Filed by Colonnade Acquisition Corp.

pursuant to Rule 425 under the Securities Act of 1933

and deemed filed pursuant to Rule 14a-12

under the Securities Exchange Act of 1934

Subject Company: Colonnade Acquisition Corp.

Commission File No. 001-39463

Transcript

Ouster Investor Day

February 22, 2021

Remy Trafelet:

Hello everyone. Thank you so much for being here. My name is Remy Trafelet. I’m the CEO of Colonnade Acquisition Corp, and we are so proud to be merging with, with Ouster on behalf of myself and my, my team and partner. [Computer audio noise in background.] Sorry, that was two computers going. But myself and Joseph Sambuco, my partner. I just want to say a couple of quick things before we get started here. Just to, you know, when we first met Angus and Mark we were unbelievably impressed by not only how thoughtful they were about the technology, but about the company that they put together. And there’s kind of three things I want to leave, I want you to think about as you’re going through the presentation here. You know, one, the company that’s been put together here, that this is the only truly LIDAR truly digital LIDAR company.

And what that has enabled this, this business to do is as automotive is super important to them and is a big part of their business because they are truly digital. They’ve been able to get into the other business, the other areas such as industrial, smart infrastructure and robotics. And then also think about the numbers that we’re presenting you today. We are extraordinarily confident with because they’re not just built off of some, you know, big TAM number times some, you know, penetration rate to come up with a revenue number they built for the bottoms up, customer by customer. So we’re extremely confident with the numbers that we’re going to be giving you today and, you know, as long-term investors we not only are we extremely excited about the relative valuation versus the other LIDAR companies, but on an absolute basis this company is extremely attractive at, you know, nine times EBITDA on 2024 and four times on 2025. So with that let, let me stop talking and hand it over to hand it over the group. We’re gonna start with a video and thank you so much for being here.

Video 1:

We’re entering the beginning stages of the next industrial revolution, where we can really change how machines are interacting with the world. In just a couple of years, everybody on earth has got to know what LIDAR technology is because they’re going to have it in their pocket and it’s actually going to be deployed on every moving object on earth. We founded Ouster based on a vision of making LIDAR affordable and ubiquitous. When we founded Ouster, LIDAR was very expensive and kind of a very niche application. But now with Ouster’s digital technology, the LIDAR industry is undergoing a major disruptive transformation.

Whenever an industry adopts it, silicon can completely changes our world. Digital technologies once they enter an old analog market very quickly displace them and become completely dominant, but it takes some imagination and it takes some invention to introduce, you know, Silicon CMOS technologies into a new industry. The adoption of LIDAR at a really mass scale

depends on it being affordable. That’s where Ouster has really focused its efforts. LIDAR can only become affordable and ubiquitous if it transitions from fundamentally analog to digital technology. You really can bet on integrated CMOS semiconductors being the cost and performance leader given their track record over the last half century. We see a parallel with LIDAR to what happened when cameras went through a digital transformation: costs dropped and performance and flexibility skyrocketed. We’re swapping out chip sets in the sensors instead of redesigning and rearchitecting LIDAR sensors every year. I’m about to show you the inside of our sensor for the very first time.

This is the OS-0, one of our newest sensors. Inside is the key to everything we do. Our digital LIDAR module is small enough to fit in the palm of your hand and yet it’s extremely high performance. Instead of hundreds of discrete analog parts, it’s made up of four key components: custom lenses that define the sensor’s range and field of view, just like a digital camera, a patented micro optical system that increases performance by orders of magnitude, a high efficiency laser array, which integrates all of our lasers onto a single semiconductor chip, last and most important is our LIDAR system-on-chip. This piece of Silicon uses SPAD photon detectors capable of counting over 1 billion photons per second. Our digital LIDAR architecture powers all of Ouster’s products.

Because of the interest around self-driving cars LIDAR, for the last five to 10 years, became synonymous with automotive applications. What we find equally exciting are all of the applications being deployed now that need LIDAR as a critical sensor. The commercial success we’ve seen in the past two years demonstrates that the market is far larger than automotive alone with demand for LIDAR expected to total 50 billion across our target markets by 2030.

We have hundreds of customers successfully deploying our LIDAR technology in the real world across a wide variety of use cases in industrial automation, smart infrastructure, robotics, and automotive.

The future of autonomy looks like a, a more convenient and safer world. Digital LIDAR is a technology that will grow to play a critical role in all of our day-to-day lives, making us all safer and more productive. You’ll have robots delivering your packages, and you’ll even be able to sleep in your car, and you’ll be safe because they’re LIDAR sensors that are measuring the world all around you billions of times a second.

We believe Ouster is core to this future. We’re going to continue to leverage our digital architecture to push the frontier of both performance and cost for many years to come. At Ouster, we are building the eyes of autonomy.

Angus Pacala:

Welcome everyone to Ouster’s investor day. Before we dive in, I’d like to turn it over to Carl Bass, our new chairman of the board. Carl served as Autodesk CEO for 15 years and was the former chairman of Zoox, a major automotive autonomous vehicle mobility player, and is also an advisor and board member across a large variety of robotics and hardware startups. So I’m incredibly pleased to welcome him to Ouster’s board and, and hand it over to him for a couple of thoughts on why he joined the company.

Carl Bass:

Good morning, I recently joined Ouster as the Chairman of the Board. So let me just take a few minutes to explain what motivated me to do this. You know, I’ve had a longstanding interest in physical products, physical products that are made better with software. So think of smart machines and smart devices. Now, in fact, I’ve spent most of my career developing engineering tools to help people build better products. And more recently, I’ve been involved with autonomous vehicles with companies such as Zoox. I’ve also been involved with autonomous construction equipment. Think of things like bulldozers and backhoes, and even installing drywall. I’ve been involved with putting hundreds of small satellites into space. Now I’ve built and I’ve used, and I’ve seen many different kinds of robots, both personal ones and industrial ones. And the thread that ties all these efforts together is that it is the software that unlocks the power of these machines, making them more powerful, more accurate, and more reliable.

Now, if you look at the definition of a robot, it’s a machine that senses, thinks, acts and communicates. And it’s that first property of being able to sense the environment that brings us here today. You know, when we look at LIDAR, it’s one of the critical components that allow our smart machines to sense their environment. Now, one place where there’s been a tremendous amount of interest in LIDAR is, of course, in autonomous vehicles. And while some may say these will be able to do without LIDAR and just use computer vision alone, just use image processing — I kind of think that’s nonsense. You know, if we’re dealing with unstructured environments like city streets, where safety is critical, if you think about it, in fact, we will always want AVs to be safer. They will never be safe enough. And for that, LIDAR is going to be absolutely essential for the foreseeable future.

Now, there were three things that drew me to Ouster. The first, it was the recognition that LIDAR was needed in areas way beyond AVs or even automotive. In fact, it was the broad set of customers representing a really diverse set of uses that got me super excited. If we’re going to have really useful smart machines, it starts with being able to get a high quality, high fidelity picture of the environment in which our machines are operating. The ability of our machines to do exceptional work is limited by the quality of the machine to map and understand its environments. And LIDAR is critical for doing that. Second, it was the idea of building digital LIDAR and being able to benefit from the exponential improvement in semiconductors, the effect of Moore’s law, that convinced me that LIDAR would see widespread adoption as the technology improved and the cost decreased.

And the third, which is even a bit closer to home is that most customers don’t want to buy raw sensors. The best products will come from the hardware and software integration of multiple sensors. Companies want to buy off the shelf solutions that fuse the output of cameras, LIDAR and radar. Very few companies will have the expertise or the deep financial resources to build their own set of suite of sensors, you know, let alone their own LIDAR. And as I looked around, it seemed to me that the company that was best positioned to take advantage of these trends is

Ouster. But let me add a final reason. And that’s the team at Ouster. Throughout my career, the thing that has been most important to me is to work with the best people. Smart, principled, capable, and passionate. And this is exactly what I found in Ouster, starting with Angus. It is amazing what he has already accomplished. Not only the product he is building, but the company and the culture he is creating. I am very much looking forward to helping them grow into a great successful company. Thank you.

Angus Pacala:

Great. Thanks Carl. And now keep in mind, this is a live event. So, from start to finish what you see is what you get. We are holding this in Ouster R &D lab in San Francisco. So behind me, we have manufacturing equipment that is in various stages of completion, about to be shipped off to Benchmark Thailand. I’ve got a customer robot. This is, Formant was generous enough to lend at Boston Dynamics robot as a prop piece. We’ve also got a live sensor feed from an OS1 128 sensor that’s directly in front of me, and we also pulled in some sensors that are going out for shipment today. And I’ve got a number of props here. I’ve got CMOS camera technology, and then our digital LIDAR technology on the table. I’m going to use that as we go through the presentation. Now we do have a pretty packed agenda.

I’m going to start with a company update, then go into tech, tech deep-dive. Then we’re going to have a guest speaker, Stephen Fantone, come in and give his own views on the technology that we’ve developed here. Then Stephen and I are going to do a technical Q&A before going into a commercial customer use case and automotive section, I’ll then hand it off to Anna Brunelle, our CFO, before we end with a general Q&A to, to complete the session. So, it’s a packed agenda and feel free to add questions to the chat box that you have on your screen. We want to get to as many questions as possible. And even if you, if you have a particular person that you’d like to answer a question — whether it’s Stephen, me or Anna — just please note it in the text field and finally for shareholders. I want to encourage everyone to vote for the merger, the completion of the merger with a deadline on March 9th, and you can go to our website, to the investor page to get more detailed details on how to vote.

So, I’d like to start with a commercial update to Ouster. One of the great things about Ouster is we have a diverse and growing business, and we had a really fantastic Q4 in 2020 to end the year. So, we have a ton of momentum leading into 2021. We announced preliminary 2020 Q4 results. We had a record revenue of $6.4 million for the quarter, record gross margins of 31% for our business. We expanded production capacity at our Benchmark Thailand facility by 61%. And we added over a hundred new customers in the quarter alone. So, we have now, well over 500 total customers spanning over 50 countries and across a diverse set of use cases and our four verticals.gain, industrial, smart infrastructure, robotics, and of course, automotive. So, we’re really hitting our stride. And we also reaffirmed guidance for 2021 of $33 to $35 million dollars for the year.

So, feeling really good about the business and proud of what we built to date and, and I expect it to be just, just up from here. Now, one of the other things that’s interesting about working here and invigorating is actually the constant stream of updates that we get from these hundreds of customers. And some of those updates are actually public and I’ve, and, and I’m showing here on the screen, but it’s a great collection and summary of the wide range of applications that our customers are using our products for. So, you can see smart agriculture applications, mobility applications with shuttles. There’s a drone that’s doing civil engineering infrastructure mapping, and, and even this semi-submersible autonomous robot just kind of highlighting how robust our products are, even in saltwater environments like this. On this next page, we have our, one of our great customers and longtime customers MAY mobility in the top left some really cool cold weather applications, more smart agriculture and more mobility solutions.

Now, I also wanted to highlight an extremely significant deal that we announced in the last two weeks with a long-time customer of ours, Plus. Now, Plus is a supplier and operator of autonomous trucking technology, and we just signed what to my knowledge is one of the largest deals, one of the largest binding deals, ever for high performance LIDAR. It’s a committed agreement to purchase 2,000 sensors for Plus’ thousand truck fleet deployment over the next two years. It’s an absolutely massive deal and they have an end customer that is committed to these volumes. And one of the things that we didn’t announce that’s so exciting about this is there is a projected forecast of 160,000 sensors on this project over the next five years. This is equivalent to a major automakers commercial run for a vehicle. So, this is just one customer. It’s a major proof point for us. Again, we’ve been working with them for a long time, and we have a number of other customers that I think can actually reach this scale, not in three years, but today, just like Plus. So, we’ll talk a little bit more about Plus later on in the session, but I wanted to highlight up, highlight that upfront.

I’d like to start the main section around technology by giving a little bit of history on CMOS Silicon technology and its track record in technology for the last 60 years. And now, first, Silicon and CMOS and digital technologies are all synonymous. CMOS technology, CMOS stands for complementary metal oxide semiconductor, and it’s a type of Silicon technology that is what you’re all familiar with. It’s what makes the chips in your smartphone. It’s what makes the chips and every personal computer and all the chips in your cars. It’s basically the backbone of our infrastructure, of our information economy is Silicon CMOS technology. And then that’s very important that you understand that that is the digital LIDAR technology that we are building. It’s a Silicon CMOS technology that, that has an unbelievable track record over the last 60 years of dominating every single industry that, that it’s entered. And it doesn’t matter, you know, if it was the CPU’s in the sixties or telecom infrastructure and DRAM or GPUs, or more modern semiconductor, or sensor technologies like temperature sensors, GPS receivers, cellular modems, and of course, camera technology in the last 20 years. Every single one of those industries adopted CMOS technology at a certain point and exploded the opportunity for that technology and got in the hands of many more people—broadened the TAM. And it’s really, for three reasons. CMOS has a unique capability to, to get, give best in class price best in class performance that’s driven by this exponential scaling law called Moore’s law that has been improving CMOS technology exponentially for 60 years. And finally, the inherent manufacturability and volume manufacturability of CMOS technology. And so, this is actually for, for those unfamiliar with how CMOS chips are made, this is a Silicon wafer. And this is, this is how all CMOS technology is actually made. It’s made in wafers at very high volume in billion dollar facilities by major companies like TSMC, ST Micro or Intel.

And you can buy these in a highly automated process at the millions or billion unit quantity, and then they get diced down into, into chips. So again, it’s that unique combination of price, performance and scale that has allowed CMOS to enter industries and then capture the vast majority of market share while also expanding the opportunity in, in many cases by orders of magnitude, which is what happened with cameras. One of the other hallmarks of CMOS Silicon technologies is their ability to simplify an architecture by pulling complexity out of a traditional technology and putting it on a CMOS chip. And that’s something that’s very intuitive in a digital camera. And this is actually the image sense of the Silicon image sensor from an industrial camera modern camera.

And you can see it’s just a square centimeter of Silicon costs, tens of dollars, but it contains all of the pixels, all of the processing, all of the command and control logic needed to operate a camera. And the only other component in a modern digital camera is the lens. And that architecture is way, way simpler than any analog camera that came before it. And it also is flexible enough to apply to all of the use cases for cameras in the world today. So that fundamental architecture of a chip and a lens has pulled complexity out of the system, put it on the chip. Silicon is great at absorbing complexity and made it flexible enough to build products for a myriad set of use cases. So, in this case, we have industrial cameras powered this by this technology, consumer cameras powered by this technology. And also I’ve got, this is actually the complete eight camera set from a Tesla model three just showing that automotive and other and other verticals all can leverage the exact same CMOS architecture for an advanced sensor type, like a CMOS camera. So there really is an ability to make a one size fits all architecture while, and then make specific products based on that architecture to hit the various needs of, of different industries.

So, on that kind of track record of CMOS dominating industry after industry, after industry, that’s why we founded Ouster as a digital CMOS LIDAR company. We’re following in a long line of other industries that have been completely dominated by this technology. And we’re, we’re betting on that track record. I mean, that’s the... Silicon Valley is named after this effect. This, this effect is, is the basis of Silicon Valley. And we’re just another company in a long string applying this extremely effectively to a new industry.

So I’m going to be using the camera to LIDAR analogy many times throughout the presentation because it’s incredibly apt. I mean, we really are trying to build sensors that look as similar to cameras as possible where we’ve really just added a light source. Every LIDAR needs a laser source. And one of the other things that we have done similar to the camera world is build a suite of products that are all based on a common architecture, the digital LIDAR architecture, but that fit the various use cases of our customers. Sometimes a customer might need multiple of our, of our products, all in working in combination, kind of like this eight camera setup in the Tesla model three, and other customers may just want one of these particular products.

Here we’ve got the ES2 fully solved device that is going to be released in a couple of years. We have OS0 and the OS1 and the OS2. You can actually think about the differences between these products just like the triple camera set up in an iPhone today where what distinguishes that triple camera set up is the focal length of the optics that go in front of those, of those digital

cameras — or there’s one camera that’s a fisheye lens, one that’s a medium focal length lens, and one, that’s a telephoto lens. And we’ve followed that exact same model. So we have a fisheye product, a mid-focal length product and a telephoto product. And they’re all useful in their own ways. They’re all useful for different applications, but you can literally see how we’re, we’re actually using the same exact chip set across all our products, but putting different lenses in front of each chip set in order to achieve different requirements and field of view, range... field of view and range, effectively. So, it’s a really intuitive way to build a set of products.

And now I do want to make sure that the audience understands the point cloud to my right. There is an OS1 128 just running right in front of me, and it’s outputting 2.6 million points per second. So, it’s a 2.6 megapixel sensor. You can think about it exactly like a digital camera that has a megapixel count. This is a 2.6 megapixel LIDAR. It’s an extremely high resolution LIDAR sensor, but instead of it outputting a 2D image, it’s outputting 3D points. 2.6 million XYZ points that we can place into a 3D environment every second. This is the fundamental data type of a LIDAR, and it’s what allows a robot to extremely reliably sense its surroundings. So this will be just running throughout the course of the show.

Now, diving a little further into the digital LIDAR architecture. What I said about cameras was that CMOS has an incredible ability to pull complexity out of an architecture and put it into chips. And that’s exactly what we’ve done with a digital LIDAR. So, fundamentally this is four components. There’s two chips and two lenses. There’s a receiver SOC System on chip, which is the Silicon component in our, in our system. It contains all of the pixels, all of the command and control logic and all of the signal processing of a LIDAR sensor. And then there’s a VCSEL laser array, Which the, the beauty of VCSELs is that they’re incredibly, incredibly complex packed and robust, low cost. And you can integrate all of the VCSEL lasers onto a single chip.

So I’ve pulled out the two fundamental chips in this device, and I have them sitting on the table. I’ve got the VCSEL laser right here, and the Silicon CMOS system-on-chip—the receiver system-on-chip right here. This is our L2 chip which I’ll explain later, but there’s a remarkable similarity between this device and the CMOS digital camera. They both contain all the pixels that are necessary to sense photons in the system. They also both contain all of the command and control logic necessary to build a complete LIDAR sensor. And so, again, fundamentally, we are pulling complexity out of a LIDAR sensor and putting it into the Silicon. Make the architecture simple, make it one size fits all. You can build a variety of products on this architecture and create a ubiquitous set of use cases given that architecture. So, LIDAR really does not have to be complex at the architectural level. That’s something that I want to make very clear here.

Now, one of the benefits of, of moving to this simplified system is the inherent manufacturability of the digital LIDAR architecture of these digital LIDAR modules. So again, we’ve outsourced all manufacturing to Benchmark Electronics Thailand. This is a video that’s playing is, is actually the Benchmark facility in Thailand. And benchmark is a multi-billion dollar CM (global contract manufacturer) employing over 10,000 people. And we’ve modeled the manufacturing process of our digital LIDAR sensors off of the highly automated and mature CMOS camera manufacturing world. And it’s one of the reasons why we’ve been able to ramp production in...

Oh, sorry. Okay. I wasn’t playing the video. Here we go. So this, this video shows the actual, the actual facility. So, we’ve been able to ramp manufacturing in this facility incredibly quickly compared to some of our peers. And it’s a real highlight for the business. Now, this facility is ITF 16949 automotive certified. We’ve had automakers come and audit this facility, give it a passing score. It’s, and it’s you can see it’s quite sophisticated. And you’ll actually notice in, in some of the following slides, the custom machinery, some of the black boxes that you see behind me, there’s a number of them already replicated at this facility. But the tools are owned by Ouster. They’re operated by Benchmark employees, but we also have full-time staff in the, in this facility. Benchmark also has a global presence. So, we’re able to, we’re able to expand to other facilities worldwide when, if, if and when necessary. So, hopefully, that gives you a sense for what we’re doing over and over in Thailand. Again, there’s inherent manufacturability to the technology when you put the complexity onto the Silicon. The Silicon comes on these wafers and that’s a highly reliable process. And so that’s where the complex manufacturing happens, but it occurs at these major semiconductor fab companies like TSMC and ST micro, Global Foundries, et cetera.

Now, I’d like to spend the remaining time before I pass this off to Stephen Fantone, to talk about ways of comparing different technology approaches in LIDAR. I think I share some of the same difficulty in understanding this, this landscape as investors. It’s, it’s really complex. There are so many different approaches to LIDAR, and it’s hard to wade through it all. But I’ve come up with a couple of criteria and a framework for assessing a variety of technologies and, and the trends, the kind of the specific criteria that indicate the trends that I think are going to allow specific technologies to, to, to flourish in this space and be dominant overall. So, those are really the capacity for the technology to be integrated down onto a single chip versus it remaining as a complex architecture. I really think that architectures are important to focus on and the simplicity of them. The second is just the approach to achieving high performance. So that’s taking an efficient, power efficient approach versus a brute force approach. And finally, is the roadmap of the company or the technology aligned with Moore’s law. Moore’s law really is that special. Moore’s law, again, is the scaling law of exponential improvement that dictates only CMOS Silicon technologies. And it’s really that special. It has basically an unbeaten track record over, over, over half a century. And, and it’s critical that a technology is very closely aligned with it. It’s going to win out long-term. And so, then comparing 905 nanometers spinning technology, MEMS, 905 nanometers MEMS scanning technology, 1550 nanometers scanning technology, and FMCW scanning LIDAR sensors.

Starting with the complexity and the capacity for a system to be reduced to the minimum number of just simple chips, building a chip based LIDAR. Instead of trying to walk through the specifics of each architecture, we’ve pulled direct patent images from the web and we’ve just posted them here. And my intention is not, these are all representative patent images of kind of the state-of-the-art for these four technologies. And my intention is not to go through and, and get into the details of each one of these. If you have questions, please about any one of these, save it for the Q&A. I’m happy to answer specific details and questions about any one of these and our comparison digital LIDAR. But really, I want you to get an appreciation for the complex architectures, these block diagrams that show the duplication of components and the multitude of components that are needed to build these analog and analog LIDAR sensors. So, we’ve labeled the laser systems. We’ve labeled the optical systems and also the detector systems in each of these four approaches.

And now, in this slide, the key here is, and I love this quote by Elon Musk. It’s incredibly apt. And I promised our team, I would only quote Elon Musk once in this presentation. So here it is, but he has a great quote saying the best part is no part. And this is really getting down to the idea of architecture should be simple, and you should put complexity onto the fewest number of parts possible. Namely CMOS semiconductors are a great way of absorbing complexity from an architecture. So, it’s a really apt statement, and that’s, that’s what we’re doing with the digital architecture. We’ve marked every place that we’ve made a component in these analog approaches unnecessary through digital integration onto this, this SOC namely onto the SOC, because that’s where all the logic goes in combination with the VCSEL laser. It’s just a massive and fundamental shift in approach to building a LIDAR sensor that strips an immense amount of complexity out of all of these systems and replaces it with these two chips.

So the second criteria that I think is really important when going after ubiquitous use cases is the approach to achieving performance in a LIDAR where the two options are brute force or efficiency. Brute force — and this is as it pertains to kind of the total power use of a system - and 1550 nanometer technology is actually a really good example of a brute force approach to achieving performance. So the idea here is use very high powered lasers at high wavelengths, which consume a lot of power to achieve performance numbers. And it’s a trade of achieving, kind of, a short-term gain to achieve that performance in exchange for long long-term drawbacks on the size, weight, power, and cost of the system. And if CMOS cameras were to go the route of brute force you wouldn’t have cameras on your cell phone. You wouldn’t have cameras on small, small consumer electronics, and you wouldn’t even have cameras — eight cameras - distributed around the EV like you do today. It just wouldn’t be possible without power efficiency. You can’t create ubiquitous deployment of a technology. And that’s actually one of the hallmarks and corollaries to Moore’s law --is not just that, that the, the performance or the scaling of transistors drops exponentially every 20, 24 months. But it’s also that the power efficiency of those transistors drops, or improves, exponentially with the scale factor so that you can put more and more complexity onto a chip with the same power draw at the system level, that’s, that’s really important. Actually, CMOS is really uniquely situated as an extraordinarily power efficient technology. And so, we focused our entire architecture around one that does more with less power. So we have some of the most power efficient devices in LIDAR today. And that’s only going to continue to improve as we improve the underlying semiconductor chip set and the device. And it can be quite difficult to understand the actual wide gap between a brute force and a power efficient approach. So in order to visualize that, I have, I have a visual here. And this glass bead represents the total photon budget that we have for our digital LIDAR sensor. So that’s the total number of photons that we output from our digital LIDAR. And this tub of glass beads represents the photon budget of a 1550 nanometer LIDAR. So there’s an, there’s a massive gap between a brute force and an efficient technology. And you do not get this kind of output for free. There’s a significant and painful trade to going a brute force approach with the technology. You can get some term gains, no question, but long-term, you’re constrained into a local minima that consumes a lot of power., costs a lot, is heavy, and, and weight constrained. So that’s really important as the second piece of criteria for building ubiquitous low cost technology.

Third criteria is just how closely aligned is the technology to Moore’s law. And Moore’s law really is this special that you want your company to be aligned with it. It has a near perfect track record of dominating every industry that it’s gone after, whether it’s CPU, GPUs, DRAM memory, solid state hard drives, cameras, GPS, nearshore measurement units, temperature sensors, cell modems — every single one of those industries has fallen to CMOS. And it’s because of Moore’s law. And we’ve paid special attention to this by aligning our roadmap as closely as possible with this exponential trajectory in the underlying semiconductors. So, while we build this product line today, we don’t reinvent these products every year from the ground up. Instead, we redesign the chip set, this laser and detector array, every year and swap those into this standard set of products to unlock exponential improvements in the technology year after year after year.

So, instead of redesigning LIDAR sensors, we redesign chips and swap them in. And this is actually standard practice. Again, this is the strategy of many semiconductor companies before us, notably companies like Apple or Nvidia. Apple tapes out new A series processors every year, and puts them in their iPhone to unlock exponential performance improvements, better battery life, new features with no real drawback to the end customer. It’s just a free lunch. And they don’t re-architect the iPhones from the ground up. They don’t re-imagine, you know, from, from the ground up, what a phone is. They’re really re-imagining the A series processor marching down semiconductor nodes year after year after year. It’s an incredibly competitive way to build a product. It’s also what Nvidia does or basically any, any semiconductor company that has mature chips and build their products around a chip set that this is the approach they take.

And so we are adopting that, and it’s a very, very successful and tried and true practice. So we have so much confidence in this approach, partially because we just have direct visibility onto where these semiconductors are going. Today, we have the L2 chip in our devices. This is a 40 nanometer chip. So these are 40 nanometer wafers. And that just dictates the 40 nanometers is the size of the transistors on the chip. But the most advanced semiconductor nodes today are at five nanometer nodes. And that’s an 8x improvement linearly on the chips, but it’s actually even better than that because chips are are 2D. So there’s 64 times the density improvement immediately available to us to march down towards over the next couple of years. So that’s 64 times the performance, 64 times the logic that we can put on these chips, 64 times the pixels, 64 times the processing with no drawback in power, in cost to the end customer whatsoever. So it’s, that’s the core and fundamental opportunity that is driving our roadmap.

So with that, those are the... that’s kind of the framework that I have that I look at when I’m comparing to other technologies. And I’d love to use that maybe as I answer questions with Stephen in just a bit, but before we do that I’d love for Stephen Fantone to talk for, for a bit about his views and his experience in optics, in the optical industry with CMOS cameras and also working with Ouster for the last four years. Now, Stephen is an optical expert in his own, right. He has degrees in electrical engineering and management from MIT and a PhD from the, from Rochester university in optics. He’s the founder and owner of Optikos corporation which is a major premier optical design consultancy out of Boston. And, and he recently stepped down as the president of the Optical Society of America. So he, his reputation precedes him. And we’re really happy to have him on the call. And again, he’s going to be available to answer questions that you all have about optical technology, digital LIDAR technology, and other LIDAR technologies just after this. So, I’ll turn it over to Stephen. Cheers.

Stephen Fantone:

Great. Thank you very much, Angus. Delighted to be able to be here today. Today, I want to tell you a little bit about Optikos, how we came to interact with Ouster and some of the things that we’ve done with them, and share with you my perspective as to where Ouster stands in product development and how they’ve taken advantage of a lot of improvements and advancements in other fields that they’re leveraging to produce a really state-of-the-art leading LIDAR system. Optikos was founded nearly 40 years ago. Our tagline is anywhere light goes. What does that mean? It means we work on consumer products, military products, medical devices, medical diagnostics, all different kinds of products for companies and helping them advance their technology and bringing it to fruition in instrumentation and products. Most of you probably have in your households a product, at one time or another, we helped design.

So oftentimes we’re operating behind the scenes, enabling optical technology for major companies as they advance their products. We’ve got a team here in the Boston area that is dedicated to that. And we also manufacture our own in house testing instrumentation and offer product development services and QC services to other companies. We first became involved with Ouster back in 2017 when they had reached out to us to have us help them assess their supply chain, the components that they were getting from that supply chain. And over the course of a year, we learned more about what their business strategy was, their product strategy, and their entire view of technological development. And I became excited by this involvement, in part, because we had seen things like this before. We’d been in business long enough to see the disruption that occurred as photography went from an analog to a digital business, and the increase in scale, the reduction in costs, the increase in quality, the greater value that you get today out of a photographic or imaging system as compared to even 5, 10, certainly 30 and 40 years ago.

And that disruption occurred again because of the advancements in CMOS technology and the development of components that weren’t necessarily developed for that particular field. People working in those fields could use those components to advance their products. We saw it again with smartphones. CMOS imagers were pioneered by JPL and research institutions. People, and as recently as 20 years ago, never thought that they would surpass the quality of CCD imagers in a major difference between CMOS imagers and CCD imagers. It’s the ultimate price trajectory, cost trajectory that they are on. And as people worked on CMOS imagers, we saw in the late nineties that there was going to be an increasing performance metric for them. There was going to become decreasing pricing. And it was really predictable that by the 2005, 2008 period that you would be able to replace an awful lot of CCDs with CMOS imagers.

And that would also enable new products that simply wouldn’t have been possible to create at that time. And the cell phone camera is, is a great example of that. Being able to put three imagers in a camera in a consumer product that most people can afford is really, really quite amazing. LIDAR is an old idea. Was really the first LIDAR systems were made within a year of the laser being invented in 1960, as they were originally configured, they were large behemoths. They were used for geospatial sensing. They flew in airplanes. They were used to assess the sea, the topography of the earth. They were used to range the distance to the moon. They were used to measure clear air turbulence in front of airplanes. In the 1960s, no one thought, outside of science fiction, that LIDAR would be used and that there would be autonomous vehicles using LIDAR.

So as you look and you say, well, why? What’s happening today? And what, what did we find at Ouster? What we found was a sensitivity to the fact that this technology does march on. That there are components to be used in leverage that weren’t available even 10 years ago. Low cost spatter rays, low cost VCSEL arrays. Those improvements driven by other fields, optical telecommunications, being the major one, brought these components forward to a point where we’re at a point in time now that is an incredible nexus where there’s the availability of these components at a descending price trajectory, an increasing performance trajectory combined with on detectors, emitters, VCSELs and SPADS on CMOS computation and a scalability. That’s going to continue into the foreseeable future. So we’re really quite excited by what we see here. It’s a unique opportunity. If this team at Ouster had tried to do this 10 years ago, it wouldn’t have been able to do it, to do it at the cost and performance today.

The components simply weren’t available. If they tried to do 10 years from now, they would have missed the market. There’s a unique market opportunity that is growing exponentially, that they’ve been able to attach to. And so when I look at the combination of these things — that you’ve got scalability, reducing costs, increasing performance, using tools and manufacturing methods that are maturing and improving each year. We’re just really very happy to be able to continue to work with them on the design of the next generation of, of, of, of LIDAR systems. So with that, I’ll turn it back to you, Angus, for our Q&A.

Angus Pacala:

Thanks so much, Stephen. So we’re going to go into Q&A for the next little bit, specifically focused on technical questions around our digital LIDAR and other technologies. And Derek Frome, our Director of Marketing is going to moderate the question & answer.

Derek Frome:

Very excited to be here with you today, Angus and Stephen. So as a reminder, after this Q&A session we’re going to go ahead and cover our industries as well as a financial update, so stay tuned for that. And then a broad Q&A at the end. So the first question is coming in from online. How does your digital LIDAR technology compare with competitors offering 40 LIDAR? And what advantages do you hold over those technologies?

Angus Pacala:

And Stephen do, do you, are you familiar with 40 LIDAR? I’d love... take it if you are. Otherwise, I can answer.

Stephen Fantone:

Why don’t you start and I’ll…

Angus Pacala:

All right, sure. Thanks. So 40 LIDAR is the concept of doing radial velocity sensing from a LIDAR sensor. So, commonly it’s associated with an FMCW LIDAR sensor. And that’s the major, I guess, talking point for a FMCW LIDAR is this idea that you can measure, measure velocity with the sensor. But the fact is that there’s a really great way to measure velocity with a traditional LIDAR sensor. And it’s just algorithmic. It’s very simple math to actually calculate the difference between one, one, one frame of the device and the next frame, and just calculate the distance, the difference in distance between those two frames at every point in the scene and get fundamental velocity vectors at every, every pixel. That’s effectively what a 4D LIDAR is doing anyway, it’s just doing it in the hardware, but we get to kind of leverage all of this CMOS technology, do the velocity sensing algorithmically. And keep in mind, hat’s the way this entire industry has developed — is actually through algorithmic velocity sensing. Companies have been doing this for 10 years. All of our customers are, are doing it this way. It’s really efficient. And, and we just don’t see it as a major selling point to have that inherent in the LIDAR versus a simple algorithm running off of the LIDAR.

Derek Frome:

Great. We have a follow up question to that. Can you talk about your differences versus Silicon photonics?

Angus Pacala:

Sure. Stephen, did you want to talk about Silicon photonics? I want to make sure.

Stephen Fantone:

Well, all of these are technology that at the right time would be incorporated into these systems. And the question is, does it still... do Silicon photonics right now present an advantage? That’s one where, should they advance to a point where there is an advantage, I think Ouster will be ready to do that. And right now it isn’t there. Maybe it’s where VCSEL arrays were and, and SPADS were 10 years ago, but this is something that is not in conflict with the fundamental architecture of what they’re doing.

Angus Pacala:

Awesome. That’s actually a really interesting answer. So I would answer it another way, but, but I think that’s a great point. There’s nothing preventing us, should the technology mature to kind of adopt some of the, the ideas of Silicon photonics into our architecture. But to Stephen’s point, it’s a very long way off. So you hear about 2024 timeframes to even have a first product on the market for Silicon photonics. nd just stepping back, you know, I, I have particular kind of

familiarity and interest in Silicon photonics. I started a previous company in the space called Quanergy Systems which was building Silicon photonic LIDAR. So I’ve literally worked directly on this technology for a number of years and, and understand it very well. And aside from the immaturity of the technology and the long time to market, one of the core problems with Silicon photonics is that it is not dictated by Moore’s law.

So while it is Silicon, it is not CMOS Silicon. It’s Silicon photonics, and it does not have an exponential scaling law associated with it. So there’s this benefit of integrating onto a chip, which is why you hear companies talking about when it comes to Silicon photonics, but integrating onto the chip then freezes that technology and time versus CMOS where it actually unlocks exponential improvement. And the, the, the freezing in time is for.. it’s actually really intuitive. Moore’s law is about shrinking feature sizes on a chip year after year after year. And you can do that with transistors because they’re electrical devices. Silicon photonics is about putting light on to chips, light guides on chips or fiber optics on the chips and light has a particular wavelength or size, and you cannot shrink the features that guide light through a chip because of that fundamental principle that light has a given size. It has a wavelength. And so that’s the reason why your device, a Silicon photonic device, is frozen in time, cannot exponentially improve beyond its, its initial kind of transfer to a chip. So I think that’s one of the biggest problems with Silicon photonics personally.

Derek Frome):

Great. And as a reminder, folks, you can type your questions into the chat bar in your window. And we have folks who are feeding me those questions right now. So, yeah.

Angus Pacala:

And, and please, if, if you have a question for Stephen specifically, just note it in your question. Okay.

Derek Frome:

Absolutely. So here’s the question about range. Can you talk about how you think about range as a metric of performance, some of your competitors that are using the brute force methods site superior range as a benefit to their respective approaches?

Angus Pacala:

Yeah, that that’s, that’s absolutely right. So, competitors that have gone to brute force route have exchanged a short term benefit of range for long-term competitiveness. That’s exactly what they’ve done. And so, we, alternatively, have a suite of products that have achieved different ranges, and we’ve actually focused much more on a combination of resolution and range. So we don’t offer today the highest range devices in the market. But what we do offer is the highest, that the best combination of resolution and range devices in the market, and that, we call that usable range or useful range because you need a combination of resolution at range to do most of the productive work of a LIDAR sensor, which is really classification of objects in the far field. And there you need lots of resolution at long range, and that’s where we really excel.

But, but also I would point out that not every customer cares about range. There are many customers that care about wide field of view sensors, like the OS 0much more so than range. So a combination of resolution and wide field of view instead of an all-in range number. And just the final point here — it’s not going, you don’t have to hold your breath for very long for our sensors to eclipse the range numbers of our competitors. Again, brute force is a short-term gain for long-term drawbacks. We are exponentially improving and the capability, the range capability of our sensors, and you won’t have to wait very long for that metric to fall to us as well.

Derek Frome:

Okay. Next question is pretty straight forward. We’ve heard a lot about 1550 nanometer. What wavelength of laser are you using?

Angus Pacala:

865. So near infrared.

Derek Frome:

Okay and some peers claim that 905 nanometrer, and sort of by extension the 850 or 865 nanometer, can have a negative impact on human eyes. Does 850 nanometers cause any safety issues?

Angus Pacala:

So I’d love for Stephen to answer this, but first and foremost, every LIDAR sensor that is sold today is eye safe, period. Maybe there some disinformation being propagated, but we are legally bound to sell safe devices. You can hold any device from anybody that sells LIDAR sensors up to your eye and be safe doing it. We are regulated by the FDA and we are all adhering to law, as far as I know. So everybody takes this super seriously, and I don’t want anyone to think that there are unsafe products in the market. But, Stephen, maybe you can talk about this high safety concept.

Stephen Fantone:

Yeah. The FDA was invited into the laser industry literally 40 years ago, maybe 50 years ago to address this issue of safety of lasers. And in the old days, part of the safety issue was the power supplies- that these were high voltage, bulky power supplies that in and of themselves were dangerous. We’re not in that situation anymore, but through this time there’s been an enormous amount of regulatory oversight in the laser business, right down to the lasers that are in CD players. And so it’s a very well-established science. When there is a case of a person’s eyes being damaged, it’s a reportable offense under OSHA. This gets a tremendous scrutiny and Ouster’s

complying with all of those regulations. So I think that anybody that puts a product out in the commercial marketplace is going to be complying with those standards. And people are very serious about them in this country. There are cases where companies have gotten sloppy about it and the FDA has come in and shut them down. Basically, they have a regulatory statutory right. They don’t even have to go to a court. They can come in and keep you from shipping product. So, I’m quite confident that this is not an issue for Ouster and it really won’t be an issue for any other major player that, you know, like most of us do, intend to comply with the rules and regulations.

Derek Frome:

Great. Thanks, Stephen. Okay. Here’s another question are all of your components in the LIDAR equally subject to Moore’s law? So that’s referencing the, the VCSELs, the spatter rays for bonded optics and so on.

Angus Pacala:

Yeah. Great question. So really there’s four components: 2 lenses, 2 chips. And certainly the only component that’s truly guided by Moore’s law is the CMOS chip on the receiver side. And that’s an important component to be locked with Moore’s law because it is effectively, I mean, it’s like 99% of the LIDAR because it’s doing all of the signal processing, all the command and control, and it has all the VCSELs for receiving information on it. That’s actually the hardest thing to improve exponentially from, from any other means. Now VCSELs—you do want to, we want to be able to continue to improve VCSELs and pack more and more and denser and denser VCSELs onto a chip. And we’ve taken that into account in two ways. First of all, it was just that VCSELs are still a relatively immature technology. They are mass produced and there are billions of VCSELs that are sold every year, but it’s only recently that high powered VCSELs for use and LIDAR have started to come to market. And that, that kind of high power route for VCSELs is still one that is exponentially improving. So I wouldn’t call it Moore’s law. It’s not right to call it Moore’s law, but there actually is an exponential kind of scaling and performance improvement that’s happening in VCSELs. One of the other benefits of VCSELs is you can make them incredibly small. So we’ve started with relatively large VCSELs on this chip, and we can just keep shrinking them down smaller and smaller to pack exponential gains into the VCSELs. So not technically Moore’s law, but, effectively we are, we are not constrained by the VCSEL technology for the foreseeable future.

So everything I said about, you know, scaling two 64x the density on the receiver is actually enabled on the VCSELside as well. That also holds for the micro optical architecture, that wafer level optics. We’ve done the same thing where the feature size of our micro optics is actually relatively kind of mid-range, and there’s a real capacity to continue to scale that smaller and smaller and smaller on the chips. That’s a great question though.

Derek Frome:

Great. Now here’s the question that maybe Stephen can weigh in on first and Angus follow up. Can you discuss your relative cost position? How much more cost effective is this digital solution and how much more cost and performance improvement do you need to see in order to drive accelerated adoption? Stephen?

Stephen Fantone:

I think from the standpoint of the optics, oftentimes, the cost of the optics scale with the size and volume of the optics. As things become smaller and more compact, you’re able to use injection molding technologies. You’re able to put more cavities in a particular mold- the price of the optics can come down dramatically and the larger, the volume, the lower the price. And this is, really, can be quite dramatic. We can talk about factors of 10 or more on issues like that. So I’m very optimistic on the optical part of it, physical component part of it that we’ll see, again, dramatic reductions in. Already the cost of Ouster’s LIDAR systems- the optical a portion of that is really a modest percentage of the overall cost today. And I expect it to continue to go down. For others that have more discrete components that are associated with glass components that becomes more of a challenge. And if they can’t get down to smaller sizes, they won’t get that scaling.

Angus Pacala:

Yeah. And maybe to fill in a bit on that, add a little more color. You know, if you look at a CMOS camera today, there’s almost an even split between the cost of the Silicon and the cost of the glass lens or the optics that go in front of that Silicon. And that’s generally correct. And the same concept actually holds true for digital LIDAR module. There’s almost equal contribution in the balm from the VCSEL ray, the Silicon ray, and the two lenses. So, it’s already a significantly cost reduced system. And like Stephen said, it’s really just scale, that is bringing costs down at this point. We don’t need, there’s nothing more to integrate on the system. It’s already simple. So it’s merely our purchasing power and our supply chain to bring down costs further, which you can see in our COGS trajectory, in our financials.

Stephen Fantone:

Yeah. And Angus, I think it’s important to note how different this is than what the photographic business and imaging business was like 20 years ago. Now, these optical systems are not just a little smaller, they’re dramatically smaller, they’re smaller by an order of magnitude, and it’s really, it’s not different in gradience—it’s a difference in kind. And so when you look at those lenses that are in your iPhone, that we all have today, those are incredibly sophisticated lenses- lenses that if they had been made for something like a 35 millimeter format would have cost thousands, if not tens of thousands of dollars. They incorporate aspheric technologies routinely. They’re made in much larger volumes than 35 millimeter camera models were ever made. And I think that is, it really, again, just a complete difference in kind today than we were just a few years ago.

Derek Frome:

That’s a great point, Stephen. Thanks. okay. This is probably a question for both of you, maybe Angus first. We continue to see advances in camera, vision and radar technologies at attractive price points. How do you see your combination of Moore’s law as well as performance compete with those cameras and radar in the future?

Angus Pacala:

That’s a great question. So, you know, I think Carl actually said it really well at the beginning. There is an, and Carl knows what he’s talking about—he was the chairman of the board at Zoox which is a premier self-driving company recently acquired by Amazon. So, and what he said was there is no limit to how safe you can build a self-driving car. There will never be a point at which consumers do not want a safer car to ride in that they have no control over. Just like there’s no limit to how safe you can make a passenger airliner. There will never be a stopping point. Only perfection is acceptable when you hand over control of your life to a machine. And from that perspective- we’re not competing with cameras or radar. LIDAR is just another complimentary sensor to give more redundancy, to drive further towards that perfection—the no crash world that we all want.

So it’s not a competition here. And our, our whole goal as a company is to make the technology affordable enough that there’s no real economic reason not to incorporate LIDAR. I am all for camera and radar technology getting better and better and better. And I want to buy cars that have 40 radar in them, advanced machine vision, and LIDAR sensors. I want it all. And that’s how I’m going to fall asleep comfortably in my car as it drives me to work in a couple of years.

Derek Frome:

Okay. The followup question is the question you probably knew you would get today. Elon Musk, who you quoted earlier, doesn’t believe AV requires LIDAR technology. Do you have a view on that or what is your point of view on that?

Angus Pacala:

Yeah, I do want to let Stephen answer. Stephen, let me know if you want to answer at any point.

But I can give my view. I mean, I basically just said it succinctly just now. There is a certain point where regulation will step in because if LIDAR is so economical that all cars will just be forced to incorporate it because there’s no limit to the safety that that consumers want, or that society expects from automated systems, notably planes. And, so I think that Elon’s comments about LIDAR were definitely a point in time. I mean, I kind of agree with him. Anyone that was trying to put an analog LIDAR into a car is doomed. There’s not an economical reason to do it. It doesn’t make any sense. What makes sense is to have small form factor digital LIDAR sensors that are a complete package of redundancy that get seamlessly incorporated into a car and don’t cost a lot. That’s the world we’re pushing towards. And that definitely makes sense. It’s effectively building camera like LIDAR systems that behave and are incorporated into cars, just like cameras.

Derek Frome:

Stephen, perspective to share on that.

Stephen Fantone:

Angus, let me… The answer that it’s going to be all one way or the other, is if someone wants to make that point, they’re just trying to win an argument rather than understand that, that we’re really trying to solve the problem. (inaudible) have their place. LIDAR has its place. You know, there are people that drive that just have one functioning eye. They don’t have great depth perception. They have depth perception by moving their head side to side, they intuit the world around them. They don’t have LIDAR that can make up the velocity measurement frame to frame, and know what the velocity is of a vehicle that’s out a hundred yards away. Similarly with cameras, a camera by itself, doesn’t do that. They need to be used in pairs and the resolution that you get out of a camera with a stereo baseline- it’s going to be dependent on what that stereo-base line is and the processing that goes on frame to frame with that.

So what’s really going to happen, and people are pragmatic about this. They want safe vehicles. And so there are going to be cases where LIDAR gives a certain kind of functionality that cameras can’t, or cameras are too expensive to do. And there are going to be times where it may be a camera that’s inside the car that’s looking at the person to make sure the driver’s awake. And I think that, you know, this one where there’s a toolset to obtain a certain level of safety in autonomous vehicles that is going to necessarily incorporate both. And this idea that need one, or you could do it all with one that seems to me to be, really be a fool’s errand.

Derek Frome:

Thanks, Stephen. Next question. Several other LIDAR companies talk about being integrated onto a chip. What is it that Ouster is doing that’s different from what they’re doing?

Angus Pacala:

Cool. I’ll take that one to start. So there are two reasons why it’s not the same. One is, well, so a lot of companies are talking about semiconductor integration, right? It’s a no brainer to try to integrate parts of the system onto semiconductors, but it matters what type of semiconductor it is -matters that it CMOS, really does. No technology is like CMOS in the world. And it also matters to what degree that the integration has happened- where whether it’s partial integration, they’re choosing a small sub-system to make a specific chip for it, or whether there’s complete and total integration onto a single chip. And that goes back to kind of the three criteria that I presented that I think are really important.

The level of integration onto a single chip and whether by extension, the technology is driven by Moore’s law because the entire device is effectively a Silicon chip. So you can just lock it, lock the performance of the, of the system to Moore’s law. And so from that perspective, you know, there are companies that are doing like 1550 nanometer technology are never going to integrate

onto entirely CMOS. They actually have strung together, indium gallium arsenide with some specialized receivers within a standard kind of FBGA or some type of signal processing backend. So there’s a series of chips that are necessary. And also they’re using scanning systems. It’s not a fully solid state system to boot. So, you know, there’s this combination by putting everything onto one chip, we get both the benefit of incredibly simple architecture and the scalability and complete solid state devices, which is something we haven’t really even covered here. But the fact that basically every one of these other technologies has some sort of optical scanning system, which is just additional complexity and cost in, in the system. So again, like Silicon photonics, the other example of putting stuff on a chip, but there are real drawbacks to doing Silicon photonics. And, and again, I’ve had direct experiences with those challenges at my previous company.

Derek Frome:

Perhaps a followup to that is. Go ahead, Stephen.

Stephen Fantone:

Yep. I think the other part is to say what’s driving the advantage of technologies. The bulk of the (inaudible) most that’s where they’re going to see the emphasis, the greatest outcome versus greatest improvement. When you look to these other areas, looking at photonics, but they still have to prove themselves. It’s absolutely critical they’re a working scale technology. When they do get to a point -let’s imagine they’re successful and it scales—it’s unlikely to scale at the rate Moore’s law scales. So if it’s proven otherwise, we’ll certainly look at that and see if it applies to making a device that’s economically competitive and provides the features that’s needed. The goodness is Ouster doesn’t need to own a CMOS fab facility. And if the technology changes a little bit, or even a lot, Ouster will will be looking and saying, well, if there’s an advantage in pulling this technology that has a long-term (inaudible) to it I’m sure they’ll look at it seriously. Right now, those are offon the horizon and still have a lot to prove.

And oftentimes in fields like this, what we see is the moving of the goalpost. Say, you know, 10 years ago, people would say we’d be in autonomous vehicles by 2020. We don’t have them yet, but they’re coming. But we’ll redefine what success (inaudible) and on a trajectory that is very confident of its huge success- not relying on technological improvements made by others. If anything, they’re only relying on technological improvements where there’s a roadmap for it in a high degree of certainty that the technology target is going to be met. And I think that should give everyone a high degree of confidence going forward and approach.

Angus Pacala:

Thanks, Stephen. And it sounds like you might be having, is he getting static on his line? Is he dropping out or is his audio good?

Derek Frome:

So the next question is what patents do you have on your digital liner technology?

Angus Pacala:

Sure. I’ll take that. So, well, we hold over 30 patents, granted patents for the technology at this point, over a hundred pending patents worldwide. We filed very broadly internationally and domestically across a wide variety of families. We have over 20 different invention families that we’re pursuing. This is a place that Mark and I, my co-founder, Mark and I really focused on from the beginning. We knew we had something special by being the first mover in this digital space. And one of the real benefits of the semiconductor industry is the ability to patent and protect patents in this industry because of kind of the unique challenges or, or the partners that you have to fabricate your technology with.

So unlike other industries where they’re using off the shelf parts or discreet electronics they can buy from a myriad of sources, in order to build a digital LIDAR sensor, any digital product that has custom chips- you have to work with massive international, reputable companies like TSMC, ST micro, Sony, Samsung. And those companies, you know, they’re the literally the only companies that can build the products that, that we build. And so it’s, we’re able to protect our IP through them because they respect the IP of their customers. And it’s very difficult for a company to come in, try to make a carbon copy of our devices with one of those companies. So, it’s a real barrier to entry that that makes the patent portfolio incredibly important. And yeah, I think it’s just something that we spend an immense amount of time on filing strategically kind of up and down the technology stack. We were the first to file in digital LIDAR. So, I think we have something quite special there.

Derek Frome:

Got it. Okay. And is your digital receiver SOC design entirely proprietary? Or are you licensing some portions of the intellectual property?

Angus Pacala:

No, so yeah, this is really important. We build completely proprietary custom chips on standard CMOS processes. So the chips that you see here, the SOCs, they cost over a hundred million dollars and five years to develop. So there’s a massive undertaking to build a complex Silicon CMOS system like that with a hundred million transistors on the chip. I mean, it’s an immense undertaking and very few companies actually successfully transitioned from conception to product in this in this field. Many fall by the wayside during this process. So absolutely proprietary custom chips built on standard CMOS processes. And that’s what every, I mean, Nvidia Apple, Tesla, where they’re self-driving chip, it’s all custom chips on standard processes. That’s the way to do it.

Derek Frome:

I think this will be our last question here before we have to let Stephen go. So are there moving parts in your design, and if so, how do you see their complexity compared with competitors? And do you see the performance of those components improving as well?

Angus Pacala:

Yeah. I guess Stephen, I’ll take this. You can give some color too, if you want. We have two product lines. We have a fully solid state product, which is truly no moving parts. It’s just two lines. I mean, it looks like this inside, right? That’s what’s going on there. So it’s a fully solid state device that yeah, fully solid state in every sense of the word. There’s no micro scanning, no macro or micro scanning. But we also, these are spinning products. And so they have one moving part and we decided to build the spinning products because that’s actually where most of the market is today. Most customers, despite what you might’ve read in the press, most customers want spinning products today because of the benefits in field of view and the benefits and the familiarity that the customer base has with these wide field of view 360 degree sensors.

And you know, we could have chosen to use some other scanning mechanism, some of our competitors. I mean, every one of our competitors has a moving system in their devices, which is something they don’t talk about very often. But if you’re going to put a moving system into a product, the best thing to do is just to put one moving system in the product, not like a 2D scanner. And so spinning is actually a really elegant way of doing that. It’s super robust and it’s simple. And again, here, the best part is no part. Instead of having a raster scanning system with two axes of scanning, like some of our competitors – a spinning product works really well. It’s what customers want. It’s highly robust and it’s super compact and power efficient to boot. So on the question of whether the spinning is going to get better, no, it’s just an electric motor. There’s nothing really to get better. It’s already cheap. So, it’s really the optics module on top that is improving. Yeah. Great. Thanks. And I’ll see you

Stephen Fantone:

(inaudible)

Angus Pacala:

Hey, Stephen. I apologize to interrupt, but your audio isn’t coming through. So I think we’re going to pause here. It’s just not coming through—it’s garbled. Yeah. Thank you so much, Stephen, for participating in the event. We’re going to move on to the next section. Thanks again. Thanks, Derek.

All right. Hopefully that was helpful. Again, we’re going to have another general Q&A at the end with me and Ana Brunelle, our CFO, and with Derek moderating again, and I’m happy to take more technical questions if you have them. So again, just keep posting any question you want answered, and we’ll try to get to it in the second Q&A session that follows this. So with the remainder of my time, I want to talk about how our products are used today in different industries and different use cases. And then go into a little more detail on the current state of our automotive business and our strategy in, in that industry. Since it’s been, you know, it’s still an intense focus for the investment community, and I want to make sure that everyone is clear on what we’re doing there.

And so in terms of actually talking about the broader use cases, I care a lot about this. I think it’s important for investors and consumers to understand how LIDAR technology is being used more broadly, and to put a picture in people’s minds of why LIDAR sensing is used—in not just automotive but in industrials and smart infrastructure, robotics as adjacent markets. And in order to do that, we are going to walk through the conceptual supply chain of Ouster’s products from raw material mining in Australia, to manufacturing in Southeast Asia, to shipping and delivery in the United States. Because one way to think about what Ouster is doing is it’s automating the entire global supply and logistics chain across the world. That’s what a vast majority of our customers are actually doing is automating that delivery of that while the production and delivery of goods throughout the entire world. And it’s, it’s really staggering in scale how much activity there is to improve the efficiency, improve the safety and improve the automation of every step along the way in the delivery chain.

And there’s another well, one of the things that I think is interesting about LIDAR is we talk about automotive deaths, you know, 40,000 people a year in the U.S. alone die in automotive accidents, but there are equal numbers of people that die in industrial accidents or get maimed in industrial accidents throughout a supply chain, like the one that we’re showing on the screen. And that is, you know, we’re going after basically the same opportunity, but expanded, just improving the safety and efficiency of powerful equipment across the world with our digital LIDAR sensors. So there’s a real great motivating force behind all of this work. So one of the start with mining, that’s where it all begins digging materials out of the ground. And I love, you know, we have a number of mining customers.

One of the differences with heavy industry mining equipment, construction equipment is it’s largely slower moving equipment. That’s extremely large high, high capital costs. And there’s a requirement for many small form factor, super rugged sensors being deployed around the vehicle. And we have a number of customers that are doing this, but they’re using OS-0 & OS-1 sensors primarily with sometimes an OS-2 sensor for the forward-looking direction, depending on how fast the speeds get up to. But this example is Sandvik, a Scandinavian major mining machinery manufacturer and operator out of Scandinavia, and they’ve deployed four OS-1s onto their mining platform. They’re automating the vast majority of their mining vehicles. This is a really cool application, but that clip kind of gives you an example of it’s such a different environment than a highway or public roads. It’s got all kinds of different constraints, but the fundamental need is for 360 degree perception around those vehicles.

So they don’t bump into walls, you know, the walls of the mine. So they’re not hitting humans that are interacting or other machines, and they’re also not damaging the equipment. And also, you know, ultimately allowing the machine to operate more efficiently with higher uptime. So the next example, after the mine, you have to manufacture your products. We do this in Thailand, and a large amount of robotics in factories is used for moving materials around the factory floor. And there are these little, these pallet robots called automated ground vehicles or AGVs, which

is a really interesting and established market for LIDAR today. It’s an industrial LIDAR market that we don’t really talk about this very much, but there’s over a billion dollar market almost for industrial LIDAR for applications like this. And here again, I’ve got a great clip of AGVs operating on a factory floor, but the state-of-the-art is kind of highly constrained movement of these vehicles with simple LIDAR sensors.

That can’t really sense the environment with a lot of clarity. They just make sure that they don’t bump into stuff basically. And our LIDAR sensors are, you know, that high enough quality and small enough and form factor and low enough cost to now bring real machine intelligence to a platform like this, to allow for a much broader range and much more flexible range of applications for automated ground vehicles. The same is true for warehouse automation. So similarly, small form factor, automated forklifts, and other vehicles that are moving around a warehouse surveying the status of stock on shelves, or actually moving, moving material on and off shelves is a major business for us. We have a number of customers in the automated forklift market, for example, and here again, these are slow moving, relatively slow moving devices, but they’re operating very complex tasks,doing very complex tasks and you need 360 degree coverage around these vehicles that go forward. They go back. Sometimes they go sideways and they also need extreme fields of view and really high resolution to see small products on shelves, but also to see to the very top of shelves. So sensors like the OS-0 that have super high fields of view, small form factors and best in class resolution. So 2.6 megapixel sensors are really are a requirement to automate a vehicle like this.

And so goods are manufactured, they’re warehoused, and then they go to ports to get shipped to the U.S., in our case. And we have a lot of great examples of port customers here as a gantry crane that, that, and we have actually installments on three continents with gantry cranes. It’s a really interesting opportunity. It’s kind of a mid-range application where you need actually pretty effective well, high field of view, but also long-range and all weather performance that’s operating literally 24/7. These docks never sleep. But dockyards are actually a beehive of activity. So you can see there’s yard logistics vehicles that we are also automating. So there’s a whole host of applications that some of them look like, you know, a self-driving car or a mobility fleet, but, but in a more constrained environment.

But then there, there are applications like these gantry cranes where we’re first making them safer. So they’re not knocking containers into a hundred million dollar or a billion dollar ship. But also making them more efficient, making them operate closer to 24/7, which is super important for just return on this serious capital investment. So again, we’ve got deployments on three continents for gantry cranes, and there’s a lot of those in the world. So products reach our shores and now we get into some more familiar applications. So this is an automated trucking application. In the U.S. long-haul trucking- major opportunity for deployment of LIDAR sensors. Obviously, we have Plus AI as one of our marquee customers in this space, but trucks really require the full suite of sensors that we provide. OS-0, OS-1 & OS-2 sensors. You need long range forward looking sensors, but also you need to blanket the vehicle and completely redundant LIDAR sensors all around. So a pretty familiar application there. And finally delivering product to the doorstep. So autonomous last mile delivery looks a lot like those smaller form factor robotics, except they’re operating in challenging outdoor environments in

close contact with humans constantly. So, again, there’s a real need for LIDAR sensing, given the safety critical aspect, you can’t be hitting humans as you’re delivering packages, but these devices are small in a form factor that you can really only fit a single LIDAR sensor onto these vehicles. So here 360 degree scanning systems are really important. Small form factor in power efficiency is super important. And you’ll see, we have a number of great customers. This is Postmates that’s actively deployed in LA and San Francisco with OS-1/128 sensors.

So, super high resolution sensors. And you need that mid range because the sensors not only have to navigate slow sidewalks at like three miles an hour, but they also have to cross roads where the traffic may be maybe running 40- 50 miles an hour. So a mid range sensor is really important there, but it needs to be in a small power and power efficient form factor. So hopefully that’s a helpful overview on kind of the myriad use cases for the technology. Again, we are, I really want to stress and make sure that everyone understands how diverse and essential LIDAR sensors are going to be across the entire logistics chain of the world. We really are automating every single step of how the world builds and delivers products to your doorstep. But I also, I wanted to end this period talking about automotive.

I don’t want investors to come away with the idea that we don’t have a core focus on the automotive business. It really does guide everything we do here. It’s a major vertical for us, and it guides all of our product development because it generally is a superset of the requirements of, of the other industries. And so on the commercial side we have significant traction in automotive today. So, top five automotive customers drove almost 5 million in revenue in 2020 alone, and they included three global automakers. And just last week, actually we signed a major contract with an OEM for a nonbinding opportunity worth $30 million over the next three years. This is actually, it’s an established customer with forecasts of $30 million. So it’s just goes to show you, Plus is one example of a great customer in automotive that’s scaling, but there are many others that are in the pipeline that have really eye-popping numbers in terms of revenue in the short time.

We don’t have to wait for 2023 to be generating tens, if not hundreds of millions of dollars from the automotive sector. And of course, we’re working with 10 autonomous trucking customers today. Companies like Daimler trucks, companies like Plus like Ike, Kodiak, all are using our sensors on the roads every day. As it pertains to the product, I do want to stress, we have the only true solid state product in development for the automotive industry. And other folks talk about solid state or pseudo solid state, but at the end of the day, there’s moving mirrors in all those other devices. And we are going to be supplying true solid state products to that market. We also have aligned our entire roadmap around achieving automotive certification for the mechanical and environmental performance of the devices. So we test all our devices to GMW 3172 which is a standard automotive spec.

And unlike some of our peers, we actually are committed to certifying every single one of the products that we build. So OS-2, OS-1, OS-0 and all the electronically scanning solid state sensors will be automotive quality and functionally safe. So ACE will be certified in accordance with ISO 26262. So I think we’re going to be one of the only companies that actually can supply a portfolio of digital LIDAR products or of LIDAR products to the automotive market and allow

customers to pick and choose what sensor is right for them, knowing that every single one was developed under the same automotive product requirements and achieving them. And then finally, we also are set apart by just our capacity to manufacture in an automotive certified facility today. So, Benchmark electronics has certified their facility to IATF 16949. And we’ve actually, we’ve had automakers come and audit that facility and give it passing marks which we’re really happy about. It’s also an inherence with ISO9001 and 14001. So we have full teams of people working to make sure that our products are certified and the manufacturing is certified. It’s a full functional group at Ouster today.

And all that progress on kind of the product and the commercial side is well and good, but I think it’s also, if we’re going to do that, we’re going to do the engineering and it really is just engineering. Although it’s, it’s difficult, it’s painful- we’re going to get it done. The strategy and viewpoint that we hold on how the auto industry is going to develop and how Ouster is going to ultimately win it is also equally important. And I think fundamentally kind of at odds with how some of our peers might be thinking about the industry. And it’s at odds on three main points: at odds on what we think the product is that automakers want, what we think the price point is, and what the expected gross margins are for the industry. And so starting with the product,

This is something that, I think is just kind of a fact, not an assumption by us, but any automaker that offers us an RFP that sends an RFP or an RFQ for LIDAR sensors is not asking for a single LIDAR sensor to deploy on their vehicles. They’re actually asking for three different LIDAR sensors to quote: short, medium and long range LIDAR. In the exact same way that CMOS camera systems like this eight camera system have fisheye medium field of view and telephoto camera sensors in the system. Automakers want a package of LIDAR sensors that are short, medium and long range. Every single automaker that sends us a quote asked for those three sensors. And it’s for quite an obvious reason- in order to transition L-2 systems to L-2 plus and L-3 aid/assist. systems, which means hands-free, eyes-free driving- they need complete redundancy in the sensors that are currently enabling the L-2 capabilities.

Meaning the cameras, cameras are what enable the L-2 product suite and capability suite, which we’ve listed out there with the check marks. And in order to make those hands free, eyes-free L-2 plus or L-3, you actually need to have redundancy and LIDAR sensing across every single one of the cameras that you have here. And we are positioning to build that suite of solid state LIDAR sensors to be seamlessly incorporated everywhere that there’s a camera on the vehicle.

And so finally this brings me to price, not only do automakers want a multi-LIDAR package to deploy, to actually ship the features that consumers want, but they are demanding a thousand dollars price point for that five LIDAR package. And so you may have heard of the thousand dollar price point, but I think that there’s just a fundamental disconnect in this industry about why there’s a thousand dollar price point. It’s enabling for a consumer. They don’t want to pay more than that, but they don’t want to pay more than that for the full suite of L-2 plus, and L-3 capabilities. And you can only do that with a multi-sensor package, really five or more LIDAR sensors. And so Ouster is uniquely positioned. We can hit that number for five LIDARs, not for one for 5. A thousand dollars, five LIDARs, all solid state digital lighter sensors that get seamlessly incorporated into the vehicle body, everywhere that there’s a camera.

So, you know, that’s one of the reasons why I have so much confidence in our automotive business is just that we were building to the product that automakers and consumers actually want. That enables the full suite of autonomy features that I think customers are actually expecting. So with that, we’re going to play a video that covers the Plus deal. Just kind to end, this section. The engineer in the video, Tim, does talk. He touches on this need for different LIDAR sensors that blanket the vehicle in, in redundant sensing and it kind of touches on this point that I’m making here. So stay tuned for that, listen and enjoy the video.

Video 2:

We are on the cusp of an autonomous revolution. I have two children that are eight years old, and I believe by the time they’re ready to drive, they won’t need to drive at all.

There are three key developments that are coming together right now that make it practical to deploy autonomous driving. The first is very high power computers. Second is deep learning. And the third is high resolution LIDAR.

Plus is applying Level 4 for autonomous driving technology to trucking today. Trucking is a really interesting use case because trucks are really big. Trucks do a lot of work. You can increase the utilization of a truck to 24 hours if you can automate it. We’re deploying our system in the U.S., China and Europe at large volume. We will have thousands of trucks on the road by the end of this year, and tens of thousands of trucks by the end of next year. So we need partners that are ready to scale with us.

Ouster is absolutely ready to scale. They are able to manufacture these sensors with high reliability and all the features we want at production volumes with production pricing. One thing that many people don’t realize about trucks is just how big they really are. You can hide a car next to a truck, and it’s very hard for the driver to see it. The reason LIDAR is so important for autonomous driving is because it tells you the occupancy of space, a very simple, direct measure of whether you can drive there or not. When driving a truck at highway speeds, if there’s something very far down the road, you still have some time to react to it. But if you have some something close to you, you have to react instantly. That’s why we try to keep a cocoon of perfect perception directly next to the truck. And that’s why the Ouster LIDAR is a key ingredient in our system. The wide field of view and the broad opening angle on the OS-1 sensor is an absolutely perfect fit for our trucking system. It makes it possible for us to have zero blind spots anywhere. The high resolution of Ouster LIDAR is absolutely critical for our perception system.

When it comes to LIDAR, there is no range without sufficient resolution. Our system needs to be able to make decisions based on the sensor data that it’s getting. The more points we can get on an object, the more confidence we have. The effective range of the Ouster sensor is best in class. We don’t put LIDAR on the truck just because it’s cool. We put LIDAR on the truck because we can’t afford to make any errors in perception. We need our system to be absolutely perfect. Ouster has put LIDAR on a chip and they can actually take advantage of Moore’s law. They’re shrinking the components and they’re doubling the performance every year. At Plus, we’ve tested nearly every LIDAR available, and the Ouster LIDAR had the lowest failure rate. If you want to build the technology of the future, Ouster is able to meet that challenge.

Angus Pacala:

That video is a great summary of what Plus is doing.

It’s really, really exciting, and you should check it out. You can go to their website and learn more about it. But, but a great summary of, of some of the benefits that we’re bringing to that customer. I’d like to introduce Anna Brunelle, our CFO Anna is going to cover the financials of the business and give an update there before we move into a general Q&A to end the session.

Anna Brunelle:

Okay, great. So I’m really excited to share some highlights from our 2020 results. We’ve been seeing really strong momentum in our business since the release of our second generation product earlier in 2020. And as a result of that, we really had our strongest quarter to date in Q4 with 6.4 million in revenue. And that contributed to 18.9 million in total 2020 revenue. So, we’re gross margin positive today. Our margins improved throughout 2020, and that allowed us to achieve about 31% gross margins in the fourth quarter of 2020. Our production capacity is expanding as well, increasing 61% quarter over quarter. And that sets us up very well for the future growth that we’re expecting. Lastly, we issued revenue guidance of $33 to $35 million for 2021, which is about an 80% increase over fiscal year 2020.

So let’s talk about some of our financial trends. A few things to note here. Our diversified multi-market approach opens up about ten times the total addressable market. This means that we’re selling existing products to hundreds of customers in multiple markets today and we believe that the Aid/Assist automotive market will take a little longer to develop than our peers. We also believe it will be the most price sensitive and, as such, if you look at our revenue forecast over the next five years, Aid/Assist automotive makes up less than 5% of our projections. And of course this, you know, is something that we’re taking a more conservative approach on because we think it might take a little bit longer, but obviously should that market develop faster, we’ll be ready to meet it and that will only benefit us. But, right now, we’re projecting that the lion’s share of our revenue will come from the other end markets.

And so looking at scale and thinking about unit volumes, our growth is compounding today. We’re going to scale our manufacturing to meet the auto market at the right time. You know, looking at the sensor unit slides here, we show that we have about 700,000 units projected by 2025, and that’ll really allow us to meet the auto market at the right time with volume manufacturing. So, as I noted before, on the last slide, we’re already gross margin positive today. We sell our products in market today with positive unit economics, and this means that as we increase volumes we’ll be able to move down the cost curve while preserving margins. And this means that we can drop price when needed in the most competitive markets. We will have both price and performance that no one else can match. So remember, our cost assumptions are not theoretical. They’re based on products we’re selling in the market today, and our costs are derived using quotes from our contract manufacturer. So we’re able to quote unit costs at specified volumes and get a high level of confidence into our cost structure years into the future. Our COGS curve is driven primarily by volume. We’re not reliant on any new technology breakthroughs to meet the unit costs in this forecast.

So, having talked about revenue, let’s walk through our opportunity graph. We have about 500 customers today. Of that, about 200 of those customers are moving towards production. So we’ve placed those 200 customers in the funnel on the left. And then, plotting their forecast that they give us against time, and applying our historical conversion rates to each stage of the funnel, we’re able to calculate that we have 5 billion in cumulative sales opportunity from existing customers through 2025. This is a bottoms-up forecast for specific projects with specific customers, and we’ve kept our win rates flat over time. So we think we’ve taken a very conservative approach to modeling this. You know, again, these are existing customers who are moving towards production with our existing products and these customers span all four verticals that we’re operating in today.

So, let’s build on the opportunity graph a little bit, but this time let’s look at our total sales opportunity, not just our sales opportunity from existing customers. And so what we’ve done here is the blue area on the graph still represents the same existing customers who are moving through our production funnel. And on top of that we’ve layered two things, the first being the dotted line. The dotted line represents our revenue projections that you just saw in the earlier slides. And what you can see here is that our existing customers are providing enough sales opportunity that we could potentially meet our revenue forecast just from existing customers alone, but of course we’ll get new customers. We added about 300 customers in 2020, about a hundred in the fourth quarter of 2020 alone. And so we’ve very conservatively modeled here in the yellow area, on the graph 75 new customer additions per year. And so adding that to our existing customers, we get to at about an 11 billion cumulative sales opportunity through 2025. And so the point that I think we need to make here is that we have an established customer base, hundreds of customers who are driving a bottoms up forecast with very little, with no customer contributing more than 5% of revenues, so highly diversified.

So, onto the next discussion. What does that mean for the profitability of the business? You know, Angus talked earlier about the shift to digital LIDAR really driving a step change in cost and how we’ve set out to be the low cost ubiquitous LIDAR provider. And I think we’re making strong progress towards that. Angus talked to you a lot about the technology, but I also want to talk a bit about the business and why we’re so confident in our cost structure and our gross margin structure. So a few things to note, were gross margin positive today, as we mentioned, 31% in Q4 of 2020. And, when we get to 2023 and beyond, our unit economics drive substantial gross profit. And so a few things about our cost structure, again. Number one, we’re leveraging one digital architecture for all of our customer needs across verticals. And as Angus spoke about earlier, you know, we have three core components in our product, and that allows us to really drive efficiency and manufacturing.

Our digital architecture reduces labor costs and allows for easier outsourced manufacturing. And that’s why we’re already up and running with our outsource manufacturing in Thailand. Second, we’re really designed to scale. We have a single form factor, and we continue to improve that over time through firmware and chip set updates. This means we’re leveraging an established consumer semi-ecosystem to drive visibility into costs and margins at scale. And at volume, our single form factor also allows us to build strong purchasing power across skews. And then third, the majority of our margin improvement is really driven by volume, and we’re already in volume production with our contract manufacturer in Thailand. So, we have high visibility and high confidence into our COGS’ trajectory and our forecasts, as these amounts are generated by quotes from our contract manufacturer. And this means we also have very high confidence in our adjusted EBITDA trajectory. So from an adjusted EBITDA perspective, we expect to be adjusted EBITDA positive by 2023. And through this transaction we’ll be fully funded to meet that adjusted EBITDA goal. Also, we do plan to use the proceeds from this transaction to make additional investments in our growth, which are represented in the 2021 and 2022 adjusted EBITDA trends on this slide. So finally, with that, I’ll turn it over to Angus to talk about the use of proceeds.

Angus Pacala:

Great, thanks Anna. So, we have an established business today and a real opportunity to grow that business by investing directly in it and seeing a fast return on investment. And it’s in three main areas that we are going to invest. The first is building out a larger worldwide sales and marketing effort, and we were able to achieve that 18.9 millionin revenue with between 12 and 15 salespeople people on our team last year. It’s relatively small, all things considered. And today actually our sales team is completely inundated and red lining on inbound interest for our technology. So there’s a huge capacity to scale that effort and just build more business that way. We’ve modeled 75 customers per year into our forecasts, but I want to add hundreds of customers every year given that there are tens of thousands of customers that we could potentially go out and win. And so there’s just an immediate and very clear way of building a bigger business and hitting our numbers that way.

The second is an increased investment in software development and actually the product and engineering teams that are developing value-added software solutions across our four verticals. This is something that Carl touched on and it’s one of the places that I’m very excited to be working with him on is how we build and expand the software side of our business. And, you know, ultimately, I think there is more opportunity to provide solutions in this space than even just the huge opportunity that is available for the hardware itself.

And it’s not a place that we focus in this presentation, but there is a monumental effort internally to build those solutions and Carl has been really integral in helping us lay out that strategy. And you’ll be hearing more about how we’re expanding the business in the solution space over the course of this year.

Now the final area to invest is just in accelerating the core product hardware roadmap that we have today. And again, this is based on taping out new chip sets that we swap into our existing products to accelerate or provide exponential feature and performance benefits to the end customer. Traditionally, this has been a two year cycle of building new chips into our products,

but with the new capital, we’re able to do that on a one-year cycle and just double the rate at which we improve the products in our pipeline. So very clear ROI from that perspective and most major semiconductor companies when they have the capacity to do so, move to a one-year model. That’s about as good as you can do. So very clear three places to use the, the, the proceeds that we’re generating from this merger. So I think with that, we’re going to move to a Q&A with Anna and I, and this is a general Q&A- all questions are welcome and we have Derek back on stage to monitor.

Derek Frome:

Great, well, let’s get right to it. The first question Angus, probably for you. Do you currently meet automotive specifications and what’s your automotive qualification timeline look like?

Angus Pacala:

Yeah. So, like I said, we are somewhat unique in committing to automotive certifying every product we ever make, all these products and any new product that we ever introduce. And that’s really because they all share the underlying components in the same digital LIDAR architecture, so we can qualify a lot of the, you know, the components just once and use them in all our products. So today actually our products hit many of the auto requirements in terms of mechanical reliability, environmental performance, quality standards, you know many of those specs are already hit. We recently announced an updated sensor product suite that has a standard two year warranty to kind of give you a proof point that we actually are doing this. It’s one of the best warranties in the industry now. And it just shows that we really are improving the quality and the reliability of these products as we’re testing to GMW 3172. Also a number of the functional safety features that are required by our automotive are already in these devices. So yeah, we’re well on our way to achieving full functional safety and product certification for all of our products. We see that happening by the end of 2022.

Derek Frome:

Great. And folks, as a reminder, you can drop a question into the chat box on your screen, and that will come through to me here, and we would love to answer your questions. So the next one is about costs. Angus, cost has been a headwind to adoption in automotive as you noted before with Tesla. What does Ouster’s cost curve look like?

Angus Pacala:

Well, we have shared our cost curve. It’s directly on the slides that actually Anna presented and you can see our COGS curve. And I guess today, well, the COGS curve is not perfectly indicative. It’s actually far from indicative of the underlying BOM cost of our sensors. And we have a capacity to enable customers with this technology now at price points that make economical sense for the vast majority of customers that we work with, you know, companies like Plus. So for most customers, even today, we have removed the economic barrier to adopting our technology. And so, yeah, but the COGS trajectory is in the slides, but it is not super indicative of the BOM costs, which we are not sharing due to just kind of competitive intelligence reasons.

Derek Frome:

Yeah, the obvious reasons. Okay, another automotive question. This one I think is really interesting. For automakers, you know, how do they source and when they’re sourcing LIDAR, what specifications are most important to them, or is it really kind of a broad range of specifications that are important, you have to fit all of them?

Angus Pacala:

Well, again, there’s really three different specs that every automaker is asking for: short-, medium- and long-range LIDAR. And all you’ve probably ever heard about is the long range LIDAR, which is 250, 250 meters range, 0.1 degree resolution to 0.05 degree resolution, and a 120 degree by 30 degree for horizontal and vertical field of view. And then, like, under 20 Watts of power, and 10 to 20 Hz frame rate. So that’s, like, the long range spec that probably everyone is familiar with, but it’s just one of the three specs that is required on these vehicles. And it’s the minority of the total LIDAR package that automakers are wanting to put on the vehicle. At this point, well, automakers want it all. They’re actually not gonna put, well, there’s two answers to the question. One is if the economics don’t make sense, they’re not going to put the LIDAR sensors onto the vehicle.

If a consumer cannot pay for the product, and this goes back to kind of Tesla and Elon Musk, it’s like, if you cannot find LIDAR sensors that are economical to put on a car, they will not go on the car. So ultimately price is dictating this market for, you know, full stop. But, beyond price it, yeah, basically automakers need every single spec that they’re pushing for met to, you know, within their bounds of 10% maybe. It’s a very demanding industry, but it’s not insurmountable if you actually look at the spec at this point, and you look at our products and our digital sensors, we’ll hit the price, we’ll hit the performance across all three of those sensor types. And I just don’t see the hitting performance is actually a real problem in this industry in the next couple of years. The technology has matured far enough.

Derek Frome:

Got it. This is an interesting one. Bloomberg reported on Friday that Apple was in talks with several LIDAR suppliers in regards to a rumored self-driving car. Can you comment if you received an RFP from them, or if you’re in talks with them, and if not, can you discuss broadly perhaps why a company like Apple may be able to leverage your digital LIDAR technology over some of your competitors?

Angus Pacala:

Sure. So I can’t comment on any specific customer like that, but thanks for asking. We actually have some interesting connections to Apple. Mark actually worked in the same group as some of the co-founders of some of the other companies in this industry. And I actually, originally before joining Ouster, I was going to go manage the entire group developing similar, you know, some technologies around some of this stuff. Mark was – I was going to be Mark’s boss. But we decided to work together instead. So we have some kind of familiarity with the Apple group from that perspective. The reason, I mean, first and foremost, the reason why Apple might work with us is because we actually have products and it’s really, you can’t work with us unless you have products to work with and they and so that would be one really good reason.

And then they’re also mature products. And they’re closely aligned with, I think, some of the ideas that Apple has adopted in their consumer brand, notably the iPhone has a digital LIDAR sensor in it today. And, hopefully, many of you are aware of that, but really interesting development, something that’s super exciting for me, there is a digital LIDAR sensor that looks just like a scaled down version of these optical modules here in every new iPhone. And so I think that they’ve realized that digital LIDAR sensors are the way of the future, and so that would be probably a pretty good reason why they might work with us. But again, I can’t comment on what actually is going on behind the scenes. Yeah.

Derek Frome:

Okay. Anna, this is perhaps a question for you. Can you talk about whether the recent commercial agreements were part of the original projections, what are the milestones and furthermore, kind of, what are the milestones and announcements that investors should be looking for as we move through 2021?

Anna Brunelle:

Yeah, I mean, I think that we’ve seen really great momentum in our business with the number of new customers coming into our funnel, exploring, using our products in their production of their own projects. And I think, as a result of that, since we showed you our slide deck about a quarter ago -it’s been posted on our website - we’ve seen improvement in our overall sales funnel. And so some of you may have picked up on that. In terms of the actual contracts that are converting, you know, we’ve had relationships with these people for a period of time, these companies, for a period of time to where they’ve been able to work with us and work with our technology and really get to know our business and we expect that we’ll continue to have additional customers converting through the funnel at the same kinds of rates that we’re seeing historically. And so, as a result of that, that’s really what our forecast is based on. We did, in our forecast, try to take a conservative stance and cap any one customer in our forecast from providing more than 5% of the revenue forecast in a particular year, and so we have done some things like that to employ conservatism. But yes, generally we have been expecting to see customers convert and they are converting and we’re very excited about the momentum in the business.

Derek Frome:

Great. Okay. This is a question about margins and selling to automotive. So typically, it’s been challenging to see gross margins of better than the mid-30s selling to automotive OEMs. What gives Ouster confidence that your gross margins can get into the 40s or higher for the company overall, Angus?

Angus Pacala:

Great, question. So actually, we are not projecting, we’re projecting 25% margins in auto. So this is a point of significant disagreement between Ouster and, I think, the rest of our peers who are projecting, you know, they’re exclusively automotive companies that, or positioned companies, that are projecting 50%, 60%, even I saw 80% margin expectation for an automotive LIDAR business. We just fundamentally disagree. We think that the auto part of our business is going to look like an auto company, and it’s going to have something around 25% margins is what we’ve modeled, and that it’s just completely unrealistic to expect anything higher than that, frankly. And so take that as you will. But the benefit that we have is that we operate diversely across our other end markets, and there are definitely higher margins, higher ASP’s in those other verticals that are boosting our blended ASP into the 50% and 60% blended region. So, we feel really great about our gross margin trajectory over time and have confidence in hitting kind of the 50%, 60% mark over time. But within auto, we, I think we’re realistic. It’s going to be 25%.

Derek Frome:

Great. Okay.

And this one perhaps Anna for you. Do the ASP’s included in your financial projections reflect actual sensor pricing, or is all revenue, including engineering revenue, getting divided by units?

Anna Brunelle:

The ASP’s in the forecast that we showed you earlier is hardware ASP’s only. It doesn’t include other revenue such as software revenue, which would then of course, boost the margins further.

Angus Pacala:

Yeah, yeah. And maybe this is a time to highlight. Our revenue comes, I mean the vast majority of our revenue, comes from products we sell. nd even the definition - so customers to us are, are companies that have paid us and we’ve delivered a sensor to them. So we’re not playing any games with, you know, partners or whatever. These are all of our customers. Those 500 companies have paid us, we’ve delivered them sensors. And that’s product-based revenue. So, that’s the basis of our business.

Derek Frome:

Got it. Okay. This question about the model, as it relates to the five LIDAR package that we just showed on the slide. So you show a five LIDAR package for a thousand dollars. In your model average ASPs are $1,706 and blended COGS are shown as $809, even in 2025. Does this mean that ASP’s or gross margins are higher outside of automotive? And can you achieve target margins at those automotive price points?

Angus Pacala:

Yeah, so I think we kind of already answered that question. We have just different margin expectations for the different verticals. We haven’t broken that out in the, you know, the blended ASP doesn’t break that out. And it’s really the blended ASP being at $800 is just because of the way that we’ve modeled a conservative timeline to high volume automotive. So it’s contributing a small part of the overall revenue by 2025. But that price point, that thousand dollar price point for five LIDAR sensors, absolutely what we’ve committed to, it’s completely achievable. It’s just a function of how we’ve conservatively modeled that industry. Again, it could, you know, it could be pulled in and that would be great for our business, if there’s faster adoption of the solid state sensors at that thousand dollar all-in price point, and it would change the blended ASP. But again, still would hit the margin numbers. Yeah.

Derek Frome:

Got it. Angus, this next question is for you,it iss about scalability and supply chain. So how does Ouster think about your global supply chain? And how do you think about, you know, can you support the scaling for the rapid increase in customers that you’re projecting?

Angus Pacala:

Absolutely. So there was a time when we were going through manufacturing hell. That time has passed. I know we had our own pain associated with ramping manufacturing. But at this point we’ve really hit our stride. Benchmark has been operating super effectively for multiple quarters now and, and really ramping their capacity. I don’t know if you saw, if you caught it in the video, but the floor area devoted to the line right now is a small fraction of the total floor area in that facility, in that floor of that facility. And these tools behind me are in the process of being built and shipped there to increase capacity further. So we’re just copying exactly what we already have there and shipping over more equipment. So I think, yeah, we have all the capacity we need to scale with the business. I don’t think there’s any risk there.

Derek Frome:

Quick follow-up question to that. Since you mentioned that Ouster went through a sort of manufacturing hell a couple of years ago, like this is actually a great follow-up question. Do you anticipate that some of your lower volume peers are going to go through a similar stage either now or in the future since they haven’t scaled to the same size that Ouster has?

Angus Pacala:

Yeah. Yeah. I mean, absolutely. It’s hard to manufacture products. Half of all of our engineers are just working on manufacturing technology, basically taking the technology that CMOS cameras have developed, and making it work for, for digital LIDAR sensors. Making a prototype is easy. Making products at scale is extremely hard by comparison. And we’ve had just a massive effort for years and years to actually get to this point. Mark and I never wanted to be the Silicon

Valley technologists that had some great ideas, but never could bring it to scale. And we brought on a core team of veterans that knew how to do manufacturing at all, and also knew how to offshore it and work with a great partner like Benchmark. And so we’ve had that team, that core group for over three years now- almost four years just at the company grinding away and getting this stuff working. So I’m very proud of that, yeah.

Derek Frome:

Absolutely. A question about supply chain, Angus. Have you been affected by the recent chip shortage and do you have sufficient supply chain to fulfill customer orders?

Angus Pacala:

We haven’t really been affected by the chip shortage. So one of the benefits of having our own custom chips is like the main components in our devices that we have control over that supply chain. And we can just buy, we buy a safety stock of these wafers basically, and the wafers just sit as safety stock, they have an infinite shelf life effectively, and we diced them up and put them in our products when needed. So we’ve had no issues with our supply chain as of yet. And I don’t really foresee it just because we have full-time supply chain staff that are constantly monitoring this and making sure that we never have any gap in our supply. It’s definitely something that we’ve had to manage for the balance of materials in the sensor, but it’s, it hasn’t disrupted the business and I don’t expect it to.

Derek Frome:

Yeah, got it. Maybe for you Anna can you talk more about how you define your pipeline? What is a customer and how do you define projected revenues?

Anna Brunelle:

Yeah, absolutely. I mean, I think Angus touched on earlier that we consider a customer, someone that we’ve shipped a LIDAR product to, and who is paying for it. So we don’t play any games or fool around with, you know, giving out free trial sensors and calling those customers, or promising a customer a product that is under development at some future date, and calling that a customer to us. When we say we had 500 customers in 2020, we’re talking about customers that we actually shipped a sensor to, that they either paid for, or, you know, are paying for it on credit within a very reasonable amount of time. In terms of building our forecast, that customer number, as I said, of those 500, about 200 have production intent, they’ve identified a project that they’re working on that they would like to use LIDAR for and start moving through our funnel. And so that is how we then, you know, built up that graph that I showed you earlier of the total sales opportunity from existing customers, as well as conservatively modeling 75 additional customers per year for new customers. And then that gets us to an 11 billion sales opportunity between now and 2025.

Derek Frome:

Great. I think this next one might be for really either of you. So, of the pipeline today, is there a way to think about how that breaks down between the, the short range OS-0, the mid range OS-1, the long range OS-2 products? Do we provide that breakdown?

Anna Brunelle:

We haven’t provided a breakdown. I mean, I think we’re selling to a very diverse set of customers across a very wide you know, wide number of use cases. And so, you know, we’re seeing our revenue forecast split pretty much equally between the United States, EMEA, and China, and you know, we’re seeing diversified use cases that are really using all of our products.

Derek Frome:

Got it. Oh, this is a great question. What’s the difference, for Angus. What is the difference between Apple’s digital LIDAR technology compared to Ouster’s, and how does that impact Ouster’s IP portfolio?

Angus Pacala:

Yeah, I figured that would come up when I mentioned that. So first of all, we were the original kind of. We filed all the original patents on digital LIDAR technology and the kind of the unlocking intellectual property and inventions that allowed us to build a digital LIDAR that is performance in a way that an Apple digital LIDAR is not. So the main difference is just an Apple digital LIDAR is a highly constrained device that’s going in the smartphone. It still uses a digital SOC and the pixel laser ray. So those two building blocks are the same, but there are a number of innovations to kind of the architectural level of the chips, the signal processing and algorithms that go on the chips. And then this wafer level micro optical system that gets layered and bonded to those two chips that actually unlocks orders of magnitude more performance than what goes into the Apple products.

And so all of that combined is a significant differentiator and IP from the Apple digital LIDAR product. So, there’s no question. I mean, yeah, they’re, they’re one in the same and I, you know, I’m happy about that. I want to see more digital LIDAR technology being developed because we benefit from it. Again, we do custom chips on standard processes, and if those standard processes get better because there are more consumer grade digital LIDARs that are being built, that’s good for us. But again, we have this differentiating IP on taking kind of a novelty sensor. That’s doing consumer entertainment, AR applications on the smartphone and making it a high performance, all weather, extremely long range, extremely high resolution sensor that can see in all environments. So yeah, there are additional innovations that separate the two.

Derek Frome:

Another question about the chips, Angus. How often do you envision bringing new chips to market in order to stay ahead of the competition? And what’s the cost for each new chip brought to market?

Angus Pacala:

We’re going to do new chips every year. That’s what we’re committed to as a company. And that’s, I mean, every premier semiconductor company does a new chip every year. And we’ve been able, yeah, we’ve been doing them every two years, basically because of capital constraints and just the size of the team constraints. We have a relatively small, or have had a relatively small engineering team. There’s two ways to answer the question about how much it costs to bring a chip to market. It’s expensive compared to like making a PCB, you know, it’s measured in the millions of dollars, not in the tens or hundreds of thousands of dollars of making, you know, kind of off the shelf, consumer electronics with off the shelf components. But it’s the only again, it’s the main, and it’s a reduced set of activity. So we unlock all that performance through just doing the chip. We don’t have to redevelop every part of the LIDAR sensor, which ultimately costs far more to do ground-up redesigns every year, then doing a ground-up redesign on just a chip. So I think overall it’s capital or op ex efficient. But a lot of the costs is then in the chip development itself. It’s measured in the millions of dollars.

Derek Frome:

Great. So the questions about the physical size of the sensors and thinking about automotive integration, perhaps integrating up to five, six, or however many chips into an automotive application and sort of concealing them in the body of a vehicle, what is the sort of smallest size you can imagine getting your sensor down to, and sort of, can you talk about the constraints of, of getting it to even smaller and smaller sizes?

Angus Pacala:

Yeah. So this is actually, this is the ES2, so this is the highest performance level of the solid state sensors that we’re releasing. And we haven’t really announced the full suite. I’ve talked a lot about the full suite, but the fact is we can make these arbitrarily smaller to make sure that they are very easily integratable all around the vehicle. So the capacity to scale these down for the shorter range, wider field of view applications, all around the vehicles there’s just a lot of it, it’s not hard to, to actually scale the technology down to hit those other those other price points and envelopes, which is, I mean, the Apple digital LIDAR is a great example of how small you can make the technology. It’s like a five by five millimeter package that they’ve shrunk into a smartphone obviously with a performance drawback there, but point is this can be as small as digital camera technology, and that’s what we intend to make it.

Derek Frome:

For you, Anna. Ouster increased customers in production from 9 to 20 since December. How much customer concentration do you see this year and next year?

Anna Brunelle:

Yeah, I mean, customer concentration is not, it just hasn’t been an issue for us because, as I keep mentioning, we have such a large total addressable market and, you know, hundreds of very diverse customers. And so, as I said, no, customer is representing more than 5% of our forecast.

Derek Frome:

Got it. Right.

Angus Pacala:

This is so different than our competitors.

Anna Brunelle:

The exact opposite. Yeah. It’s hard for people to understand.

Angus Pacala:

A lot of our competitors are one customer away from having zero revenue. That’s the way I think about it. They’re one customer away from having zero revenue. If they miss their target, they miss some spec on their product and it pisses off the customer, then it, you know, that’s gone. So I think this is a real benefit keeping we have this highly diversified business and we’re going to keep it that way.

Anna Brunelle:

Yeah. And I think also the fact that you were pointing out the numbers of how many people are moving through and converting in the funnel, those are new customers moving into production that we’re very excited about. And we have more momentum there than I think any of our peers.

Derek Frome:

Well, here’s a question about just that. Of the 500 customers you have, how should we think about the likelihood of seeing serial production agreements with those customers?

Anna Brunelle:

Yeah, I mean, I mean, we’re, we’re working towards signing multi-year agreements with customers wherever possible and in time as the LIDAR industry matures and as our business continues to grow, I’m sure that that will become more and more normal. As I say right now, we have 200 customers moving through our funnel into production and we’re signing agreements quite regularly, so we’re very excited about the momentum and we’re very happy with where we are today.

Angus Pacala:

Yeah. And I mean, on that note, we really should give ourselves more credit for this. There’s 20 customers in serial production today. Plus is a great example of a customer. I mean, it’s the scale of that agreement, it’s equal in size to a major automotive you know, automakers production run for a technology like this. Potential for hundreds of thousands of sensors over the next five years with binding, you know, with a major binding component. So, we’re in serial production with a number of, well with 20 customers at this point. And we’ve been, we’re going to just continue to have a steady beat of these being able to be announced.

Derek Frome:

Here’s sort of a good follow-up question. What is the Ouster team’s near term visibility into customer demand and how far out do customers provide visibility on their future orders?

Angus Pacala:

Sure. Well, the near-term visibility on demand – well, one way of saying this, we have we’re inundated with inbound interest right now. Our sales team is completely a red lining on just the inbound interest, which is one of the great things about this SPAC merger process is just our ability to get out there and be more visible and more credible as a company. On the visibility of current customers and their demand forecasts, this is core to how we forecast the business in general, is the bottom’s up customer information that we get from every customer on their own expectations of demand over time. So we have a lot of visibility across our customer set on what they expect to buy from us, because we asked them. This is part of how we engage with customers is we negotiate pricing and we, in tandem with the demand forecast that they provide us, so that we can better model the company and be more repeatable and just have better visibility into the long-term. And it doesn’t mean that every customer gives us a five-year forecast. Some, we try to make sure that they at least give us a three-year forecast and a lot of them give us a five-year forecast, but it’s always, multi-year.

Derek Frome:

Got it. Angus for you. What are your software plans and how big is that opportunity?

Angus Pacala:

Great question. So I touched on this a little bit. We have big plans for software. There’s an inherent need for software solutions across a broad swath of our customer base. Ultimately there is more opportunity and more value to be created from the solutions that we provide on top of the hardware than on the hardware itself. And that’s not to diminish the opportunity for the hardware in any way, shape or form. It’s just, there’s so much opportunity. Given that every one of our industries is effectively a legacy industry that is now adopting a completely new autonomous technology set, and that inherently opens up a massive opportunity for new companies like us to, to move in. So we’re going to be telling this story over the course of this year. Like, we don’t have time to go into the full strategy around what we’re doing.

And honestly, the strategy differs by vertical. We have very different dynamics in industrial and smart infrastructure, robotics, and automotive, and the products that we’re going to deliver in those verticals varies quite significantly. And really this capital is allowing us to build specific teams to go after the biggest opportunities in each one of these industries. It’s not like we’re going to have one team building one solution that somehow applies to everyone here. There’s a lot more specificity on what the product is that we’re delivering the total solution into the end market. So stay tuned, I guess.

Derek Frome:

Stay tuned. Okay. Here’s a question about the data output of the sensor. Do Ouster LIDAR sensors have any unique advantages for machine learning, perception, or SLAM?

Angus Pacala:

I can take that one. The answer is, yes. We do have unique advantages for the algorithm side. But first and foremost, in order to enter this market at all, you have to perfectly, you have to build a sensor that is as capable as the sensors already in the market. That’s something we really took to heart. And so our sensors can output great quality point clouds, just like the incumbent sensors that came before it. You have to be in that position to play in the industry, but we have also, because of the advantage of the Silicon to kind of absorb new complexity and features, we’ve been able to push unique capabilities into the Silicon that other LIDARS don’t have. One example of this is just our ability to produce ambient camera like images from the LIDAR sensor. So we actually, every one of our sensors can sense sunlight, I mean, just like a camera and produce a camera, a monochromatic camera image, which is a great input for machine learning. And we see customers starting to adopt these unique data layers that are output by our device. And that creates a stickier customer as they incorporate that into their, their algorithmic set.

Derek Frome:

One more question about software. When you model your sales pipeline is software part of the sensor sale, or is it a separate line item? Will we provide a breakout for those?

Anna Brunelle:

We will. We’ll be required to. Once software is more than 10% of your revenue you’re required to report it separately for GAAP accounting, so there will be guidance on that going forward as the software business grows.

Derek Frome:

Great. okay. And are you looking into national security or defense customers, Angus?

Angus Pacala:

Yeah, we actually have, we debated where to put defense customers, in the four verticals because they actually span defense could be its own vertical, but you can think of them as almost robotics or smart infrastructure. We have a number of defense customers already today. It could be a potentially huge opportunity for us. I think we’re pretty well positioned there. We have really robust, reliable sensors, which is important for, for mil-spec applications. We actually do test to some mil-spec standards just for our defense customers. So yeah, I think we’re actually pretty well positioned there. It’s upside for the business. And, so I guess we’ll see.

Derek Frome:

Great. Okay. Here’s a question about cold weather performance. If a car or a truck with your product sits outside on a cold and icy winter day — I’m assuming this question comes from the East coast — where the products are covered with snow or ice overnight, will the product still perform okay when you fire up the car?

Angus Pacala:

Yeah, so Silicon is really good at cold temperatures and we have actually introduced the best- in-class cold weather performance in our sensors. So we meet the full envelope of cold weather performance for automotive specifications, which is negative 40°C operation. So our products will start up and operate just fine and negative 40° =C temperatures, which I think negative 40°C is, is the same in Fahrenheit. So it’s really cold and that’s one of the unique advantages. We have a lot of customers working in cold climates. Some of our mining customers are operating in mines in Finland and Scandinavia and Siberia. And so we’ve seen a lot of traction in cold weather climates. If there’s snow or ice on the sensor window, you do need a defrosting system. Like any automotive sensor, actually, has defrosting systems that go along with the actual sensor. So depending on the climate, you need a defroster, but that’s it.

Derek Frome:

Okay. Question about a specific competitor. Angus, you mentioned Velodyne earlier in the presentation. How is your technology better or how does it compare with them in automotive applications?

Angus Pacala:

Yeah, sure. Well, Velodyne sells a number of different products they’re most known for their analog spinning sensors. And there, there’s a very clear difference in technology. I mean, their analog sensors have hundreds or thousands of duplicated parts, all strung out on complex circuit boards. They talk about kind of micro, electronic integration, but it’s still at the end of the day, hundreds or thousands of microelectronics just strung out on circuit boards. It’s super complex and costly. And we just have a fundamental, different approach with digital technology. It’s way simpler, it’s way cheaper. We have really competitive pricing against their products. They also produce some MEMS-scanning products. I haven’t really seen those kind of gain market tracking traction yet. So there though, if their MEMS-scanning products like the Velarray ever gain traction they would be mostly competing with our complete solid state devices. And I think we have the inherent advantage there both in simplicity of the product, but also robustness of the product given it’s fully solid state versus a MEMS based scanning system.

Derek Frome:

Okay. What is preventing your competitors from accessing the other LIDAR market sectors outside of automotive?

Angus Pacala:

This goes back, I mean, there are a number of different reasons. It depends on which competitor we’re talking about, why they can’t access the other sectors, but there are rarely, I mean, digital CMOS is really one of the only hardware technologies that can play across markets, and it’s proven it can play across markets, because it hits a super set of the of the product requirements that are needed across all markets. Again, it follows that efficiency approach to performance that allows CMOS devices to be the smallest, the lowest cost, the most power efficient, the lightest weight, and the most robust and most performance sensors across all industries, which makes them applicable to all industries. And so it’s very, I think if you just look at competitive products, none of them are nearly as small as these products on the table here, which are real products by the way. And we’ve shipped thousands of these units worldwide versus kind of even prototype sensors from our competitors are much larger than these. And you need to hit a super set of requirements in order to play across this broad array of, of end markets.

Derek Frome:

Right. I think we have time for two more questions. So second to last question. Angus, how is your product different or better than Luminar sensors?

Angus Pascal:

Different or better? Well, this is analog versus digital. This is efficient versus brute force. So if you just want to go spec for spec, we’re smaller, we’re more power efficient, lighter, we’re cheaper, we’re higher resolution and we output more, like, unique data layers and work. Yeah, I think, and we’re mature. The only thing they still have us with is range. And again, that goes back to the brute force versus efficiency approach. They’ve exchanged kind of short-term gains in range for locking their system into an analog or exotic semiconductor space that I think is ultimately, well, it’s not the trade we’ve made. You know, you’ve heard our reasons for why we’re going this route. And I think we’ll be able to eclipse or hit the range spec that they’re hitting in quite short order.

Derek Frome:

Okay, and our last question, open-ended. What is the single most exciting use of your technology that you foresee in the near future?

Angus Frome:

We should both answer this question. We should, both. We debated putting up, there’s some really interesting, like there’s a museum that just sent us some pictures. That’s like this interactive, interactive digital art display. We decided not to show that because it’s not going to be volume. Not a volume opportunity. For me, I’m excited about any time big, powerful machines are doing safe, productive work with our sensors. That’s, what’s so cool about this business is like, an automated semi is cool, but so is an automated dump truck or a huge automated gantry crane, or maybe it’s not even automated, but it’s just a fundamentally safer operation because it’s augmenting a human operator and keeping an eye on what they’re doing to prevent injury or a workplace accident. So, I’m excited by the broad array of applications that are big power, powerful machines doing productive work in the real world.

Anna Brunelle:

Yeah. I mean, I would have agreed with Angus. He, he kind of took my favorite, but I would just perhaps add to that, that I think if you think about it from the people side of things, I mean, if you are working in these dangerous environments and have LIDAR kind of protecting you, I think there’s a human interest or a human element to what we’re doing, that’s really important, and that is ultimately going to change the world. And so I think taking the kind of tactical things that Angus spoke about and just layering on top of that, how it improves lives and improves our culture. I think that’s really exciting.

Derek Frome:

So Angus, over to you to wrap it up.

Angus Pacala:

Great. Yeah. Well, thank you everyone for joining in that, that concludes the presentation for today. Hopefully it was informative and thanks for all the guest speakers that showed up today. Everyone have a good day. Cheers.