This conference call transcript was computer generated and almost certianly contains errors. This transcript is provided for information purposes only.EarningsCall, LLC makes no representation about the accuracy of the aforementioned transcript, and you are cautioned not to place undue reliance on the information provided by the transcript.
spk07: Good afternoon. My name is Vaishnavi, and I will be your conference operator today. At this time, I would like to welcome everyone to AI's fourth quarter and full year 2021 earnings conference call and webcast. All participant lines have been placed in a listen-only mode. Should you need assistance, please signal a conference specialist by pressing the star key followed by zero. Joining us on the call today will be Blair LaCourte, Chief Executive Officer and Bob Brown, Chief Financial Officer. Opening remarks by AI management will be followed by a question and answer session. To ask a question, you may press star, then one on a touchtone phone. To withdraw your question, please press star, then two. Please note this event is being recorded. I'll now turn the call over to AI.
spk01: Thanks, and welcome everyone to AI's fourth quarter and full year 2021 earnings call. With me today are Blair LeCourt, our Chief Executive Officer, and Bob Brown, our Chief Financial Officer. Earlier today, we announced our financial results for the fourth quarter and full year 2021. A copy of our press release can be found on our website at investors.aey.ai. Before we start, I would like to remind participants that during this call, management may make forward-looking statements including, without limitation, statements regarding our future performance, growth strategy and financial outlook. Forward-looking statements are based on our current expectations and assumptions regarding our business, the industry and other conditions. These forward-looking statements are subject to inherent risks, uncertainties and changes in circumstances that are difficult or impossible to predict. Our actual results may differ materially from those contemplated by the forward-looking statements. We caution you, therefore, against placing undue reliance on any of these forward-looking statements. You can find more information about the risks, uncertainties, and other factors in our reports filed from time to time with the Securities and Exchange Commission, including in our annual report on Form 10-K for the year ended December 31, 2021. all information discussed today is as of march twenty eighth twenty twenty two and we do not intend and undertake no obligation to update any forward-looking statements whether as a result of new information future developments or otherwise except as may be required by law In addition, today's discussion will include references to certain non-GAAP financial measures. These non-GAAP measures are presented for supplemental informational purposes only and should not be considered a substitute for financial information presented in accordance with GAAP. A reconciliation of these measures to the most directly comparable GAAP measures is available in our press release, and you should refer to our reconciliations of non-GAAP financial measures to the most directly comparable GAAP measure in our earnings release. With that, I'll pass it over to Blair.
spk05: Thank you for joining us today for AI's quarterly investor update. For those of you I have not met before, my name is Blair LaCourte and I am the CEO of AI. Joining me here today is my CFO, Bob Brown. This is our second public earnings call and our first full year report. We are very excited to share our progress here today. First, we're going to provide some perspective on how the LIDAR market has developed and where we believe we are in the cycle of commercialization. I will then touch on our progress in 2021, our key investment themes, and highlight AI's business model and product differentiation. Bob will then take us through our Q4 and full year 2021 financials. He will share a more in-depth review of 2021 accomplishments, as well as our financial projections for 2022. I will finish by highlighting two key culture initiatives and this year's strategic objectives. We will then transition to questions. As some of you may know, LiDAR is not a new technology. It was invented in the 1960s for specialized applications because of its deterministic capabilities in measuring the exact distance to an object with higher resolution, especially where other existing interpretive sensors like radars and cameras may be at a disadvantage. For the next 40 years, LiDAR was used to collect and transfer information in complex spatial environments. In the early 2000s, governments around the world began to look for ways to transition this technology into broader commercial markets. For example, the DARPA Grand Challenge was extremely successful in spawning over 85 LiDAR companies to implement sensors into automotive and the wider industrial marketplace. Fast forward to 2021, and as with most foundational technology transitions, not only have the markets expanded, but the original 85 plus privately financed companies led to eight IPOs in 2021, which raised a significant amount of public capital. We believe AI is now one of two companies with the capabilities to scale from simple to ultra-high performance applications. We also believe we are the only company with an adaptive, software-based sensor platform that can be customized across markets. Most importantly, AI's software-defined platform is well positioned to benefit from the trends and high demand for smart assets and software-definable vehicles. 2021 was a very productive year at AI. The dedication and high level execution of our team allowed us to deliver on plan with strong performance on many fronts. AI's focus was on productization, as well as building and enhancing the company's infrastructure and human capital to enable scalability. We finalized the next generation of our products, which are designed for manufacturability. We also began the transition to volume production with the announcement of collaborations in both industrial and automotive manufacturing. We secured world-class partners to help us bring this technology to market, and we engaged in key pilots in all of our strategic segments. We intend to take this momentum and continue to execute on our path to scale and profitability. Let's now walk through our investment themes. First and foremost, we are an automation company. Our technology acquires and processes high quality spatial information to allow assets or vehicles to make better decisions when a human is not engaged. In short, we enable automation on demand across many markets. Second, we have a unique business model that leverages the existing global value chains to increase innovation and drive the velocity of adoption. In the industrial markets, we sell software configured solutions, both direct to customers and in partnership with systems integrators. In the high volume automotive ADAS markets, we license our intelligence software platform to global tier one automotive suppliers for scalability. Third, we have built our products and business model on top of a disruptive software platform that resides on the sensor. This platform allows our LiDAR sensors to adapt to their environment or use cases and to deliver the right information at the right time to enable autonomous features and functions. By enabling smart assets and software-definable cars to be more intelligent in how they collect and integrate information, AI is enabling new business models for our customers. Fourth, AI utilizes capital light manufacturing and the existing global automotive supply chain for cost optimization and scalability. We believe we're the only company that has multiple global manufacturers investing in building out high volume production facilities. Finally, you can bet on a plan, but what you're ultimately betting on is a team with integrity and discipline that has a long track record of success. Now, we're going to shift gears, excuse the pun, and take a quick dive into our first three investment themes. First, high-quality information, where I will give you a preview of our new Foresight M product. Second, our unique business model, where we will hear from one of our partners and one of our customers on how they evaluated the LiDAR marketplace and why they chose AI over our peers. And third, an overview of our disruptive software platform, where we will share several videos of some remarkable new capabilities that have never been shown before. Then I will transition to Bob Brown, our CFO, to review the financials. So now let's expand on our first theme by explaining what we mean by delivering the highest quality spatial information by sharing with you for the first time the remarkable performance from our latest Foresight M platform. As many of you know, LiDAR is a deterministic sensor that provides resolution at range to accurately see and navigate around small obstacles at speed. even in poor conditions. We believe while many sensors can assist the driver, only high performance LiDAR in conjunction with other sensors can replace the driver and power autonomy on demand or full autonomy. As you can see, AI's 1550 nanometer LiDAR gets a significant amount more resolution than 4D radar. and it also detects pedestrians even when a high-end HD camera may not have the appropriate lighting conditions. Later in this presentation, we will also show these Foresight M capabilities at industry-leading range of over 500 meters. When architecting our solution, we took some cues from human biomimicry. We believe the quality of information is always heightened by the integration of multiple senses. So while we do believe LiDAR offers a unique value, our platform was designed, when appropriate, to also seamlessly integrate with onboard radars and cameras. Later in the presentation, you will also hear how Continental not only intends to sell our LiDAR technology as a standalone product, but also has recently integrated it into a system with their radar, cameras, and ADCUs. To demonstrate the value of our system's design and flexibility, we took a quick fly-through of our office and one of our employees. Sorry, Joel. To illustrate the power of our architecture and how the integration of multiple data sources together creates higher quality information for autonomous decision-making. While this may look like a video, it is actually a 3D LiDAR image that has RGB data fused at the point of acquisition. This is to further illustrate our point that is all about information and a network of sensors that increases the quality of that information. In this example, the LiDAR provides absolute deterministic resolution at range through its point cloud. but also uses the platform to integrate color information, which can be extremely helpful when navigating through environments that were designed for humans who use embedded color cues. For example, is it a white line or a yellow line? Let's transition to how our business model and how our partners are leveraging this platform to drive the adoption of LiDAR. We looked at autonomy across multiple markets and realized that if information is needed to be integrated to make decisions, we should evaluate how to work with the leaders who integrate information in each market. You can see here some of our announced partners, including Intetra, Komatsu, Hitachi, Mitsubishi, 2Simple, and Continental. Our partners are able to leverage AI's intelligence sensing platform to deliver specific applications to their customers. Why is this important? Because it shows the power of software that we can customize our sensing platform to meet the unique needs of our customers. Our partners have the ability to use their domain expertise to build impactful solutions faster. As an example, in industrial, Hanbin Lee, CEO of Seoul Robotics, will talk to us from Korea about how his company is leveraging our Foresight M product. He will explain how our software definability and high performance capabilities truly differentiate AI. Hanbin?
spk00: Thanks, Blair. Hi, I am Hanbin Lee, founder and CEO of Saw Robotics. I'm happy to be able to tell you a little about how AI and Saw Robotics have developed a strong and mutually beneficial partnership. We started working on LiDAR perception software in 2017, and since then, We've successfully developed and deployed end-to-end solutions at factories, airports, intersections, and many other domains. Over these five years, we've gotten a chance to work with every major LIDAR company in the world, and we have never seen the fidelity of data that we are seeing from AI today. By optimizing performance modes on AI's software-definable foresight architecture, we can take a customer-centric approach and change the field of view as well as scanning behavior to always maintain very high point density for specific use cases. This makes the data much more meaningful in terms of perception capabilities, and today we are showing something that's never been done before. Using a mode optimized for highway monitoring, AI and solar robotics are tracking all vehicles including small cars and large trucks up to 500 meters, enabling a paradigm shift for the world of smart infrastructure to tackle interstates all around the world. We will be improving incident detection, optimize traffic flow, and reduce downtime caused by lane obstruction for all municipalities. So we are very excited to pursue future opportunities together with AI. Back to you, Blair.
spk05: Thanks, Hanbin. I would also like to publicly state I did not require him to wear a Boston Red Sox hat, but I do appreciate the gesture. Now let's talk about our business model with one of our customers in the highly regulated, high volume automotive ADAS and autonomous driving markets. Note again, we use the same sensing platform as we do for industrial markets, but we license it to tier one automotive suppliers who build, manufacture, and distribute their own products based on our architecture for their large installed base of OEM customers. This model delivers AI a high margin licensing stream with considerable scalability. Joining us from Continental, one of the largest automotive companies in the world, who is licensing our software platform to make their own product, is my good friend, Gunnar Juergens, to give us an update on how our joint product is progressing. Gunnar?
spk02: Thank you, Blair. Thank you for the opportunity to say a few words on your quarterly update call. Actually, the collaboration between Continental and AI is really gaining momentum. We are making significant progress both on the industrialization of the product and in building the business pipeline. With a history of more than 20 years and selling more than 20 million LiDAR sensors to the automotive markets, Continental is actually uniquely positioned to anticipate the LiDAR needs of the markets. And what we believe is that a long-range, high-performance LiDAR is really the missing piece of the puzzle to enable safe, hands-free, eyes-off, autonomous driving, both on the highways as well as in urban scenarios. And this technology is what we are selling together with AI, both as a standalone sensor as well as part of a full-stack solution together with our short-range LiDARs, radar, camera, ADCU, and software. Let me show you. This is actually the product called HRL131 that we are delivering to the markets together with AI. This is currently being produced as a B sample in our continental facilities. And on the business side, we are already engaged with numerous customer acquisition processes. And we have actually shown this B sample in a live demonstration in a private customer event in our facilities in Auburn Hills and Michigan two weeks ago. On the collaboration side, I must say we're very happy and it feels like it's a seamless collaboration between the teams of AI and Continental. And together, I feel we are progressing to our shared vision of delivering cost-effective, reliable, and a safe solution both to ADAS and to autonomous driving.
spk05: Thanks, Gunnar. So let's talk about our third investment theme, our differentiated technology, and how our adaptive software platform adds unique value to our partners and customers. I would like to start with a simple diagram and then show you three videos demonstrating some of the remarkable capabilities that have never been shown before. On the left of your screen, you can see an illustration of how many traditional passive LiDARs work. They collect data in a fixed serial manner, which they then pass this detection data to a perception engine. It's a one-way flow of data from a sensor into an application software layer. The idea being, collect as much raw data as possible and turn it into information later. In high speed or complex environments, this can be a challenge. On the right, you will see that we took a very different path by building a software platform on the sensor, which gives us several unique capabilities. The first advantage is that we can now control hardware individually using software to change the way the sensor works, depending on different environments and or use cases. Second, we've added feedback loops between our perception and application software and the sensor software platform. This two-way communication capability allows the applications to ask the sensor for the information they need and ultimately enhances the quality of the information. Third, on the right side of this illustration, you can see how Foresight M doesn't silo itself from other sensors. We can use maps, cameras, radars, and IMUs to trigger the LiDAR so we can be more intelligent and efficient when collecting critical information. Finally, this software-defined architecture is natively compatible to enable OTA, so we can change the way the hardware works through software updates, allowing our customers in the future the capability to upgrade to completely new features and functionality. In this first video, I promised we would show you something remarkable. Our adaptive LiDAR will use multiple performance modes to allow the vehicle to be situationally aware and to adjust depending on the application, environmental conditions, or the maneuver the vehicle is trying to perform. I would like you to focus your attention on the icons in the middle of the screen. This will show you how the car is changing the scan patterns to support different driving scenarios. When in urban mode, range is reduced as resolution is increased on the sides to detect lateral entry of pedestrians or other vehicles entering your path. During lane changing mode, it puts more resolution on the adjacent lanes. Finally, as we shift into highway mode, where speed is increased, the lighter extends the range and the density on the road surface to look for small objects to potentially avoid. What you see at the bottom of the screen is the traditional point cloud that a computer uses. I hope this was helpful to understand how our adaptive sensing system differs from many passive systems, how it can provide higher quality information to allow a vehicle to interpret the world and make better decisions. In the second video, we're going to show you how adaptive LiDAR can capture either radial or lateral velocity depending on its defined use case. If you focus your attention on the top right of the screen, you can see how radial velocity information is used to feed a traffic flow application. The view on the left side of the screen is demonstrating how the system can modulate performance modes depending on use cases, such as short-range tolling versus long-range traffic congestion and monitoring. Finally, at the bottom of the screen, the system provides both lighter intensity and camera data for object classification. In this third and final video, we demonstrate how feedback loops between the perception engine and the adaptive platform can distribute extra density on key objects. I would like you to focus on the crosswalk when the light is red and how the system will apply more focus to those areas to ensure pedestrian safety. Note when the light is green, more density is also applied to cars passing through the intersection. This demonstrates how we can improve pedestrian safety in congested urban and suburban areas. As many of you know, pedestrian deaths have grown dramatically over the past two years, given drivers' dependence on passive safety systems. Now let's move on from technology to finances. I am going to hand it over to our CFO, Bob Brown, and then I will come back to wrap up our presentation with an update on some of our governance and cultural initiatives. Bob?
spk03: Thanks, Blair, and good afternoon, everyone. As Blair mentioned, 2021 was an exciting year for our team. We achieved several significant milestones, both in the fourth quarter and over the full year. I want to echo the feeling of encouragement with the progress we made during the year, both from an operations and execution standpoint. We successfully completed the process of becoming a public company in August by listing on NASDAQ as LIDR and raising over $200 million net of fees. We also eliminated over $50 million of outstanding debt at the closing of the transaction. In addition, we announced a $125 million common stock purchase agreement in December, providing us with access to additional liquidity. Turning to our products, prior to our IPO, we established a goal to clearly and measurably demonstrate the performance advantages of AI's LiDAR technology in 2021. I'm pleased to share that we successfully achieved that target and established LiDAR performance standards for range, speed, and resolution in third-party tests. Importantly, we also validated our superior weather performance. We received initial automotive bee samples from the Continental line and remain on schedule for start of production in 2024. Meanwhile, we began initial production of our Foresight-M LiDAR sensor at Sanmina, our contract manufacturing partner. The Foresight-M will be sold into industrial markets. In the area of customers and partners, we demonstrated strong commercial traction with leading players in the market. We're pleased to have developed important relationships with key ecosystem players, including with Continental in ADAS, TuSimple in Trucking, NVIDIA in Mobility, Komatsu in Construction, Hitachi in Rail, and Mitsubishi, Econolite, Intetra, and Sol Robotics, all in support of our recent ITS launch. With respect to governance, our goal was to establish a strong board of directors to represent our shareholders. Each of the individuals that were recently elected to our board contributes significant knowledge and industry expertise that will allow us to drive future shareholder value. During 2021, a key focus for our management team was to scale the company. We succeeded in those efforts and doubled our employee base, and we expanded internationally in Germany, Japan, and South Korea. Finally, we had a strong year in innovation, including reaching a milestone of over 100 total patents filed. Now let me turn to our financial performance during 2021. Revenue for the fourth quarter of $1.8 million was up by over 1,320% compared to the third quarter of 2021. Revenue for the full year of $3 million was up 90% compared to the prior year. The growth in revenue for the quarter and full year was driven by higher prototype sales and an increase in revenue from development contracts. Now turning to more information on the fourth quarter, net loss was $25 million on a GAAP basis and GAAP EPS was a loss of 16 cents. The loss for the quarter was driven by operating expenses of $23 million on a GAAP basis. Operating expenses increased from $17 million on a gap basis in the prior quarter due to increases in headcount as we scale our business, a full quarter of public company costs, and a bonus accrual for 2021 that was approved in the fourth quarter. We continue to invest in advancing our disruptive technology and building out our team to support the growth opportunities that we see in front of us. Net loss for the fourth quarter was $20 million on a non-GAAP basis, and non-GAAP EPS was a loss of 13 cents. We had access to total cash and available liquidity of $289 million at year-end 2021, which includes $164 million of cash, cash equivalents, and marketable securities on our balance sheet, and access to up to $125 million of cash through the common stock purchase agreement that we announced in December. Looking at the full year in more detail, we generated revenue of $3 million during 2021, up 90% compared to $1.6 million in fiscal 2020. Net loss for 2021 was $65 million on a GAAP basis, and GAAP EPS was a loss of 60 cents. Net loss for 2021 was $54 million on a non-GAAP basis, and non-GAAP EPS was a loss of 49 cents. Now let me discuss our guidance for 2022. We're targeting a revenue range of $4 million to $6 million this year. We expect revenue growth to come from development contract revenue primarily related to our automotive business and product sales in our industrial business following the production ramp of our Foresight M LiDAR starting mid-year. We expect that revenue in the first half of 2022 will be lighter prior to that mid-year production ramp, so the majority of our 2022 revenue is targeted for the second half of the year. Non-GAAP net loss for 2022, which excludes stock-based compensation expense, is expected to be approximately $100 million. This reflects the expansion of our high-caliber team and investments to support the growth opportunities driven by our Foresight Intelligent Sensing platform. Our licensing model in the automotive market and contract manufacturing relationships in the industrial market helps us minimize capital expenditures. As a result, we're expecting CapEx to be in the range of $3 million to $4 million for 2022. I'm pleased with our team's performance during 2021. We executed well against our strategic milestones, and we look forward to sharing further progress against our financial, commercial, and technical objectives in the coming quarters. With that, I'll pass it back to Blair to wrap things up before we open the line for questions.
spk05: Thanks, Bob. I'd like to touch briefly on our people and culture, two things that feed AI success as a company. We've previously discussed the depth and breadth of our management team and board, and we continue to add world-class governance, bringing on industry leaders with deep experience and success building and managing publicly traded companies. The latest addition to our board is former Apple global marketing executive, Sue Zeifman. Sue has led global marketing and communications for some of the world's best known technology and consumer brands. She has been working with us on our advisory board for the last year, and I'd like to welcome her onto AI's board of directors. In addition to investing in our people, we are deeply committed to impacting our local and global community for good. With the devastation happening in Ukraine, we felt compelled to act. Many people are not aware that there are over 2,000 high school, college and graduate students in the United States today from Ukraine who may not have a place to return over summer break. AI's management team and employees are committed to making a difference by creating internships for these students. We are announcing today that we have launched a formal internship program and partnered with UC Berkeley to provide two students with summer internships, one in business and the other in engineering, to ensure their continued education and financial stability while they are unable to return home. We will also be challenging other companies to follow our lead and make a difference in students' lives. In addition, many are not aware, as the security level threats have risen in the world today, many of our special operators have been deployed in both support and defensive positions around the world. This impacts not only the brave men and women who serve, but also their families, who will be without a parent while they're deployed. AI employees have made a commitment to support the Navy SEALs and the SEAL Kids organization, who provide tutoring, after-school sports activities, and emergency medical expenses. We are proud of our people and culture, and as I transition to talking about 2022 objectives, I would be remiss not to mention the recent recognition we have received for our leadership and innovation by our peers, customers, and industry analysts. In 2022, we've already been recognized by two of the most prestigious organizations, the Consumer Electronics Show, CES, and by Fast Company Magazine for producing innovative products that will change the world. I would like to personally thank our employees for their hard work and dedication and congratulate them on this recognition. In conclusion, I want to leave you with a preview of AI's focus for 2022. We will be, in addition, giving specific updates on these themes in each of our quarterly earnings calls. 2022 for AI is all about execution into market. As you have seen with our preview of the Foresight M product, we are completing commercialization of our products for both industrial and automotive segments based on a revolutionary shared architecture. In addition, we will be transitioning the production of our Foresight M industrial product to our contract manufacturer, Samina, and transferring production of Continental's HRL 131 for automotive to their world-class manufacturing facility in Germany. As we launch our newest Foresight M products this year, we also intend to announce our roadmap for our first set of lower price product extensions. We will also be expanding and accelerating partnerships, pilots, and programs throughout the year. As always, we intend to perform against our financial targets and continue to invest in our people and in creating a team-oriented and winning culture. Thank you for investing your time today. Now let's open it up for questions. Operator?
spk07: We will now begin the question and answer session. To ask a question, you may press star, then one on your touchtone phone. If you are using a speakerphone, please pick up your handset before pressing the keys. If at any time your question has been addressed and you would like to withdraw your question, please press star then two. At this time, we will pause momentarily to assemble our roster. Our first question comes from Joseph Osha with Guggenheim Partners. Please go ahead.
spk06: Hello, this is actually Hillary on for Joe, and I just wanted to first touch on the mobility and industrial side of the business. And we've obviously seen quite a few exciting announcements there, and it was really great to hear from Soul Robotics this evening. I was just wondering if you could provide any comments in regards to conversations you're having with other players in those markets, perhaps highlight any areas that are particularly of interest. and any guidance in terms of when we might see additional announcements there.
spk05: Sure. In the industrial mobility markets, you can see from our highlight of Hanbin that the ITS market has picked up in the U.S. and actually globally, smart cities and ITS applications. In fact, there's a tremendous, a record-breaking amount of funding actually in the next federal budget, the transportation bill. So that's one area we see that's moving very, very quickly. Another is trucking, where the highway-based hub-to-hub applications are ROI-driven and that they're actually accelerating in time and speed. The third is in aerospace and defense, where now a lot of these technologies that came from aerospace are actually coming back. They're extremely excited to have a commercial off-the-shelf product that they can buy, that they can also adapt and expand on, given that the sensor is actually a platform for software programs. So those are the big areas. Now, we're doing pilots beyond that, but I'd say that's our emphasis for this year.
spk06: Okay, great. And then second, I just wanted to touch on the equity financing that you guys announced and just wondering if you can kind of share strategically how you're thinking about drawing that down and if we'll see it kind of, you know, evenly over the next couple of years or perhaps see you kind of manage to a cash number. And that's it for me. Thank you.
spk04: Yeah. Hi, Hillary. It's Bob. So as we mentioned in the prepared remarks, we've got about $289 million of total liquidity at 1231. So that includes about $164 million of cash and marketable securities and then access to $125 million, as you noted, through the common stock purchase agreement we announced in December. So we feel like we've got a very strong partner with that facility with Tumumstone, which is the counterparty there. And we have a strategy for accessing that facility at the appropriate time. We don't have specifics that we want to lay out in terms of timing today. But we do have three years to use that facility. And we feel comfortable that we'll be able to access it when and if we decide to do so.
spk06: Great. Thank you.
spk07: Our next question comes from Suji De Silva with Roth Capital. Please go ahead.
spk10: Hi, Blair. Hi, Bob. Congrats on the progress here. So I want to get a sense on the automotive of Continental, the B-sample. I appreciate having a speaker on the call. Whether the B-sample is customer-specific or it's Continental still, and then C-sample, if that comes next, goes to more customer-specific programs, and what's the timing of the transition toward that versus the current B-sample industry?
spk05: Sure. Thank you, Suji. It's actually a great question and goes to the heart of our business model with tier ones. The product, the technology that they've licensed, as you know, the Foresight platform, software platform and hardware designs are specifically combined to be one product for Continental so that it can run it on a high volume line for multiple customers to actually drive down both the component cost and the manufacturing cost. So they have the capability using the platform to both integrate it after the fact, which they've already done with custom system configurations like radar and camera and ADCUs. They also have the capability to open up the software and to make custom adjustments for the OEMs specifically. So what we heard from them is, Look, we want one system, but we need to open the system up so that the hardware manufacturing can be done on one line. And with your software-definable hardware components, we can go back in after the fact, customer by customer, and tweak how we want either the hardware to perform or what features or integrations we want to actually add after the fact. Does that answer your question?
spk10: Yes, it gives me some color insight in there. Thank you. And then perhaps for Bob, on the revenue guidance, just to help understand, are there production non-auto units in the second half? Is it prototype units versus the NRE? Any color on kind of one click down from what you gave would be helpful, Bob.
spk04: Yeah, you bet. As we mentioned in the prepared remarks, we're expecting to really get into more of the volume production in mid-year with the Foresight M, which is targeted at the industrial side of it. So we think the development contract revenue will probably be a third to maybe a half of the revenue in 2022, and then the rest will be Foresight M products that we'd be selling. So that's how we're thinking about it right now in terms of the balance between those two. Right.
spk05: And there was a very subtle nod in the presentation to the fact that we made a very big strategic decision. And that was to actually drive the convergence of the industrial platform and the automotive platform faster to actually reduce costs a year and a half faster than we thought we could. And what I mean by that is, And our original plans, we were going to come out with a product from Semina. Continental was going to come out for a product on their, you know, manufacturing line. And then we were going to merge the best of breed from both those products into the hardware design in the next cycle. What we found in working with Continental was that we could accelerate some of the things that we were doing together and actually integrate them into our industrial products. increasing performance and reducing costs because now we're sharing the exact same component supply chains. So for us, it did push us back on timing about when we get that industrial product out. We lose a quarter or so, but we, as I said before, we accelerate our cost reduction capabilities by over a year and a half. And what we're finding in the marketplace today is that the original plan of driving down hardware costs to increase adoption, the market has pushed that even faster than we thought it would. You can see it in many of the presentations from our peers, that the average ASP is coming down.
spk10: Okay. Appreciate all the color, Blair. Bob, thanks, guys.
spk05: Thanks, Sujit.
spk10: Thanks, Sujit.
spk07: Our next question comes from Hans Chang with DA Davidson. Please go ahead.
spk08: Hi, Blair, Bob. Thank you for taking my question. So I have a follow-up question on the industrial mobility side of business. So I just wanted to know, like, is there any potential risk about the supply chain risk? I mean, we have industry-wide supply chain semi-supply chain constraints, right? And I don't know if that could be a potential risk about the production on our fourth IM product, or it's already embedded into a guidance, or it's probably not at all. And then the second question, I just want to follow up the last question about the effort you have done to actually bring the cost reduction like one-half year ahead. And can you elaborate more maybe from technology perspective? What have you done to really achieve that milestone? Thank you.
spk04: You bet. So this is Bob. So I'll take the first part of that question, and then Blair can take the second part. So relative to the supply chain risks and the overall geopolitical risks that we're all seeing in the world today, Yes, we are taking that into account as we reflect on the ramp of the Foresight M production for this year. So you can assume that that has been baked into our guidance. So we did think very carefully about that. And we've tried to err on the side of caution in this environment, basically, relative to the guidance that we gave. So, Blair, why don't you take that technology question?
spk05: Sure, Hans. A great question. So part of our plan, if you've heard us speak in the past, which you have, and thank you for your report, has been to drive costs down in three specific areas. One is in system design. The second is in innovation and new materials through a large global supply chain. And the third is in converged architecture, where we drive volumes up by combining the same components that we use in automotive with the industrial components and therefore have bigger runs and lower prices. So the advantage we have in coming to a converged architecture earlier is that the majority of the components in both products are exactly the same, are already qualified and will be functionally tested together so that the industrial markets will benefit from the higher volumes in automotive and the automotive markets will benefit from the fact that there's been more customization done in the software and the industrial market. So we really see, we thought this would be done in a serial basis. We'd get one done, we'd get the other done, and we'd pick the best of breed. We're basically bringing the two products together. Although they're produced on different manufacturing lines, they're sourcing from the same components. Does that make sense?
spk08: Yeah, that totally makes sense, and that's helpful. One more question, if I may. I think you guys have the partnership with Two Simple. Can you give more cover about that or what kind of partnership? Will that be in the trucking business or still in the automotive business? Also, recently they have a shuffle in the top management team. So Will that even could be a potential impact to AI business with them?
spk05: Sure. We have two different initiatives with TuSimple. One is on the development side, which is across everything that they're doing on the base level and base architecture. The second is specifically trucking. Given that our products are high performance, long range, and can see the highest level of density. When you take a look at hub-to-hub trucking, it looks a lot like automotive highway autopilot. So we're very focused with them on that application today. I think that what TuSimple has said, and I believe from my experience, is that they've decided to make some changes in their management team but they're on track with the exact customer base that they had before and that their production schedules and their engineering teams have stayed intact. So we don't assume, or we haven't seen, any changes from the customer side, the Too Simple customer side, which is we partner with Too Simple to actually provide technology for them to integrate for their OEMs. Got it.
spk08: That's all I have. Thank you.
spk07: Our next question comes from John Roy with Water Tower Research. Please go ahead.
spk09: Thank you. So, Blair, you were talking a lot about software-driven cars and things like that, and certainly electric vehicles are definitely making a big push. Is there some kind of special connection between those two, or is it just the type of company that's doing that?
spk05: No, there's actually a very strong connection. I mean, one of the reasons that... that many of the car companies in the world are moving to EV. You know, one reason is the environment. The second reason is that when you move to an EV, you limit the number of moving parts and you allow, rather than try to build intelligence from an existing system, which has many ADCUs around the car, to a much simpler system. One of the things that Tesla has benefited from is being able to build software on top of a system that was designed on top of a battery and was designed to be controlled by software. So the EV trend itself is also a software-defined car trend. Given that we have a software-definable sensor, what we've been finding in discussions is that we can accelerate that new set of features by being able to apply hardware today, but over the period a car is owned, to actually add new features using the same hardware, using that diagram I showed you where we can change the way the hardware works. So I think the EV cycle is actually very, very correlated with how many new features you're going to actually see in your car. I remember at Autodesk, we were a software company in the CAD business, In the old days, back in the early 90s, we used to do an architectural release, which would take us 18 months, and then we do a feature release, right, which would take us 18 months. So we change the architecture, then we'd be able to add new features on top of it. What you're seeing today, you know, 20, 30 years later, is the ability to actually do an architectural release that will enable multiple feature releases into these smart assets. That's why we actually use the term smart assets in industrial, and software-divinable vehicle. At the end of the day, the analogy could be made when you took a look at your flip phone, you'd have to buy a new flip phone before you got new features. It was beautiful. I loved it. But when you took a look at your iPhone, in between iPhone hardware upgrades, you can bring down apps anytime you want and use your phone differently. I think we've already entered that cycle, and I think it's going to be a very exciting cycle for consumers.
spk09: Yeah, as a follow-up, your team's experience in writing software for military applications, did that give you a little bit of a leg up on the competition? Because certainly, you know, the software is pretty critical to the safe operation of the vehicle. So do you guys go through extra steps? Is there a verification process or certification?
spk05: I would say that, you know, the word software means many different things to many different people, which is why we kind of took that simple diagram and I would say that maybe as my dad would say, where you have been always informs where you will go and how you define the problem defines the solution. And so the way we define the problem having worked on complex fast moving systems was that you had to actually put the complexity in the software because you never knew what the use case you were going to go after was. And as you saw in the LiDAR industry, the aerospace, telecom, and the GIS industries are actually large spatial networks of information. It's really about aggregating, integrating, and transferring information. And so I think the advantage that we may have, and by the way, there'll be a lot of different models, and I'm not saying ours is the only one, is we looked at making the sensor a software platform, i.e. in biomimicry, you'd call that edge processing. You process 80% of your vision and your visual cortex, not in your executive function. Right. So we focused on building that software, um, and then feeding information from that software into say perception software or application software. Um, so when you talk to someone about software, you have to decide, you know, which piece are you talking about? Signal processing, embedded software, a software platform that's embedded like ours, or are you talking about perception or are you talking about application? All of the pieces are important. Our philosophy, and I think what we did gain from our military experience, was that if you can collect information more intelligently, you will actually make the decisions happen faster and with more fidelity. And I think that's the difference. I will end with saying that there will be different types of cameras, radar, and LIDARs out there. You know, our focus is on high-performance, high-value LiDAR that can both collect spatial data but also can integrate with orthogonal data from other sensors. So we really have a point of view that we are in the information business, and that two years from now, you're going to find that the winners in this marketplace built a platform that collects information intelligently, and that's where the value is going to end up.
spk09: Great. Thanks, Blair.
spk05: Thanks. So Bob was very unhappy with that question because it gave me a chance to be philosophical. But thank you for everyone for jumping on today and investing your time. I know that everyone's very busy and there's also a lot of companies in our peer group. So thank you for making the investment.
spk04: Thanks, everybody. Operator, that concludes the call. Thanks, everybody, for joining us.
spk07: The conference is now concluded. Thank you for attending today's presentation. You may now disconnect.
Disclaimer