah, how do you design these simulations?Peter [00:38:02]: This is honestly the This is what makes simulation so hard, right? it's because you Simulation is fundamentally about you're trying to optimize the development of a system, right? Like, how can I build this system faster and better and cheaper and what are all the levers that I have to actually accomplish that? 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 inPeter [00:38:36]: but we can actually scale the simulation environment now with additional real world data and that also unlocks a whole new field of robotics.Qasar [00:38:46]: There is a meniscus line where you cross where still doing real world testing is better. there's, in this, sim-to-real gap, you can reproduce reality at exceedingly expensive costs and this So nothing is free. So really you have to you're 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 it in the real world. At some point it, that doesn't make sense. And so even, from our earliest days in autonomy, our view was you're still gonna do real world testing. You There's, there's not, there's not this, magical land where you're not gonna do that. And maybe even like a more nuanced version of this in like traditional software development is, most of your testing for software in a vehicle, 95% of that can be like traditional CI/CD kind of, flows that you would have in traditional web development. But once you have Now you, let's say you have a truck. Well, you can do like 4% of those in like a rig which has all the components, the electrical and electronics of a truck, but doesn't have, it doesn't have the tires and it doesn't have the And then you have the 1%, which is actually the vehicle. There's something There's a similar analogy in terms of using simulation for intelligent systems. You can do a lot in a simulator, but in using world models, but ultimately it's, it's physical AI. So you're gonna deploy it on physical machines andQasar [00:40:17]: the freezer example comes to, comes to light.Alessio [00:40:20]: The world model thing has been to me the hardest thing toAlessio [00:40:22]: wrap my head around. Like we have Faith Eliyon on the podcast.World Models, Hydroplaning, and Cause-Effect LearningQasar [00:40:25]: We've been doing a small series with like another Intuition company, General Intuition as well.Qasar [00:40:31]: yeah, and I mean, lots of, lots of coverage on NeRFs and yes.Alessio [00:40:34]: Yeah. It feels like we talk with about, 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 hydroplaning is one thing I think about, is 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 it's like the real thing is like when you're doing both cars and the highway in Japan versus the excavator in a mine in,Qasar [00:41:13]: ArizonaAlessio [00:41:13]: wherever you're Arizona, wherever you're deploying them.Alessio [00:41:15]: 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 that makes sense?Peter [00:41:28]: Yeah, totally. So yeah, I can say at a pure engineering level, I think 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, before you go bankrupt. So there is just a very practical mindset of like, world models 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. most fundamentally, world models are all about It's understanding the world, but also understanding what's going to happen. It's like the cause-effect relationship.Peter [00:42:01]: Right? And so like it, right, if you have a take some sort of construction tool, and that construction tool is gonna be doing some work on the Earth in some way, it's gonna be moving earth, 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 and now I have things that are over here and not over there anymore and that cause-effect, relationship. data obviously is a is a big problem. The hydroplaningPeter [00:42:26]: one 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 well this road, has, let's say the appropriate curvature to it so 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. And there are a lot of visual nuance, very nuanced visual cues in the scene and so I do think in the world model concept there's a good chance that the model actually would learn that you should just drive slower when these visual cues exist, and that's obviously the beautiful-The beauty of, these kinds of models where they just, they learn these non-obvious things.Swyx [00:43:14]: It doesn't need to know about hydroplaning to know that it needs to drive slower.Peter [00:43:17]: Yes.Swyx [00:43:17]: I guess it's Yeah. I wanna ask questions about, also deploying models. I presume, like, you use a lot of these world models for training data and simulation, but what about deploying it onto the systems in production? Presumably you have you have, like, GPUs on deviceOnboard vs. Offboard: Latency, Embedded ML, and DistillationSwyx [00:43:36]: but they're I keep saying on device. What's the what's the right term for that?Peter [00:43:40]: On machine.Swyx [00:43:41]: On machine.Peter [00:43:41]: Or embedded, yeah.Swyx [00:43:42]: Yeah. What is the embedded world like? because for people who are not used to that world, this is very alien.Peter [00:43:49]: Yeah. So it's actually We call it onboard and off board.Peter [00:43:52]: So like, onboard software and off board software.Peter [00:43:54]: And the great thing about off board software is you don't have to care about time, and you can run really large models, right? So you can, you can say, “Well, this model, I don't care if it takes one second for it to give me a result or 10 seconds for it to give me a result, because we have time.” And the models can be really big, and they can run, in a data center or on a on a huge GPU and you can obviously have distribute to compute, et cetera. But onboard you don't have any of those benefits. You're 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 it's like truly efficiency and like, literally every fraction of a millisecond counts. And you can't have a situation where the model takes too long because then the vehicle can't actually function.Peter [00:44:42]: And so you can, you can still use a lot of the same techniques, and the models themselves you can think of as like a derivative 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.Qasar [00:45:03]: Yeah. And I think like, the broader point I think which, maybe is not obvious but it's worth saying is in 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, it's actually deploying them inSwyx [00:45:19]: The hardware they give you.Qasar [00:45:21]: Yeah. On the hardware you give you.Qasar [00:45:22]: And so And there's just a reality is of safety critical systems. So those end up being the your limiting factorsQasar [00:45:29]: rather than, let's say, a limiting factor for, a foundation model companyQasar [00:45:34]: is gonna be just capital maybe or researchers.Qasar [00:45:38]: So we're, we're in that way dealing with, for us as people who kind of come in that realm with like a very interesting Those constraints force creativity.Swyx [00:45:47]: And I imagine, nobody was deploying or giving you the hardware for transformers back in 2018, whatever, but now they are. What's the evolution like? just peel back the curtains a little bit.Peter [00:45:59]: Yeah. Transformers first off, I think the paper was originally published in 2017.Swyx [00:46:02]: 2017.Swyx [00:46:02]: So there's no time.Peter [00:46:04]: And ISwyx [00:46:05]: But I'm just saying I guess I'm saying, like, embedded ML systems usually, like, a lot less parameters, a lot less compute, and now, like, orders of magnitude more.Peter [00:46:14]: Yeah. absolutely. what I was gonna say though was I think in the in the original paper in 2017, maybe it's in the last paragraph, somewhere in the paper they talk about, like, “Oh, by the way, this technique might be useful for, like, images and videos as well.”Peter [00:46:30]: These last subjects.Peter [00:46:31]: And it took a few years for that impact to really hit. But like, now, we're seeing transformers are everywhere.Swyx [00:46:39]: Yeah. Vision transformers.Peter [00:46:40]: And then then the compute just keeps getting better and better. But you do have this fundamental trade-off, right? It's like you have power, you have cost, and performance and like, getting the right, getting the right mix of those things in an embedded package that can also be, like, shaken and baked in all thePeter [00:47:00]: 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, we know the rate of improvements of these systems.Swyx [00:47:11]: Yeah. So like, Google just released the Gemma 2B modelSwyx [00:47:15]: that effective 2B model. Is that useful to you guys or is that too big?Peter [00:47:18]: You can run that model on an embedded system, definitely.Peter [00:47:21]: the So yes, it's, it's useful in that regard. The bigger question is, like, what do you use it for in an embedded system? Like, you actually need to customize it quite a bit to make it useful for something. But yeah, you could run a two billion parameter model, definitely.Swyx [00:47:35]: It also interesting, like, what percent is a custom ML model that only does that thing versus a generalist LLMSwyx [00:47:41]: which probably is not that useful actually for your context.Peter [00:47:46]: Like, you, like, you can imagine different use cases, right?Peter [00:47:48]: So theSwyx [00:47:49]: The voice stuff, yes.Peter [00:47:49]: Yeah, the voice test. Totally, yes.Peter [00:47:51]: So for the actual, autonomy elements, that's 100% in-house. We do every bit of that, the data simulation, the model, everything. But when you get into the more generic use cases like voice or voice assistant kind of thing, that's where these more generalist models like Gemma actually can be quite, can be quite useful.Swyx [00:48:09]: Yeah. And then there's also obviously a trade-off between, like, what percent must you do on machine, versus just call home.Peter [00:48:16]: Yeah. It's all about latency.Swyx [00:48:17]: Latency.Peter [00:48:17]: It's all about latency. Yeah.Swyx [00:48:18]: Yeah. Well, like, I think actually in a lot of contexts, especially in the US, you can just have a connection to the web.Qasar [00:48:26]: Yeah. I think though most of our universe is everything has to be fairly, embedded and local because just the nature of Even in the US there's a lot of likeSwyx [00:48:39]: PatchinessQasar [00:48:40]: don't haveQasar [00:48:41]: have coverage, right? And if you look at, like, the old world of autonomy within mining, which is, like, long before transformers and kind of, neural networks, in the like CNN and kind of a universe, they were really just hand-coded, systems. They were just like, this machine is gonna run to that place with thisPeter [00:49:03]: That was our GPS, like very accurate GPS.Qasar [00:49:05]: Yeah. And so that worked, and that worked for 20 years, so why would we actually need to use transformers or kind of 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 m-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.Alessio [00:49:28]: I looked up RTK, real-time kinematic, one to two-centimeter accuracy.Qasar [00:49:32]: Yeah. Fantastic. But the and fantastic in faraway lands where there's not gonna be cell phone coverage.Peter [00:49:39]: Yeah, so it's widely used on the legacy mining and agricultural autonomy systems today. So like, for example, a combine that can be precise within one or two centimeters as it's driving down the field, they use RTK.Qasar [00:49:53]: Yes.Peter [00:49:53]: But it's, it's expensive.Qasar [00:49:54]: Yeah. And it's, it's, it's autonomy, but it's not intelligent in the way that I think all of usQasar [00:49:58]: if in twenty-six we'd be talking about intelligence.Alessio [00:50:00]: 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 you probably wanna remove that?”Peter [00:50:14]: We have a diversified bet strategy internally, and the reason we've done that is because we operate in now a bunch of industries, a bunch of geographies, and each of the approaches has, obviously a different risk to them.Peter [00:50:27]: And so like, we're not going to put all of our eggs in a single basket for a single approach because that approach may no

Qasar Younis is the co-founder and CEO of Applied Intuition, a $15 billion AI company that adds intelligence to cars, tractors, planes, submarines, and other vehicles—essentially, Tesla or Waymo without the hardware. He was previously COO of Y Combinator, started his career as an engineer at GM and Bosch, and was born on a farm in Pakistan.We discuss:1. Why the biggest AI revolution will play out in mining, farming, construction, and trucking over the next 5 to 10 years, not in software2. Why Qasar intentionally stayed under the radar for nearly a decade while building Applied Intuition, and why most founders shouldn't do that3. The truth about China's AI capabilities and why comparisons to American companies are fundamentally flawed4. The company values that drive Applied Intuition: speed above everything, laugh a lot, half the work is follow-up, never disappoint the customer5. The biggest lessons from Qasar's stint as YC's COO, including that the most successful companies show traction very early6. How reading old books is the best way to build taste—Brought to you by:Omni—AI analytics your customers can trustVanta—Automate compliance. Simplify security.Lovable—Build apps by simply chatting with AI—Episode transcript: https://www.lennysnewsletter.com/p/the-most-successful-ai-company-youve-never-heard-of—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Qasar Younis:• X: https://x.com/qasar• LinkedIn: https://www.linkedin.com/in/qasar• Website: https://qy.co• Reading list: https://qy.co/books—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Qasar and Applied Intuition(04:01) The optimistic vision: How AI will create abundance(08:49) Why anxiety about AI comes from misunderstanding—and how to fight fear with knowledge(12:58) The market sell-off explained(16:31) Self-driving cars: Why 30,000 annual deaths prove we need autonomy now(20:22) The spectrum of physical AI(28:00) How AI is coming just in time(33:26) Why comparing Chinese AI companies to American AI companies is a category error(39:12) Why Qasar finally joined Twitter after staying silent for a decade(45:08) Why successful companies almost always show early signs of traction(50:40) Applied Intuition's core values(56:00) Why the company cleans its own office—and never spent a dollar of raised capital(58:50) Quasar's reading philosophy(01:06:14) How to operationalize listening to naysayers(01:12:53) The importance of decisiveness(01:14:55) Removing emotions from decisions(01:19:02) Why most Silicon Valley CEOs don't have great taste—and how to develop it—Referenced:• Applied Intuition: https://www.appliedintuition.com• Marc Andreessen: The real AI boom hasn't even started yet: https://www.lennysnewsletter.com/p/marc-andreessen-the-real-ai-boom• Elad Gil's website: https://eladgil.com• Bosch: https://www.bosch.com• Berkshire Hathaway: https://www.berkshirehathaway.com• Naval Ravikant on X: https://x.com/naval• Y Combinator: https://www.ycombinator.com• Waymo: https://waymo.com/• Tesla: https://www.tesla.com• DeepSeek: https://www.deepseek.com• Rivian: https://rivian.com• Crate & Barrel: https://www.crateandbarrel.com• OpenClaw: https://openclaw.ai• Sam Altman on X: https://x.com/sama• Peter Ludwig on LinkedIn: https://www.linkedin.com/in/peterwludwig• What Steve Jobs really meant when he said ‘Good artists copy; great artists steal': https://www.cnet.com/tech/tech-industry/what-steve-jobs-really-meant-when-he-said-good-artists-copy-great-artists-steal• 7 quotes on the power of reading from Charlie Munger: https://www.neil.blog/articles/7-quotes-power-reading-charlie-munger• Andreessen Horowitz: https://a16z.com• John Doerr on LinkedIn: https://www.linkedin.com/in/john-doerr-03248211• Gandhi's quote: https://www.azquotes.com/author/5308-Mahatma_Gandhi/tag/truth#google_vignette• Steve Ballmer on X: https://x.com/Steven_Ballmer• General Motors: https://www.gm.com—Recommended books:• House of Huawei: The Secret History of China's Most Powerful Company: https://www.amazon.com/House-Huawei-History-Powerful-Company/dp/0593544633• Maintenance: Of Everything, Part One: https://press.stripe.com/maintenance-part-one• The Autobiography of Malcolm X: As Told to Alex Haley: https://www.amazon.com/Autobiography-Malcolm-Told-Alex-Haley/dp/0345350685• High Output Management: https://www.amazon.com/High-Output-Management-Andrew-Grove/dp/0679762884• The Emperor of All Maladies: A Biography of Cancer: https://www.amazon.com/Emperor-All-Maladies-Biography-Cancer/dp/1439170916• Made in America: https://www.amazon.com/Sam-Walton-Made-America/dp/0553562835• My American Journey: https://www.amazon.com/American-Journey-Autobiography-Colin-Powell/dp/0679432965• Guns, Germs, and Steel: The Fates of Human Societies: https://www.amazon.com/Guns-Germs-Steel-Fates-Societies/dp/0393317552• Collapse: How Societies Choose to Fail or Succeed: https://www.amazon.com/Collapse-Societies-Choose-Succeed-Revised/dp/0143117009• SPQR: A History of Ancient Rome: https://www.amazon.com/SPQR-History-Ancient-Mary-Beard/dp/0871404230• A World Appears: A Journey into Consciousness: https://www.amazon.com/World-Appears-Journey-into-Consciousness/dp/198488199X—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Applied Intuition builds the kind of AI you don't see, but can't live without. Co-founders Qasar Younis and Peter Ludwig share how their $15 billion company powers vehicle intelligence across cars, trucks, tanks, mining equipment, and defense systems operating in some of the most demanding conditions on earth.They explain why combining AI with safety-critical systems raises the stakes, how a single mistake can destroy an entire company, and why so many autonomy startups ended up in the “graveyard.” The conversation explores the slow, methodical path to real autonomy, the hidden complexity of machines that run nonstop, and why consumer AI metaphors break down once software meets the physical world.Qasar and Peter also reflect on how Applied uses AI internally, how their principle of “radical pragmatism” keeps innovation grounded, and what it takes to move fast without breaking things when lives and livelihoods are on the line. From six-figure labor shortages in remote mines to the future of defense and logistics, this episode reveals how AI is quietly transforming the physical world — one carefully coded system at a time.Key Takeaways:Safety changes everything about AIWhen AI moves from the screen to the real world, the rules change. Qasar and Peter explain why building for trucks, tanks, and jets demands a different kind of discipline — one where precision and safety replace speed and iteration.The graveyard of autonomy is realThere's a long list of companies that underestimated what it takes to build safe, reliable autonomy. Applied Intuition's founders share what went wrong — and why moving slower has been their biggest advantage.Radical pragmatism is the hidden differentiatorInside Applied Intuition, “radical pragmatism” isn't a slogan — it's a practice. Qasar and Peter describe how it guides product decisions, culture, and leadership, helping them innovate in places where failure isn't an option.The next frontier of AI is off the screenFrom mines to military systems, the future of AI won't be chatbots — it will be machines that think, move, and decide in the physical world. Jeremy and Henrik reflect on how that shift raises the bar for builders, leaders, and the technology itself.Applied Intuition: http://applied.co/LinkedIn: linkedin.com/AppliedX: https://x.com/Applied00:00 Intro: Safety Critical Systems00:33 Meet the Founders of Applied Intuition01:09 Understanding Applied Intuition's Unique Approach03:02 The Human-Machine Teaming Concept07:26 Challenges in Autonomous Driving16:39 AI in Industrial Applications28:27 Future of Fighter Jets and AI29:50 AI in Applied: Coding Tools and Beyond33:16 Radical Pragmatism and AI Integration36:03 Challenges of AI Adoption in Large Organizations39:56 Human and Technical Challenges in AI42:02 Innovation and Organizational Structure48:38 Reflections on AI and Future Prospects

Qasar Younis, CEO and Peter Ludwig, CTO, Co-Founders of Applied Intuition joined Grayson Brulte on The Road to Autonomy podcast to discuss why Applied Intuition continues to be one of the most interesting companies in autonomy.The conversation explores Applied Intuition's growing portfolio of partnerships, including a major deal with Komatsu and the launch of their new SDS (self-driving system for automotive). Qasar and Peter share how first-principles thinking, diversification across verticals, and a relentless focus on engineering have allowed the company to expand while continually de-risking the business.As OEMs weigh the long-running build-versus-buy debate around autonomous driving systems, China's automakers are rapidly advancing their capabilities with a strong emphasis on in-vehicle software. From Tesla's software-driven model to legacy OEMs navigating the transition to software-defined vehicles, this episode of The Road to Autonomy highlights how Applied Intuition's Vehicle OS and SDS offerings are designed to meet automakers where they are today, while positioning them for what's next.In a future where software increasingly defines brand and customer experience, Applied Intuition is building the infrastructure that will power both vehicles and autonomy. Episode Chapters0:00 What's Next for Applied Intuition? 1:44 Self-Driving for Automotive (SDS)7:15 Managing Risks12:45 Komatsu Partnership16:32 Breakthrough Technology 21:38 Vehicle OS23:48 OpenAI Partnership25:05 L2/L2+ Demand32:42 Licensing Autonomous Driving Systems35:18 Maintaining SDS42:50 Cadillac44:09 Does Software Defines a Brand? 46:10 Planning for Automotive Software 49:29 What's NextRecorded on Friday, September 5, 2025--------About The Road to AutonomyThe Road to Autonomy provides market intelligence and strategic advisory services to institutional investors and companies, delivering insights needed to stay ahead of emerging trends in the autonomy economy™. To learn more, say hello (at) roadtoautonomy.com.Sign up for This Week in The Autonomy Economy newsletter: https://www.roadtoautonomy.com/ae/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Qasar Younis is the co-founder and CEO of Applied Intuition, a leading vehicle intelligence platform that helps companies develop and deploy autonomous systems at scale. In June 2025, the company raised $600M at a $15B valuation. Before Applied Intuition, Qasar was the COO and a group partner at Y Combinator, and earlier founded TalkBin, which was acquired by Google. He's also held engineering roles at General Motors and Bosch. In today's episode, we discuss: • The two founder traits Silicon Valley undervalues • How to get 1–3 extra months of work done every year • Lessons from YC on pattern matching and founder feedback • The battle-tested startup formula Qasar used at Applied • Why co-founder fit is make-or-break • Applied's playbook: vertical SaaS, product-led GTM, and leveraging VC networks • Why Applied went multi-product in the early days • Contrarian takes on startup culture, compensation, and cost control • Why domain expertise is making a comeback • And much more… Referenced: • Applied Intuition: https://www.appliedintuition.com • Ansys: https://www.ansys.com • Bilal Zuberi: https://www.linkedin.com/in/bzuberi • Bosch: https://www.bosch.com • Elad Gil: https://www.linkedin.com/in/eladgil • General Motors: https://www.gm.com • “Google's Acquisition of TalkBin”: https://techcrunch.com/2011/04/25/google-acquires-talkbin-a-feedback-platform-for-businesses-thats-only-five-months-old/ • “High Output Management”: https://www.amazon.com/High-Output-Management-Andrew-Grove/dp/0679762884 • Kyle Vogt: https://x.com/kvogt • Marc Andreessen: https://x.com/pmarca • “Only the Paranoid Survive”: https://www.amazon.com/Only-Paranoid-Survive-Strategic-Inflection/dp/0385483821 • Paul Graham: https://x.com/paulg • Peter Ludwig: https://www.linkedin.com/in/peterwludwig • Sam Altman: https://x.com/sama • TalkBin: https://www.crunchbase.com/organization/talkbin • “The History of the Standard Oil Company”: https://www.amazon.com/History-Standard-Oil-Company-Volumes/dp/1519455860 • Waymo: https://waymo.com • Y Combinator: https://www.ycombinator.com • Zoox: https://zoox.com Where to find Qasar: • LinkedIn: https://www.linkedin.com/in/qasar/ Where to find Brett: • LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ • Twitter/X: https://twitter.com/brettberson Where to find First Round Capital: • Website: https://firstround.com/ • First Round Review: https://review.firstround.com/ • Twitter/X: https://twitter.com/firstround • YouTube: https://www.youtube.com/@FirstRoundCapital • This podcast on all platforms: https://review.firstround.com/podcast Timestamps: (01:26) Two founder traits Silicon Valley undervalues (04:23) Gain 1-3 extra months of productivity yearly (05:52) Why founders should read outside the startup canon (07:27) Lessons from YC (13:44) Why it's harder to start than to quit (15:52) The moment you become a real founder (20:24) How great founders master luck (21:46) Qasar's battle-tested startup formula (25:37) The founding insight for Applied (31:42) How Applied expanded beyond automotive (38:05) Why Applied went multi-product early (45:45) What no one says about startup secondaries (49:02) Why being cheap is a startup superpower (51:04) The myth of "competition doesn't matter" (53:50) Early scrappiness: The Sunnyvale house setup (54:50) Why domain knowledge is making a comeback (58:32) The mentors who shaped Qasar

Qasar Younis, CEO and Peter Ludwig, CTO, Co-Founders of Applied Intuition joined Grayson Brulte on The Road to Autonomy podcast to discuss Applied Intuition's evolution from a tooling platform to a multi-domain vehicle intelligence powerhouse.As Applied Intuition continues to grow, they remain focused on staying paranoid, avoiding arrogance, and learning from companies such as Microsoft to navigate technological shifts. Whether it's building autonomous trucking stacks, enabling collaborative autonomy, or creating seamless digital passports for drivers and passengers, Applied Intuition is positioning itself to become the de facto platform for vehicle intelligence.Episode Chapters0:00 What's Next for Applied Intuition? 9:16 Autonomous Trucking11:46 Autonomy for Defense 15:37 OpenAI Partnership 21:59 Applied Passport25:10 Microsoft of Autonomy30:04 Vehicle Intelligence42:33 Over-the-Air Software Updates 45:34 Meta V-JEPA 249:29 Licensing 54:44 HiringRecorded on Friday, June 20, 2025--------About The Road to AutonomyThe Road to Autonomy provides market intelligence and strategic advisory services to institutional investors and companies, delivering insights needed to stay ahead of emerging trends in the autonomy economy™. To learn more, say hello (at) roadtoautonomy.com.Sign up for This Week in The Autonomy Economy newsletter: https://www.roadtoautonomy.com/autonomy-economy/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

When will fully autonomous vehicles see widespread adoption? According to Applied Intuition, that future is closer than you may think. Applied Intuition's CEO, Qasar Younis, and CTO, Peter Ludwig, talk with Elad Gil about how now is the best time to both work on self-driving vehicle technology and monetize it. Qasar and Peter discuss the advantages of developing their own OS in-house for their autonomous applications, self-driving technology's potential to drive re-shoring of vehicle manufacturing to the United States, and how best to gauge the bar for safety in autonomous systems. Plus, they explore how self-driving technology may reshape the designs of not only vehicles, but cities themselves. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @qasar | @AppliedInt Chapters: 00:00 Qasar Younis and Peter Ludwig Introduction 01:28 A Primer on Applied Intuition 11:08 Applied Intuition's Customers 12:04 Impact of Chinese Vehicles Manufacturers 15:44 EV Policies in the European Market 20:49 Can Robotics and Automation Re-Shore Vehicle Manufacturing? 21:53 Training Models for Autonomous Vehicles 26:41 Gauging the Bar for Autonomous Vehicles Safety 32:03 Timeline for Large-Scale Autonomous Vehicle Adoption 36:28 Rethinking Urban Design for Autonomous Vehicles 38:47 How Applied Intuition Uses AI for Tooling and OS 42:09 Designing for User Experience 43:31 Applied Intuition's Hiring Strategy 45:01 Conclusion

CNBC's Morgan Brennan sits down with Applied Intuition Co-founder and CEO Qasar Younis to discuss the company's work to bring AI to more civilian and military vehicles, and the important of American innovation in the space as global competition heats up.

This week, we're featuring a panel discussion from the 2025 Hill and Valley Forum with Josh Wolfe (Lux Capital), Senator Jack Reed (RI), and Qasar Younis, Founder and CEO, Applied Intuition They address talent attraction and retention, China's espionage and surveillance state, immigration policies, dual-use technologies, and the challenges and strategies in defense innovation and data security in AI and autonomous vehicles. —

Qasar Younis, co-founder and CEO and Peter Ludwig, co-founder and CTO, Applied Intuition Topic: Applied Intuition counts nearly every major automotive and truck maker among its customers for bespoke development of software and tools for range of autonomy use cases Follow the Truck Tech Podcast Other FreightWaves Shows Learn more about your ad choices. Visit megaphone.fm/adchoices

Lenny's Podcast: Product | Growth | Career ✓ Claim Key Takeaways Check out the episode pageRead the full notes @ podcastnotes.orgMike Maples, Jr. is a legendary early-stage startup investor and a co-founder and partner at Floodgate. He's made early bets on transformative companies like Twitter, Lyft, Twitch, Okta, Rappi, and Applied Intuition and is one of the pioneers of seed-stage investing as a category. He's been on the Forbes Midas List eight times and enjoys sharing the lessons he's learned from his years studying iconic companies. In his new book, Pattern Breakers: Why Some Start-Ups Change the Future, co-authored with Peter Ziebelman, he discusses what he's found separates startups and founders that break through and change the world from those that don't. After spending years reviewing the notes and decks from the thousands of startups he's known over the past two decades, he's uncovered three ways that breakthrough founders think and act differently. In our conversation, Mike talks about:• The three elements of breakthrough startup ideas• Why you need to both think and act differently• How to avoid the “comparison trap” and “conformity trap”• The importance of movements, storytelling, and healthy disagreeableness in startup success• How to apply pattern-breaking principles within large companies• Mike's one piece of advice for founders• Much morePre-order Mike's book here and get a second signed copy for free. Limited copies are available, so order ASAP: patternbreakers.com/lenny.—Brought to you by:• Enterpret—Transform customer feedback into product growth• Anvil—The fastest way to build software for documents• Webflow—The web experience platform—Find the transcript at: https://www.lennysnewsletter.com/p/how-to-find-a-great-startup-idea-mike-maples-jr—Where to find Mike Maples, Jr.:• X: https://x.com/m2jr• LinkedIn: https://www.linkedin.com/in/maples/• Substack: https://greatness.substack.com/• Website: https://www.floodgate.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Mike's background(03:10) The inspiration behind Pattern Breakers(08:09) Uncovering startup insights(11:37) A quick summary of Pattern Breakers(13:52) Coming up with an idea(15:30) Inflections(17:09) Examples of inflections(28:10) Insights(36:58) The power of surprises(47:36) Founder-future fit(55:33) Advice for aspiring founders(56:41) Living in the future: valid opinions(55:34) Case study: Maddie Hall and Living Carbon(58:40) Identifying lighthouse customers(01:00:53) The importance of desperation in customer needs(01:03:57) Creating movements and storytelling(01:24:22) The role of disagreeableness in startups(01:34:42) Applying these principles within a company(01:40:43) Lightning round—Referenced:• Pattern Breakers: Why Some Start-Ups Change the Future: https://www.amazon.com/Pattern-Breakers-Start-Ups-Change-Future/dp/1541704355• Justin.tv: https://en.wikipedia.org/wiki/Justin.tv• Airbnb's CEO says a $40 cereal box changed the course of the multibillion-dollar company: https://fortune.com/2023/04/19/airbnb-ceo-cereal-box-investors-changed-everything-billion-dollar-company/• Brian Chesky's new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach• The Unconventional Exit: How Justin Kan Sold His First Startup on eBay: https://medium.datadriveninvestor.com/the-unconventional-exit-how-justin-kan-sold-his-first-startup-on-ebay-4d705afe1354• Kyle Vogt on LinkedIn: https://www.linkedin.com/in/kylevogt/• The State of Telehealth Before and After the COVID-19 Pandemic: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9035352/• The Craigslist Killers: https://www.gq.com/story/craigslist-killers• The social radar: Y Combinator's secret weapon | Jessica Livingston (co-founder of Y Combinator, author, podcast host): https://www.lennysnewsletter.com/p/the-social-radar-jessica-livingston• Michael Seibel on LinkedIn: https://www.linkedin.com/in/mwseibel/• The Airbnb Story: How Three Ordinary Guys Disrupted an Industry, Made Billions ... and Created Plenty of Controversy: https://www.amazon.com/Airbnb-Story-Ordinary-Disrupted-Controversy/dp/0544952669• Scott Cook: https://www.forbes.com/profile/scott-cook/• Chegg: https://www.chegg.com/• Aayush Phumbhra on LinkedIn: https://www.linkedin.com/in/aayush/• Osman Rashid on LinkedIn: https://www.linkedin.com/in/osmanrashid/• Okta: https://www.okta.com/• The Man Who Makes the Future: Wired Icon Marc Andreessen: https://www.wired.com/2012/04/ff-andreessen/• Peter Ludwig on LinkedIn: https://www.linkedin.com/in/peterwludwig/• Qasar Younis on LinkedIn: https://www.linkedin.com/in/qasar/• Paul Allen's website: https://paulallen.com/• Louis Pasteur quote: https://www.forbes.com/quotes/6145/• What was Atrium and why did it fail? https://www.failory.com/cemetery/atrium• Patrick Collison on LinkedIn: https://www.linkedin.com/in/patrickcollison/• Drew Houston on LinkedIn: https://www.linkedin.com/in/drewhouston/• William Gibson's quote: https://www.goodreads.com/quotes/681-the-future-is-already-here-it-s-just-not-evenly• Maddie Hall on LinkedIn: https://www.linkedin.com/in/maddie-hall-76293135/• Living Carbon: https://www.livingcarbon.com• Zenefits (now Trinet): https://connect.trinet.com/• Sam Altman on X: https://x.com/sama• Steve Wozniak on LinkedIn: https://www.linkedin.com/in/wozniaksteve/• Horsley Bridge Partners: https://www.horsleybridge.com/• David Swensen: https://en.wikipedia.org/wiki/David_F._Swensen• Judith Elsea on LinkedIn: https://www.linkedin.com/in/judithelsea/• 7 Powers: The Foundations of Business Strategy: https://www.amazon.com/7-Powers-Foundations-Business-Strategy/dp/0998116319• Business strategy with Hamilton Helmer (author of 7 Powers): https://www.lennysnewsletter.com/p/business-strategy-with-hamilton-helmer• Lyft's Focus on Community and the Story Behind the Pink Mustache: https://techcrunch.com/2012/09/17/lyfts-focus-on-community-and-the-story-behind-the-pink-mustache/• Logan Green on LinkedIn: https://www.linkedin.com/in/logangreen/• John Zimmer on LinkedIn: https://www.linkedin.com/in/johnzimmer11/• Storytelling with Nancy Duarte: How to craft compelling presentations and tell a story that sticks: https://www.lennysnewsletter.com/p/storytelling-with-nancy-duarte-how• Steve Jobs Introducing the iPhone at MacWorld 2007: https://www.youtube.com/watch?v=x7qPAY9JqE4• Jonathan Livingston Seagull: https://www.amazon.com/Jonathan-Livingston-Seagull-Richard-Bach/dp/0743278909• The paths to power: How to grow your influence and advance your career | Jeffrey Pfeffer (author of 7 Rules of Power, professor at Stanford GSB): https://www.lennysnewsletter.com/p/the-paths-to-power-jeffrey-pfeffer• Robin Roberts on LinkedIn: https://www.linkedin.com/in/robin-roberts-393a934b/• Skunkworks: https://www.lockheedmartin.com/en-us/who-we-are/business-areas/aeronautics/skunkworks.html• Vision, conviction, and hype: How to build 0 to 1 inside a company | Mihika Kapoor (Product at Figma): https://www.lennysnewsletter.com/p/vision-conviction-hype-mihika-kapoor• Hard-won lessons building 0 to 1 inside Atlassian | Tanguy Crusson (Head of Jira Product Discovery): https://www.lennysnewsletter.com/p/building-0-to-1-inside-atlassian-tanguy-crusson• Figma: https://www.figma.com/• Atlassian: https://www.atlassian.com/• Vinod Khosla: https://www.khoslaventures.com/team/vinod-khosla/• Top Five Regrets of the Dying: A Life Transformed by the Dearly Departing: https://www.amazon.com/Top-Five-Regrets-Dying-Transformed-ebook/dp/B07KNRLY1L• Chase, Chance, and Creativity: The Lucky Art of Novelty: https://www.amazon.com/Chase-Chance-Creativity-Lucky-Novelty/dp/0262511355• Clay Christensen's books: https://www.amazon.com/stores/Clayton-M.-Christensen/author/B000APPD3Y• Resonate: Present Visual Stories That Transform: https://www.amazon.com/Resonate-Present-Stories-Transform-Audiences/dp/0470632011• Ferrari on Prime: https://www.amazon.com/Ferrari-Adam-Driver/dp/B0CNDBN672• Montblanc fountain pens: https://www.montblanc.com/en-us—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Lenny's Podcast: Product | Growth | Career ✓ Claim Key Takeaways Check out the episode pageRead the full notes @ podcastnotes.orgMike Maples, Jr. is a legendary early-stage startup investor and a co-founder and partner at Floodgate. He's made early bets on transformative companies like Twitter, Lyft, Twitch, Okta, Rappi, and Applied Intuition and is one of the pioneers of seed-stage investing as a category. He's been on the Forbes Midas List eight times and enjoys sharing the lessons he's learned from his years studying iconic companies. In his new book, Pattern Breakers: Why Some Start-Ups Change the Future, co-authored with Peter Ziebelman, he discusses what he's found separates startups and founders that break through and change the world from those that don't. After spending years reviewing the notes and decks from the thousands of startups he's known over the past two decades, he's uncovered three ways that breakthrough founders think and act differently. In our conversation, Mike talks about:• The three elements of breakthrough startup ideas• Why you need to both think and act differently• How to avoid the “comparison trap” and “conformity trap”• The importance of movements, storytelling, and healthy disagreeableness in startup success• How to apply pattern-breaking principles within large companies• Mike's one piece of advice for founders• Much morePre-order Mike's book here and get a second signed copy for free. Limited copies are available, so order ASAP: patternbreakers.com/lenny.—Brought to you by:• Enterpret—Transform customer feedback into product growth• Anvil—The fastest way to build software for documents• Webflow—The web experience platform—Find the transcript at: https://www.lennysnewsletter.com/p/how-to-find-a-great-startup-idea-mike-maples-jr—Where to find Mike Maples, Jr.:• X: https://x.com/m2jr• LinkedIn: https://www.linkedin.com/in/maples/• Substack: https://greatness.substack.com/• Website: https://www.floodgate.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Mike's background(03:10) The inspiration behind Pattern Breakers(08:09) Uncovering startup insights(11:37) A quick summary of Pattern Breakers(13:52) Coming up with an idea(15:30) Inflections(17:09) Examples of inflections(28:10) Insights(36:58) The power of surprises(47:36) Founder-future fit(55:33) Advice for aspiring founders(56:41) Living in the future: valid opinions(55:34) Case study: Maddie Hall and Living Carbon(58:40) Identifying lighthouse customers(01:00:53) The importance of desperation in customer needs(01:03:57) Creating movements and storytelling(01:24:22) The role of disagreeableness in startups(01:34:42) Applying these principles within a company(01:40:43) Lightning round—Referenced:• Pattern Breakers: Why Some Start-Ups Change the Future: https://www.amazon.com/Pattern-Breakers-Start-Ups-Change-Future/dp/1541704355• Justin.tv: https://en.wikipedia.org/wiki/Justin.tv• Airbnb's CEO says a $40 cereal box changed the course of the multibillion-dollar company: https://fortune.com/2023/04/19/airbnb-ceo-cereal-box-investors-changed-everything-billion-dollar-company/• Brian Chesky's new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach• The Unconventional Exit: How Justin Kan Sold His First Startup on eBay: https://medium.datadriveninvestor.com/the-unconventional-exit-how-justin-kan-sold-his-first-startup-on-ebay-4d705afe1354• Kyle Vogt on LinkedIn: https://www.linkedin.com/in/kylevogt/• The State of Telehealth Before and After the COVID-19 Pandemic: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9035352/• The Craigslist Killers: https://www.gq.com/story/craigslist-killers• The social radar: Y Combinator's secret weapon | Jessica Livingston (co-founder of Y Combinator, author, podcast host): https://www.lennysnewsletter.com/p/the-social-radar-jessica-livingston• Michael Seibel on LinkedIn: https://www.linkedin.com/in/mwseibel/• The Airbnb Story: How Three Ordinary Guys Disrupted an Industry, Made Billions ... and Created Plenty of Controversy: https://www.amazon.com/Airbnb-Story-Ordinary-Disrupted-Controversy/dp/0544952669• Scott Cook: https://www.forbes.com/profile/scott-cook/• Chegg: https://www.chegg.com/• Aayush Phumbhra on LinkedIn: https://www.linkedin.com/in/aayush/• Osman Rashid on LinkedIn: https://www.linkedin.com/in/osmanrashid/• Okta: https://www.okta.com/• The Man Who Makes the Future: Wired Icon Marc Andreessen: https://www.wired.com/2012/04/ff-andreessen/• Peter Ludwig on LinkedIn: https://www.linkedin.com/in/peterwludwig/• Qasar Younis on LinkedIn: https://www.linkedin.com/in/qasar/• Paul Allen's website: https://paulallen.com/• Louis Pasteur quote: https://www.forbes.com/quotes/6145/• What was Atrium and why did it fail? https://www.failory.com/cemetery/atrium• Patrick Collison on LinkedIn: https://www.linkedin.com/in/patrickcollison/• Drew Houston on LinkedIn: https://www.linkedin.com/in/drewhouston/• William Gibson's quote: https://www.goodreads.com/quotes/681-the-future-is-already-here-it-s-just-not-evenly• Maddie Hall on LinkedIn: https://www.linkedin.com/in/maddie-hall-76293135/• Living Carbon: https://www.livingcarbon.com• Zenefits (now Trinet): https://connect.trinet.com/• Sam Altman on X: https://x.com/sama• Steve Wozniak on LinkedIn: https://www.linkedin.com/in/wozniaksteve/• Horsley Bridge Partners: https://www.horsleybridge.com/• David Swensen: https://en.wikipedia.org/wiki/David_F._Swensen• Judith Elsea on LinkedIn: https://www.linkedin.com/in/judithelsea/• 7 Powers: The Foundations of Business Strategy: https://www.amazon.com/7-Powers-Foundations-Business-Strategy/dp/0998116319• Business strategy with Hamilton Helmer (author of 7 Powers): https://www.lennysnewsletter.com/p/business-strategy-with-hamilton-helmer• Lyft's Focus on Community and the Story Behind the Pink Mustache: https://techcrunch.com/2012/09/17/lyfts-focus-on-community-and-the-story-behind-the-pink-mustache/• Logan Green on LinkedIn: https://www.linkedin.com/in/logangreen/• John Zimmer on LinkedIn: https://www.linkedin.com/in/johnzimmer11/• Storytelling with Nancy Duarte: How to craft compelling presentations and tell a story that sticks: https://www.lennysnewsletter.com/p/storytelling-with-nancy-duarte-how• Steve Jobs Introducing the iPhone at MacWorld 2007: https://www.youtube.com/watch?v=x7qPAY9JqE4• Jonathan Livingston Seagull: https://www.amazon.com/Jonathan-Livingston-Seagull-Richard-Bach/dp/0743278909• The paths to power: How to grow your influence and advance your career | Jeffrey Pfeffer (author of 7 Rules of Power, professor at Stanford GSB): https://www.lennysnewsletter.com/p/the-paths-to-power-jeffrey-pfeffer• Robin Roberts on LinkedIn: https://www.linkedin.com/in/robin-roberts-393a934b/• Skunkworks: https://www.lockheedmartin.com/en-us/who-we-are/business-areas/aeronautics/skunkworks.html• Vision, conviction, and hype: How to build 0 to 1 inside a company | Mihika Kapoor (Product at Figma): https://www.lennysnewsletter.com/p/vision-conviction-hype-mihika-kapoor• Hard-won lessons building 0 to 1 inside Atlassian | Tanguy Crusson (Head of Jira Product Discovery): https://www.lennysnewsletter.com/p/building-0-to-1-inside-atlassian-tanguy-crusson• Figma: https://www.figma.com/• Atlassian: https://www.atlassian.com/• Vinod Khosla: https://www.khoslaventures.com/team/vinod-khosla/• Top Five Regrets of the Dying: A Life Transformed by the Dearly Departing: https://www.amazon.com/Top-Five-Regrets-Dying-Transformed-ebook/dp/B07KNRLY1L• Chase, Chance, and Creativity: The Lucky Art of Novelty: https://www.amazon.com/Chase-Chance-Creativity-Lucky-Novelty/dp/0262511355• Clay Christensen's books: https://www.amazon.com/stores/Clayton-M.-Christensen/author/B000APPD3Y• Resonate: Present Visual Stories That Transform: https://www.amazon.com/Resonate-Present-Stories-Transform-Audiences/dp/0470632011• Ferrari on Prime: https://www.amazon.com/Ferrari-Adam-Driver/dp/B0CNDBN672• Montblanc fountain pens: https://www.montblanc.com/en-us—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Lenny's Podcast: Product | Growth | Career ✓ Claim Key Takeaways Check out the episode pageRead the full notes @ podcastnotes.orgMike Maples, Jr. is a legendary early-stage startup investor and a co-founder and partner at Floodgate. He's made early bets on transformative companies like Twitter, Lyft, Twitch, Okta, Rappi, and Applied Intuition and is one of the pioneers of seed-stage investing as a category. He's been on the Forbes Midas List eight times and enjoys sharing the lessons he's learned from his years studying iconic companies. In his new book, Pattern Breakers: Why Some Start-Ups Change the Future, co-authored with Peter Ziebelman, he discusses what he's found separates startups and founders that break through and change the world from those that don't. After spending years reviewing the notes and decks from the thousands of startups he's known over the past two decades, he's uncovered three ways that breakthrough founders think and act differently. In our conversation, Mike talks about:• The three elements of breakthrough startup ideas• Why you need to both think and act differently• How to avoid the “comparison trap” and “conformity trap”• The importance of movements, storytelling, and healthy disagreeableness in startup success• How to apply pattern-breaking principles within large companies• Mike's one piece of advice for founders• Much morePre-order Mike's book here and get a second signed copy for free. Limited copies are available, so order ASAP: patternbreakers.com/lenny.—Brought to you by:• Enterpret—Transform customer feedback into product growth• Anvil—The fastest way to build software for documents• Webflow—The web experience platform—Find the transcript at: https://www.lennysnewsletter.com/p/how-to-find-a-great-startup-idea-mike-maples-jr—Where to find Mike Maples, Jr.:• X: https://x.com/m2jr• LinkedIn: https://www.linkedin.com/in/maples/• Substack: https://greatness.substack.com/• Website: https://www.floodgate.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Mike's background(03:10) The inspiration behind Pattern Breakers(08:09) Uncovering startup insights(11:37) A quick summary of Pattern Breakers(13:52) Coming up with an idea(15:30) Inflections(17:09) Examples of inflections(28:10) Insights(36:58) The power of surprises(47:36) Founder-future fit(55:33) Advice for aspiring founders(56:41) Living in the future: valid opinions(55:34) Case study: Maddie Hall and Living Carbon(58:40) Identifying lighthouse customers(01:00:53) The importance of desperation in customer needs(01:03:57) Creating movements and storytelling(01:24:22) The role of disagreeableness in startups(01:34:42) Applying these principles within a company(01:40:43) Lightning round—Referenced:• Pattern Breakers: Why Some Start-Ups Change the Future: https://www.amazon.com/Pattern-Breakers-Start-Ups-Change-Future/dp/1541704355• Justin.tv: https://en.wikipedia.org/wiki/Justin.tv• Airbnb's CEO says a $40 cereal box changed the course of the multibillion-dollar company: https://fortune.com/2023/04/19/airbnb-ceo-cereal-box-investors-changed-everything-billion-dollar-company/• Brian Chesky's new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach• The Unconventional Exit: How Justin Kan Sold His First Startup on eBay: https://medium.datadriveninvestor.com/the-unconventional-exit-how-justin-kan-sold-his-first-startup-on-ebay-4d705afe1354• Kyle Vogt on LinkedIn: https://www.linkedin.com/in/kylevogt/• The State of Telehealth Before and After the COVID-19 Pandemic: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9035352/• The Craigslist Killers: https://www.gq.com/story/craigslist-killers• The social radar: Y Combinator's secret weapon | Jessica Livingston (co-founder of Y Combinator, author, podcast host): https://www.lennysnewsletter.com/p/the-social-radar-jessica-livingston• Michael Seibel on LinkedIn: https://www.linkedin.com/in/mwseibel/• The Airbnb Story: How Three Ordinary Guys Disrupted an Industry, Made Billions ... and Created Plenty of Controversy: https://www.amazon.com/Airbnb-Story-Ordinary-Disrupted-Controversy/dp/0544952669• Scott Cook: https://www.forbes.com/profile/scott-cook/• Chegg: https://www.chegg.com/• Aayush Phumbhra on LinkedIn: https://www.linkedin.com/in/aayush/• Osman Rashid on LinkedIn: https://www.linkedin.com/in/osmanrashid/• Okta: https://www.okta.com/• The Man Who Makes the Future: Wired Icon Marc Andreessen: https://www.wired.com/2012/04/ff-andreessen/• Peter Ludwig on LinkedIn: https://www.linkedin.com/in/peterwludwig/• Qasar Younis on LinkedIn: https://www.linkedin.com/in/qasar/• Paul Allen's website: https://paulallen.com/• Louis Pasteur quote: https://www.forbes.com/quotes/6145/• What was Atrium and why did it fail? https://www.failory.com/cemetery/atrium• Patrick Collison on LinkedIn: https://www.linkedin.com/in/patrickcollison/• Drew Houston on LinkedIn: https://www.linkedin.com/in/drewhouston/• William Gibson's quote: https://www.goodreads.com/quotes/681-the-future-is-already-here-it-s-just-not-evenly• Maddie Hall on LinkedIn: https://www.linkedin.com/in/maddie-hall-76293135/• Living Carbon: https://www.livingcarbon.com• Zenefits (now Trinet): https://connect.trinet.com/• Sam Altman on X: https://x.com/sama• Steve Wozniak on LinkedIn: https://www.linkedin.com/in/wozniaksteve/• Horsley Bridge Partners: https://www.horsleybridge.com/• David Swensen: https://en.wikipedia.org/wiki/David_F._Swensen• Judith Elsea on LinkedIn: https://www.linkedin.com/in/judithelsea/• 7 Powers: The Foundations of Business Strategy: https://www.amazon.com/7-Powers-Foundations-Business-Strategy/dp/0998116319• Business strategy with Hamilton Helmer (author of 7 Powers): https://www.lennysnewsletter.com/p/business-strategy-with-hamilton-helmer• Lyft's Focus on Community and the Story Behind the Pink Mustache: https://techcrunch.com/2012/09/17/lyfts-focus-on-community-and-the-story-behind-the-pink-mustache/• Logan Green on LinkedIn: https://www.linkedin.com/in/logangreen/• John Zimmer on LinkedIn: https://www.linkedin.com/in/johnzimmer11/• Storytelling with Nancy Duarte: How to craft compelling presentations and tell a story that sticks: https://www.lennysnewsletter.com/p/storytelling-with-nancy-duarte-how• Steve Jobs Introducing the iPhone at MacWorld 2007: https://www.youtube.com/watch?v=x7qPAY9JqE4• Jonathan Livingston Seagull: https://www.amazon.com/Jonathan-Livingston-Seagull-Richard-Bach/dp/0743278909• The paths to power: How to grow your influence and advance your career | Jeffrey Pfeffer (author of 7 Rules of Power, professor at Stanford GSB): https://www.lennysnewsletter.com/p/the-paths-to-power-jeffrey-pfeffer• Robin Roberts on LinkedIn: https://www.linkedin.com/in/robin-roberts-393a934b/• Skunkworks: https://www.lockheedmartin.com/en-us/who-we-are/business-areas/aeronautics/skunkworks.html• Vision, conviction, and hype: How to build 0 to 1 inside a company | Mihika Kapoor (Product at Figma): https://www.lennysnewsletter.com/p/vision-conviction-hype-mihika-kapoor• Hard-won lessons building 0 to 1 inside Atlassian | Tanguy Crusson (Head of Jira Product Discovery): https://www.lennysnewsletter.com/p/building-0-to-1-inside-atlassian-tanguy-crusson• Figma: https://www.figma.com/• Atlassian: https://www.atlassian.com/• Vinod Khosla: https://www.khoslaventures.com/team/vinod-khosla/• Top Five Regrets of the Dying: A Life Transformed by the Dearly Departing: https://www.amazon.com/Top-Five-Regrets-Dying-Transformed-ebook/dp/B07KNRLY1L• Chase, Chance, and Creativity: The Lucky Art of Novelty: https://www.amazon.com/Chase-Chance-Creativity-Lucky-Novelty/dp/0262511355• Clay Christensen's books: https://www.amazon.com/stores/Clayton-M.-Christensen/author/B000APPD3Y• Resonate: Present Visual Stories That Transform: https://www.amazon.com/Resonate-Present-Stories-Transform-Audiences/dp/0470632011• Ferrari on Prime: https://www.amazon.com/Ferrari-Adam-Driver/dp/B0CNDBN672• Montblanc fountain pens: https://www.montblanc.com/en-us—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Applied Intuition is developing an off-road autonomy stack to address the growing demand for autonomous solutions in industries such as mining, agriculture, and defense. Qasar Younis, CEO and Peter Ludwig, CTO, Co-Founders of Applied Intuition joined The Road to Autonomy Founder Grayson Brulte on Autonomy Insights to discuss Applied's approach to off-road autonomy. The company's approach to developing an off-road autonomy stack is driven by industry needs, particularly labor shortages in remote locations and safety concerns. Applied Intuition's off-road strategy involves creating a modular, adaptable solution that can be customized for various vehicle types and industries. This approach allows them to distribute development costs across multiple customers, making it more economical for companies to adopt their technology rather than developing in-house solutions. Their technology is designed to work with a range of vehicles and tasks, with the philosophy that if a human can control a vehicle off-road, their autonomy stack should be able to accomplish the same task.As the company grows, they are positioning themselves as a key enabler in the emerging autonomy economy, providing the tools and technology necessary for companies to succeed in implementing and scaling autonomous solutions across multiple sectors.Episode Chapters0:00 Why Develop an Off-Road Autonomy Stack?9:48 Off-Road Autonomy and Defense10:54 Off-Road Autonomy as a Growth Market13:20 What's Next For Applied Intuition?--------About The Road to AutonomyThe Road to Autonomy® is a leading source of data, insight and commentary on autonomous vehicles/trucks and the emerging autonomy economy™. The company has two businesses: The Road to Autonomy Indices, with Standard and Poor's Dow Jones Indices as the custom calculation agent; Media, which includes The Road to Autonomy and Autonomy Economy podcasts as well as This Week in The Autonomy Economy newsletter.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Mike Maples, Jr. is a legendary early-stage startup investor and a co-founder and partner at Floodgate. He's made early bets on transformative companies like Twitter, Lyft, Twitch, Okta, Rappi, and Applied Intuition and is one of the pioneers of seed-stage investing as a category. He's been on the Forbes Midas List eight times and enjoys sharing the lessons he's learned from his years studying iconic companies. In his new book, Pattern Breakers: Why Some Start-Ups Change the Future, co-authored with Peter Ziebelman, he discusses what he's found separates startups and founders that break through and change the world from those that don't. After spending years reviewing the notes and decks from the thousands of startups he's known over the past two decades, he's uncovered three ways that breakthrough founders think and act differently. In our conversation, Mike talks about:• The three elements of breakthrough startup ideas• Why you need to both think and act differently• How to avoid the “comparison trap” and “conformity trap”• The importance of movements, storytelling, and healthy disagreeableness in startup success• How to apply pattern-breaking principles within large companies• Mike's one piece of advice for founders• Much morePre-order Mike's book here and get a second signed copy for free. Limited copies are available, so order ASAP: patternbreakers.com/lenny.—Brought to you by:• Enterpret—Transform customer feedback into product growth• Anvil—The fastest way to build software for documents• Webflow—The web experience platform—Find the transcript at: https://www.lennysnewsletter.com/p/how-to-find-a-great-startup-idea-mike-maples-jr—Where to find Mike Maples, Jr.:• X: https://x.com/m2jr• LinkedIn: https://www.linkedin.com/in/maples/• Substack: https://greatness.substack.com/• Website: https://www.floodgate.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Mike's background(03:10) The inspiration behind Pattern Breakers(08:09) Uncovering startup insights(11:37) A quick summary of Pattern Breakers(13:52) Coming up with an idea(15:30) Inflections(17:09) Examples of inflections(28:10) Insights(36:58) The power of surprises(47:36) Founder-future fit(55:33) Advice for aspiring founders(56:41) Living in the future: valid opinions(55:34) Case study: Maddie Hall and Living Carbon(58:40) Identifying lighthouse customers(01:00:53) The importance of desperation in customer needs(01:03:57) Creating movements and storytelling(01:24:22) The role of disagreeableness in startups(01:34:42) Applying these principles within a company(01:40:43) Lightning round—Referenced:• Pattern Breakers: Why Some Start-Ups Change the Future: https://www.amazon.com/Pattern-Breakers-Start-Ups-Change-Future/dp/1541704355• Justin.tv: https://en.wikipedia.org/wiki/Justin.tv• Airbnb's CEO says a $40 cereal box changed the course of the multibillion-dollar company: https://fortune.com/2023/04/19/airbnb-ceo-cereal-box-investors-changed-everything-billion-dollar-company/• Brian Chesky's new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach• The Unconventional Exit: How Justin Kan Sold His First Startup on eBay: https://medium.datadriveninvestor.com/the-unconventional-exit-how-justin-kan-sold-his-first-startup-on-ebay-4d705afe1354• Kyle Vogt on LinkedIn: https://www.linkedin.com/in/kylevogt/• The State of Telehealth Before and After the COVID-19 Pandemic: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9035352/• The Craigslist Killers: https://www.gq.com/story/craigslist-killers• The social radar: Y Combinator's secret weapon | Jessica Livingston (co-founder of Y Combinator, author, podcast host): https://www.lennysnewsletter.com/p/the-social-radar-jessica-livingston• Michael Seibel on LinkedIn: https://www.linkedin.com/in/mwseibel/• The Airbnb Story: How Three Ordinary Guys Disrupted an Industry, Made Billions ... and Created Plenty of Controversy: https://www.amazon.com/Airbnb-Story-Ordinary-Disrupted-Controversy/dp/0544952669• Scott Cook: https://www.forbes.com/profile/scott-cook/• Chegg: https://www.chegg.com/• Aayush Phumbhra on LinkedIn: https://www.linkedin.com/in/aayush/• Osman Rashid on LinkedIn: https://www.linkedin.com/in/osmanrashid/• Okta: https://www.okta.com/• The Man Who Makes the Future: Wired Icon Marc Andreessen: https://www.wired.com/2012/04/ff-andreessen/• Peter Ludwig on LinkedIn: https://www.linkedin.com/in/peterwludwig/• Qasar Younis on LinkedIn: https://www.linkedin.com/in/qasar/• Paul Allen's website: https://paulallen.com/• Louis Pasteur quote: https://www.forbes.com/quotes/6145/• What was Atrium and why did it fail? https://www.failory.com/cemetery/atrium• Patrick Collison on LinkedIn: https://www.linkedin.com/in/patrickcollison/• Drew Houston on LinkedIn: https://www.linkedin.com/in/drewhouston/• William Gibson's quote: https://www.goodreads.com/quotes/681-the-future-is-already-here-it-s-just-not-evenly• Maddie Hall on LinkedIn: https://www.linkedin.com/in/maddie-hall-76293135/• Living Carbon: https://www.livingcarbon.com• Zenefits (now Trinet): https://connect.trinet.com/• Sam Altman on X: https://x.com/sama• Steve Wozniak on LinkedIn: https://www.linkedin.com/in/wozniaksteve/• Horsley Bridge Partners: https://www.horsleybridge.com/• David Swensen: https://en.wikipedia.org/wiki/David_F._Swensen• Judith Elsea on LinkedIn: https://www.linkedin.com/in/judithelsea/• 7 Powers: The Foundations of Business Strategy: https://www.amazon.com/7-Powers-Foundations-Business-Strategy/dp/0998116319• Business strategy with Hamilton Helmer (author of 7 Powers): https://www.lennysnewsletter.com/p/business-strategy-with-hamilton-helmer• Lyft's Focus on Community and the Story Behind the Pink Mustache: https://techcrunch.com/2012/09/17/lyfts-focus-on-community-and-the-story-behind-the-pink-mustache/• Logan Green on LinkedIn: https://www.linkedin.com/in/logangreen/• John Zimmer on LinkedIn: https://www.linkedin.com/in/johnzimmer11/• Storytelling with Nancy Duarte: How to craft compelling presentations and tell a story that sticks: https://www.lennysnewsletter.com/p/storytelling-with-nancy-duarte-how• Steve Jobs Introducing the iPhone at MacWorld 2007: https://www.youtube.com/watch?v=x7qPAY9JqE4• Jonathan Livingston Seagull: https://www.amazon.com/Jonathan-Livingston-Seagull-Richard-Bach/dp/0743278909• The paths to power: How to grow your influence and advance your career | Jeffrey Pfeffer (author of 7 Rules of Power, professor at Stanford GSB): https://www.lennysnewsletter.com/p/the-paths-to-power-jeffrey-pfeffer• Robin Roberts on LinkedIn: https://www.linkedin.com/in/robin-roberts-393a934b/• Skunkworks: https://www.lockheedmartin.com/en-us/who-we-are/business-areas/aeronautics/skunkworks.html• Vision, conviction, and hype: How to build 0 to 1 inside a company | Mihika Kapoor (Product at Figma): https://www.lennysnewsletter.com/p/vision-conviction-hype-mihika-kapoor• Hard-won lessons building 0 to 1 inside Atlassian | Tanguy Crusson (Head of Jira Product Discovery): https://www.lennysnewsletter.com/p/building-0-to-1-inside-atlassian-tanguy-crusson• Figma: https://www.figma.com/• Atlassian: https://www.atlassian.com/• Vinod Khosla: https://www.khoslaventures.com/team/vinod-khosla/• Top Five Regrets of the Dying: A Life Transformed by the Dearly Departing: https://www.amazon.com/Top-Five-Regrets-Dying-Transformed-ebook/dp/B07KNRLY1L• Chase, Chance, and Creativity: The Lucky Art of Novelty: https://www.amazon.com/Chase-Chance-Creativity-Lucky-Novelty/dp/0262511355• Clay Christensen's books: https://www.amazon.com/stores/Clayton-M.-Christensen/author/B000APPD3Y• Resonate: Present Visual Stories That Transform: https://www.amazon.com/Resonate-Present-Stories-Transform-Audiences/dp/0470632011• Ferrari on Prime: https://www.amazon.com/Ferrari-Adam-Driver/dp/B0CNDBN672• Montblanc fountain pens: https://www.montblanc.com/en-us—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Qasar Younis and Peter Ludwig built Applied Intuition differently from most other startups. At a time of profligate spending at the peak of the tech bubble, they kept expenses low — and the company cash-flow positive for several years now. When every other company was moving toward remote work or a hybrid setup, they doubled down on the in-person, five-days-per-week office (while continuing a no-shoes philosophy). And when it comes to culture, they don't just post their corporate values on a wall, but encode them right into the very software that runs the company. The results? Applied reached a new milestone valuation earlier this year of $6 billion as well as announced a strategic partnership with automaker Porsche. It's a moment of success years and even decades in the making, with both Qasar and Peter growing up amidst the milieu of America's auto capital Detroit. Yet, it wasn't just friends and family working in the auto industry that led them to invent the future of the car, but also a willingness to learn from Silicon Valley's most thoughtful startup growth practices. Alongside host Danny Crichton and Lux general partner Bilal Zuberi, we weave a conversation about automotive and autonomy while we discuss the key decisions that founders must make when building a startup. We talk about the pressure of capitalism on company execution, using software to manage a growing organization, why Google exported so much talent in the early 2010s, how to protect engineering productivity with a customer-centric culture, how to construct a useful board of directors, and finally, why markets just “whomp” any other factor of success for entrepreneurs.

Qasar Younis is the co-founder and CEO of Applied Intuition, which creates software solutions to help automakers, Tier 1 suppliers, and companies in the trucking, construction, and agriculture industries transition to next-generation vehicles. Before founding Applied Intuition, Younis was a partner and COO of Y Combinator. In this conversation with Stanford adjunct lecturer Ravi Belani, Younis gives practical advice for aspiring entrepreneurs, especially students, and shares insights he's gathered from his experience as an investor and founder.

Qasar Younis is the co-founder and CEO of Applied Intuition, which creates software solutions to help automakers, Tier 1 suppliers, and companies in the trucking, construction, and agriculture industries transition to next-generation vehicles. Before founding Applied Intuition, Younis was a partner and COO of Y Combinator. In this conversation with Stanford adjunct lecturer Ravi Belani, Younis gives practical advice for aspiring entrepreneurs, especially students, and shares insights he's gathered from his experience as an investor and founder. (EDS NOTE: THIS TALK INCLUDES EXPLICIT LANGUAGE.)

Are you ready to go behind the scenes and uncover the secrets of the company playing a pivotal role in the future of autonomous and electric vehicles? In this riveting episode, we dive deep into the world of Applied Intuition, the Silicon Valley company partnering with automotive giants such as Porsche to develop groundbreaking software that will make self-driving cars a reality.Join us as host Grayson Brulte sits down with Qasar Younis and Peter Ludwig, the visionary co-founders who are fusing cutting-edge artificial intelligence with decades of automotive expertise. You'll gain unprecedented insights into their bold mission to accelerate safe autonomy across industries – from transforming in-vehicle experiences to tackling defense applications.But that's not all! Brace yourself for insights into Applied Intuition's pioneering work, including their multi-stack strategy to future-proof technology, ambitious vehicle software platform to revolutionize mobile electronics, and the innovative ways they're empowering automakers to control the consumer experience like never before.Don't miss this opportunity to understand the forces driving autonomy and witness the birth of a new era in intelligent machines. Listen now and immerse yourself in a world where the boundaries of possibility are constantly being redefined.Episode Chapters0:00 The Road to Autonomy Index Introduction0:56 Series E Funding3:44 Applied Intuition AI Roadmap5:58 AV 2.010:00 Insights into the Chip Market11:04 Applied Intuition Trust Layer14:37 Autonomous Driving24:36 Applied Intuition x Porsche27:45 Software Development with OEMs36:54 Applied Intuition Defense Business39:24 Future of Applied IntuitionRecorded on Friday, March 29, 2024 --------About The Road to AutonomyThe Road to Autonomy® is a leading source of data, insight and commentary on autonomous vehicles/trucks and the emerging autonomy economy™. The company has two businesses: The Road to Autonomy Indices, with Standard and Poor's Dow Jones Indices as the custom calculation agent; Media, which includes The Road to Autonomy and Autonomy Economy podcasts as well as This Week in The Autonomy Economy newsletter.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Qasar Younis and Peter Ludwig of Applied Intuition join Erik and Jack for episode two of “1 to 1000." If you're looking for SOC 2, ISO 27001, GDPR or HIPAA compliance, head to Vanta: https://www.vanta.com/1000  --- SPONSORS: Are you building a business? If you're looking for SOC 2, ISO 27001, GDPR or HIPAA compliance, head to Vanta. Achieving compliance can actually unlock major growth for your company and build customer loyalty. Vanta automates up to 90% of Compliance work, getting you audit-ready in weeks instead of months and saving 85% of associated costs. 1 to 1000 listeners get $1000 off at: https://www.vanta.com/1000 Metaview is the AI assistant for interviewing. Metaview completely removes the need for recruiters and hiring managers to take notes during interviews—because their AI is designed to take world-class interview notes for you. Team builders at companies like Brex, Robinhood, Quora, and Replit say Metaview has changed the game—see the magic for yourself for free on your first 5 interviews: https://www.metaview.ai/1000 Pesto Tech is a hiring marketplace that makes finding great remote developers fast and easy. They use large language models to evaluate developers along dozens of parameters, including code quality, performance, and security. If you need to start hiring developers fast, all you have to do is answer 5 simple questions on their website: https://pesto.tech --- X / TWITTER: @qasar (Qasar) @jaltma (Jack) @eriktorenberg (Erik Torenberg) @AppliedInt @TurpentineMedia --- TIMESTAMPS: (00:00) Episode Preview (00:42) The idea maze of Applied Intuition (06:21) Qasar and Peter's thoughts on YC's thesis for why founders start companies (09:39) Younger vs. older founders and what risks they should take on (13:51) Sponsor: Vanta (14:49) How to be a good founder: through experience (16:33) What industries should founders start companies in? (19:27) People, team, culture at Applied Intuition (21:05) In-office policy vs. remote policy (27:55) Sponsors: Metaview | Pesto Tech (29:33) Unpacking "radical pragmatism" (32:52) The discipline of living out company values (36:51) The challenges of values (40:06) What's Qasar changed his mind on since YC? (42:14) On recruiting (44:16) Qasar and Peter's unique insights on PMF (47:22) How did Applied Intuition know when they had PMF? (48:27) Multi-product companies (50:26) Last thoughts from Qasar RECOMMENDED PODCAST:  Every week investor and writer of the popular newsletter The Diff, Byrne Hobart, and co-host Erik Torenberg discuss today's major inflection points in technology, business, and markets – and help listeners build a diversified portfolio of trends and ideas for the future. Subscribe to “The Riff” with Byrne Hobart and Erik Torenberg: https://link.chtbl.com/theriff

In der Mittagsfolge sprechen wir heute mit Paula Hübner, Principal von La Famiglia, über die Auflage von zwei neuen Fonds mit einem Gesamtvolumen von über 250 Millionen Euro.La Famiglia ist ein europäischer Risikokapitalfonds mit einem Fokus auf Technologieunternehmen in der Seed- und Wachstumsphase. Der Kapitalgeber wird von einer Auswahl an global aktiven Unternehmerinnen und Unternehmern aus verschiedenen Branchen unterstützt, die den Portfoliounternehmen einen wertvollen frühen Marktzugang, einflussreiche Partnerschaften und umfassendes Know-how bieten können. Die investierten Business Angels arbeiten ebenfalls operativ mit den Foundern zusammen, um eine umfangreiche Unterstützung zu gewährleisten. Das Unternehmen hat derzeit mehr als 70 Startups in seinem Portfolio, darunter die mit 12 Milliarden Euro bewertete HR-Plattform Deel sowie Personio und Forto. Im Gründungsjahr 2017 wurde in Berlin der erste Seed-Fonds in Höhe von 35 Millionen Euro geschlossen. Seitdem hat der Fonds mehr als 350 Millionen Euro eingeworben. Der zweite Seed-Fonds in Höhe von 60 Millionen Euro wurde im Jahr 2019 geschlossen. Das leitende Investmentteam von La Famiglia besteht zu 60 % aus Frauen und zu 60 % aus Personen mit Migrationshintergrund.Der VC hat nun über 250 Millionen Euro für zwei neue Fonds aufgebracht. 165 Millionen Euro werden für die Auflage seines dritten Seed-Fonds verwendet und 90 Millionen Euro fließen in den ersten Wachstums-Co-Investmentfonds des Kapitalgebers. Family Offices, die hinter Markennamen wie Valentino, Adidas, Swarovski, Hapag Lloyd und Estée Lauder stehen sowie Business Angels wie beispielsweise Ilkka Paananen, Ross Mason, Qasar Younis, Hanno Renner und Michael Wax haben die Auflage der Fonds unterstützt. Aus diesen sollen jeweils bis zu 5 Millionen Euro in die Startups fließen. Die in Zürich ansässige Qualitätsmanagement-Plattform Ethon.ai ist bereits die erste Investition aus dem dritten Seed-Fonds.

Michael Ma is the founder & CEO of CreatorDAO, a decentralized community that accelerates creators with capital & technology. He is also a VC and founded Liquid 2 Ventures alongside legendary football player Joe Montana after helping First Round Capital launch Dorm Room Fund in Boston. Prior to venture capital, Michael got his MBA at Harvard Business School and before that sold his startup TalkBin to Google 7 months after cofounding the company with Qasar Younis. In this conversation we discuss: How Michael started & sold TalkBin to Google in 7 months Choosing to sell vs. scale your startup How he met Joe Montana (and started a VC firm with him) What he got out of his time at Harvard Business School The value of an MBA degree YC vs. Harvard Business School What he looks for as an investor What is CreatorDAO And much more

What if you could test hundreds of thousands of traffic and road scenarios before your autonomous vehicles ever even touched asphalt? Imagine billions of miles and an untold number of road events encountered in the virtual world before your car or truck even left the factory floor. . Talk about a head start when it comes to safety and efficiency. . Using a comprehensive suite of products created from the best practices in software development, the minds at Applied Intuition work with clients to design virtual test environments that simulate everything the road can throw at them. They are creating a world where software-enabled vehicles are an integral part of society across all industries where there are vehicles – roads, yes, but also factories and ports as well. .  In this episode of SAE Tomorrow Today, Qasar Younis, Co-Founder and CEO, and Peter Ludwig, Co-Founder and CTO of Applied Intuition, discussed how they're combining Detroit engineering know-how with Silicon Valley digital ingenuity to design differentiated solutions for customers such as VW, GM and Toyota, enabling game-changing AV development with speed and scale. .  We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today (and give us a review) on your preferred podcasting platform. .  Follow SAE on LinkedIn, Instagram, Facebook, Twitter, and YouTube. Follow host Grayson Brulte on LinkedIn, Twitter, and Instagram.

Qasar Younis and Peter Ludwig both grew up in the shadow of American automotive giant General Motors in Detroit, but didn't cross paths until their days as product managers at Google. Since then they've pooled their affinity for cars and technology as the co-founders of Applied Intuition, a fast-moving startup that has already achieved greatness in the world of advanced simulation software for autonomous vehicles. Mike Maples, Jr of FLOODGATE interviews Younis and Ludwig to discuss how the company has made its mark so quickly, and why so much of their success is due to the intentional design of their team, business opportunity, culture and category.

Qasar Younis, CEO & Co-Founder, and Peter Ludwig, CTO & Co-Founder, Applied Intuition joined Grayson Brulte on The Road To Autonomy Podcast to discuss simulation and why a simulation first approach to autonomy is key to building and scaling autonomous vehicles.The conversation begins with Qasar talking about what the marketplace looked like when he co-founded Applied Intuition with Peter in 2017. This was the same year that Waymo began testing autonomous minivans in Chandler, Arizona without a safety driver on public roads. Reflecting on this, Peter shares his take on the marketplace.Generally speaking, there is not really winner take all dynamics in the automotive ecosystem. There is always going to be many companies. There are going to be many players, [with] Waymo being sort of in front in autonomy technology. What is great for Applied is that they are showing the world what is possible and that we are building tools which frankly enable any automotive company to compete at that level. – Peter LudwigQasar expands upon this to share his perspective on how the autonomous vehicle industry operated in 2016, 2017.In 2016, 2017 the only pattern was the Waymo pattern. Which is raise tons of money and build everything in-house. That's just not the case anymore. I do not think there a single sophisticated in-house sim team that isn't also working with somebody in some capacity that is not inside. – Qasar YounisBuilding upon this, Qasar dives into the economics of build versus buy and why it makes economic sense to buy instead of building in-house simulation tools. With technology advancements over the past four and a half years and new powerful chips being introduced, Applied has been able to close the sim to real gap.You want simulation to be as close as possible to the real-world performance of the system, while still being cost-effective to run. – Peter LudwigAs Applied matures as a company, the company has begun to assume a leadership position in the autonomous vehicle industry. Applied has recently published their Best Practices for The Testing and Deployment of Autonomous Vehicles guide that can be downloaded here.In the guide, Applied summarizes best practices for the testing and development of autonomous vehicles. It is an important guide that can be incorporated into your development workflow today.Our goal of the company is to move the entire autonomy ecosystem forward. – Qasar YounisTaking a step back for a moment, Qasar discusses simulation and references an interview where a Waymo Senior Director of Product Management stated that simulation is roughly responsible for 80 to 85% of their progress.Fundamentally there are many things that you cannot test safely in the real world that are necessary for ensuring the safe operation of the vehicle. You can model those scenarios in simulation. – Peter LudwigIn a 2018 interview with Bloomberg, Peter spoke to Mark Bergen about scenarios. Grayson asks Peter how the team comes up with scenarios to model in simulation. Taking it to a local level, Grayson shares several scenarios and Peter explains how simulation can help to prepare autonomous vehicles for those ODDs (Operational Design Domains).Shifting the conversation from autonomous vehicles to autonomous trucks, Grayson asks Peter what are the main differences between simulation for autonomous vehicles and autonomous trucks. Peter explains in-depth how there is a large difference in the approach to simulation for trucks due to the fact the way trucks are built and how they are driven.While there are different forms of simulation, Applied has been solely focused on autonomy since day one.Fundamentally we think that the autonomous industry will be very, very large. We believe that everything that moves will be autonomous. We want to enable that reality. – Qasar YounisExpanding different forms of simulation, Peter explains how Applied's simulation platform differs from a system designed to generate images for movies and video games.Wrapping up the conversation, Qasar and Peter discuss why everything that moves will be autonomous.Follow The Road To Autonomy on Apple PodcastsRecorded on Thursday, June 17, 2021.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

IN THE MIX: Automotive software executive Qasar Younis discusses the future of autonomous vehicles…and how soon our cars will be chauffeering us around!.

Tooling has always been an auto-industry backbone. In a software era, Qasar Younis and Peter Ludwig, the CEO and CTO, respectively, at Applied Intuition, argue it’s all the more important. The two discuss software and simulation that let others develop and test AVs, and their experiences bridging Detroit and Silicon Valley.

Guest: Qasar Younis, Founder and CEO of Applied Intuition

Today's guests are Qasar Younis (Founder & CEO) and Varun Mittal (Senior Engineer) at Applied Intuition. In this episode we discuss why advanced simulation is important for AV development, the cultural differences and similarities between Detroit and Silicon Valley, market dynamics as companies choose between vertical and horizontal development strategies, and predictions for the future of the autonomy revolution.

with Peter Ludwig, Qasar Younis (@qasar), and Sonal Chokshi (@smc90) When people talk about autonomous vehicles, we hear everything from "we're much closer than you think" to "we're much further than you think". So where are we, really, in the widespread reality of autonomous vehicles today? It depends, of course, on how you define autonomy -- which is where a handy recap and update of the SAE (Society of Automotive Engineers) levels of autonomy comes in. But still, given everything out there from self-driving shuttles to Teslas, it's really hard to tell just where we are and where the nuances of, say, Level 2-plus vs. Level 3 might come in. This episode of the a16z Podcast takes a quick pulse on where we are in the state of autonomy in 2019 when it comes to autonomous cars, shuttles, robots -- basically any "autonomous" and/or "self-driving" vehicle out there -- as well as the analogy of mobile for understanding the space: where it works, where it breaks down. But did even the mobile industry itself really have a clear iPhone "moment"? When did mobile devices that seemed so limited -- or seemed like just "toys" -- suddenly (or not so suddenly) go to an apps layer that we use every single day? How do we build "the rails" and "the trains" at the same time in this case? And perhaps most importantly, where will the spoils of this new wave of innovation go -- to Silicon Valley or Detroit? Or outside the U.S.? Who are the players? How do regulatory -- and quite frankly, nationalistic -- concerns come into play here? And finally, how does one balance the desire to embrace innovation in an open and fast, yet still very thoughtful and safe way? The answers, according to Applied Intuition co-founder and CEO Qasar Younis and CTO Peter Ludwig (in conversation with Sonal Chokshi), have to do with commodities and capitalism, with science and science fiction, with simulation and software as infrastructure, and more... And really, how we define autonomy now, and in the future.

On this episode of Venture Stories, Erik is joined by two exciting guests: Qasar Younis (@qasar), co-founder of Applied Intuition, and Alexandr Wang (@alexandr_wang), CEO of Scale.Both Qasar and Alex are creating software that is transforming the way autonomous vehicles are being developed. The three of them have an expansive conversation about where autonomous technology is at today and how the technology and industry might evolve in the future. Throughout the conversation the founders peel back the curtain on the autonomous vehicle development process and put forth are a number of ideas about autonomous technology that run counter to the prevailing narrative in the media today.They begin by talking about some of the specific ways that software is transforming the auto industry and in what ways the tools the founders are building are being used in the development of autonomous technology. Erik asks about the pros and cons of being a horizontal company vs. a vertical company in the space, and Qasar and Alex discuss the extent to which existing car manufacturers have modularized the parts that go into traditional vehicles and why this trend will continue with autonomous technology.Qasar and Alex point out that the key question now is not if, but when, autonomous technology will be deployed at mass scale, and say that even three years ago it was unclear whether it was going to happen at all. They compare the industry as it exists now to the early days of the iPhone and say that like the iPhone app explosion, self-driving cars will be only one application that emerges on top of autonomous technology, which will itself be a much bigger market than the smartphone market. Qasar and Alex both agree that robo-taxis are overly focused on by the media and that other applications of autonomous technology such as in trucking, last-mile delivery and warehouses will arrive sooner and will be both much larger and more consequential than robo-taxis. Alex says that he believes autonomous vehicle technology will be only one part of a broader “robot revolution” in society.Thanks for listening — if you like what you hear, please review us on your favorite podcast platform. Check us out on the web at villageglobal.vc or get in touch with us on Twitter @villageglobal.Venture Stories is brought to you by Village Global, is hosted by co-founder and partner, Erik Torenberg and is produced by Brett Bolkowy.

On this episode of Venture Stories, Erik is joined by two exciting guests: Qasar Younis (@qasar), co-founder of Applied Intuition, and Alexandr Wang (@alexandr_wang), CEO of Scale.Both Qasar and Alex are creating software that is transforming the way autonomous vehicles are being developed. The three of them have an expansive conversation about where autonomous technology is at today and how the technology and industry might evolve in the future. Throughout the conversation the founders peel back the curtain on the autonomous vehicle development process and put forth are a number of ideas about autonomous technology that run counter to the prevailing narrative in the media today.They begin by talking about some of the specific ways that software is transforming the auto industry and in what ways the tools the founders are building are being used in the development of autonomous technology. Erik asks about the pros and cons of being a horizontal company vs. a vertical company in the space, and Qasar and Alex discuss the extent to which existing car manufacturers have modularized the parts that go into traditional vehicles and why this trend will continue with autonomous technology.Qasar and Alex point out that the key question now is not if, but when, autonomous technology will be deployed at mass scale, and say that even three years ago it was unclear whether it was going to happen at all. They compare the industry as it exists now to the early days of the iPhone and say that like the iPhone app explosion, self-driving cars will be only one application that emerges on top of autonomous technology, which will itself be a much bigger market than the smartphone market. Qasar and Alex both agree that robo-taxis are overly focused on by the media and that other applications of autonomous technology such as in trucking, last-mile delivery and warehouses will arrive sooner and will be both much larger and more consequential than robo-taxis. Alex says that he believes autonomous vehicle technology will be only one part of a broader “robot revolution” in society.Thanks for listening — if you like what you hear, please review us on your favorite podcast platform. Check us out on the web at villageglobal.vc or get in touch with us on Twitter @villageglobal.Venture Stories is brought to you by Village Global, is hosted by co-founder and partner, Erik Torenberg and is produced by Brett Bolkowy.

In this episode, we interview Qasar Younis and Matthew Colford of Applied Intuition about simulation and its role in developing and testing autonomous vehicles, including the safety and policy implications. --- Support this podcast: https://anchor.fm/smarter-cars/support

This special live episode was recorded at the Atrium offices in San Francisco on June 20 2018. Village Global co-founder Erik Torenberg hosted a fireside chat with Daniel Kan, co-founder and COO of Cruise Automation and Qasar Younis, former COO of YCombinator.They discussed all things fundraising, providing an inside look into the world of VC funding and exposing some of the subtler points of fundraising for seed and Series A rounds. They discuss topics like dealing with VCs, refining your pitch and the importance of metrics in a Series A round. They also talked about prepping for meetings with VCs, what motivates VCs, how to efficiently backchannel via your network and transitioning from a seed round to a Series A round.They finished with an enlightening Q&A session, taking questions from the live audience at Atrium.Thanks for listening — if you like what you hear, please review us on your favorite podcast platform. Check us out on the web at villageglobal.vc/podcast or get in touch with us on Twitter @villageglobal.

This special live episode was recorded at the Atrium offices in San Francisco on June 20 2018. Village Global co-founder Erik Torenberg hosted a fireside chat with Daniel Kan, co-founder and COO of Cruise Automation and Qasar Younis, former COO of YCombinator.They discussed all things fundraising, providing an inside look into the world of VC funding and exposing some of the subtler points of fundraising for seed and Series A rounds. They discuss topics like dealing with VCs, refining your pitch and the importance of metrics in a Series A round. They also talked about prepping for meetings with VCs, what motivates VCs, how to efficiently backchannel via your network and transitioning from a seed round to a Series A round.They finished with an enlightening Q&A session, taking questions from the live audience at Atrium.Thanks for listening — if you like what you hear, please review us on your favorite podcast platform. Check us out on the web at villageglobal.vc/podcast or get in touch with us on Twitter @villageglobal.

As cars become more like iPhones and less like just, well, cars — everything changes, from data to mapping to interfaces to security and more. How so? Where are we anyway, given all the hype around when self-driving cars will appear everywhere? And where are new opportunities in the space? This episode of the a16z Podcast, based on a panel discussion from the most recent a16z Summit, features a16z research and deal team head Frank Chen in conversation with various companies doing different things in the autonomous space. Guests include: Taggart Matthiesen, head of product at Lyft, which is developing autonomous car technology; James Wu, CEO and co-founder of DeepMap, which focuses on full-stack HD mapping for autonomy; and Qasar Younis, CEO of Applied Intuition, which provides advance simulation software for autonomy. ––– The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation. This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments and certain publicly traded cryptocurrencies/ digital assets for which the issuer has not provided permission for a16z to disclose publicly) is available at https://a16z.com/investments/. Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information.

Episode 16 of Startup School Radio: Host Aaron Harris interviews YC partner Qasar Younis. Also on the show: Frederick Hutson, founder and CEO of Pigeon.ly.