Dear Shareholders, In this special letter, I want to talk about the seismic shift across any industry by generative AI and large language models (LLMs). AI represents a pivotal development of this decade. This transformative technology is set to disrupt industries, from those seeking the next innovation S-curve to those grappling with shrinking margins. The fact is, today’s EV battery market is completely different from that of three years ago, or even just one year ago. The incumbent battery players dominate the global market. The next-generation battery companies must deliver something completely different and lightyears ahead to become rel-evant. We cannot compete on their terms. Previously, we announced that we are entering the air mobility market, in-cluding urban air mobility, or UAM (larger aircrafts), and drones (smaller air-crafts), in addition to our existing EV work. For next-gen batteries to compete with incumbent batteries, we must overcome three hurdles at commercial scale: quality, safety and future material development. The traditional human-based approach simply takes too long. That’s why the introduction of next-gen battery technologies has always been very slow. As the world’s leader in Li-Metal and the first to enter automotive A-sample and B-sample joint development agreements (JDAs) with global automakers, we have strategically integrated AI into our operations, encompassing tech-nology development, manufacturing, and aftermarket support. Since em-barking on embedding AI into Li-Metal, we have realized that the value of AI materializes when it fundamentally reshapes the business model. By adopt-ing a thematic approach with a platform-building mindset, we aim to gener-ate both internal and external value. We are excited to share the preliminary outcomes of our initiatives. Today, we are introducing a paradigm shift. Our AI solutions will accelerate the commercialization of all next-gen battery technologies. Li-Metal repre-sents the forefront of this new approach, but our AI will ultimately be agnostic to any battery technology. "Remembering that you are going to die is the best way I know to avoid the trap of thinking you have something to lose. You are already naked. There is no reason not to follow your heart." Steve Jobs

1. EV Last quarter, we announced our B-sample joint development partnership with Hyundai to build a line within their Electrification Center in Ui-Wang, South Korea. With our latest update, I am glad to share that we are on track to hit our target of completion of the line in the fourth quarter of this year. This will yield one of the largest capacity Li-Metal lines globally and will man-ufacture 50 Ah to 100 Ah large automotive Li-Metal B-sample cells. We con-tinue to work with our automotive OEMs with a goal to reach EV-C sample in 2025 and start of production (SOP) in 2026. 2. UAM and Drones We continue to see strong demands from UAM and drone markets. For UAM, we are converting our previous EV A-sample lines to UAM lines. We expect to complete Field Acceptance Test (FAT) in August, Site Acceptance Test (SAT) in September, and start producing cells in October. Both UAM lines will make 20Ah to 30 Ah medium Li-Metal cells and modules. We are making great progress testing these Li-Metal modules based on the rigorous safety tests for aviation certification. We have already entered a few cell testing agreements with leading UAM OEMs and expect to enter a few more later this year. For drones, we are seeing growing demand from both industrial and defense customers, especially for small swarm drones. The drone market was esti-mated to be $28 billion in 2023 (Skyquest), about 1.8x the $16 billion esti-mated market size for AR/VR goggles in 2023 (Consegic Intelligence). We have already converted our small cell lines to make 4 Ah to 6 Ah small Li-Metal cells and modules (configuration ranging from 2 cells to 12 cells). 3. Data Collection Without data, there cannot be intelligence. The development of artificial general intelligence (AGI) relies extensively on the tremendous amount of data that our human species accumulated, including stories, books, writings, social media profiles, and online data. However, the equivalent battery data does not exist today, at least nowhere near the same scale. Collecting large amounts of well-labeled and high-quality data becomes imperative to the successful development of AI for batteries.

AI for Manufacturing The traditional approach to optimizing cell design and process and im-proving manufacturing quality is through human experience, where the human engineers define and optimize quality specifications, typi-cally takes at least eight years. Battery manufacturing is often more of an art than a science, especially between the good ones and the very best. While this traditional human experience-based approach has worked well in the past and works today for mature Li-ion cell technologies, it slows down large-scale commercialization of next-gen battery technologies. We believe AI for Manufacturing can accelerate this timeline by 10x. It uses ma-chine learning to define and fine-tune quality specifications based on manufacturing process data collected, which is much faster and more accurate than human engineers. Our EV-B sample, UAM and drone lines produce an enormous amount of data, the largest manufacturing data of Li-Metal cells anywhere in the world. We produce more than 1,000 cells per line per month and growing. There are more than 1,000 quality check points per cell and growing, including both time-series data and images such as CT, X-ray, ultrasound, and vision. There are thousands of process steps with complex individual and group relationships. Our AI for Manufacturing model has already been pre-trained on more than 15,000 Li-Metal cells. We are very excited to announce the installation of AI for Manufactur-ing on ALL of our Li-Metal lines, from EV-B sample to UAM to small drones. We expect it will provide very detailed and accurate individual cell and process step level quality and group steps relationship analysis. This will further accelerate the optimization of manufacturing quality, preparing us for EV-C sample and larger scale UAM and drone manufacturing. In addition to in-house AI for Manufacturing development, we also plan to partner with big tech companies and incorporate the latest AI for Manufacturing approach from the semiconductor industry. As we continue to work with our automotive OEMs for EV-C sample and SOP, this AI for Manufacturing capability allows us to bring enormous value to our auto OEM and large battery manufacturing partners. AI tools are developed to assist CT analysis of a cell during manufacturing