Awesome. Good afternoon, everyone. Thanks for joining us here today. My name is Jash Patwa. I am a member of the Automotive Equity Research Team here at JP Morgan. It is my pleasure to be joined by Matt Fisch, Chairman and CEO of AEye, as well as Conor Tierney, Chief Financial Officer. It is always great to have you at the conference, so thanks for being a supporter. To kick things off, I will hand it over to Matt to walk us through a few slides. Then, we will get into Q&A, and take questions from the audience. Thanks, Matt. Okay, great. Oh, yeah, good to be here. Fourth year for me. 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 here, and then save most of the time for Q&A. Yeah, the question since I have been here last year, key question is LiDAR still essential? Is it still central to autonomy? I love this recent quote from Rivian. It is not the only one. But still fits nicely between that spot. It is complementary, especially in automotive autonomy between radar and camera. It sees very far, like radar can, but it sees a lot sharper and a lot resolution. When you see the comments here about the Level 4, so we had Don here on the stage earlier. I think that is an important factor of how the direction for the tech stack gets influenced, moving forward. Quick picture, this is our car that we work on jointly with the University of Toronto. It is their WinTOR driving program. LiDAR is still the only sensor that can see into direct sunlight. Rain is practically invisible to our sensors. 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 would say when I was standing here on stage last year, there was this notion of having a LiDAR in a passenger vehicle. I think that has changed quite a bit. It is called a monolithic LiDAR. It is one size fits all that takes on many tasks inside the vehicle. I think what we are seeing now is a much more specialized set of use cases, which play well to the architecture that we have. We will get into that in a second here. Supply chain resiliency. We are 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 is passenger vehicle, robotaxi, and trucking. In Western Europe and the U.S., it is table stakes at this point to have a very resilient supply chain. Well, good thing Don is not here. 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 a position about LiDAR being essential in this space. One of the things that also ties back to what I mentioned earlier about moving away from monolithic LiDAR. We talked a lot about cost and integration cost of a new sensor last year. There is a couple of things. One is just the BOM cost 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 mentioned it earlier here when he was on stage about you need to train sensors. There is software integration incompatibility that needs to happen. This is one of the areas that AEye, with our Apollo sensor, and what I am going to be talking about next. SDV, software-defined vehicle. We are the SDV of LiDAR. Imagine a sensor, we talked last year, we have a kilometer of range in our sensor. Think about that as a checkbook. Where do you take all that horsepower and 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 is a new industry. That was great, huh? The understanding of what exactly the sensor needs to do is evolving month -to- month and year-o n- year. We are super flexible in that regard. We can sort of, here is a case where the red is, we are spending a lot of our budget in the sensor. The cooler colors are we are spending less. We can rebudget this, depending on the particular OEM passenger vehicle versus truck. We are seeing this becoming a very important factor as we are moving forward. I mentioned supply chain resiliency. Same as last year. We are partnered with experienced Tier 1 automotive supplier. Our footprint is global and flexible, but most importantly, today, we are able to do manufacturing in North America. Again, this is a checkbox. You walk into an OEM purchasing department today, if you are not able to check this box, it's, "See you later." Capital light. This is a value we have clung to at the company. Because our assembly and supply chain process is so very modular, we are able to scale up and scale down manufacturing as needed. Really what drives the cost for us is the working capital and the components, not the initial investment of the line itself. We are 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 are walking into a purchasing department at an OEM is, "Okay, can you produce thousands of these things to support our first vehicle line?" I know there was a question that came in the pre-notes about NVIDIA. Integration cost. That is really what everybody needs to think about and what we are thinking about here. It is not just how much does the hardware cost, but it is also how much software integration and training work that you have to do to integrate a new sensor into the self-driving stack. NVIDIA has really been a great partner for us in this place. They have got their hardware, at least they have stated in 35 + OEMs. It is really powerful to be able to walk into an OEM and be pre-qualified and very compatible with that platform. It is in essence, making a statement about the maturity of the product and the integration cost. They have been such a great partner, not only just about pushing us on the representative automotive sensing requirements, but also the automotive grade piece. We are 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 are hardened and robust by being a part of their Halos safety lab. We got smaller since last year. I am happy to show here. This is our STRATOS sensor. It fits nicely in the palm of my hand. We had a 1 km sensor last year called Apollo, and this is now 1.5 km. Essentially double the budget, double the checkbook size of performance. You will see it tucked neatly under the rear view mirror in a vehicle application that is actually slightly larger. That is the Apollo sensor. It is slightly larger. This is just really an output of that learning, meaning, monolithic sensors not required. That was keeping the cost of LiDARs in general higher. Imagine for example, an OEM who would use a long- range sensor, be up above the mirror, and then two shorter- range sensors in the side view mirrors or the headlights. That is actually a cheaper solution than one single monolithic sensor. This has allowed us to really take out some of the over-engineering that has been done for this. This one lighter has to do the entire work for the passenger vehicle, and allowed us to increase in place performance where the OEM needs it most, and also drive down the cost of the product and make it smaller at the same time. Last but not least. 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 AI. LiDAR has certainly had a place in the broader market of physical AI, and that is where the thinking part of the machine interacts with the physical world. I will tell you, defense has been a very hot topic for us. There has been a lot of inbound for us with unmanned ground vehicles in 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 are incredibly good due to the tech stack to see power lines that are 3 cm in thickness at hundreds of meters away. The power of long distance sensing we brought into the automotive space is now paying off in defense. These guys fly low altitude missions. Power line is a big hazard. These are very expensive drones. They are not disposable. For example, we have been able to add a key technology there. Last but not least, counter- UAS, that is the swarm size drones because of our long range and ability to focus energy out far and ability to see very small objects. We have been very busy in this space. In fact, it's been making up the largest chunk of our revenue here the first half of the year. 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 a 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 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 p hysical AI market continues to develop. Our balance sheet is solid. We have a clean balance sheet. Thank you, Conor. A strong cash position with a large customer pipeline. 25 customers paying revenue today. We have the balance sheet to bridge that gap, we believe, to that sweeter spot in revenue. Ecosystem is diverse. NVIDIA's leading the pack here. Relationship with them has been great. Again, integration cost being key there. We've expanded our partnership since we were last here that help us provide solutions for other markets like defense, data center security in other places. That's it. With that being said, here's an example of an airport security application we have. You can see sort of this is how a machine sees, not 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. Thanks for having us. Great. Maybe before we get started in a specific direction, just a question around the long range. Is that a function of the LiDAR being SDV? The fact that it is software defined, does that make it long range? Or is it more a hardware element or hardware design choice that leads to--? The range, as I like to talk about it is the photon budget, because LiDAR is all about sending light 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 bistatic 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. Typically, we have channeled that into range, but there is other ways. Like, for example, in the commercial win we have with Alive3D, we have taken that range, brought it in shorter and hit 60 Hz frame rate, which nobody else can do. That is why we won that business. Understood. And that is more like the flexibility in terms of the photon budget that is enabled by the software-- That is right. -- approach that you've -- 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. You touched on different adjacencies or other sectors outside of automotive that the LiDARs are addressing today. Maybe while we stick to automotive, is range the gating factor or where you see the most conversations with automakers drifting towards or are there other factors that come up in conversations more often? I think, I would say just in general, a theme that we see is the range and the fact that we have something that we can uniquely advertise and that's 1 km or greater. That's bringing people in the door, both in the automotive space and the non-automotive. As you see in the picture that I had a few slides ago, we can put this up behind the windshield above the mirror. That when you shine a laser through glass, you lose a lot of range. We have so much headroom in that case that we can meet the 85 mph driving spec 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. In the case of Alive3D, for example, a non-automotive market, we brought it in up close and given them 60 Hz 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 sealing 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 just 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? Yeah. I think, look, one of the things, if we start just from the automotive space, we're seeing that each OEM is doing things slightly differently. Certainly, a robotaxi has many LiDARs on it. For example, as they may transition to highway driving, they need to start seeing further ahead, higher speeds, longer distance. But still, when it's navigating 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. In each of those two 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. They're going to go after that long range, narrower field of view. Just from a financial perspective, that allows us just to ship one piece of hardware and service those different vehicle lines or each of those three different markets. A drone case flying at 400 kph, they just need to see very far ahead. Now we're back to a kilometer. They can see a very narrow field of view because they're just looking at avoiding a crash into something directly in front of it, and do that last-minute maneuver around them. You saw a picture up there earlier. We have a product called OPTIS, which is a combination of our sensor plus NVIDIA's Jetson platform that's sitting at an intersection in Detroit. We also have one in the Bay Area. That one wants to see wide because two 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 m out and very wide. There you have three or four different use cases that just need to see things drastically different, see the world in a drastically different way. When you have-- 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. I appreciate that. Just while we're on the technology and the hardware element, I was curious if you could talk about the resilience against dirt. 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? Yeah. 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 may have heard about VCSEL- SPAD LiDARs, for example. They have much shorter range. That's the one LiDAR 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 down. 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 a prism device. For example, we're just wrapping up a very deep discussion right now and testing with a customer that required us to have 1000g shock resilience. 1000gs is not something that a spinning mirror is likely to survive long term. That, again, this MEMS architecture that we have, which is the only moving part in the system, is only a 1 mm. It's smaller than a pencil eraser. It's 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 aerospace 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. I see. We have resiliency that's unique for long-range LiDAR. Understood. Organizationally, how are you balancing between Apollo and STRATOS? Which of those sensors are directed towards which applications and platforms? Not to give away too many 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 far and seeing more narrow. Our optics, it's like an eyeglass prescription. We can swap in and 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 minimal work that STRATOS brings up. I'd say more than 90% of the work, which is mostly software, is shared between the two solutions. Understood. That is very encouraging. I think that is a good segue into pricing. Maybe just a state of the union on where ASPs sit, how you think about the longer-term trend line for ASPs and gross margins? We are going to give Conor a chance to jump in. Yeah. What I would say is, look, Matt brought it up earlier, but you think about the versatility of the sensor, right? We do not need many different product variants because we can change a lot of things through the software. What that does for us? It allows us to compete in many different verticals. I think one area where we can command a lot of pricing power is in the high-performance verticals. I think about aerospace and defense, anywhere where you have a customer that is willing to pay a premium for the value proposition. That is where we have a lot of opportunities to really lean in, improve our margins, and it is not unusual in 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 are selling directly to the OEMs. That is certainly an area where we need to be a little bit more competitive. That said, I think we have a lot of leverage in terms of how we design the product. Also, we have what is called our capital light business model, which makes us leaner than the competition. We kind of live and breathe that in our DNA. Got it. I think that's a great segue into just talking about the capital light business model. But maybe on automotive, specifically on the ASPs, is there a specific price point which automakers are looking to get LiDAR sensors under to embrace them more meaningfully? Is it like $500, $600? Yeah, 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. Then, in non-automotive, 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. 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 one 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, we'll call it military grade sensor. We're eating into that performance range. So as Conor said, we're learning a lot, but there's a lot of room to work with. Understood. Now, we will 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? Yeah. I think that's probably more a longer-term play. Right now we're selling probably directly to the end customer. 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. I think the great thing about a licensing model is we can really lean into that 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. Yep. That's clear. 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 is for new applications, and why are LiDAR sensors better positioned for these applications? Yeah, look, I think it's a combination of both. I think we talked two earnings calls ago and in the most recent earnings call as well about power line detection. Again, back to that high-speed drone, low altitude missions, very expensive. It crashes into something. There's really nothing that can see a power line that far away today. Again, we're talking about something, if you're 500 m out, imagine trying to see something about the thickness of a golf ball. It's invisible to radar. It's certainly out of the range of a camera flying at that speed. So there's a case where LiDAR is enabling, we'll call it a new TCO reduction. Let's talk about counter-drones. For example, I think as we understand, a lot of these systems work today by listening. They may listen from a microphone. They're tuned to the sound, and then they point a radar in a direction and see if they can see drones coming in. Radars in this space to keep the cost reasonable, again, they're not billions of dollars worth of radar, can see about 200 m, but it can't distinguish a drone from a flock of ducks, for example. This is where LiDAR is complementing an existing set of sensors that say, you know what? Because we have that finer resolution versus radar, we can actually see, by the way, a little bit further than a radar can. We can eliminate those false negative type of scenarios. So there's an example of where it complements the existing set of sensors. Sure. So it's both. Understood. I have a question. Go ahead. I have a question for Matt. Yes. I saw that one. That is OPTIS, yeah. OPTIS. Yes. The one you hold? Yeah. We call this one STRATOS. Oh, okay. Do we need to charge it? When you put it in the drone? Yes. Remember, this sensor weighs about 1 kg. This would typically operate in a larger drone that is probably running off of a jet engine, not necessarily an EV- type application. That type of drone generates its own power. Again, this isn't sized or fitted for an Amazon package delivery drone, for example. It's a much larger fighter drone. This only takes about 15 W-18 W of power. It could run for many hours off of a battery. Generally, when you put it on a drone-type vehicle, it has a combustion engine that is generating its own electric power because it's a larger combat or surveillance drone. May I know how much of it? How much is the-- Cost. Yeah, the cost. Conor mentioned this earlier. In the automotive space, because volumes are much higher, they are expecting prices in the hundreds of dollars. Of course, they want as cheap as possible, but it needs to be in the hundreds. Somewhere between $500-$1,000. In the defense space, it can be thousands of dollars, if not over $10,000, in that case. One more question. When the business model, you are going to do to be on the retail? Oh, retail? No. How to sell this one? You are going to partner with a drone company or you-- I see. Yeah. To be or to-- It's definitely a B2B product, just to be clear about it. Typically, our partners are either the OEM directly or a system integrator, which would be the equivalent of a Tier 1. For example, in the automotive industry or in the defense space. For example, the primes, if that term makes sense. It could be both in the automotive and trucking. It tends to be more with the OEM themselves. In other markets, it's with an integrator, a defense prime or similar. The model varies. Great. I know we have a few minutes. Just on defense, just rounding out that discussion, have you seen any signs of pricing compression in that segment, as some of your peers have also increasingly focused in that domain? I think, I suspect that's going to vary based on the use case. When we're in the markets and the solutions that we're competing in where, say, 1 km is important, high frame rates are important, high resolution's important, we're enjoying a distinction of differentiation in that product, so n ot yet. We have some very unique product characteristics, so we enjoy that differentiation for now. The answer is no. I don't think- Yeah. -- pricing is a factor either, especially with a lot of these customers. They are more focused on the problem and how to solve that problem, and they are willing to pay a premium to do that. Sure. Okay. Obviously impressive technology. Have you seen any interest from a sensor- fusion- type application for security cameras? Absolutely. By the way, we may have, when we go do our earnings, I think we bucket border security, for example, into our defense application. This falls into our OPTIS solution generally, where we are combining the perception. We have a partner, for example, in this area called Flasheye, and they have created a perception application that works really well, for example, for data center. Inside the data center, security where you watch people, hey, are they going someplace they are not supposed to be going? Or perimeter. State-of-the-art today for perimeter security tends to be, well, you cross a laser beam and you break it. It triggers an alarm. We can actually, with this partner, for example, identify animals versus humans versus bushes, seeing things in actual 3D, eliminate those false alarms, and lower the TCO, so a bsolutely. There is other, I think, especially for security, if you think about existing sensing solutions as well, things like radar. If you are looking at a chain link fence, there is a lot of splattering. But with something like LiDAR, you can go right through the fence and you can see behind it. So there are unique things to do with the technology that makes it very competitive vis-a-vis other sensing modalities. We may spot the object because we can see far in the dark. Then may, in some cases, hand it off to a camera that zooms in. Yeah. Awesome. I think we have time for one more. So maybe just in terms of, you have helpfully pointed to a liquidity runway into 2028. How should we think about capital needs in a scenario where a big contract win requires a step-up in investment? How do you balance preserving the balance sheet against funding that kind of growth? Well, look, I don't think we're going to change our business model. We've always believed in a capital light model, and that's really how we operate the business. What I would say is if there's a growth opportunity, or there's a growth catalyst, for sure we would evaluate that. We would do what's in the best interests of our shareholders, and also what we think is in the best interest of the company. That's probably how we would position it. Great. I think that's a great place to end. Yeah. Thanks, Matt and Conor, and thanks, everyone. Thanks for having us. Yeah. Thanks for having us.