Rivian Automotive, Inc. (RIVN) Earnings Call Transcript

Okay. Great. Thank you, everybody, for joining us. My name is Mark Delaney, and I cover Rivian for Goldman Sachs. I'm very pleased to have with me today James Philbin, SVP of Autonomy and AI; Vidya Rajagopalan, SVP of Electrical Hardware; and Chip Newcom, SVP of Investor Relations. Thank you all for coming.

Great to be here.

Well, one of the really exciting things out of Rivian recently has been R2 and the company recently launched that product, which, as many of you know, is a 2-row SUV at a more affordable price point than the 3-row and more premium R1 line that's been out previously. Chip, maybe I can start with you for this one. Can you share more on what Rivian's seeing with respect to R2 demand and feedback now that the vehicle has launched?

Well, first, Mark, thanks for hosting us today and great to speak with there. Hopefully, many out there have already gotten a chance to go out and demo drive an R2. But still early days in terms of what we're seeing on the R2. But most importantly, from a media perspective, the early feedback we've gotten, it's been absolutely phenomenal. I mean, this weekend, the New York Times posted their review. We've seen any number of comparisons against both. ICE vehicles, EVs and across the board, it's been really great feedback. So I think from our perspective, as a company, it's showing that from a product market a bit, we're absolutely hitting the right points for what the consumer is going to be looking for. And now of course, it's all about execution and continuing to ramp our supply chain, continuing to ramp within our plan to normal but very excited about the early feedback we've seen.

One of the metrics the company shared on the last earnings call was that conversions from the preorder retrotracking ahead of what you had anticipated. I don't know if you can share more on how you're measuring that. Any context you can give on that front?

Yes. I mean I think it comes down too. So for a little bit of context for everyone out there. So we've been taking $100 refundable deposits for our potential customers if they'd like to purchase an R2. And then we've been inviting folks in waves to then be able to come in to our systems, configure their vehicle and put in their intent to order and purchase and then starting to deliver against that. And so as we've been inviting those ways in seeing increasingly a very good conversion rate of folks actually saying, okay, yes, I do want to buy the vehicle, and it's interesting, we're now introducing new trims. For example, Coastal Cloud, we just launched, seeing as we introduce each of those new waves, more interest coming through from customers. So continuing to see good progress there.

That's great. And in order to meet the demand of the company has been scaling up production. So you'd already started a single shift in normal the R2. The plan was to add a second by the end of 3Q, so this current quarter or so just a few weeks left. How is that ramp progressing?

Yes, still planning to add a second shift here in the third quarter. So working through the ramp process, as you would expect.

Any key bottlenecks for the company to solve?

Another thing that we've talked about continuing to work through as with any ramp working through the process of all of our suppliers, we can only move as fast as the slowest supplier in our supply chain. So working through that process.

Great. Well, it's a helpful overview of the R2. We are very privileged to have James and Vidya from the tech team. I know you have a lot of various projects you're working on and a great opportunity for us to dive into some of those. Well, one of the key themes, of course, at this conference is AI and applicable for Rivian in a few ways, but including some of the autonomy products. James, maybe I can direct this one to you. Where does the company stand with its plan to have supervised point-to-point driving available later this year and then eyes off for 2027?

Yes. So I think the team has been very hard at work on point-to-point recently, so scaling up the model, spinning up the data and the validation of that model and then actually putting the vehicles on the road in a sort of test framework so that we can get feedback and feed it back in. So I think we're seeing very good progress. Actually, we recently gave demos to the Uber CEO and CFO. I think one of those folks posted about it on Twitter. So yes, great feedback, and I'm very excited to get that feature out towards the end of the year.

And what are the biggest technical challenges remaining?

I think mostly around sort of dialing in a good humanistic behavior in all cases. So specifically around making sure the model is not too conservative, that it's sort of tracking speed as you would expect. I think one thing we've seen is actually very good behavior around relatively complex scenarios like construction or narrow negotiation, so very excited about the progress that's being made. Obviously, the validation needs to happen so that we can make sure it's safe before we ship it.

And so more as you think about moving from the point-to-point supervised the eyes off in 2027 sorts of things you're working through?

Yes, it's essentially the same system. So just imagine the continued development of this end-to-end model, where we gain confidence, we can validate on more long tail cases. We can scale up the number of miles that, that system sees. And eventually, you get to a confidence level together with the system architecture that can allow you to take that eyes off.

One of the things we've heard so far today at the conference, more broadly, has been about tightening compute capacity to train AI. And as you think about kind of some of these milestones, are you able to get enough supply to train your AI?

Yes. So thankfully, we have obviously very close relationship with Amazon, one of our biggest investors. And through AWS, we have a great supply of GPUs that at least for the next 6 to 9 months, is going to support as well. Past that, of course, we'll need to see and I'm very interested to see how this market develops in terms of compute.

And just maybe for any of you, and Chip, feel free to chime in on these as well. But the company has indicated that autonomy plus adoption is trending positively. What have you learned so far about customer willingness to pay for this product?

So I think, yes, generally tracking actually better than we expected. I think 1 thing I'd say is that the product is very kind of sticky in the sense that once customers have these features, they really struggle to go back. And I've actually seen this myself, testing point to point is -- I really hate going back to vehicles now they don't have that feature set, even though it's at an early stage. So I think in general, those products are very sticky. People really love to use them I think, over time, it's going to become increasingly table stakes for any new vehicle to have these advanced autonomy capabilities. So that's kind of what we see. And yes, all of that is driving, obviously, demand R2s and R1s and also Autonomy plus.

As you're engaged with customers around people purchasing R1, R2, what are they saying around whether or not those buying decisions are being influenced by what Rivian offers on this autonomy software side? And is that leading to incremental sales or maybe even any lost sales as you think about where you stand in the competition?

So I think you typically see 2 maybe cohorts of customers. One cohort comes from the traditional ICE well, where obviously, the autonomy levels are much, much lower. And there, I think people are looking much more around the size of R2, its capability, how fun it is to drive, the stance you have in it. And that's where I think, yes, R2 is really shining. And then on the EV side, where people may be coming from Tesla, which have higher expectations, I think Universal Hands Free, which is what we launched last year, is going quite a long way with those customers, but then the final set of that cohort is really looking for a point-to-point time feature. So we're hoping we can really knock it out of the park with both cohorts at the end of the year.

Well, that software doesn't need to run on hardware. So, Vidya, I can go to you for this next one. And at the AI day, get back into this number you presented and we not take some of some of our questions. You outlined a number of initiatives, including some custom silicon, the RAP1 processor. Maybe talk about why you made that choice and how important is custom silicon to Rivian's long-term competitive position in technology?

Absolutely. I've always said that 3 reasons we've built our own silicon, and they're performance, cost and velocity. I'll start with the cost. It's pretty obvious. When you buy merchant silicon, it does a lot of margin stacking going on. And in this day and age, AI silicon commands quite a bit of premium. And if I can shave off a big piece of it, that really helps us offer more at the same price point. So that's the number 1 reason. Number 2 reason is performance. And in particular, because we are a vertically integrated company, we actually understand the vehicle top to bottom. We understand the application really well, and we're able to actually design a piece of silicon that is tailored towards autonomy tailored to its physical AI. It is not a piece of silicon that was designed for a data center inference product that then migrated over here. It's really purpose built for this purpose, and we believe that you can actually get -- have super fluid stuff that comes from previous vestigial piece of hardware that's sitting around there. And at the same time, I've spent a long, long time prior to this in chip companies and often chip companies designed to benchmark or sample applications or open source models, but actually having the real model we run, having that and co-developing the hardware along with it helps you really design the hardware optimally. So you get really good performance for it. And then finally, the most important -- another very important and most important part is the velocity. As James' team and my team are co-located and when we find issues, we can work through them very quickly, which may seem like a trivial thing until you actually work at a company and work through that process. If you have -- buy silicon from anybody else, you find a problem. You file a ticket. Then their solutions or support team looks at it. They're not able to solve it. They escalate it to the engineering team. And then it takes usually it's a matter of weeks before it gets taken care of. And when we're working with our teams, usually, the teams walk over to each other and discuss it and get fixed much sooner. And that's important because that's a virtuous cycle. You can move and iterate much faster. And all 3 of these over generations really help you build them out, and that's what we're after.

All right. As you think about the challenges that come with developing that silicon and some of the milestones you still need maybe solve for the Gen 3 hardware, what does that mean for time lines? And is the Gen 3 hardware stack still on track for late 2026?

So building silicon, and that's a very good point. It's not for the faint of heart. It comes with a lot of risk because a simple matter of fact is the wafer, single wafer cost to tape-out costs a lot of money. And then it's very expensive. And then when you put it in the oven, it comes back after 4 months. And so the cost of a mistake is very high. So you have to spend a lot of time doing validation pretape out, which I'm happy to say we're at the tail end of this. We've had silicon in-house for more than 1.5 years at this point, so we feel very comfortable in the health and quality of the silicon. Because it goes into an automotive use case, we have to go through more rigorous testing, and all of that is underway and pretty much wrapping up, I should say, within the next month, 1.5 months. We should be pretty much done and sort of the hardware characterization testing piece of it, we have vehicles that have the silicon that are driving around. We -- the computer that actually uses the silicon is also built and well tested. We actually demoed that at AI Day last year. So we've had that in-house for over a year, too. And more recently, it's not enough to have hardware. You have to have software running on it. So more recently, we have proven that the actual application, the autonomy application can be mapped to it. It is -- has mapped and it's actually executing as expected. We're exercising the features that are released to the public, universal hands-free, lane change and command, highways, all of the features, all of the features have been exercised and are working as expected. And not only are they working functionally, but we're meeting the performance targets we have set for ourselves. We had set a goal to at least before excess performance, and we are achieving that as yet without fully optimized software and our compiler tools. All of the stuff that needs to work in order to make the silicon work is all on track.

What should investors think about for when the Gen 3 hardware architecture can make its way on to the R1 platform?

We haven't actually announced that yet. So that's something we're evaluating, but we don't have -- we haven't announced a time line on that yet. But the important thing, which I think is sometimes lost in people is that when we come out with point-to-point, it will actually be available on Gen 2 and Gen 3. so what the RAP1 chip, our custom silicon chip and LiDAR enable is really functionality at a later date. The IZO functionality, we've talked up for later. So there shouldn't have been no difference at launch.

And For the Gen 3 hardware stack in terms of costs, how does that compare relative to Gen 2?

So part of the reason we designed it was for costs. So the way to look at it is Gen 3 will enable a few different performance points. So the lower performance point will be cheaper than the Gen 2 hardware, and the higher performance point will be on par or slightly higher. So enable -- gives us 2 different performance points and a lot of cost savings. But it's cheaper dollar per top if you look at it, as dollar per performance unit.

I wanted to talk on AVs. And as a part of the Uber robotaxi announcement, the company plans to deploy AVs starting in 2028 with initial deployments in San Francisco and Miami. And I think the company is planning to deploy some R2 vehicles with safety drivers in San Francisco and Miami later this year. Maybe this one is for you, James. Can you share more on how this effort is going and the broader development there?

Yes. I think the progress towards that later milestone is tracking very well. We had actually Uber review a few weeks ago. So I think that everyone is very pleased with the progress. As you said, part of that milestone involves testing in 3 urban areas by the end of the year. So that's San Francisco, Miami and Chicago. We already have expert driver teams already driving around collecting data and validating the system that we wanted to show. So yes, everything looks good from that point.

And Chip, maybe you can speak a bit on the financial implications of the Uber deal, both in terms of that business on a stand-alone basis, but also what it might mean selling R2 vehicles as well as the capital considerations.

Yes. So a lot of benefits in terms of the partnership. First is, of course, the capital that we're getting from Uber as a partner, where in total across the next several years, we expect to get $1.25 billion of equity capital from Uber. So we already received earlier this year an initial $300 million of capital from them as part of the signing. So James' point later this year, we expect to achieve the second milestone, which will then unlock another $250 million, and then we'll have 3 more milestones thereafter to unlock the final $700 million. And those milestones are going to be around achieving certain technical hurdles associated with an L4 capable R2 for the robotaxi service and then the expansion of that service to up to 25 markets around the world, including at least 1 in Europe. So from a capital perspective, one, that helps us enable the acceleration of spend on our R&D around autonomy. And that's a big part of what we need to be doing in the near term. Then over the medium to longer term, once we have an L4-capable R2. Of course, we've got an initial agreement for them to purchase 10,000 vehicles, either Uber or their fleet partners to then operate on the Uber network within another option for another 40,000 thereafter. And an important part that we will get with that once these vehicles are out on the Uber network is the software licensing fees associated with the cars actually driving around. And I think that's an important part that many investors miss, is that a really attractive part of the economics is the fact that it's going to be Rivian's driver that's going to be driving around over time.

I don't know that you have, but any quantification of what that license would look like?

We haven't described what that licensing fee is going to look like as of yet.

Okay. Maybe talk about what the technology looks like for an L4 vehicle relative to what Rivian is currently working on for consumer vehicles, both from a hardware and software perspective and maybe how similar is it versus maybe some differences either on the hardware or software side.

So ultimately, we think the software stack should essentially be the same. So you can imagine the same stack and data loop that's ultimately powering point to point, getting better and better and better. And as you scale up in terms of the number of data, the amount of driving actually the customer fleet is doing and you're also validating further and further over more miles, then that system gets better and better, and then you can transition it to increasingly driverless domains. Now supporting that, you need a robust safety architecture, so that's something that very much we're thinking through. It actually comes with the RAP silicon, and there will also be some additional sensor changes for the final L4 products that we're working through the details of now. But essentially, you can think of the stack, the same stack powering the consumer fleet ultimately also powering the robotaxi fleet.

Vidya, anything?

Yes. I think in the hardware space, what we will be doing is really having more sensing. And so I would say more and more capable is the way to look at it in order to make sure that all the corner cases are fully covered. In particular, robotaxi has to start from a stationary spot with nobody in the vehicle, which is not typically a problem you have to solve for a consumer vehicle. So that means there's a higher need for sensing around. And so we -- there'll be additional sensing to augment the cases that are not covered in a normal consumer vehicle. And then we're just going to provision for, obviously, more compute because you want to really provision for really having all the compute you might need as the models get more sophisticated.

And I think James made an important point here for investors is that the future data is not just purely about commercial applications like robotaxi. It really is around this concept of personal L4 over time. And so the idea that all of us could collectively get our time back so that your commute now, you can be productive in ways that is not focusing on the road, like that for a lot of consumers, there's a lot of value to that over time.