Rivian’s Roadmap to AI Architecture and Autonomy with Founder and CEO RJ Scaringe

Autonomous vehicle technology has moved past human-coded rules and into an era of neural networks and custom computer chips. And to solve the most difficult driving scenarios, electric vehicle company Rivian abandoned its original technology platform to build a vertically integrated data stack. Sarah Guo sits down with Rivian Founder and CEO RJ Scaringe to explore the seismic shift in the automotive industry toward AI-driven, software-defined vehicles . RJ discusses the move away from function or domain-based architecture for vehicle electronic systems to software-defined architecture, which allows for dynamic, monthly updates to features in Rivian’s vehicles. RJ also talks about the upcoming launch of Rivian’s R2 model, which aims to be a distinct, affordable, mass-market alternative to the Tesla Model Y. Plus, RJ shares his vision for a future where vehicles don’t just drive us, but inspire personal freedom and exploration. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @RJScaringe | @Rivian Chapters: 00:00 – Cold Open00:35 – RJ Scaringe Introduction0:58 – Rivian’s Autonomy Evolution05:19 – Why Rivian’s Tech is Vertically Integrated10:06 – Levels of Autonomous Driving Technologies14:00 – Importance of a Software-Defined Architecture19:28 – Differentiating Autonomous Vehicle Models23:20 – R2: The First Mass Market Autonomous Vehicle25:02 – Do Americans Want EVs?29:05 – How Our Relationship to Vehicles is Evolving30:45 – Conclusion

• 0:00Speaker 00

  By 2030, it'll be inconceivable to buy a car and not expect it to drive itself. Every single one of our cars, we want to have the ability for it to operate at very high levels of autonomy. Radars are extremely cheap, LiDARs are very cheap. But the really expensive part of the system is actually the onboard inference. An order of magnitude more expensive than any of the perception stack. My view is EV adoption in the United States is a reflection of the lack of choice. As consumers, we need lots of choices. We need to have variety. We self-identify with the thing we drive. The world doesn't need another Model Y. The world needs another choice.

• 0:37Speaker 01

  Hi, listeners. Welcome back to No Priors. Today, I'm here with RJ Skarinch, the founder and CEO of Rivian. We're here to talk about their autonomy strategy, proprietary chips, their coming R2 model, whether Americans want EVs, and what our relationship to cars is going to be in the age of AI. Let's get into it. RJ, thanks so much for doing this.

• 0:57Speaker 00

  Thank you for having me.

• 0:58Speaker 01

  So Rivian's already an incredibly cool company. How did you decide it was going to become an autonomy company? When did that happen?

• 1:04Speaker 00

  From the beginning, we thought of it as a transportation and mobility company. And in fact, even before Rivian became Rivian, when I was thinking about what's the first products, it was unclear what kind of car would be, but Rivian was a car. But it was always clear we wanted to be at the front edge of hoping to redefine what does it mean to have access to personal transportation? And so autonomy has always been part of the strategy, but it's now fully coming to life with the technology that we're building.

• 1:29Speaker 01

  And you think about the function of Rivian. There's transportation. There's also the experience. Like, when, how long ago did you guys start investing in the autonomy strategy here?

• 1:40Speaker 00

  Yes, we launched R1 in very end of 2021. And we used what I'll broadly characterize like a 1.0 approach to autonomy. So we had a perception platform. We used a third-party front-facing camera that was essentially a third-party solution that then plugged into an overall framework that we built, but it was all rules-based. So the camera is fed a rules-based planner. The planner would then make a bunch of decisions around the feeds from the perception. And it was, you know, the moment we launched, we knew it was the wrong approach, but it was the thing we'd started working on. Well, before the launch. And so at the end of 2021, beginning of 2022, we made the decision to completely reset the platform and

• 2:20Speaker 01

  Was that a hard decision?

• 2:22Speaker 00

  No, because it was so clear. We made, we made it. You know, when you're building something like this, you're, you recognize you're going to spend many, many billions of dollars creating it. So we knew this, like, at the core of transportation is, is driving. And at the core of that is a shift to having the vehicle be kept while driving itself. And so we made the decision to redo it, like, clean sheet. You know, no legacy of what we had built in the Gen one. And that first launch from a hardware point of view in the middle of 2024. So it was with our Gen two vehicles. You're not a single line of shared code, not a single piece of common hardware on the perception or on the compute side. And, uh, and then we had to build like the actual data flywheels. We had to grow the car park to build enough of a data flywheel to then start to train the model.

• 3:08Speaker 00

  And what we showed in our autonomy data late last year, late in 2025, was the beginnings of the series are really like super exciting steps of how this is going to grow and expand. I say this all the time. I, I think of not just for Rivian, but I'd say for the auto industry in general. The last three years compared to the next three years are going to look very different. So the rate of progress that we saw in autonomy between, let's say, 2020 and 2025, or 2021 and 2025. And what we're going to see between today and let's say 2029, 2030 are, they're completely different slopes. And that really comes back to, you know, entirely new architectures now being used to develop

• 3:47Speaker 00

  self-driving, actually truly AI architectures, whereas before, these were not AI architectures in the, in the true sense. They were, they were using machine vision, but really rules-based environments that we defined as, as humans, you know, we codified them, which is very different to how they're now built today.

• 4:01Speaker 01

  You might actually have perfect timing here in that I got to be part of investing in sort of the first wave of independent autonomy bets that were working with the OEMs at my last investing.

• 4:12Speaker 00

  Okay.

• 4:13Speaker 01

  But this is. Say eight, 10 years ago.

• 4:15Speaker 00

  Yeah.

• 4:16Speaker 01

  And as you mentioned, there's several architectural revolutions since then.

• 4:21Speaker 00

  Yeah.

• 4:21Speaker 01

  And so for companies to make that shift from, you know, we're going to have these separate reception and planning systems to more end to end neural networks.

• 4:29Speaker 00

  Yeah.

• 4:30Speaker 01

  I ask because I felt it was actually quite a hard decision for people and choosing their partners and in general, from a, from a technical perspective.

• 4:36Speaker 00

  Well, I think it, I mean, you can see it. So there's, if you go back to the very beginning of the idea of self-driving, a lot of effort, a lot of spend happened for companies to build these rules-based environments and to build these more classic systems. And when transformer-based encoding came along, you know, just a couple of years ago, and it shifted very rapidly to, it was clear that the future state was going to be neural net-based. It was hard because if you're a company that's built all these systems, it's like, do I keep investing what I had? What do I, what do I do with all this work that was, was built before? And the reality is, is a lot of it is, the vast majority of it's going to be pure throwaway. Because it wasn't like a gradual shift, it was a complete rethink of how things are architected.

• 5:20Speaker 01

  How did you decide that this was going to be an in-house effort versus a partner effort? That given, most people who made cars said, we're going to go partner or buy something here.

• 5:31Speaker 00

  I guess the emotional slash philosophical is, on things that are really important, we've taken the approach of vertically integrating them. So electronics, our software, all the high voltage systems in the vehicles, things like motors and converters, all the power electronics, these are all things we, we develop and build in-house. And in a few cases, you know, we had to start with something that was either off the shelf or partially off the shelf. But today, all of that's completely in-house. And in the case of self-driving, we knew that long-term, it needed to be something that was developed internally. We started, as I said, with a mobilized-centric solution, which a lot of folks did.

• 6:07Speaker 01

  Right.

• 6:08Speaker 00

  Particularly in like, you know, that 2015 to 2021 timeframe. But when you really look at what's necessary to, to be successful in a neural net-based approach.

• 6:18Speaker 00

  There's a core set of ingredients that very few people have, and I think we uniquely have them. So first and foremost, you need to have complete control of the perception platforms. You have all the, everything that the, the, the system is capable of observing, whether that's cameras, radars, or lidars, or some combination of all three. You need to have control of that, meaning there's no intermediary company that's like processing some of that information. And so that's powerful because you can then feed raw signals into your system. The system needs to be capable of triggering unique or interesting or noteworthy events that you can then use to train. That triggered, you know, those triggered moments need to then be captured, saved on the vehicle. And then when the, when the time arises where

• 7:00Speaker 00

  you have Wi-Fi ideally, send it up. And the reason I say Wi-Fi, these are, this is a lot of data. So you could of course do it over LTE, but it's expensive. And so you have to have a really robust data architecture in the vehicle. Then you need to be able to send it off, off board and use that with a lot of, uh, training. So with a lot of GPUs to train a model. Companies that are either developing independent solutions that are not a car company, they typically don't have access to the type of mileage that we do. So the, the huge amount of data that our vehicles generate. Uh, if you're developing this from a sensor set point of view, you typically don't have the vehicle architecture and the vehicle car park. So we just came to the view that we have all these ingredients to do it really well. And it's like not a optional thing. It's the companies that do

• 7:43Speaker 00

  this well will exist. The companies that don't do this well, like have feel really strongly this, they will not exist. They will shrink to, shrink to nothing, last scientifically approach, you know, zero.

• 7:53Speaker 01

  You think it can only be delivered and really a vertical, vertically integrated?

• 7:57Speaker 00

  No, I think, I think there's more than one, less than five companies outside of China that have the necessary ingredients to do this. The capital, the GPUs, the, the car park with, you know, enough vehicles generating enough data. I said more than one, less than five.

• 8:12Speaker 01

  And the control of that whole training loop.

• 8:14Speaker 00

  It's probably like more than one, less than. Three, maybe four, like there's very small number of companies that can do this. I think the unique spot we are in time right now is the one dot.

• 8:23Speaker 01

  Can I ask explicitly then, it's you, it's Tesla, it's Waymo. Is that the three?

• 8:28Speaker 00

  I would include all three of those, yeah. And there's maybe one or two others in the mix. But I think the challenge is you have to look at the, not just the moment in time for performance where we are today. Do you have the ingredients to continue making progress at a very high, like high rate over the next four or five years? And so a lot of the solutions that are more one dot O based and are sort of stuck in that framework. I think have a, like a truly a zero percent chance of progressing to be competitive with a neural net based approach. And then our own best approach does take a lot of time. You have to build a ton of inference on the, you have to either buy it or build it. A lot of inference, we decided to build it. So we built an in-house chip to do this. You need to have a car park this large.

• 9:11Speaker 01

  You just mean enough onboard compute to actually run the models in the car?

• 9:15Speaker 00

  Yeah, in the vehicle. And so you could, you could buy that. Of course, Nvidia makes those, but you need to be able to do that at scale and have it in every car. And so we took the decision to make our chip in-house.

• 9:26Speaker 01

  Is that more a capability decision or a cost decision?

• 9:30Speaker 00

  It's a cost and like, we want to have it on everything. So every single one of our cars, we want to have the ability for it to operate at very high levels of autonomy. And so we design, spec and build the cameras. Radars are extremely cheap. LiDARs are now, you know, very, very cheap. But the really expensive part of the system is actually the onboard inference. And so that's like an order of magnitude more expensive than any of the perception stack. I think people focus on the perception because it's the things we can like visualize.

• 9:57Speaker 01

  Right.

• 9:57Speaker 00

  But the brain is actually the most expensive part. And so we brought that in-house as a way to remove costs from the system so that we can easily deploy this on, on every car.

• 10:07Speaker 01

  You are taking like a sort of, no, step by step approach to levels of autonomy.

• 10:13Speaker 00

  Yeah.

• 10:13Speaker 01

  And how do you think about how quickly you approach like level four or, you know, the safety case around each of these things? How fast your team goes against this?

• 10:23Speaker 00

  Yeah, I mean, this is even this question is unique. It's just a few years ago, 20, 2019, 2021, even. There was like very, like very clearly delineated.

• 10:34Speaker 00

  Ways to approach autonomy. There was a level two approach,

• 10:37Speaker 01

  Yeah.

• 10:37Speaker 00

  which was camera heavy, maybe with a few radars. And then there was a level four approach, which was, of course, had cameras, but had a lot of lighters. It was sort of inconceivable to think of the level two system becoming a level four. And similarly, the level four system was way overbuilt to even, like conceivably, think about putting that on every consumer vehicle.

• 10:56Speaker 01

  Well, you didn't want the, the big

• 10:58Speaker 00

  Yeah, you didn't want all these parts. Yeah.

• 11:00Speaker 01

  Yeah.

• 11:01Speaker 00

  Tens of thousands of dollars of perception. So what's happened is those two world systems, I think, have just started to very clearly emerge where the delineation between a level two, a level three, and a level four in terms of perception and in terms of compute has started to fade. And it's now essentially just remove, like, how capable the system is at addressing all these corner cases. And, you know, this is what's hard for a consumer to recognize. If you're driving a level two system, or a level three system, or a level four system, for 99.9999, like, feels identical.

• 11:38Speaker 01

  Right.

• 11:38Speaker 00

  The difference is, like, the fifth or sixth or seventh, ninth on that is these, like, extreme corner cases. And so I think it's actually led to a lot of confusion where you'll be in a level two system, like, the car could drive itself. And you're like, yes, it can under

• 11:54Speaker 01

  Most of the roads, millions of miles.

• 11:56Speaker 00

  Except these very unique corner cases. And so, to your point on safety cases, the question then becomes is, like, how confident are we in the system capability in covering these really obscure, unlikely, rare events, which, of course, if they're not covered well, it can lead to really, you know, terrible outcome, you know, the vehicle in a bad collision. And so that's where the neural net-based approach has just changed things a lot. So the capabilities are so much stronger and the ability now, I think, for us to Deploying a lot more vehicles, have a car park that's very large. So we went from, you know, a few years ago, state of the art was you'd have a test development fleet of maybe, maybe

• 12:37Speaker 00

  a few hundred vehicles, maybe, maybe like high hundreds of vehicles to now like thousands and thousands, every single car on the road is part of your data fleet that's identifying these unique car cases and then running the model against them to test. And now, of course, we're simulating those unique cases and we can do a lot there. So the, just the whole nature of it's changed so dramatically that, and I think by, by 2030, it'll be inconceivable to buy a car and not expect it to drive itself. You know, maybe that sooner, like we hope it sooner, like we're targeting a little sooner than that, but certainly in like a very, very near future, like that will become a must have in a car. Sort of like, it's hard to imagine buying a car today without airbags or buying a car today without air conditioning.

• 13:21Speaker 00

  These things at a moment in time were optional. I think in not too, not too much time, couple of years, it'll be hard to concede buying a car that can't drop you at the airport or pick up your kids from school.

• 13:31Speaker 01

  I would argue that right now, um, most of the biggest car makers do not have the ingredients that you described to make this a reality. So do you think that, um, that's gonna play out in the market where like autonomy will be so important as a driving feature, core feature of the car, that there's just gonna be a big market share shift to those, those who can figure it out? I know, I know you're biased here, but I'm like-

• 13:55Speaker 00

  No, no, I think it's, it's a hard question to answer. So I think it's, uh, I always characterize like this. I think it's inconceivable for a car company to continue to operate at scale, like mass market. I think very niche enthusiast realms, sure. But like at scale, without a software-defined architecture, which is even before you get to autonomy, just like can you do OTAs? Do you have control of a-

• 14:20Speaker 01

  Sorry, can you define software-defined architecture?

• 14:23Speaker 00

  Yeah, that's like, before we get to autonomy, it's like, these are like basics. So the way car-

• 14:27Speaker 01

  The core thesis of your-

• 14:28Speaker 00

  Yeah, yeah. So the way car electronic systems have been designed and built and have evolved. With the exception of Tesla and Rivian, every car on the road has what is called a domain-based architecture. So you could also call it a function-based architecture. So all the functions across the vehicle, let's say chassis control, or door system control, or HVAC, your air conditioning system, all have little computers associated with them.

• 14:53Speaker 01

  Right.

• 14:54Speaker 00

  What we call ECUs, electronic control units. And in a modern car, you might have 100 to 150 of these. And each of these run their own little island of software, and that little island of software is written by a supplier, more likely a supplier to the supplier. So you go to a tier one, and they hire a tier two who writes the code base to run your HVAC.

• 15:13Speaker 01

  That's why it's impossible to debug, like a software system.

• 15:17Speaker 00

  It's also why it's really hard to do an update. So imagine you have 100 different islands of software written by 100 different teams that all have to coordinate. And so if you want a feature, you know, something that manifests as a feature often involves combining functions from different domains. So a simple one to visualize is when you walk up to your car to get into it. You want it to automatically unlock. You want the HVAC to go to your preset. You want your seats to adjust. You want it to make an audible noise in the outside. You want the lights to do something. You probably want the audio system to do something. Those are all different little ECUs in a traditional car. And the coordination cost in it is really high. It's very unlikely that a car company will make a change to that sequence because it involves coordinating amongst maybe 10 different players. In contrast, on a, on a approach where you build a zonal architecture, where you have

• 16:02Speaker 00

  a very small number of computers, I believe, you know, one, two, maybe three, depending on the size of the car, that are running one operating system that control everything, it's very easy. See, that sequence you could make updates to, you know, in a matter of minutes, maybe an hour, you could change the whole sequence of what happens. You walk up to the car, issuing over the update, and it's very straightforward.

• 16:22Speaker 01

  How often does Rivian update?

• 16:24Speaker 00

  We do about one a month. And it's typically, you know, we add a couple of new features. We add refinements to existing features. We're listening to like what customers are seeing and asking for. But, you know, every month, the car gets like notably better. And it's created this really amazing dynamic where customers are like excited for the, for the update. Like, when's the next OTA gonna drop? The irony of all this is these domain-based architectures goes back to like, how do we arrive at this? It actually goes back to fuel injection systems. So up until early 1960s, like every car on the road was completely analog. So there's no computers at all in the car. It was 100% analog. And the first computers were there to drive the fuel injection systems and car companies

• 17:08Speaker 00

  said, this isn't a core competency. Let's push that little computer or in the fuel injection system to a supplier and the supplier will make that. You know, and this is where you saw things like the Bosch fuel injection systems and never planned, it's sort of like a field of weeds. Then over the next like seven, 60, 70 years, everything that became, you know, computer controlled to any degree is suddenly starting to have a little ECU, a little computer associated with it. And it just like grew into this absolute disastrous mess that is, you know, today, the the network architecture that's in truly every car on the road with the exception of two companies. That what I just described is what underpins. We did a large software licensing deal, a $5.8 billion deal with Volkswagen Group, the

• 17:52Speaker 00

  second largest car company in the world, to essentially leverage our network architecture and ECU topology for their, you know, all their various brands. And so it's an interesting final point there on the, on your first question, which is, you know, what happens to market share? So I think it's inconceivable that car, if to be at scale, that you don't have a software defined architecture that allows your features to become better and better. And particularly thinking about how AI starts to integrate into the features, that's number one. Secondly, it's inconceivable to think about a car company existing at scale without the vehicles having very high levels of autonomy. And so car companies have a choice on both of those. They can either accept that they're going to shrink. That's choice one.

• 18:34Speaker 00

  Choice two is go build it themselves, which is really hard because they don't typically have these skill sets or not software electronics companies in terms of like their organizational DNA, or they can find a third party to source it from. And in both cases, there's not great third parties to go to. And in the case of autonomy, most of the third parties that that did emerge over the last 10 to 15 years tend to be very much like classic rules based, like call it like AV or autonomous vehicle 1.0 solutions. And those work pretty well for the business construct of selling like a sensor and a function. But that structure is really flawed when you want to have like a large data flywheel and it's constantly learning and evolving and you're issuing updates constantly.

• 19:19Speaker 00

  It's just, it's really hard to imagine that with an arm's length transaction. And so I think the vertically integrated stacks are going to naturally have some big advantages.

• 19:28Speaker 01

  So this might be an irrelevant question, but I am curious. Do you think that the autonomy, like the models that maybe the three, maybe the one, maybe the five companies that come up with this develop are fundamentally different over time? Because I spent a lot of time in the AI ecosystem and the, let's say, the language oriented foundation models like feel like they're converging at this moment in time. I look at a Rivian, I'm like, I don't know, people like venture in that thing. Do you actually want it to do different things, have different styles or capabilities? Or is it really just like as much autonomy as possible, safety case?

• 20:06Speaker 00

  Well, first, this is a great, this is a great question.

• 20:10Speaker 01

  I want my car to drive.

• 20:11Speaker 00

  Like in the LLM world, a lot of it has converged because it's the training data sets nearly the same. Yeah. So we're taking the breadth of knowledge that's contained on the internet and we're training models off of that. In the case of driving a vehicle, there is no internet of driving data. And so you need both a robust sensor set to be able to capture the data and you need a car park that has enough vehicles in it. And so of course Tesla has the largest car park of vehicles by far. Our approach to this is we have a higher level of capability on our perception stacks. We have better cameras, we have radar, and of course with R2, we'll have a LiDAR as well. A huge part of that strategy is not only those cover corner cases better, so the cameras have incredible low light and bright light performance.

• 20:55Speaker 00

  So the dynamic range of the cameras is stronger, we have more cameras, a lot more megapixels. We have radar, which is great for object detection. And a LiDAR, which is, it's a very powerful tool for training the models. And so imagine. Eight hundred feet in front of us, there's a little speck into a camera. It's hard to figure out what that is. And historically, what we would do to train that is you would have a LiDAR sitting on the vehicle on a, like a ground truth fleet to help train your cameras. Putting that on every single one of our cars is, turns our entire fleet into this amazing training platform, this data acquisition machine. That was a core part of how we thought about our strategy is we're going to go, you know, not as heavy as let's say a Waymo on perception,

• 21:38Speaker 00

  but heavier than let's say Tesla to build a really robust data platform on a vehicle by vehicle basis. And then with a car park that's going to grow significantly with the expansion with R2. Yeah. So I, I think first and foremost is there is no common Internet data. So the data sets that we're going to be picking up though are going to be very similar, but, but you have to go acquire.

• 22:01Speaker 01

  But there's still different decisions about what data you care about acquiring. Yeah.

• 22:04Speaker 00

  Well, I think this is what to like, how does a car feel ultimately? It needs to be safe. And the differences in the way it drives or feels are going to be more about like, what's the UI, the user interface of it? You know, like even we just updated some of our features. We have three settings for how the vehicle drives mild, medium, and spicy. Spicy is the highest one.

• 22:23Speaker 01

  Yeah.

• 22:24Speaker 00

  And so it's just like a little bit more aggressive. Over time, and we've spent time thinking about this, I think this will start to become part of a key decisions. How does the vehicle behave? And there's work we're doing to, to think about how the vehicle can behave in a way that against a set of heuristics drives like you. So

• 22:43Speaker 01

  Very spicy.

• 22:44Speaker 00

  The overall model is trained on how to perform in a safe way, but it actually learns some of your, you learn some of your driving preferences and creates a model around you. And of course, in a world where you never drive the car because you're just, it's always driving for you. There's a way for you to set, I'd like it to aggressively change lanes. I'd like it to reside in the right-hand lane, like those kinds of decisions. And those are, those are less around the tech, more on what's the, the product or the UI, I feel like.

• 23:10Speaker 01

  Right. The ability to collect those preferences.

• 23:12Speaker 00

  Yeah. So it's preference based. And I think we will see that. I know that'll be a decision like a Tesla makes that may be different than how Rivian makes it. You know, it's hard to say today.

• 23:22Speaker 01

  Can we talk about what the R2 means for like the company and some of the key design decisions here? I was just talking to Jonathan, one of your lead designers about the constraints and, you know, aiming for more mass market and more volume here.

• 23:35Speaker 00

  I mean, yeah, you said it. It's a R1. It's a flagship product. So it's average selling price is around $90,000. It's the best selling, the R1S is the best selling premium electric SUV in the country. So that's electric SUVs over $70,000. And we're the best selling premium SUV, electric or non-electric, in the state of California. So it sells really well. You know, it outsells everything in its class, like a model Tesla Model X, it outsells like two to one. But because of the price, it's just limiting in terms of how much volume we can achieve with that platform. And so R2 is the, our first truly mass market product with pricing that's, as we've said, going to start at 45 and allows people that are

• 24:19Speaker 00

  in that, you know, the average price of a new car in the United States is $50,000, in that like 45 to 55,000 dollar price range, I think to have a really great choice. And to date, there haven't been a lot of great choices there. You know, there's, I'd say there's like sort of singular set of great choices with a Model, Model 3, Model Y. And of course, that's, that's shown through extreme market share capture, 50% roughly market share goes up or down, but around that, call it half the EV market is Model 3 or Model Y. So there's just such an untapped opportunity to pull customers out of ICE vehicles, out of internal combustion vehicles with a choice that's, you know, has characteristics that are different and unique relative to a Tesla.

• 25:03Speaker 01

  These are like two substantive to be rapid fire questions, but they're, they're important for me to ask you. Do Americans want EVs? Like, why haven't they adopted them faster?

• 25:11Speaker 00

  Well, yeah, I think to the last question, I think causality is always a hard thing to. Yeah, really understand, but let's zoom out here. The overall adoption rate in the United States of EV is around 8%. The vast majority of vehicle buyers are buying vehicles that are under $70,000 with the average sale price of about 50. And so if you look at the number of vehicle choices you have at a price point that's under $70,000, depending on the year, this of course changes year to year, there's well in excess of 300 different vehicle model line choices, putting aside trims and performance packages, but just in terms of like overall vehicle types. And she can buy hatchbacks, minivans, SUVs, you know, two seaters, convertibles. I mean, there's a whole array of different things you can buy.

• 25:57Speaker 00

  And in the EV space, I think, and this is, I think there's more than one, less than three great choices, and I'd say Tesla with the Model 3 Model Y is absolutely one of those. But there's so few choices that if you are looking for a form factor that's not a Tesla.

• 26:17Speaker 01

  So you think it's just missing product set that people are gonna want?

• 26:20Speaker 00

  Yeah, an extreme lack of choice is how you put it. Like a shocking lack of choice. And this is what gets into interesting like corporate psychology. But because of the success of the Model Y in particular, the EV choices that do exist that are outside of Tesla are often very similar to a Model Y.

• 26:39Speaker 01

  Sure.

• 26:40Speaker 00

  So if you were to like draw like an outline, if you looked at the side view profile of a lot of its alternatives and draw a profile and then put it next to a Model Y, it's almost identical.

• 26:49Speaker 01

  There's a design sketch over here of basically the Model Y and all its competitors.

• 26:54Speaker 00

  They're all basically the same. It's like if you want a Model Y, buy a Model Y versus getting.

• 26:58Speaker 01

  You want something different.

• 26:59Speaker 00

  Yeah, so you have all these companies that are trying to create their own version of Model Y and it's like, it's unfortunate because they didn't say, well, what can we do that's unique and different? And so for us, we think the Model Y is a great car. I've owned one, many folks on our team have owned one, but the world doesn't need another Model Y, the world needs another choice. And so I think this is a reframing of just how we look at transportation is it's such a big space. It's such an area of personal expression that we need as, as consumers, we need lots of choices, we need to have variety, we self identify with the thing we drive, we just haven't had it. So I think. My view is the EV adoption in the United States is a reflection of the lack of choice. There's one set of really great choices with Model 3 and Model Y. I think

• 27:44Speaker 00

  there needs to be many more. And so even looking at our partnership with Boltzrang Group, a big motivator for that, which ties to our mission, was can we take our technology platform and allow that to be expressed through a variety of really interesting and very storied brands and different form factors, different price points, of course, different segments. And I think the more choices we have, the more it's going to lead to broader based adoption of electric vehicles, which creates, I think, a very positive level of momentum around the space. It's worth noting on that point, when we look at how we develop a car, like take R2, we don't think of it as this is someone who's going to buy an EV, let's

• 28:29Speaker 00

  make it good. We think of it as let's make the best possible vehicle, you know, we can imagine so incredible performance and, you know, great range, great dynamics, tons of storage. And the person buying it will be drawn into electrification because the car is just the best choice they have. And we took that same view with R1. And on R1, the vast majority of our customers are first time ever owning an EV is Rivian, which is was really good. If if all we were doing is moving customers between one or two brands, it wouldn't be accomplishing. Well, we have to create new EV customers with products that are so compelling that it just draws people in.

• 29:06Speaker 01

  So that leads into my very last question here. I grew up thinking like a car is a huge part of my identity. Love cars, drew them. Still think they're pretty cool. And, you know, as they become more like utilitarian services with the rise of robo taxis as a concept of like, you know, serving some of the function that your car did before. How do you think our relationship with cars changes or vehicles over time?

• 29:33Speaker 00

  I do think it's we're going to see a shift. It's interesting, like philosophical question, why, why are cars such a part of our society? And why do we have this affinity for them in a way that we don't have that feeling for other? Things in our lives that are really important, like I don't, I don't look at my refrigerator and think, I really love that. In the same way that I do with a car. And I think part of it is a car enables this personal freedom. It allows you to explore.

• 30:00Speaker 00

  It's, it's something that you not only ride in, but it becomes part of an expression itself. And I think that's probably going to continue to some degree, but it is going to evolve. And, and the way we look at it with our products and even how we've laid out and contemplated the, the purpose of the brand, we really look at it through the lens of the vehicles and the products we make need to both enable people to go do the kinds of things, you know, that they would hope to have memories of years to come. So we, we often say the kinds of things you'd want to take photographs of. But more than just enabling it, which is a functional requirement, like can it drive there? You know, can it fit the stuff, your, your pets, your gear, your friends, your, your, all of your stuff. More than just enabling it, can it inspire it?

• 30:45Speaker 00

  And so can the brand and the way we present what we're building and the way we make design decisions inspire you to go do the things you want to remember for years to come. And so there's little like design decisions we take that link to that. So a flashlight in the door is an invitation to explore. It's an invitation to go look at things the night.

• 31:06Speaker 01

  Or the treehouse.

• 31:07Speaker 00

  Yeah, there's exactly. So there's all these little decisions you made throughout the whole car from these that are just designed to like engage that element of inspiring people to go like imagine that life they want to have.

• 31:21Speaker 01

  Awesome. Thank you so much, RJ. Congrats on the R2 and on the autonomy program.

• 31:25Speaker 00

  Thank you.

• 31:28Speaker 01

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