AudioEye, Inc. (AEYE) Earnings Call Transcript & Summary

Awesome. Good afternoon, everyone. Thanks for joining us here today. My name is Josh Batwa. I'm a member of the Automotive Equity Research team here at JPMorgan. It's my pleasure to be joined by Matt Fisch, Chairman and CEO of A.EYE as well as Conor Jurney, Chief Financial Officer. It's always great to have you at the conference. Thanks for being a supporter. Just to kick things off, I'll hand it over to Matt to walk us through a few slides, and then we'll get into Q&A and take questions from the audience. Thanks, Matt.

Okay. Great. Well, yes, good to be here, fourth year for me, and no hurricane coming or anything like that. So nice relaxing time. We have a few slides to go through in the presentation. We have a little bit of a product introduction that are here and then save most of the time for Q&A. So the other question since I've been here last year, key question is, is LIDAR still essential, is it still central to autonomy? And I love this recent quote from Rivian. It's not the only one, but it still fits nicely between that spot. It's complementary and especially in automotive autonomy between radar and camera. It sees very far like radar can, but it sees a lot sharper and lot resolution. And when you see the comments here about the Level 4, so we had Don here on the stage earlier, I think that's an important factor of how the direction for the tech stack gets influenced moving forward. Quick picture of our -- this is our core that we work on jointly with the University of Toronto. It's their wind tour driving program. LIDAR still the only sensor that can see into direct sunlight rain is practically invisible to our sensors are driving in bad weather, lidar is still really a key piece to the puzzle in the tech stack. One of the things we learned since we were here last year about the OEM needs, especially on the automotive side. I'd say when I was standing here on stage last year, there was this notion of having a lidar in a passenger vehicle. And I think that's changed quite a bit. It's -- I call it monolithic lidar. It's 1 size fits all that takes on many tasks inside the vehicle. And I think what we're seeing now is a much more specialized set of use cases, which play well to the architecture that we have. We'll get into that in a second here. Supply chain resiliency. We're an American company. We manufacture in America, and this has become a front and center topic when talking with purchasing departments in OEMs, regardless of whether it's passenger vehicle, robotaxi and trucking. In Western Europe and the U.S., it's table stakes at this point to have a very resilient supply chain. And good thing Don is not here, but no intention to support any particular trucking manufacturer, but one of the things we saw in the first half of the year is an enormous amount of capital coming into the Level 4 players particularly Autonomous trucking, Uber sponsored a lot of cash for that. This L4 mindset that you saw early at the beginning of the presentation is starting to influence and strengthen the position about lidar being essential in this space. One of the things that also ties back, I thought I mentioned earlier about moving away from monolithic lidar, we talked a lot about cost and integration cost of a new sensor last year. And there's a couple of things. One is just the [indiscernible] costs about adding hardware to a vehicle, be it a truck or a passenger vehicle, but the other piece is just the overall integration cost. Don can did earlier here when he was on stage about you need to train sensors, there's software integration and compatibility that needs to happen. And this is 1 of the areas that AI, whether Apollo sensor and what I'm going to be talking about next software vehicle we are the SDV of lidar. And imagine a sensor, we talked last year, we have a kilometer of range in our sensor. And think about that as a checkbook. Where do you take all that horsepower in performance? Does a car need to see a kilometer ahead maybe not, but we can do other things with it and be super flexible it's a new industry, the -- that was great. The understanding of what exactly the sensor needs to do is evolve month-to-month and year-on-year. We're super flexible in that regard. We can sort of -- here's a case where the red is we're spending a lot of our budget in the sensor and the cooler colors are spending less. We can rebudget this depending on a particular OEM passenger vehicle versus truck. And we're seeing this becoming a very important factor as we're moving forward. I mentioned supply chain resiliency. Same as last year, we're partnered with experienced Tier 1 automotive supplier. Our footprint is global and flexible, but most importantly, today, we're able to do manufacturing in North America. And again, this is a checkbox you walk into an OEM purchasing department today. If you're not able to check this box, it's see you later. Capital light, this is a value we've clung to at the company and because our assembly and supply chain process is so very modular. We're able to scale up and [indiscernible] manufacturing is needed. Really what drives the cost for us is the working capital and the components, not the initial investment of the line itself, and we're ready to scale. The current line that we announced late last year is ready to ramp up to 60,000 units a year. Again, this is a checkbox when you're walking into a purchasing department at an OEM is, okay, can you produce thousands of these things just for our first vehicle line. I know there was a question that came in the pre-notes about NVIDIA. Integration costs, that's really what everybody needs to think about and what we're thinking about here. It's not just how much does the hardware cost, but it's also how much software integration and training work that you have to do to integrate a new sensor into the self-driving stack. And NVIDIA has really been a great partner for us in this place. And they've got their hardware, at least they've stated in 35-plus OEMs. And it's really powerful to be able to walk into OEM and be prequalified and very compatible with that platform. It's in essence making the statement about the maturity of the product and the integration cost, and they've been such a great partner, not only just about pushing us on the representative automotive sensing requirements, but also the automotive grade piece. We're a tech company. automotive reliability is something that is evolving and developing for a tech company like us. NVIDIA has been a great partner in helping make sure that we're hardened and robust by being part of their Halos safety line. So we got smaller since last year, and I'm happy to show here. This is our Stratus sensor fits nicely in the palm of my hand. We had a 1-kilometer sensor last year called Apollo, and this is now 1.5 kilometers. It's essentially double the budget double the checkbook size of performance. And you'll see it tucked neatly under the rearview mirror in a vehicle application. That's actually a slightly larger. That's the Apollo [indiscernible] slightly larger, and this is just really an output of that learning, meaning monolithic sensors not required that was keeping the cost of [indiscernible] in general higher Imagine, for example, an OEM who would use a long-range sensor be up above the mirror and then 2 shorter range sensors in the side view mirrors or the headlights, that's actually a cheaper solution than 1 single monolithic sensor, and this has allowed us to really take out some of the overengineering that's been done for this well. This 1 lighter has to do the entire work for passenger vehicle and allowed us to increase employee performance where the OEM needs it most, and also drive down the cost of the product and to make it smaller at the same time. Last but not least, so now we -- when we were here last year, we talked about autonomy as the market, and that has expanded since then to more generally what we call physical A.EYE. And lidar has certainly had a place in the broader market of physical AI, and that's where the sinking part of the machine interacts with the physical world. And I'll tell you, defense has been a very hot topic for us. There's been a lot of inbound for us with unmanned ground vehicles high-risk situations, drones, in flight and also manned vehicles. These are not sort of the hobby level drones, but larger drones that need to avoid things like power lines. Like we have in the picture there, we're incredibly good due to the tax stack to see power lines that are 3 centimeters in thickness at hundreds of meters away. So that power of long distance sensing, we brought into the automotive space is now paying off in defense. These guys fly a low altitude missions, power line is a big hazard. These are very expensive drones that are not disposable, and for example, we've been able to add a key technology there. And last but not least, counter UAS, that's the swarm size drones because of our long range and ability to focus energy out for an ability to see very small objects. we've been very busy in this space. In fact, it's been making up the largest chunk of our revenue here in the first half of the year. So as physical AI ramps, we feel like we're in a solid position. We continue to have strong differentiation, the ability to point performance in the way that an OEM or other markets need it. They have a big budget. We give them a large budget and they can spend it how they want. If you followed our earnings, this is the recent commercial announcement that's been -- that was the underpinning. We had competition there that was fierce. Our ability to put the performance into a high frame rate allowed us to be unmatched in that particular market. Manufacturing North America and that high flexibility as we continue to learn, physical AI market continues to develop. Our balance sheet is solid. We have a clean balance sheet. Thank you, Connor, and a strong cash position with a large customer pipeline, 25 customers paying revenue today, and we have the balance sheet to bridge that gap, we believe, to that sweeter spot in revenue. Ecosystem is diverse, NVIDIA is leading the pack here, relationship with them has been great. Again, integration cost being key there. And then we've expanded our partnership since we were last year that help us a lot on is for other markets like defense data center security in other places. That's it. With that being said, I'll have -- here's an example of an airport security application hub. You can see sort of -- this is how a machine sees and how a person sees, but we're very proud of the detail and consistency of the data that's coming into the machine in this case. We believe the sensors are world class in that regard.

Awesome. Thank you. Thanks for all the great color. Great. Maybe before we get started in a specific direction, just question around the long range. Is that a function of the lidar being SDV, like the fact that it's software defined, does that make it long range? Or is it more a hardware element or hardware design choice that leads to?

the range. And as I like to talk about it, it's the photon budget because lidar is all about sending white photons out and collecting them back, the bigger budget you have, the more things you can do with Range. For example, because of our wavelength and because of the unique architecture we have here, we have this biostatic architecture where the transmit and receive are different. This allows us to have a much larger photon budget than the rest of the market, we believe. And so typically, we've channeled that in a. But there's other ways, like, for example, in the commercial win we have with the Live 3D, we've taken that range, brought it in shorter and hit 60-hertz frame rate, which nobody else can do, and that's why we won that business.

Understood. And that's more -- like the flexibility in terms of the Photon budget that's enabled by the software. That's the approach that you've...

So we can crank it out to a kilometer for a high-speed drone that's flying and trade off some other things. And then we can also bring it up close and give incredible resolution like you saw with that plane behind.

Got it. I appreciate that. And you touched on different adjacencies or other sectors outside of automotive that the LiDRs are addressing today. But maybe while we stick to automotive, is range the gating factor or where you see the most conversations with automakers referring towards? Or are there other factors that come up in conversations morrow.

I think in -- I would say just in general, the theme that we see is the range and the fact that we have something that we can uniquely advertise, and that's 1 kilometer or greater, that's bringing people in the door, both in the automotive space and the nonautomotive. As you see in the picture that I had a few slides ago, we can put this up behind the windshield above the mirror and that when you shine a laser through glass, you lose a lot of range, but we have so much headroom in that case. That we can meet the 85-mile per hour or a that we see in the automotive space and give the OEM a packaging differentiation. They don't need the taxi sign on top of the roof. So we've traded that range for the ability to punch through glass so to speak. And in the case of a live 3D, for example, a nonautomotive market, we brought it in up close and given them 60 hertz, so they can track high-speed sports option. Range brings them in the door, the ability to customize the sensor that their use case is what's selling the deal.

Got it. Understood. That's clear. We discussed the software-defined nature of the lidar sensor. You mentioned there are different use cases or flexibility in terms of what you can do. You showed us one, but wondering if you could spend a few minutes discussing some of the key use cases and how they could address different applications like maybe personal autonomous vehicles versus robotaxis? How does that flexibility help you in that regard?

Yes. I think, look, 1 of the things, if we start just from the automotive space, we're seeing that each OEM is doing things slightly differently. Certainly, robotaxi has many lidars on it, for example. And as they may transition to highway driving, they need to start seeing further ahead, higher speeds, longer distance, but still, it doesn't -- when it's navigating in the city, they still need to see very great detail around them. That's a different vehicle architecture than, say, a passenger vehicle that can't have or can't afford to have 10 sensors on it and uses lidar, for example, to enable highway at speed driving. It's a different vehicle architecture and in each of those 2 cases, even within the passenger vehicle space, and let's take trucking since Don was here earlier, they're using lidar because the braking distances are incredibly great. So they're going to go after that long range, narrower field of view. And just from a financial perspective, that allows us just to ship 1 piece of hardware and service those different vehicle lines are each of those 3 different markets. a drone case flying at 400 kilometers per hour, they just need to see very, very far ahead. Now we're back to a kilometer and they can see a very narrow field of view because they're just looking at avoiding the crash into something directly in front and do that last minute maneuver around them. You saw a picture up there earlier. We have a product called Optus, which is a combination of our [indiscernible] Jetson platform that's sitting at an intersection in Detroit. And we also have 1 in the Bay Area, that 1 wants to see why because 2 intersections coming together, by the way, it replaces the need to have multiple cameras at that intersection, that needs to see kind of medium distance like 200 kilometers out and very wide. So there you have 3 or 4 different use cases that just need to see things drastically different see the world in a drastically different way. And when you have -- so we'll call it that old school, lidar with a spinning mirror on it, you don't have that flexibility without adjusting and redoing the hardware.

Makes sense. So I appreciate that. And just like well around the technology and the hardware element, I was curious if you could talk about the resilience against that. It seems like that's been a factor that comes up every now and then with some of your peers that have talked about their experience with being onboarded onto automotive platforms. I'm wondering how the Apollo is differentiated in that sense.

Yes. So I think, look, in this day and age with the technology that's out there, if you want to have long range, you need some mechanical element. So you heard about Vlad, lidars, for example, they have much shorter range, that's the 1 line of that doesn't need a mechanical spinning device in it. As soon as you throw that in there, your reliability and resilience is going to take a step now, and so this is where we're very unique is that we have that ultra-long range without having to project the laser beam through some kind of spinning or PRISM device, for example, we're just wrapping up a very deep discussion right now in testing with a customer that required us to have 1,000 G shock resilience. And 1,000 G is not something that is pinning mirrors likely to survive long term. So that, again, this MEMS architecture that we have, which is the only moving part in the system is only 1 millimeter smaller than a pencil eraser is really giving us that extreme reliability and durability. Let's look at this from another perspective. The fact that you can put this behind the windshield in a car, allows you to use a 100-plus-year-old cleaning solution to keep the viewpoint clear. We're not in a surface by and large, in those types of vehicles. If you go into airspace and defense, Typically, this is going to be in some other kind of disclosure. I think you asked a question about, hey, is LIDAR going to have a self-cleaning solution built into it in the vast majority of applications we've seen we don't need it. And we have resiliency that's unique for long-range lighter.

Understood. And how do you -- like organizationally, how are you balancing between Apollo and Stratus, like which of those sensors are directed towards which applications and platforms?

Not to give away any secrets. But basically, from a hardware perspective, you can think of this as a manufacturing SKU versus a redesign. There is really no redesign between this and Apollo. Two things. One is that we can actually remove logic when the game is seeing for and seeing more narrow. And our optics, it's like an eyeglass prescription, weekend swapping out different lenses that give us optimal characteristics. These are things we handle at manufacturing time. It doesn't trigger new design engineering cycles, except for maybe the case that goes around it. very, very minimal work that Stratus brings up. So I'd say, like more than 90% of the work, which is mostly software is shared between the 2 on the solution.

That's very encouraging I think that's a good segue into pricing, maybe like just of the unit on where ASPs said, how you think about the longer-term trend line for ASPs and gross margins?

We're going to give Conor a chance to jump in.

Yes. So what I would say is, look, Matt brought it up earlier, but you think about the versatility of the sensor, right? And we don't need many different product variants because we can change a lot of things through the software. And what that does for us, it allows us to compete in many different verticals. And I think 1 area where we can command a lot of pricing power is in the high-performance verticals. So I think about aerospace and defense, anywhere where you have a customer that's willing to pay a premium for the value proposition. And that's where we have a lot of opportunities to really lean in, improve our margins. And it's not unusual and those sectors, especially to get margins north of 60%, right? I would say in automotive, margins are going to be more compressed, especially when you're selling directly to the OEMs. So that's certainly an area where we need to be a little bit more competitive. But that said, I think we have a lot of leverage in terms of how we design the product. Also, we have what's called our capital-light business model, which makes us leaner than the competition, and we kind of live and breathe that in our DNA.

Got it. No, I think that's a great segue into the -- just talking about the capital-light business model, but maybe on automotive, specifically on the ASPs, is there like a specific price point, which automakers are looking to get lidar sensors under to be -- to embrace them more meaningfully. Is it like $500, $600?

Yes, I mean, it depends, right? I think certainly, at volumes, you're talking probably less than $1,000 for automotive. That's for sure. That's what we're hearing. Obviously, at lower volumes when you're talking about sampling you can command a higher price point. And then in nonautomotive, it's a completely different picture, right? You're talking sometimes about tens of thousands of dollars, right? When you think about just the hardware and then the software that goes on top of that and then services and everything like that. So to a certain extent, our pricing model is still evolving. And there's things that we can do on the customization side that we haven't really even leaned into. So there's lots of opportunity there to drive improved margin and obviously more favorable pricing there, too.

I think I'd just add 1 thing to that. Just think about defense and aerospace for a moment and lidar sensors that can see a kilometer or more. These guys are accustomed to paying hundreds of thousands of dollars for that kind of, call it, military grade sensor we're eating into that performance range. So as Conor said, it's -- we're learning a lot, but there's a lot of room to work with.

Understood. No. We'll touch on defense. We have roughly 10 minutes left. So maybe just double clicking on the capital-light business model. Could you just talk about the licensing aspect of that? What do you see as the most distinctive about how you structured it, and who do you envision as the national licensees of that technology over time?

Yes. I mean I think that's probably more a longer-term play. Right now, we're selling probably directly to the end customer. And I would say that with automotive, when you're talking about higher volumes, millions of units, you're probably going to lean in more with the Tier 1s. At that point in time, you pivot more to a licensing model. And I think the great thing about a licensing model is we can really lean into that capital model, capital-light partnership model that we're known for, while at the same time, commanding higher margins, right, because you don't need the same amount of head count, your overall cost structure is going to be a lot lower.

Understood. Yes. Just wanted to pause and check if there were any questions in the room? All right. I'll go on. Maybe just switching gears to the defense segment. Lots of engagement and white space opportunity there. Could you maybe talk about whether lidar solutions are replacing existing solutions? Are they creating a new market that was like -- that is for new applications? And why do lidars sensors -- why lidar sensors better positioned for these applications?