AEye, Inc.

Before we start, I'd like to remind participants that during this call, management may make forward-looking statements including, without limitations, 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 the circumstances that are difficult or impossible to predict. Our actual results may differ materially from those contemplated by these 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 quarterly report on Form 10-Q for the period ending March 31, 2022. All information discussed today is as of May 13, 2022, 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 information purposes only and should not be considered as 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 measures in our earnings release. With that, I'll pass it over to Blair.

Thank you, Clyde. And thank you all for being here today and investing your time to participate in our quarterly update. As you have seen in our earnings release today, we finished our first quarter solidly, meeting both our financial and operating expectations. In addition, we remain on track to achieve our full year plan. While we continue to follow external global events closely and monitor market volatility, our main investor themes and objectives for 2022 remain consistent, and our focus on execution remains paramount. In our year-end earnings call, we outlined our go-forward strategy and our progress to date building product partnerships and infrastructure to meet our key objectives. We also spent time differentiating our unique business model and disruptive technology platform versus peers. In our last call, you also had a chance to hear from several customers in the automotive and industrial markets directly. They shared with us the value the AI intelligence sensing platform brings to their solutions. We would like to first emphasize the importance of 2022 as we intend to both begin shipping the Foresight product for industrial markets with our partner, Samina, as well as transferring the B sample of our first joint automotive ADAS product to our partner, Continental. In today's call, we intend to do a quick review of the market dynamics, the differentiation of our disruptive intelligence sensing platform, and illustrate why we and our partners believe AI's sensor-based operating system is uniquely positioned to enable the evolution of smart vehicles, infrastructure, and assets. We will use the majority of our time today to focus on our execution with an update on the fourth key investment theme, commercialization, industrialization, and capital light manufacturing. We will touch on both the foresight product line, as well as our joint continental ADAS product. We believe we will be the only company in our peer group to bring up volume production capabilities with multiple manufacturers. The market headline is, sensors are a highly desired addition to many vehicles, infrastructure, and other assets. Cameras and radars are interpretive sensors with unique strengths and weaknesses, but have one attribute in common. They collect information and intelligently guess. LiDAR is a deterministic sensor which can provide definitive data for many decisions, enabling new value-added features that can be standalone, like hub-to-hub trucking or highway autopilot for consumer vehicles. Or LiDAR can also complement radar and cameras to increase reliability or accuracy for existing features, such as in slower-speed traffic jam assist. What is clear is that LIDAR's commercial performance has continued to increase substantially over the last several years. Concurrently, its manufacturability is maturing, and therefore size, weight, power, and cost continue to be optimized as LIDAR is being applied across numerous industries. Many of us already have a LiDAR sensor in our smartphones, advanced driver assistance systems in our cars, and we experience traffic flow optimization on toll roads and other parts of our infrastructure. We believe LiDAR has a wide range of applications well beyond what most people have imagined. That said, all LiDARs are not the same. With many traditional LiDAR systems, data is collected in a fixed and limited manner and then passed along to a perception engine. This is a one-way flow from the sensor into an application software layer. AI software on the edge is different. First, we can control hardware components individually using a software-based operating system located on the sensor with two-way communication to change the way the sensor works, depending on different environments. In addition, the Foresight operating system does not silo itself from other sensors. Customers can create unique systems that can use maps, cameras, radars, and IMUs to trigger the LiDAR so they can be more intelligent and efficient when collecting critical information, as recently demonstrated with Continental's integration of its current ADAS suite, including radar and camera, with our joint LiDAR product. Finally, and most importantly, this software-defined architecture is natively compatible to manage data over its local sensor network and to be enabled for over-the-air updates. So we can change the way the hardware performs through software, allowing our customers in the future the ability to upgrade and to add new features and functionality. While this seems too good to be true, you only have to look to your smartphone to see the path that is already being taken by many durable goods manufacturers and infrastructure providers. In automotive specifically, the acceleration of EVs provides a natural greenfield opportunity to create software-definable platforms for cars. The future is now. One powerful example of this software definability is adaptive placement. The Foresight platform enables automotive OEMs to embed the same LiDAR sensor in various integrated locations using AI's proprietary sensing software. This optimizes performance for the vehicle-specific packaging and integration without detracting from design or limiting performance. AI's operating system provides OEMs with the ability to transform the sensor performance and enhance data capture across various mounting locations and vehicles. This is in contrast to most traditional sensors today, which cannot be optimized for placement, tolerances, and applications, making them suboptimal across a platform with multiple brands and models. At the end of the day, the ability to change the mounting locations and the height, as well as correct for curvature and transmissivity of external surfaces, allows us to increase platform adoption, optimize feature implementations, and reduce cost and complexity. This same adaptive placement capability and software definability conversely allows AI to customize across markets, allowing the use of the same hardware on a roof mount at 4 meters and a negative 40 degree angle on a Class A truck as a grill mount at 65 centimeters and a negative 15 degree angle on a trendy sports car. Up until this point, we have been talking about how our adaptive systems can add intelligence into current vehicles, infrastructure, and assets. So let's take a step back and discuss the future and what differentiates the software-defined vehicle from a traditional vehicle today that has intelligence siloed in many subsystems. On the left, you see a vehicle with all of its technology and functionality set when you purchase it. In many cases, you would need to physically change or alter a component to adjust the hardware functionality of the vehicle. On the right, you see a vehicle with a more streamlined platform reference design, reducing complexity and allowing for the flexibility to control the hardware more efficiently as part of an overall system. As we continue to advance cars with software, you will see systems begin to consolidate into software-definable platforms with more connectivity both within and outside the vehicle. With this added connectivity and distributed intelligence within the vehicle, the opportunity to add value and increase revenue from software expands. The AI operating system model is architected to complement this migration. Focus not on hardware alone, but on collecting the best data for decision-making, adding features to add safety and performance for the consumer and driving profitability for the OEM. For example, in the future, a rain sensor may trigger a rain performance mode, or a camera may trigger a lidar to confirm an object. This distributed intelligence is key for what we consider a software-enabled vehicle. In a recent report, it was estimated that Tesla today makes 67% of its profits from these types of software-enabled features. While our products already have the adaptability to be definable across multiple applications using the same hardware, the real power in the future, where cars may be driven for 10 years, may be the ability to continue to adapt over time and update remotely using OTA, an acronym for over-the-air updates. As an example, as new vehicles increase software content, OEMs will be able to update software over the life of the vehicle, similar to how your phone gets updates today. Vehicles will be able to send and receive data, enabling them to continuously increase in value. These updates will allow new features and functionality, translating to improved safety and performance. As vehicles and infrastructure head towards over-the-air evolution, we believe our software defined sensor will be a key enabler of these new business models. In summary, we believe the power of AI's unique sensor platform is that it is intended to be a set of hardware components that can be manufactured, then configured for any high-value use case in the software. For instance, OEMs or Tier 1s could use the sensor's operating system to enable ADAS features that can be bundled for a range of consumer vehicles. The same operating system could be used by system integrators in the ITS or intelligent traffic systems market who are able to optimize the sensor for a pedestrian safety at intersections or forecasting traffic flow on toll roads. Trucking can leverage high performance, high reliability sensors designed for first mile, last mile or hub to hub applications. In the high demand rail and aviation markets, each sensor can be optimized for the extreme range and the resolution they require. So let's talk about execution and our progress around commercialization and scalability. There's no better place to start than our latest product, the Foresight M, which we intend to transfer to volume production later this year. I would now like to introduce Tom Fallon, Executive Vice President of Strategic Business Development at Samina, our Foresight manufacturing partner. Take it away, Tom.

Thanks, Blair. Sanmina is one of the world's leading integrated manufacturing solutions provider. Headquartered in Silicon Valley with a global footprint, we have earned a reputation for innovation, reliability, and quality with a passion for customer success. It is important to understand that we only win when our partners win. So we are very selective in where and when we invest in new processes and emerging companies. Each year, with our customers, we bring about 3,000 new products to market, so we are approached by a lot of companies. We proactively choose to partner with the companies where we see a mutual alignment around ideas and processes. We also look for a well-defined market opportunity that is large and rapidly approaching. With AI, we found that alignment. We also found that AI has a compelling vision and business model. we believe AI's smart software-definable sensors will be a driving force in the automation of cars, infrastructure, and assets across many industries. At the core of our relationship with AI, there are three fundamental pillars we have found important to increasing the probability of success. First, AI decided early not to build a factory, but rather invest their time and resources in designing their systems for outsourced manufacturability with an eye toward optimizing efficiency and cost without compromising on industry-leading performance and reliability. Second, AI's innovative approach of aligning component suppliers with their reference system design Utilizing modular components sourced from proven automotive grade suppliers not only allows accelerated innovation, but also is a tremendous advantage in helping us to scale and harden our global supply chain. We believe this approach creates a strategic differentiation from others by optimizing time to market, volume, quality, and cost. Third, AI has transferred much of the system complexity from hardware to the software layer and its unique sensor-based operating system. We don't usually see companies make that leap until four or five generations of product release cycles. This allows one manufacturing line to produce the sensor hardware at scale, and software is used to customize the sensor per market or partner and to enable continuous enhancements in functionality over time. Most importantly, AI and Sanmina have worked as one team. From the beginning, we have leveraged each other's strengths to develop integrated design, manufacturing, and testing processes that will bring the AI Foresight LiDAR system to the market faster and with greater reliability and performance. Sanmina believes that what we make makes a difference. We are very proud of our partnership with AI. Back to you, Blair.