What is the current Innoviz Technologies Ltd share price?

The current share price of Innoviz Technologies Ltd is US$ 0.58535

How many Innoviz Technologies Ltd shares are in issue?

Innoviz Technologies Ltd has 221,365,334 shares in issue

What is the market cap of Innoviz Technologies Ltd?

The market capitalisation of Innoviz Technologies Ltd is USD 129.58M

What is the 1 year trading range for Innoviz Technologies Ltd share price?

Innoviz Technologies Ltd has traded in the range of US$ 0.58 to US$ 2.54 during the past year

What is the PE ratio of Innoviz Technologies Ltd?

The price to earnings ratio of Innoviz Technologies Ltd is -1.57

What is the cash to sales ratio of Innoviz Technologies Ltd?

The cash to sales ratio of Innoviz Technologies Ltd is 3.05

What is the registered address of Innoviz Technologies Ltd?

The registered address for Innoviz Technologies Ltd is 5 Uri Ariav Street, Rosh Haayin, 4809202

What is the Innoviz Technologies Ltd website address?

The website address for Innoviz Technologies Ltd is innoviz.tech

Which industry sector does Innoviz Technologies Ltd operate in?

Innoviz Technologies Ltd operates in the Vehicles sector

Innoviz Technologies【INVZ】銘柄情報

期間 †	前日比	前日比 %	始値	高値	安値	平均出来高	VWAP
1	-0.06465	-9.94615384615	0.65	0.66	0.5847	2543887	0.61347257	CS
4	-0.12475	-17.5679481763	0.7101	0.85	0.5847	2474833	0.69634099	CS
12	-0.07895	-11.8846906518	0.6643	0.97	0.58	2848376	0.70961392	CS
26	-0.56965	-49.3203463203	1.155	1.38	0.58	3307584	0.86855414	CS
52	-0.62965	-51.8230452675	1.215	2.54	0.58	5604653	1.47462567	CS
156	-2.53465	-81.2387820513	3.12	4.375	0.45	4469761	1.50228761	CS
260	-9.84465	-94.3878235858	10.43	10.93	0.45	3280162	2.09467477	CS

期間 †

前日比

前日比 %

始値

高値

安値

平均出来高

VWAP

-0.06465

-9.94615384615

0.65

0.66

0.5847

2543887

0.61347257

-0.12475

-17.5679481763

0.7101

0.85

0.5847

2474833

0.69634099

-0.07895

-11.8846906518

0.6643

0.97

0.58

2848376

0.70961392

-0.56965

-49.3203463203

1.155

1.38

0.58

3307584

0.86855414

-0.62965

-51.8230452675

1.215

2.54

0.58

5604653

1.47462567

156

-2.53465

-81.2387820513

3.12

4.375

0.45

4469761

1.50228761

260

-9.84465

-94.3878235858

10.43

10.93

0.45

3280162

2.09467477

Replay of Innoviz CFO interview at TD Cowen 54th Annual Technology, Media & Telecom Conference, May 28, 2026 9:05 am EDT, available at the following link https://ir.innoviz.tech/news-events/ir-calendar/detail/9949/td-cowen-54th-annual-technology-media-telecom-conference.

Unedited transcript below:

Okay. Good morning everybody. Thank you so much for joining us. Very pleased to kick off day two of the TMT conference with a fireside chat session with Innovus to talk about automotive LiDAR technology, as well as physical AI from the company. We're very pleased to have the CFO, Eldar, Segler, with us today. Thank you. We'll go through some Q&A. If you do have questions in the room, feel free to raise your hand and we will certainly get to you. So great to see you. Thanks so much for being back at the conference.
Thank you. Thank you very much for having me.
Great. So maybe just to kick off as a as a broader question, maybe for those who are also maybe a bit newer, what is the state of the LiDAR industry today, both in automotive and non-automotive and kind of how is it evolved in the last few years?
Yeah. So I think you can put it all Maybe jumping directly into the physical AI theme. So in the past, we didn't know that we are doing physical AI, but it's very clear that you have these kinds of systems that move from digital systems that were residing mainly on our computers using data, images, movies, clips, and now turning into system that need to interact with the world in real time and need to do it very efficiently, safely. They need to understand the environment and under and these are basically the physical AI system that require very good perception. And for a very good perception. They require very good sensors. So you have already cameras which are at the very widely used, but today you have more and more. 3D sensors, which are the leaders and the benefit of a 3D sensor for an AI system. That wants it interacts with the world. It can give it a 3D image, pinpointing every object in the environment. So we see actually a very strong momentum for the for leaders, for the need of, of leaders. It started with the automotive industry. I think the automotive industry went through a few cycles, hype cycle and then slowed down.
And I think today we are in a much more mature state where the industry understands the problem much better. It understands what it needs to do much better. It understands the need of the different sensors in order to reach autonomously, safe, autonomously driving. And also you have the ecosystem. You have the different players like Mobileye, like Nvidia that are doing the brains are using the sensors in order to make smart decision. And this is we are seeing this momentum both in level four for commercial application, like robotaxis, shuttles, and trucks. And we do see it picking up again in level three application for consumers, for consumer cars, starting with the Europeans, mainly the German. OEMs. And we see more and more momentum. In the US. And I believe eventually it will trickle down also to Japan and Korea. Outside of the automotive space Again, under this umbrella of physical AI, we see the need of good sensory. Because basically, in every application that you think of using a camera, you actually can use a 3D camera, which is the lidar, which again gives you some benefits that a camera doesn't have, or even coupling a camera together with the LiDAR has a lot of benefits.
And we see more and more applications. And demand for that from, from application like construction, smart infrastructure, smart cities, drones, humanoids, and also security and defense, which became a hot topic again, automated systems that use different levels of AI again. Physical AI again, they need good sensors that can capture the environment in 3D and pinpoint every object that is around. So we we, for leaders, we see a lot of momentum through these kinds of applications.
Terrific. So I want to maybe deep dive a little bit here on the Non-auto physical AI. I know it's still emerging. It's still seems like it's relatively early, but of the end markets that you described, which are the ones that you're most excited about in the next 2 or 3 years? And ultimately, how can investors sort of size these opportunities?
Yeah, sure. So so we Innovis has announced over a year ago that it started to pivot or to add additional market segments to its, its marketing efforts and interests. So we were in a sort of an exploration mode, understanding what the market needs. Where can leaders do the best work or fit the best? Where is the need? Where are the low hanging fruits? We focused initially in application like smart cities. It's basically smart infrastructure, smart, smart cities. And. Then we looked at construction applications and different kinds because we are also constantly approached from different players. And lately, and this is something also we have announced publicly. We see a growing demand from security and defense application from situational awareness. Applications through perimeter security, through automated systems that need to operate. In an environment or navigating an environment. And we see that there is a real sense of urgency in these kinds of applications. And a actual. Missing link. Or sensory level, which is not accommodated for by cameras and radars because they have certain shortcomings. Not that the. World will stop using these kinds of sensors, but they need to add another sensor and. A LiDAR is a perfect fit, and we see a lot of momentum and security in defense.
Our latest partners arc are from these kinds of sectors. We are not trying to be a tier one as we are in the automotive sector and automotive sector. We are approaching. We are competing directly on programs we know how to run a program. We know how to qualify for it and so on and so forth. In the Non-automotive space, we, and specifically in security and defense, there are very strong players, integrators, and there we will play as a tier two or tier three, providing our products and solutions, coupled with software to these kinds of applications. And again, we see there the greatest sense of urgency, the biggest market. So if we try to size, it's very hard to size these kinds of market in terms of time. But if we are looking the market research is mainly on the system as a system or specifically around the well, sensors. Like cameras and radars. So our our understanding of this market is that it at least 10 billion and beyond. Tam for the next few years, up to 2030. Very fast growing a lot of interest.
Interesting. And so within defense security, maybe can we dig a little bit more in terms of what the ladder is being used for? Is it mostly perimeter? Is it a lot of drone detection, which has become obviously a greater focus kind of where is that? And I think you mentioned software as well. I'm curious, kind of your role in software with defense as well.
Yeah. So I guess. All of the above. Basically. And even more, I think. Maybe I'll address a few applications, the ones you mentioned. So for perimeter security, there are already solutions in place today that are mainly using cameras and radars. The problem is that the reason why they are using cameras and radars, the radar has a wide field of view. And. It can scan a wide area and once it detects something, it can turn the camera, which doesn't have this field of view and needs to be focused around a very specific area. It can turn the camera into. However, a radar. Unfortunately, has a lot of of shortcomings, especially when you take a radar that usually looks to the sky, which is very clean and noisy. Environment and pointed to the ground there. You have a lot of noise. You have trees, you have vegetation, you have metal objects, fences, which radars are not very good at. And if the vegetation moves or you have wind. Then you get a lot of false alarms and there are other shortcomings. And if you replace the radar with with the lidar, you solve the problem. The lidar has the resolution.
The lidar has the field of view. It has the resiliency, and it can provide for already an existing solution and much better solution. At the same price point. So we see a lot of demand in perimeter security, both for mainly for civilian applications like airport defense, utility defense, but it can go also to special government facilities. It can protect borders if needed. So and, and this is, it seems like a very potent application. And with the ability to grow very fast because they are. Making their integrators have already. The programs, the projects in place. They just need to set the right technology. In place. So this we see as a very interesting, interesting area that we are focusing today. We are working in the US. We have a special guy and team that is working in the US for that matter. Usually you don't provide just the LiDAR. You need to provide also some kind of software that interfaces with the lidar that gives meaning to what you see. So we have partners that we are using in order to provide also the software side. This means obviously that the business model can be not only selling ladder, but also selling software, recurring revenues and so on.
So this is on the security on the perimeter security, situational awareness means many things. One of the hottest topic, I think, in the world today, you see it in Ukraine. You see it in Europe, you see it in the Middle East. Is the threat of drones and the reason drones are such a great threat, because nothing can detect it effectively today, because radars, again, they are very good. If you fly high. But drones, these kinds of drones practically literally fly under the radar. It's very difficult to identify them. They are small. They fly very close to the ground. So it's a very noisy environment. How do you differentiate between a flying drone and a bird? You don't want to protect yourself against a bird. It's a false alarm. You would like to identify the drone. So again here we're all other sensing technologies fail. A ladder is a very good solution. Again, you need a very good leader with high performance resolution. Enough range frame frame rate, ability to work in outdoors rather than indoors. So we are a perfect fit for that. This is also a very hot topic. As I said. So we see also a lot of applications and a lot of companies are moving towards offering these kinds of solutions.
With a leader. So basically when. You need sensing wherever you think of a camera, think of a lidar doing a better job might do a better job.
Terrific. Maybe one more. And then we'll go to audience question. Or maybe a two part question. One sticking with defense and maybe broader non-auto maybe talk about the ASP ranges that you can have relative to auto. And of course, the margin profile. And then secondly, what does a sales cycle look like in defense? Non-auto. We know auto can take years to go from the first phone call to a production program, given the urgency, particularly in the defense, what could that sales cycle look like from the moment you're engaging with prospective customers?
Yeah. So first of. Answer on the ASPs. So in the auto. Industry, the the name of the game on one hand is volumes. You get these. Very high volumes. It's still the biggest opportunity for innovation in terms of volumes and revenues. Aggregated revenues. And with that comes a very high pressure on pricing. So the industry at the end of the day, or partially is working with consumer, a consumer vehicles, you need you cannot offer a solution that costs hundreds story thousands of dollars. It it doesn't make sense. So the industry wants to see that you scale up to high volumes, but then in return, they want to see very cost effective solutions. So there is a constant price pressure there. Once you pivot to other applications like security and defense. A, the volumes are different, at least initially. The volumes. If we are talking about in the auto industry, hundreds of thousands per year and maybe can go up even to more than that in aggregate. And currently what we are seeing in security and defense is thousands, tens of thousands. So obviously the ASP is much higher. So if in the auto industry for high volumes, you send, you sell your leaders at hundreds of dollars.
Obviously, if you. Sell. To somebody a few hundreds or few thousands, you will sell it at much higher ASPs. Maybe a few thousands. Which result in a very. Financially. Positive. Contribution to top line and a very positive contribution to the gross margin. So the margin profile is much better and basically you can sell lower volumes, have a very meaningful contribution to the top line and very meaningful contribution to the gross margin profile in terms of the cycles. Automotive yes is a very long cycle to win an automotive program may take even a few years and then the development process takes another 2 to 3 years until. So until you get to SOP, start of production may take 4 or 5 years, even from the day you meet the first time the customer in the non-automotive space, at least from what we see and the sense of urgency that we encounter today, we see much shorter cycles, even within months. The decisions are being taken from the moment you meet somebody, then you do a pilot and to do. Employment agreements. So cycles here can be shorter. Yes. In the defense industry, sometimes it takes more time. But currently what we are seeing is a great sense of urgency to bring in good solutions with good sensors and, and you must.
Remember, I'm acting as a tier two or tier three, so I'm meeting the program in a much more mature state than when I'm at tier one and I'm starting with very early with an RFI and an RFQ and so on and so forth. So we there is a good momentum also in terms of timing.
Perfect. Question.
Yeah. So you mentioned that you are serving as a tier two, tier three in these non-auto opportunities. And also mentioned that you're providing the hardware and software solutions pertaining to, to that piece within the tier supply. But when we think about like the defense and aerial opportunity that exists for you guys from a detection perspective, Point cloud, is that something that you guys are working on from a classification standpoint, or is that the integrator that was responsible for taking the point cloud and creating like the perception oriented classification around that?
So Basically in offers a wide range of software solution, basically, we can do perception. We have done it for automotive in these cases, we are not at this point of time, we are not doing it. We have announced a partnership with a company by the name of cognitive. So they are doing the perception. They are providing the software solution, but we are the tier two. They are the tier three. They are providing a solution. Their solution. And we are offering not only the LiDAR, but the leader with the software as a solution. So this is the role we are trying to to play because there are different applications. We try not to to be fast and efficient when approaching this market. And go to market strategy. So we are trying to focus on what we are good at. It's leader and some supporting software, and then bring some partners to do the the perception layer for the software..
And if you did, like you mentioned, you have obviously the capabilities to do it. If you did the tier three, tier two componentry of it, would there be an ASP inflection relative to the current offering, or would it be just more like an embedded solution downstream?
Yeah. You are correct. It's an add on. So we are pricing it as an add on as an addition. So the leader itself can can be. Few thousands of dollars. And then you have the software components. And we are selling it together. The software we provide all the regular software like services, recurring recurring revenue elements to it and it's less. Less. As a SaaS service, but more the maintenance service and so on. Yeah.
So maybe I'm switching to auto. I wanted to cover your latest observations on the kind of level three, level four sourcing pipeline. It seems like we've been kind of waiting for this sourcing inflection. It's taken a little bit longer, but we have seen some signs from at least a few automakers of starting to put L3, including LiDAR standard on some programs. I think mostly for a few EV makers. What are you seeing? What are your expectations for decisions and wins along these fronts this year?
Okay, so I think we need to differentiate between the German OEMs and the American OEMs. Basically, the Germans are usually also higher and vehicles in the German OEMs. It's already a standard. It's not a question. If there will be an auto. Enormous feature in the car, it will be it will be a level three. This is true for Mercedes. This is true for false. False vag and Audi. Porsche cars. This is true for BMW. This. This is already embedded. And as part of their offering, the. American OEMs. We are seeing now re-engaging in these kinds of offering level three. Offerings. Both for GM, Stellantis, all of them are thinking. And also the newcomers like Rivian and other players are all into it or. Thinking how. And announcing also their. Timelines in order to introduce level three applications. Maybe. Stellantis took a step back in that. In that respect, but definitely. Ford and GM have announced publicly. So. And also Rivian. So we see the momentum is back in place. There was a certain delay, a certain setback in the. In the. US market, but I see it. Reinstated. And the understanding that this is important for them.
This is a differentiator. And if they don't do it, they will lose the competition with other players from the Germans through the Chinese. So. And I think the Japanese and the Koreans will probably adopt it a bit later. We see a lot of momentum in level four applications. All over the world for. The commercial application makes sense. In terms of the business model is different. You take a vehicle. You. You convert it into a level four vehicle that can drive itself. You eliminate the need for a driver, and then you have a platform that you can monetize off 24 over seven. So it makes a lot of sense to put such to, to do the investment, because you need to load the car with a lot of sensors, you need a stronger compute model inside the car. But it makes more sense because the business model makes sense. So we see many programs we had yesterday. Round table. And we saw many players in that many new comers, new companies, new OEMs that are playing in this field like Kodiak Mobility and, and others. So we see a lot of momentum in the level four commercial application.
And this is true, by the way, for the ID base of Volkswagen, which we launch towards the end of this year, beginning of next year, together with Mobileye. So and they are. Putting a lot of strategic value in this platform.
Absolutely. And maybe sticking with the level three, the pushback sometimes one gets on level three as well. You know, the automakers will do it. But on on very low volume, like the the highest trim levels, most expensive vehicles. And we've seen at least a couple automakers sort of say, hey, you know what? We're going to do this on, on all of the next generation vehicles like, like the R2 from Rivian being one. Are you seeing any of those signs in your pipelines where the programs that are coming may have higher attach rates or even even, I mean, maybe standards going a little bit too far, but but even standard on some vehicles, or is it still the next three, four, five years going to be limited in terms of the penetration of a particular program? Volume?
So what is different today than 2 or 3 years ago? It's the maturity level and the cost. So today, in order to introduce an autonomous feature, there is a much more knowledge of what does entail. In order to put this in place, what kind of development, what kind of technology you need A, B, you have the ecosystem, the different parts, or some of them are already ready. Like Mobileye platform like Nvidia platform. So it's not that it is plug and play, but you have somebody that has sold for you. The problem. So chauffeur from the level three solution from Mobileye. Chauffeur. You can simply adopt it or integrate it into your platform. You. Have the leaders as well. Much more mature. So. The sensory is in place and with that, with the maturity. Also the cost is reduced, which means you can introduce it to more and more model, not only to high end, maybe start thinking about mid-range. Do I think that level three will be in the next two years? Very abandoned? No, but I do think it will continue to trickle from the high end down to the mid-range and down, and then to be a common standard, because volumes will go up.
Pricing will come down. And this is the regular process. I think we we are now starting to see the recovery, but it will take time as as is the case in the automotive industry, there is one exception, which is what happens in China. China can be an accelerator for all of these processes because if the. World will see that the Chinese are starting to push not only EVs, but these EVs are demonstrating autonomous level three autonomous features. I think the Western OEMs will have no choice but to accelerate their efforts. So that might happen as well.
Great.
And on this journey, maybe talk about the Innovis three. I'm curious about maybe three parts to that one. Maybe talk about the importance of the behind the windshield. How many automakers are expressing that as a really kind of a must have in the next several years to maybe the cost of Innovis three versus Innovis two? And lastly, any things you can share on recent feedback conversations as you're kind of developing that next generation?
Yeah. So we, we are saying that the behind the windshield is sort of the holy grail of the industry. They don't like to see this bump of the ladder on the rooftop. They don't like to see the ladder and the grille, which is not the best place. You want to have the LiDAR higher up. So behind the windshield is the perfect place. It doesn't change the design of the vehicle. It's behind the windshield. So you have the wipers. You have the and embedded cleaning system, and it makes the most sense. So all, all the OEMs would, if they would have had a LiDAR that can fit in behind the windshield, they would do it. The Innovis three is exactly that. The form factor is is designed in order to fit behind the windshield. We even offer to integrate the camera inside the LiDAR. So you save even additional space and the integration is simpler inside the car. Cost reduced. We reduced the cost by 35% from the Innovis two to the Innovis three. The performance is as good as the Innovis two, and we hope that as we continue to develop this platform, maybe we can improve it even more.
The form factor, the power consumption. All the resolution range, all of that is. A very good solution for the OEMs. And they are looking after it. I don't remember the other questions.
Just the the feedback From, the feedbacks are amazing. So every. Gem that we show him the three or offer him to test it. There is. There is great enthusiasm. Again, this is something that the industry is looking for and Innovis three is really a great product with many lessons learned from all our years of experience and I think it has gotten a. Great reception so far.
Great. So maybe two rapid fire in the last minute or so. First, maybe a word on the competitive landscape outside of China. In Europe and US, North America kind of. What are you seeing in right now?
So in the automotive space, I would say the the competition has diminished greatly. Many companies either disappeared or stopped serving the automotive industry. And I think there basically 2 or 3 companies that are serving, one of them being innoviz. So we always predicted that the industry will consolidate and it happened exactly as we predicted. And we hope that Innoviz can keep its seat around the table. And be one of the 2 or 3 companies that will serve the industry outside the automotive space. There are many players. Which are. Offering different solutions. I think once you bring an automotive grade LiDAR with the performance of automotive grade range resolution power. Assumption. Form factor. It's a very potent competitor in the market and I think for specifically for security and defense, there is almost no other leader that can bring the same performance as we do.
Great. And maybe at a high level and without providing guidance, how would you frame the 2030 opportunity, whether it's units penetration for investors to think about what the next few years., at least our projection is hundreds of thousands of units a year. So maybe north of half a million. But we still need to to win these programs and aggregate the volumes. But I do believe that we are on this kind of trajectory of hundreds of thousands of units towards 20, 30.
Terrific, great conversation. I think we have to leave it there, but thank you so much for being back in.

Transcript from this morning's Webinar and Q&A on the Rise of Physical AI.

Hello everyone, and welcome to the Innovis physical AI webinar. Before we get started, I would like to remind you that our discussion today will include forward looking statements that are subject to risks and uncertainties relating to future events in the future. Financial performance of Innovis actual results could differ materially from those anticipated in the forward looking statements. Forward looking statements made today speak only to our expectations. As of today, and we undertake no obligation to publicly update or revise them. For a discussion of some important risk factors that could cause actual results to differ materially from any forward looking statements, please see the Risk Factors section of our form 20 F filed with the SEC on March 4th, 2026. Hey, so Omar, nice to be with you today. My name is David. I'm director of industry Solutions here at Innovis. I'm leading our implementation of our leader in Non-automotive spaces and, and Omer. Yeah. So I'm Omer, I'm Omer Keilaf. I'm the CEO and founder of Innovis. And I'm happy to have this session today. Great. So I'm super happy to be with you. We're going to talk about your white paper that you've just recently released, and we posted a request for questions so we can go through some of these questions today and hear your take on physical AI. Yeah.

Great. So let's start talking about the physical AI and the world models. Why is it so important in your view, and why did you decide to deep dive into this topic right now from his perspective? Sorry.

Yeah, sure. So I think obviously AI has been a very. Very big topic over the last couple of years. And every company talks about how they utilize AI. And I think it's really hard for people to really make the difference between AI. That you just utilize language models. What I call what they call digital AI. Digital AI is where you take. Data such as text or images, and you use language models in order to generate the next pixel, the next word, and you generate a lot of content with it. And there are many companies have been utilizing those language models to do that. But there is a term which is called physical AI, which I think is very important to understand because it differentiates between the digital AI and physical AI in a way that help us to. Explain what we do. Physical AI has been practiced for many years, and the first application that was designed to scale was autonomous driving. When you think about autonomous driving. It's where you take real data and unstructured mechanism where there is a car that needs to maneuver. In many uncertainties, environments and roads, and you need to utilize AI in order to make sense of everything you see around you. And make decisions on how to proceed. Now, on top of physical AI, which is now growing to different domains, there is another term that is important to understand, which is the world model. You are now companies. That are building platforms for the next generation of physical AI, where they intend to provide platforms that allow you to train models that would learn how our physical world behaves. Nvidia just released just a month ago.

I think a new platform called Cosmos where they. Plan to allow developers to study and train algorithms that are meant to predict how the world will be. Save based on trained data Most of the data. I would say the platform that they are developing is based on simulations on emulations of how these environments operate. They talk about digital Twin Cities. When I think about digital Twin Cities, it's very exciting because you can create a model that allows you to monitor. And experience and analyze and study and train. But I, when I think about it, it makes a lot of sense to me that these simulations, I call them statues that you generate either by simulations or 3D reconstruction. When I see the the capabilities that our sensors can add. To create those digital twins, I see it as if we are bringing life to those world models and a. You can use these sensors not only to utilize the application, but you can use it to study. You can create a digital twin city in real time and use it in order to really analyze how the world behaves and build those real models. World models in a more accurate manner, manner. And why now?

So why did you decide to deep dive into this topic now? So.

I, you know, autonomous driving was the first application of physical AI and while AI has been tremendously developed over the last few years, and there is a desire to use these capabilities on more applications, again, coming from. For example, very standard process, like a production line, right, where you have a very strict process of how things work. Robots that move things from one point to another, where AI actually brings a lot of values. When you have behaviors that are where there is a lot of uncertainties and we live in a world where it's very hard to really predict how things will really happen. And you need to use, if you want to really be able to automate and to, to optimize these applications, you need to create models that are trained by real data. And now that there is a desire to take this AI to additional applications on top of autonomous driving, it's clear. That there is a missing part. There is a missing part in the story of how those applications will eventually be developed and how they will be utilized. When when I am asked about what Innoviz is doing. So usually people, when I tell people that we are developing a LiDAR, then the. The first instinct or the first reaction is, oh, I know leaders from autonomous driving and it's. It's like as if Innoviz is developing leaders that could only be used for one application.

And it's obviously not the case. When we started to operate or offer our leader to other different industries, it was very hard to even explain. You know, which industries are we working in? And I think physical AI is, is actually setting a certain umbrella where. You can explain that what Innervates is developing is a technology that can grow. Physical AI and can be an infrastructure for physical AI. And I think those are terms. It's not like it's a new it's a new world because as I said, autonomous driving was physical, I. But it's a new term that allows people to understand better about the capabilities of such technologies to other industries. Right. And then, yeah, of course, perception is a bottleneck to physical AI, right? So why do you think 3D sensing is so critical to the space? And why do you think a lidar is the solution here? I'll start with the fact that when even autonomous driving people refer to lidar as ground truth, right? Even when you are trying to train an algorithm for a vision based system, you use lidar as ground truth. And that's a very strong statement because. When you're trying to develop a physical AI or world models based on inferred data, or you're trying to develop your model based on simulations, obviously you are deferring from the truth. You are reaching a bias in your model. When you use a 3D sensor with high capabilities, you are getting an error less data, and you can create 3D models that can help you eventually get to results, which is better. So I think the affiliation to library is obvious, and I think it's also important, you know, for me as a CEO of a lidar company is to be able to educate investors to understand the potential right of, of the company and where things are going to be. And through this paper, I'm, you know, the paper is, is split between two parts. We released the, the first part. Several weeks ago.

It talks about the terms of a physical AI and world models and the size of the market, etc.. And then in the second part, I'm trying to also try to give a certain prediction on where things would be because there are still several LiDAR companies or LiDAR technologies less than they were in the past, but I believe there will be continuous convergence. And I'm trying to explain how lidar, which is going to be a very meaningful and important infrastructure of physical AI. And how it's going to shape out. And it's, you know, the latter space have changed quite dramatically over the last ten years. And that's part of what I'm trying to do. And explain in the second part. Right.

And. Tell us more about the space of automotive. I mean, Innovyze has been playing very strong in this space. And how is this transformed over the past decade with respect to autonomy? Yeah.

So eventually. It's a it's a long game, right? You know, over the last decade, there were probably 200 or maybe more. LiDAR companies. And in the paper, I'm trying to explain. You know, how. The how the market evolved because. Like any industry that went through disruption. There, there is a certain way these markets behave. And I'm referring to the Gartner hype cycle, which I, I, I appreciate it. I think it's a good way to, to study any industry, you know, Gartner every year they are publishing a report that tries to pinpoint where each industry is at in the hype cycle. Understanding that it's a very traditional trend. You know, it's there's always a certain disruption. It's a disruption is an opportunity for new comers to step in and take a big part of, of a very big market. And this is a case where investors jump on the opportunity because they they want to put their chips on the players they believe will eventually be. Come the, the, the meaningful players and. You can see it in many sectors and you can see it on different reports of Gartner. But taking specifically on the automotive space in 2015, 2016, autonomous driving was a huge hype because everybody knew that this is going to change. And it does.

It is changing. And there were probably 200 lidar companies understanding that lidar is one of the biggest challenges of the of the decade. You know, and behind it is a huge market. So billions of dollars have been invested in hundreds of companies. But as always, in these hype cycle, there is this trend where you can see wake up. To the reality that the market. Eventually cannot absorb too many suppliers, especially in automotive. They tend to consolidate around very few suppliers. So there was a race to get there and it was a obviously when when you see 190 companies fail, then you have 190 CEO's going around the market telling, it's not my fault. It's a really bad market. You know, it's like you have a lot of people now try to push away the, you know, the failure by saying there is no market, there is no opportunity. It's not true. Okay? There is a market just there is not a market for everyone. And the automotive market have moved. It's target all the time. So you have the requirements that you've got that you got for a leader in 2013 is very different from the spec that you got in 2016. In 2019, and in 2026. Okay.

Because your customers always become more educated about what's more important and they, they, they've experienced these programs for the first time. And as long as the requirements, the knob. Continue to, to change, to move. You've seen less and less companies that were able to meet the cut. And one of the things that I'm talking about also in this white paper is that it's going to continue. And I'm. Talking about the different phases of of the automotive market in the first phase. 2015, 2016, you had many startups with prototypes that were able to demonstrate certain capabilities, but many of them did not meet the cut, you know, and, and in, in the second phase, I think it was all also around many situations. Macro situations such as the Covid 19. And the semi. Conductor situation and the EV. You know, there were many disruptions. Bad disruption to the automotive space, but eventually the there were much less lidar companies and. Some. Of them managed to, I would say, get an award for for an OEM, even myself. There were times that I would say because it was super noisy and crowded. The space of the LiDAR. I would say, you know, the only real way, way for you, for investors to really appreciate if a company successful or not is whether they have or they don't have an award by an OEM, because in a way, you can see it as a good quality. By the by the customer that he says, okay, this is the right one. I want. And today. I'm saying it's actually I was wrong. I don't I don't think it's the right way to look at it because now when you see in perspective of the last five years, there were several lidar companies that got an award, but failed. So it's not enough to get an award. And when you realize how difficult and challenging it is to get from an award to a to deployment, you understand that it's it's not enough to just flag, oh, I got one customer to get excited about what they do. And believe my promises. It takes a bit more than that. And to me, it helps to kind of push back on, on some of the claims that I see because, you know, you see a lot of press releases by many and but the reality is slightly different. And

I'm on, on the white paper on the second part, at least I'm trying to explain a little bit about those challenges. Like what really is needed to get to serious, real, serious production, not just, you know, getting someone to, to be convinced. I'll pause here for a more questions. Yeah.

So as we see the lidar being implemented and driven through the automotive industry. How. Do you see what happens in the automotive market? How do you see that impacting other non-automotive physical AI applications? Yeah.

So again, I think that the automotive market is is still setting the standards and. There is still no other market that can provide any leader company, any, any contract to volumes that are offered in the automotive space. Okay. Because of the size of the market, on the other hand, it's also setting a very high bar on the requirements and those requirements. As I said, they keep moving. They are they are moving all the time. One of the requirements, for example, that I'm seeing in automotive, which I know that people are less familiar with, but I see them as very dramatic, is related to the lidar. Not only people know about range and about resolution and frame rate. Those are the KPIs of 2019. You know, it's like in the old days where you would compare digital cameras by the resolution, right? That's the only way to compare it's. Not the case. The quality of the image is also affected by many other things. When you talk about autonomous driving and safety. Then I usually like to talk about what I call the dirty secret of the LiDAR space. When you think about an autonomous, when I think about autonomous car, I think about a car where you have a family sitting in the back seat and the car is, is driving without any ability of anyone to intervene. Right?

That's, that's where we are targeting now. The claim to fame of lidar in the space is that it provides redundancy to the camera. Right? That's what we are. All flagging. We are providing safety by providing redundancy to all of the failures of the camera. Now, where? The camera fails, it fails in direct sun. It fails in weather conditions. It fails in rain droplets. Right. And you'll be surprised that many latter solutions architectures are actually unable to work under those conditions. To me, it was surprising, but the. There is another. Element that is usually disregarded or not thought, well, I talk, I talk about the dirty secret is when. When you think about the functional safety, it talks about the correlation of the failure mode. If the camera fails. By a certain. A certain failure model, you need to prove that the other sensor does not fail simultaneously together. That's the only way to get redundancy. Now, if the car drives over a puddle of mud and the. Mud is now thrown on the vehicle. It is very hard to prove or to even claim that both sensors will not become covered by mud and. How do you solve this problem? Because

I'm not talking about an unrealistic situation. Because it could be mud. It could be bugs. It could be many things. I'm not familiar with any leader, but ours that deals with it and to. Me, it's shocking that for a level four system, you know, we can argue about level three, that you have a driver that is maybe reading a book, but it can be called. To re-engage while the system is cleaning the car. The sensor in. In a few seconds. But when there is no driver at all like level four to me it is a catastrophic, really catastrophic and alarming situation. Assuming the. That the sensor is incapable of dealing with this. Situations. The reason that Innovis was able to solve this problem is because we already launch our first generation Innovis one, and we tasted the road. You know, and by understanding real world problems, we knew that in the second generation we need to bring it to a different level. So we redesigned the system. So even if you throw mud at it. You will still see very well. And to most customers, it's like black magic. And they. They think that we are doing things in software or AI or something, but it's not. It's, it's really just optics and to. Me,

I sleep well at night. You know, when I, when seriously, when, when, when I think about I'm offering a product that I believe can actually work in level four. And I think that the others can. So to me, it gives confidence that I have a robust roadmap, you know, to actually enable these these applications. I don't know how others can. Can can do that, you know, because it's a, it's a very unique design that we've done. And I don't understand how anyone can use anything other than that. Exactly. Yeah.

Yeah. So I think that just to kind of push the nail on this. There is a difference between a lidar for level two. A lidar for level three and a lidar than level four. And. I think people are not very familiar with, with these distinct. You know, this differentiation because they usually think. If the latter has higher resolution, then it's good for level four or if it has good range and no, it's actually nothing to do with that. The only thing that differentiates between level two, level three, and level four is the availability of the sensor in level two, you have a driver that is has his eyes on the road and is ready to engage. Theoretically, you can say the lidar can not work. Basically, you can. It's it's kind of like you don't you have the driver as a redundant sensor, even if the lidar works, sometimes it's fine. You know, it does not cause the vehicle to lose. It's a function safety. When you go to level three and the driver doesn't look on the road any longer, you need a very robust sensor. You need a sensor that is highly available. The only cases that you are allowed that the driver is requested to re-engage is if the sensor is now has a certain degradation. For example, if it's covered with mud, right? And then the driver is called to re-engage, take the wheel and let the sensors to be clean.

When you go to level four. It's done like there is no option for any degradation. In a way. So you need a highly available sensor. So the level of quality between level two, level three, and level four is quantum leap. It's like several orders of magnitude and I believe that kind of maybe closing kind of the brackets on the question. Autonomous driving is, is setting the requirements. It's driving the volume and eventually it will help to companies like Innovis to offer really the best technology to any physical I application because eventually, if you talk about security, it's also safety, right? And if you talk about logistics where you have machines that are moving in environments where there are people, it's also safety.

And certainly intelligent traffic systems. And.

And when you talk about. Yeah. Intersections, it's also safety. So functional safety, it's not only autonomous driving and the compliance that we are having for autonomous automotive is meeting the requirements for all of those other markets. And of course, the higher performance, the higher resolution. And of course, also the lower cost potential coming from the volume from automotive will eventually drive the physical AI market. Yeah.

You definitely you managed to connect those two dots right between the automotive and non-automotive.

I try to remember what you really.

Asked.

So let's talk about technology. I know you you. You know, you're very strong in technology. And there's been a lot of excitement about different lidar technologies over the years. Years. Can you talk about the evolution of the various approaches and where you think we are now? Yeah, so I love this topic because look. At any point of time. You will have an emerging technology that is calling out to change the world. And basically be the technology that the whole market would pivot to. Unfortunately. It didn't work. Well, for many of those technologies, you know, when I founded Innovis on the first day I started the company. It was the 7th of January 2016 on. The same day. There was a press release by a company called Quanergy. At the time, it was a different company. They. Said that they have a solution based on a technology called OPA Optical Phased Array. You can look up in the internet, you can find the 7th of January 2016 press release on a product called S3. And there was a huge hype around OPA. It was the future of LiDAR. It was fully solid state. It was $250. Can I remember the the they said they can see a. 8% and at the target reflectivity at 200m. It's a mass production. It already had like partnerships with Mercedes and like it was. And I remember looking at this and asking myself, what am I doing? First day? And it's like, I'm only starting.

And this company is already claiming that they are in mass production with all of the companies and it's meeting performance that I can only dream of.

And this was a totally different technology, right? Yeah.

And lucky for me, you know, I was stubborn enough to, to learn a. Bit about technology. You know, what the real technology is about? And I actually came to the conclusion very quickly. I think after a week, I already it was clear to me that technology doesn't work. And I talked to many professors and like, people were actually involved in developing the technology, the technology did not work. And. For three years. I was asked continuously by investors, how am I capable of competing with such a technology that that is already in mass production? And I was really confused. Like, seriously, do your due diligence. What are you talking about? The technology does not work. And obviously the project never came to life, even though they raised a lot of money and this has never really reached us. The. And after that, there was a different type. It was 1550. I remember five years ago I was asked daily or. 9 or 5 can ever compete with 1515 nanometer. And like you said, I'm. Driven, you know, I go into the details and I, I couldn't understand how people could really be that confused and I just didn't know other than giving evidence by. Looking at my product, look at the competing product and make your own assessment, you know, put aside all of, all of, I'm saying, you know, about the technology. And now there's a new hype, right? There is always a new hype. And the problem is that people don't really spend enough time to understand the differentiation. Technology.

And now in essence, automotive is a very difficult market. The requirements are very high. And as I said earlier, the more you turn the knob, you will live less margins, you know, less capable of technologies in. 2016, due to the hype. Any way you can imagine developing a LiDAR, you would find a company trying to do it. You know, for leaders and raising a lot of money around it. And there are really very, very research driven, interesting ways to look at it. But there are an they are not. They they have nothing to do. With this capability, with the requirements. And I always tell people like, just see the product, see the size of the product, see the performance of the product, see the power consumption of the product. These are things that other companies don't really show. You know, I, I tend to show real footage and videos of our point cloud because I think it's really the only way that's my product. I need to show it, right. That's the only way for me to give confidence to, to investors and, and customers to really evaluate like, you know, I can always say I'm the best, I'm the best, I'm the best. But look, here's my product. And when I see others not showing their product, you know, they, they, and I see them showing videos that are processed, which I have a very sharp eye to see when someone is lying about the data they're showing to me, it's. The best evidence that, you know, I that they just don't have anything to really to show. Now, now. For the automotive market is going into a new phase. I call it phase three. And I'm talking about where I believe eventually the market will consolidate and look, I was saying. The.

The OEMs have always increased their demand. They want it smaller, cheaper, etc. and there were different types of installations. Grid installation, roof installation. They've always hated those options. They didn't. They. They're always wanted to put it behind the windshield because aesthetically it looks the best. The problem was that in order to bring it behind the windshield, you need to have a very small device because there is no room in that area. You. Have very small area. It takes all of the flux from the sun. So it needs to be consuming significantly less power consumption than the solutions that were available. And you need much higher performance because you need to overcome the attenuation you get from the window. So those extra requirements. Probably five years ago would kill any, any, anyone ever able to offer something. So they were willing to compromise, put it in the grid, put it. Sorry, on the roof, but. The Holy Grail was always behind the windshield. And I think today, now stepping into our third generation, we can benefit from the fruits of our work that we've done with this one. Reaching level three. To the road and Innovis two. It's a product that is going to serious production. End of the year with Volkswagen Level four. Gives us a very robust baseline that allowed us to take another step and develop innovative three, which is you can compare it to Innovis two, right? It's just totally. The size reduction. It's it's really is a very small product. Now,

I. I believe that this phase has many implications. One. I think it will stop the, the madness, you know, even for us moving from one generation to another requires a. Lot of effort. And in terms of scale, you want to stabilize. You want to keep in one, you know, have a design that. Meets every. Reinvent the wheel every time. Now, because of the. The desire of the OEMs to move to behind the windshield, I believe that it will stay there. It will no longer move from, you know, from from the grill to the roof behind the windshield. I don't think it will move to behind the seat or something. I really hope so. But I think that this is where the market will eventually converge to. And I understand that also these extra demands would likely to move to live in the history of the leader space. More companies that will not be able to meet it because their solutions are too big. They consume too much power and their performance is not good enough. So I think that this is a good reason, at least for me, to believe that the leader space is going to even further decrease. And I think that those technologies emerging. Like

OPA 1550 and FMC W, those are all technologies that are not a good fit for the automotive and the only technology that has the sufficient capability in terms of volume, in price, in performance, in power consumption. The only technology that's has managed to do so is a time of flight. 9.5 nanometer. And and this is how this is this is these are the lenses I wear when I really look on what's my competition? Great.

Actually, that's a, that's a great step to talk about the competition. And how do you see the landscape, the competition landscape today? Okay, so after we, you know, you know, again, so I try to put the different other companies out there, the different buckets of which are the technology they're using. And to me, it's clear that that the technology of 905 time of flight is the right one for automotive. And under those glass. Or lenses. The competition landscape looks very different. And I'm talking about automotive. I'm not talking about non-automotive in the automotive, you have many solutions that were possibly not good enough for automotive. So they they started to develop a certain other, technology, other applications. But when I kind of put it down on like, who are the sensors that are offered today with time of flight 905 in the automotive. It's in the West, it's primarily US and Valeo. And then you have the Chinese sensors, right. And the Chinese vendors, they've I think they've went through all of the solutions. They've they've learned very well from many suppliers on how to do, how to bring up leaders. They've benefited tremendously by the fact that one, the leader is strategic technology by the government of China and got a lot of funding. It's because the new electric vehicles, companies that had to leapfrog into the market and compete with the Western market, they had to bring software that was premature and make it more robust by using L2 with leaders. And that's the main reason why L2 in China. Was really flourishing and provided a lot of business to the Chinese market. There is a different problem where all of those cars are sold at a loss, but put that aside, it helped the ecosystem to develop. Now there is there are several acts now in in the US about the use of leaders from other countries, from from China due to national security. And you can already see two acts that are being pursued. One is the leader Safe act. And there is a new act just came a few days ago from from the Dot. And the reason is because eventually a leader is a mapping tool. You know, and going back to the physical AI, a leader is, is the is going to be the infrastructure for the perception of the future. AI, physical AI, you will have leaders not only in cars, you will have leaders in the infrastructure, in security borders is going to be in robotics, in, in drones. Eventually, a leader, which is a 3D sensor, is going to displace all added to any 2D sensor in any point of perception. You will eventually have a 3D sensor and the richness of the data and the ability to create those Twin Cities. Digital

Twin Cities and live digital Twin Cities. It obviously creates a lot of sensitive information, and from that perspective, I believe I cannot predict, but I believe that the market. Will stay split between leaders that were that are offered by Western suppliers and, and Chinese suppliers to be operated in China. And so when I kind of nail down, you know, this competition, you can see that eventually there is not a lot of competition. Of course, there are the emerging technology, but they are likely, in my point of view, not to be good enough for the automotive space. Yeah.

So I think it's almost time to start wrapping up, considering everything we've discussed so far about the need for perception, the requirements in the auto space, automotive space, and lidar technology, can you try and tie that all together and tell us how you think of is, is positioned in the market right now? Yeah.

Well, look, eventually it's a. It's a long journey. We are developing a new ecosystem. Okay. LiDAR is going to be a dominant sensor in the future. When you look in the white paper, I try to estimate the size of the market. The Tam, the total addressable market for leaders. Now the obvious one or usually you'll find a lot of coverage is analyst. Talk about the time of leaders in autonomous driving because it's, as I said, it's like the obvious almost only application for leaders. And there were many studies around it. And it's a, it's a market of its own. And those. Figures talk about $10 billion in the next decade. Now, the more difficult. I would say, analysis is to understand the size of the market for leaders in physical. I. And here it's more difficult because it's. Less discussed. People are not familiar. Most of the people are not familiar about the capabilities that LiDAR can offer. I'll give just one example. We've started to offer our leader to the security market. The security market, the dominant sensor sensors are camera and radar. The use of radar in physical security is, a. To me, it's a you know, just it's hard for me to explain because it's so horrible. It just doesn't work well. But it's the only technology that is available.

It is available. And when you, when you see the, the ASPs, the average selling point of these radars. It's a, it's boggles my mind, right? It's a, it's, it could be tens of thousands or thousands of dollars and, and, you know, in, in volumes that will blow your mind. Now, when we show these customers, what our ladder is capable of in terms of range and resolution and ability of solving those very obvious problems you want to deal with in physical security. You know, a very stupid kind of example is radar cannot see beyond a fence. How? How could be that for security system, you have a system that doesn't see beyond the fence? I don't know, but that's the reality. And now in in those situations, I look on the the time of radars and I try to. There is a lot of work because radars are around for a long time and you can actually see the size of those markets already today. And if LiDAR is going to displace radar in security, which I believe will happen massively, you can actually get an estimate of what might be the size of the market for leaders in security. Now,

I'm not saying it will displace all of the radars in those applications, but it will take a bite, a nice bite, and those markets are continuously growing. And you see the same in agriculture and in smart cities, in logistics. Eventually, in my point of view, a radar is a good technology for a space that is empty void in air. If you. Want to see something in clear sky, radar is a good option. Once you talk about a complex visual scene. Radar is pretty useless because of the very lack of resolution and noise and LiDAR is excellent. There. And if you take a lidar like ours, which is automotive grade resilient to dirt and can see a very long ranges, you know, we have things coming up in terms of, you know, improving. Our capabilities in those domains. It's actually a really it's going to be a huge success. So. I see eventually a innovis position in the automotive as a ground base. Line. The market will continue consolidate. I said it's a long journey. So we are now entering the phase three. And I expect phase three to leave behind less companies. And that will be our base. Base camp from where we can start penetrating to different markets. We started with security because it's I see it as a premium market because it demands performance that other leaders cannot offer. We try to see who will compete with in the lidar space in security, and there's no one, because no one is actually able to meet those requirements. And that leaves us a nice premium. But nothing holds us back for offering our leader to other, more established markets. Where in customers are already familiar with the leader space and already understand the disadvantages with the solutions they already have. So I think that I see it as a long game. I don't check the score. Because the game didn't end yet.

You know, I understand that it's a long journey. You need to make bets on your strategy. We made our bet on our position in automotive, and I believe it will be in our strength in the non-automotive. So eventually I see a innoviz as as the market leader in the future, and I expect that it will be. Very fun. Thing to do. Moving forward. Maybe just the last question on my side is some of the proof points that you're seeing in terms of customer wins and traction in the automotive space and beyond. So you touched you touched on that a bit. If there's anything you want to expand on there. Sure.

So right now we are working towards a launch with Volkswagen for. For with the ID buzz, it's the first series production robotics in Europe and. By an OEM. And we. Are working in collaboration with Mobileye. So. And through that relationship, we also reached a commercial agreement with Mobileye where they are using our sensors on other programs that they are involved with. So they are using the same set of sensors. We are using nine liters per vehicle in level four, and they're using it on other platforms. For example, Holland, it's another company that is going to SOP. Also after the ID buzz and. And then we have Diana track where we. Are. We've been selected for the sensors and it's also multiple sensors around the vehicle. And we are working. And there is a program with Audi for, for level three and, and we are continuously working with the industries on the different Rfis and Rfqs. And I expect this year that will be able to get more awards either from Innoviz two or Innovis three really depends on the customer. If it comes to the windshield integration, it'll be three. But if it's robotaxi and they want to launch tomorrow, then Innoviz two is a great product. It's a, I think it's two is an amazing product. I think the best in class today. And I think it will definitely will be successor. Very sweet one. Yeah.

Great.

It's been amazing speaking to you, Omar. And I think for me personally, one of the nice parts about selling LiDAR is the initial reaction that you get showing someone for the first time, a point cloud, and it's like showing them the future. And I think, yeah, definitely your piece on physical AI definitely gives us a bit of a glimpse to the future. So it's been really great talking to you today. Thank you. Thanks very much for your time. Thanks