SES AI Corporation

Good afternoon. Thank you for attending today's SES AI second quarter 2024 business and financial results. My name is Cole and I'll be the moderator for today's call. All lines will be muted during the presentation portion of the call with an opportunity for questions and answers at the end. If you'd like to queue for a question, you can do so by pressing star one on your telephone keypad. I'd now like to pass it over to Kyle Pilkington. Please go ahead.

Hello, everyone, and welcome to our conference call covering our second quarter 2024 results. Joining me today are Qi Chaohu, founder, chairman, and chief executive officer, and Jing Nialis, chief financial officer. We issued our shareholder letter after market closed today, which provides a business update as well as our financial results. You'll find a press release with a link to our shareholder letter and today's conference call webcast in the investor relations section of our website at ses.ai. Before we get started, this is a reminder that the discussion today may contain forward-looking information or forward-looking statements within the meaning of applicable securities legislation. These statements are based on our predictions and expectations as of today. Such statements involve certain risks, assumptions, and uncertainties, which may cause our actual or future results and performance to be materially different from those expressed or implied in these statements. The risks and uncertainties that could cause our results to differ materially from our current expectations include, but are not limited to, those detailed in our latest earnings release and in our SEC filings. This afternoon, we will review our business as well as results for the quarter. With that, I'll pass it over to Chi Chau.

Good afternoon, and thank you for joining us on our second quarter earnings call. 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 now dominate the global market. The next generation battery companies must deliver something completely different in light years ahead to become relevant. We cannot compete on their terms. Previously, we announced that we are entering the air mobility market. including urban air mobility, or UAM, and drones, 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. We are the world's leader in lithium metal. We were the world's first to enter automotive A-sample and B-sample joint development agreements with global automakers. We have developed very exciting capabilities in materials and manufacturing. We have strategically integrated AI into our operations, encompassing design, technology development, manufacturing, and aftermarket support. Since embarking on embedding AI into lithium metal, we have realized that the value of AI materializes when it fundamentally reshapes the business model. By adopting a thematic approach with platform building mindset, we aim to generate both internal and external value. We've worked diligently to achieve this and are excited to share the preliminary outcomes of our initiatives. Today, we're introducing a paradigm shift. Our AI solutions will accelerate the commercialization of all next-gen battery technologies. Lifted metal represents the forefront of this new approach, but our AI will ultimately be agnostic to any battery technology. Let's start with the EV sector. Last quarter, we announced our B-sample joint development partnership with Hyundai to build a line within their electrification center in Wuhan, South Korea. I'm glad to share that we're 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 lithium metal lines globally and will manufacture 50 amp hour to 100 amp hour large automotive lithium metal B sample cells. We continue to work with our automotive OEMs with a goal to reach EVC sample in 2025 and start of production SOP in 2026. For UAM and drones, we continue to see strong demands. For UAM, we are converting our previous EVA sample lines in South Korea and Shanghai to UAM lines. We expect the Korea UAM line to complete Field Acceptance Test in August, Site Acceptance Test in September, and start producing cells in September. We expect the Shanghai UAM line to complete both FAT and SAT in September and start producing cells in October. Both UAM lines will make 20 amp-hour to 30 amp-hour medium lithium metal cells and modules. We're making great progress testing these lithium metal modules based on the rigorous safety test 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're seeing growing demand from both industrial and defense customers, especially for small swarm drones. The drone market was estimated to be $28 billion in 2023, according to SkyQuest. about 1.8x the $16 billion estimated market size for AR VR goggles in 2023, according to ComSegic Intelligence. We have already converted our small cell lines to make 4m power to 6m power small lithium metal cells and modules. Now let's talk about our AI solutions. We have three, AI for manufacturing, AI for safety, and AI for science. First, AI for manufacturing. The traditional approach to optimizing cell design and process and improving manufacturing quality is through human experience, where the human engineers define and optimize quality specifications typically 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 human-based approach has worked well in the past, and works today for mature lithium 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 machine 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 EVB sample, UAM, and drone lines produce an enormous amount of data, the largest manufacturing data of lithium 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 checkpoints 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 lithium metal cells. We're very excited to announce the installation of AI for manufacturing on all of our lithium metal lines, from EVB sample to UAM to small drones. We expect it will provide very detailed and accurate individual step quality analysis and group of steps relationship analysis. This will further accelerate the optimization of manufacturing quality, preparing us for EVC sample in larger scale UAM and drone manufacturing. In addition to in-house AI for manufacturing development, we also partner with big tech companies and plan to incorporate the latest AI for manufacturing approach from the semiconductor industry. We continue to work with our automotive OEMs with a goal to reach EVC sample in 2025. and SLP in 2026. This AI for manufacturing capability allows us to bring enormous value to our auto OEM and large battery manufacturing partners. Second, AI for safety. Traditional vehicle battery health monitoring and safety prediction are based on a set of boundary conditions determined by humans, physics-based models. These would include, for example, state of health, SOH, state of charge, SOC, capacity, voltage, temperature, current, time, to name a few. While the boundary conditions are well understood by humans, they are not enough to actually predict battery remaining useful life and incidents. AI is far more accurate and powerful at detecting anomalies than even the best human engineers. In AI for Safety, rather than relying solely on human-developed boundary conditions, we have pre-trained our LLM with a cell cycling data of more than 15,000 lithium metal cells under various mission profiles, including more than 100 actual flight hours of drones using our lithium metal modules. Interestingly, the LLM identifies features that can detect anomalies and send early warning signals far more accurately. These AI-developed features work remarkably, and we are working on improving the explainability of these models. With more vehicle battery data training, we believe that AI for safety can help guarantee near 100% safety in the field, addressing the core issue of lithium metal and all next-gen batteries with higher energy densities, which is safety. In working with our OEM partners, our air for safety model has been able to predict 100% of more than 40 incidents. Our model predicted the incidents 10 to 30 cycles earlier than they occurred and sent warning signals. We also continue the cycle test until the actual incidents to verify the prediction accuracy. In comparison, our human based models were only able to predict around 80% of incidents. Third, AI for science. Human research and development on battery materials has been the single slowest step in commercialization of next-gen battery technologies. For example, the entire global lithium ion industry spent 30 years studying less than 1,000 unique molecules when there are 100 billion, that's 10 to the 11th, unique molecules that could be studied and should be studied. On average, it takes human scientists 10 years to introduce a new battery material. We believe AI for Science can do that in one year. Unlike AI for Manufacturing and Safety that collect actual data from the lines and vehicles, AI for Science requires an enormous molecular property database that currently does not exist. Synthesizing this property database requires massive computing power. Recently, we started a new initiative called Molecular Universe, whose goal is to crowdsource subsidized and free computing resources to map the properties of small molecules. Several universities, national labs, and big tech companies have participated in this initiative, and we have already mapped about 10 to the sixth molecules. With more GPUs, we expect to map a large enough molecular universe that our AI model training will reach sufficient accuracy. Once we have this map, we can accelerate material discovery for any battery problem. This includes not just lithium metal for EV, UAM, and drones, but also lithium ion batteries for consumer electronics, power tools, automotive, and other applications. Most of these molecules are completely new and not commercially available. That's why we built ElectroEye Foundry, which has been operational since April this year. This ElectroEye Foundry employs some of the best organic synthesis chemists in the world. Now we have complete ability from molecular mapping to generative AI models for new molecules to molecular synthesis and purification to high throughput electrolyte formulation screening, and to small and large cell testing. No one in the battery industry has such a complete capability. So how do we monetize all this? These three AI solutions represent what we expect to be exciting and sooner than expected revenue streams, as well as the future of electric transportation. In AI for manufacturing and safety, To truly ensure near 100% safety in the field, manufacturing quality and vehicle safety data must be integrated. Here's where SCS AI comes in. Our lithium metal cells for EV, UAM, and drones will be the first time that manufacturing and safety data are integrated to ensure near 100% safety. We're also working with some of our peers in both next-gen lithium ion and lithium metal batteries to consolidate manufacturing and safety data for our model training. The larger and more diverse the data, the more accurate the models become. We expect the pricing could be structured as a premium valid for the entire warranty period. The value proposition for these OEMs is that incident prediction can prevent costly recalls and more accurate remaining useful life prediction can help extend battery lifetime. In AI for Science, SES-AI has the strongest battery electrolyte development capability. Many battery companies and OEMs do not have the resource to develop good electrolyte materials. We can insource intelligence and help them solve their challenges. We will start by seeking to beat the lithium metal electrolyte Coulombic efficiency records set by human scientists. We will then expand to lithium ion applications such as low temperature performance and fast charge, non-volatility, and expand from automotive to consumer electronics to grid storage and many other applications. This type of in-source intelligence for the AI for Science business model can find an analogy in the pharmaceutical industries that enjoy much higher profit margins. The pricing structure may be based on a development fee and recurring licensing royalty. We have been applying this to lithium metal material discovery and expect to apply to lithium ion material discovery.