Mobileye Global Inc. (MBLY) Earnings Call Transcript & Summary

Good morning, everyone. I'm Samik Chatterjee, and I have the pleasure of hosting Mobileye for the fireside chat, hosting here on Day 3. Dan Galves, Chief Communication Officer, is with us. And thank you Dan for taking the time to be at the conference.

I'll kick it off with a few questions here to the audience. We'll open it up a bit later for your questions as well. Dan let's start off with more broader question about full autonomy. Mobileye was public last time around, then went private, now is back in the public market. Over that time horizon, the auto industry's perception of how realistic full autonomous driving is, seems to have evolved. Can you just start with outlining for us where we stand today in the auto industry relative to posting full autonomy relative to partial autonomy use cases like highway driving?

Sounds good. Maybe I'll just start with like a couple of basics for 30 seconds about Mobileye, and then I'll get into your questions. So most of you probably know the basics. For many years, Mobileye has had a leading position in driving assist systems, our system on chip with software and hardware design in-house supports a front vision system for vehicles that supports an additional safety layer and enables our customers to meet constantly tightening safety standards globally. This business is very profitable, generated about $2 billion in revenue last year at a 70% gross margin. This funds our entire operating expenses of around $800 million last year, the bulk of which is R&D plus substantial free cash flow on top of that. I'd note that about 80% of our R&D is related to advanced products that are still low in volume and leaves -- yes, some of these advanced products like SuperVision are already in production. Supervision represented about 6% of our revenue in 2023, but on only 0.2% of the volume. The power of these advanced products is that they carry selling prices that are 20x to 60x higher than our current high-volume products. And this has been showing up in the annual bookings we report. For example, in each of the last 2 years, we've been awarded design wins from automakers that project to around 60 million units of future volume, and we actually shipped 37 million units in 2023 and project to around $7 billion of future revenue, against our actual revenue in 2023, which was $2.1 billion. So this kind of bookings to billings ratio is very high for us. The potential for success of these advanced products, it's been the main focus of investors since we've been public. Confidence level of the market can move up and down based on the pace of design wins and other macro factors and is at a relatively low point now. But nothing has changed in our view that the market for hands-free products will develop into a very large new TAM, and we have the right technology, the right cost and the right relationships to take a leading position there. Now to your question, I think we have to think about kind of the use case of autonomy. I think for -- and you can think about it in terms of autonomous vehicles can be deployed in networks of cars that move people around a particular city or move goods around or they can be deployed in consumer products. I think if you go back 5 or 6 years to the time when we were acquired by Intel, most of the attention in the industry was around this kind of fleet deployed robotaxi type of vehicles. And no one was really thinking too much about consumer level autonomous vehicles, but we were. And so if you're building technology for a robotaxi fleet, it's okay if it only operates in 1-city or 2 cities. You work on your system in that city, get it up and running. And apparently, the thought was the demand would come. But then you have to scale to different cities to build your business. It's okay, if -- your vehicles are $50,000 or $100,000 kind of incremental cost versus a normal vehicle because you're replacing a driver, which is very expensive. And theoretically, you can get to kind of a cost parity with human-driven vehicles. But the technology doesn't really translate to consumer level vehicles because consumer owned vehicles, you can't expect somebody to pay $50,000 extra to have an autonomous vehicle. It needs to be in kind of the single-digit thousands in terms of incremental cost. And obviously, you can't sell somebody a car and say, like, we've got this amazing system, but it only operates in a few different cities. It needs to be scalable, geographically. So our approach was always to think about a technology that would serve both businesses. And kind of starting from that standpoint, you have to engineer your system for scale, both cost-wise and geographically. And that's really what we've been doing for the last 6 or 7 years. And I think the other aspect here is you can design a system you can use -- our North Star has always been fully autonomous vehicles, but the way we approach the problem, these systems can be used in semiautonomous vehicles, which is the system that we are in production with Now. So I think a lot of the skepticism, or the negative press around autonomous vehicles is more in this robotaxi field, which really hasn't turned into a scalable business yet. It may someday. And in the meantime, automakers have become much more aggressive or assertive in terms of wanting to deploy vehicles with increasing levels of autonomy over time. Starting with highway point-to-point navigation, which is kind of where the target is today, but the ultimate goal of the automakers is to get to an eyes-off system, where you can start giving people their time back on their commute at least. So I think that that's our perspective on the industry.

Okay. So maybe just then going into the products. How is SuperVision helping you sort of bridge that gap when you think about going from L2 to L4, how critical is SuperVision and the capabilities that you're going to deliver through that?

Yes, it's very critical. So I think that -- again, back to kind of what is the end goal. At this point, we see the most interest from automakers or the most strategic thinking about how to profit from this type of technology is to ultimately be able to sell vehicles where at least on the highway, people can do other things while the car is driving them. We call it yes-off/hands-off. But to get there, the performance requirements in our view, you would have to be able to demonstrate and validate that the system is more accurate and safer than a human-driven vehicle, right? And we've had a lot of work with our automaker customers about kind of what that human accuracy level is because you can think of it in terms of a significant crash or a fender bender, these types of things and what the industry is centering around is you really need to have a meantime between critical interventions of about 1 million hours, right? So the performance requirements are huge to get there. SuperVision provides a bridge to eyes-off, right? So SuperVision, the way we build it is essentially having 11 cameras around the vehicle and the software inside the vehicle that interprets the data from those 11 cameras to create what we call a sensing state, which is essentially a picture of the environment. We integrate our crowd-sourced mapping, which boosts the accuracy of that kind of view of the world by adding in information about what's the common speed of a particular road, which traffic light is relevant to the left-hand turn, the straight away, many different pieces of information within the map. Then you need a decision-making software that uses the information from that sensing state to make decisions. So this kind of camera-only system that we call SuperVision, our target is to get to about 1,000 hours between intervention -- between critical intervention. We're on the right path to get there. Right now, this is being supported by the EyeQ5 platform, our chipset called EyeQ5. There's significant amounts of new technology within the EyeQ6 platform, which launches first half this year, and then kind of the lab and the testing environment with samples of this new platform, we're seeing like very significant increases in the meantime between failure. But that's still not good enough to allow a driver to disengage for the OEMs to take on the liability and the risk that, that would entail. So to move from SuperVision to SOFR, which is our eyes-off system, we add a second perception layer made up of radars and LiDARs that would have an equal type of mean time between failure. And so if you have those 2 independent perception systems, then the chances of both of them failing at the same time, go way down, and this is how we get to the million hours. Now from a kind of a business and a commercial perspective, the OEMs by adopting SuperVision are actually developing and validating most of the Chauffeur system, because really all they need to do to move from SuperVision to Chauffeur is validate the radar and LiDAR system. So it creates this scalable bridge. It also, I think, protects in a way against potential delays in regulatory, right? Because I think this is not the kind of thing that the regulators are going to allow you to just like check a few boxes, and say, it works. They're going to want proof that these systems are safer than humans. So if that takes longer, the automaker still has a really nice high functioning system for their consumers that they're profiting from. So that's really how we approach the problem.

Got it. And maybe we move a bit to talking about how you're supporting the OEMs and their aspirations to sort of go through this road map. Obviously, some of the OEMs have their own in-house aspirations of what they can do in-house. What was those -- what is that like a typical engagement with an OEM look like? How are you accommodating their own in-house aspirations and still sort of what do you offer to them to make sure that they use most of your stack rather than theirs?

Yes. So I think, again, if you kind of go back into history, 5 or 6 years ago, where there was interest and kind of investment into consumer level, AV systems, it was generally happening through self-development of the OEM or that was really their direction was we need to become a software company. So this is a high-value area of software. This is a good place to put our investments. You had, I think, probably some thinking of -- if Tesla can do it, why can't we do it. And I think at the time, Mobileye didn't have a production level system that we could offer, right? We were still working on the SuperVision system over the last 5 or 6 -- and I think also from a control perspective, the OEMs rightfully realized that with an ADAS safety system where all you're doing is trying to avoid collisions with the car in front of you or avoid moving out of the lane and provide a warning to the driver. It doesn't really require much customization, but when you're thinking about a system where the car is going to be driving for the owner of the car, then you have to think in terms of is it going to feel comfortable right? Like what's the breaking profile? How quickly should I -- when we stop, when should we start breaking in front of a stop sign? Should it be super gradual and start very early, should it be kind of more at the end. These decisions are, I think, rightfully so decisions that the OEM feels like they need to own, because they needed to own that, they thought, well, we need to own the entire decision-making software of the car. And because the decision-making is very integrated with the perception, they said, well, we better own the perception too. So I think that this is kind of the reason why you saw so much kind of investment in self-developed software systems for semiautonomous vehicles. It hasn't worked, right? There's been many examples of kind of multiple years of heavy investment, without really any level of success or success in terms of a system that is too expensive, doesn't really scale and doesn't have a path from eyes-on to eyes-off. Over the last couple of years, the sense of urgency about having this type of technology in vehicles has increased. Part of it is Tesla, continuing to improve their system. That really -- that pressure really ramped up a few months ago when Tesla cut the price of FSD and started giving free trials, making it seem like more of a commercial strategy. The Chinese automakers have also been successful in being kind of navigate on pilot intelligent driving systems on the road and kind of the western OEMs understand that they're coming to Europe. So need to compete, but we still have this kind of roadblock of where does Mobileye's role and the OEMs roll begin. And I've kind of tried various things over time. We opened up the architecture of the chip to enable the OEMs to put their decision-making software on our chip, which would create better integration, lower cost. But I think that the -- the view is that they won't have a decision-making software that can support these types of performance requirements. So over time, we realized that really, it's really only the driving experience that they want to control. So what we've done is create an API, where you can essentially take the universal parts of the software, the things that are related to safety or interpreting the environment, the things that consumers won't ever see, you can take that from Mobileye and then you have essentially kind of tuning knobs on different parameters of the driving experience that the OEMs can code themselves. And then after the vehicle is in production, if there's complaints like, hey, this seems too reckless or this seems too assertive then they don't have to come back to us and wait for us to fix it. They can do it themselves. So we call this driving experience platform, and we feel like it's really the sweet spot in terms of enabling the OEMs to have the control that they need, but not -- but they don't have to take the risk of trying to develop the core parts of the system themselves and they can take advantage of our scale.

Yes. So now if we go back to just talking about the portfolio and the differentiation there. You start with basic ADAS and on the other end is Chauffeur mobility as a service as well. How do you think about -- the differentiation clearly is probably higher as you go towards Chauffeur and mobility as a service, but the threat of disruption or the threat of other sort of competitors coming in, particularly, you do have advantage on the basic ADAS on the cost side. So when you think about disintermediation in terms of the wins that you have, where do you see the bigger threat?

So I think that the basic ADAS business is very strong. I think we have significant competitive advantage. One is scale, right? We did 37 million chips last year, which means that there's 37 million units of capacity to put the chip on a circuit board and to by the camera and to turn it into a system. This is all the work that's done with the Tier 1s. And this is really a cost business because it's not something that the OEMs make money on, right? They need it for safety rating compliance. And so keeping costs low is extremely important and also not causing recalls like we've never been involved in a recall, or we've never been the kind of the driver of a recall. There was a couple of OEMs that tried to second source, diversify away from us a few years ago and both experienced recalls in the last 3 or 4 months. So that's also a big advantage for us. I think in China, there is some competition on the low end for low-priced vehicles that probably didn't have ADAS a few years ago for very simple systems and we're dealing with that competition. We always have some level of competition, but these systems won't work outside of China. They don't -- they're not up to the standards of kind of the western countries. And so we feel good about our positioning in ADAS. And we won 26 million units of new ADAS business in Q1. That's not going to be kind of a quarter-after-quarter type of number, but it shows kind of how we continue to win business at a very high rate. I think that Chauffeur, on the other hand, right, on kind of the further end is something where we have not seen or heard of a real approach to get to the performance requirements of this kind of million hours between failures. And I think we've been very visible in terms of our true redundancy concept of having the 2 independent perception systems. And so the idea of -- we'll use Tesla, as an example, which right now is maybe 10 hours between intervention, which is actually very good and creates a good product to get from 10 hours between intervention to 1 million hours between intervention with only a camera system, with only trying to kind of find corner cases and incrementally improve the system, it doesn't seem viable to us. So we feel like we have a very large competitive advantage in terms of getting to the performance requirements that are necessary for an eyes-off system. Now in between is eyes-on is more competitive, right? Because I think there's entities in China that are have [ good ] systems like this on the road, XPeng, Li Auto, there are some suppliers in China that are kind of pursuing this market. And I think that there's no like specific performance requirements that you need besides a system that works. Now that where we see ourselves being benefiting in this area is basically this type of system inevitably is going to be a balance of performance and cost, right? You want high performance, but how much are people willing to pay for a system that you still have to keep your eyes on the road. It's not going to be $6,000, $7,000, $8,000. So cost is going to be very important. When we look at the systems on the road in China, we see multiple LiDARs and multiple radars, the same number of more cameras. We see NVIDIA, Orange chips being used, which have like anywhere from 10 to 20x the processing power of our chipset, which means cost, which means power consumption. And then we know that some of these automakers, which are not -- these startups are not particularly like financially strong, have 2,000, 3,000 engineers working on this. So if you think in terms of 100,000 per engineer, that's $200 million to $300 million of spending on engineering for these systems, if you put it in 100,000 cars, that's $2,000 to $3,000 extra. So -- we -- our system is essentially $1,800 all-in to the automaker. We see systems, our benchmarking would say, just the material cost of these systems is $3,000 plus and then you have to add into kind of the engineered cost as well. So we think we have a very large cost advantage that will play out over time.

Okay. Interesting. Sticking with SuperVision on that front end. One of your big customers is ZEEKR. How should we think about the impact of the recent EV tariffs that were put on Chinese OEMs by the U.S. administration in relation to growth aspirations for ZEEKR and eventually then impact on SuperVision's growth?

So yes, the geopolitical situation is kind of tough to keep up with. So I think it's one of these good and bad things, right? It's our main customers are legacy automakers, right? They're not start-ups. And we've seen market share shifts over the last few years from legacy automakers to start-ups or domestic Chinese automakers. It happened in China, but we're also seeing like significant production growth in exports out of China. And we have good position with these OEMs, but it's not like we have 100% of their business for most of them, right? So I think protectionism in North America and Europe in some ways helps us because it helps our main customers. But it hurts us in a way because we want this competitive pressure to lead to our main customers moving faster to deploy these types of technologies. And I think the U.S. tariffs don't affect ZEEKR because they didn't have any ambition to come into the U.S., but they are selling cars in Europe. There's obviously talk of protectionism there as well. So I think for now, no impact. But I think we would want our customers like ZEEKR and Polestar to be able to have success outside of China because I think it helps to kind of push our other customers to move faster.

Okay. Good. Can you give us an update on wins for SuperVision? And particularly, when do we start to see a bit more diversified OEM exposure that reduces the sort of custom concentration risk for SuperVision?

Yes. Yes, good question. So right now, we have production agreements with 4 automaker groups, ZEEKR and Polestar within the Geely Group. These are in production today. So this is what's driving our volume in 2023, 2024. We launched a system with FAW, which is a state-owned automaker in China in Q4 of this year. That is expected to drive significant volume growth in 2025, but still with mostly focused on China. We have a design win with Mahindra that should launch sometime in 2026. That will be pretty low volume. But then the kind of the first global agreement we were able to sign is with Volkswagen Group, and that launches in the first half of 2026. So that's -- right now, we have 5 vehicle models in production. There's another 4 or 5 coming next year. The Porsche and Audi inside VW Group is for 17 models to launch over a period of a couple of years. So that should drive a lot of diversification globally, customer-wise and a lot of volume growth as well. And then beyond that, we're in -- what we call advanced discussions with 10 additional automakers in total, those 14 total -- almost half of the industry in terms of production share. So these design wins are really important to continue this diversification process and also generate more maybe FOMO is a good word for competitive pressure to move fast and deploy these systems. And this has been a huge expansion of the pipeline. Because if I go back to when we were IPOing in late '22, I would have said we were either production agreements or advanced discussions with 3 OEMs, now it's 14, which kind of demonstrates the impact of lack of success in internal systems, more competitive pressure and kind of the proof points that we've provided by launching the system in China.