Q2 2024 SES AI Corp Earnings Call
Qichao Hu, Unknown Executive, Jing Nealis
Cole: 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. We have 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. And now, I'd like to pass it over to Kyle Pilkington. Please go ahead.
Cole: 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.
Cole: If you'd like to queue for a question, you can do so by pressing star one on your telephone keypad. And now I'd like to pass it over to Kyle Pilkington. Please go ahead.
Kyle Pilkington: Hello, everyone, and welcome to our conference call covering our second quarter 2024 results. Joining me today are Qichao Hu, founder, chairman, and chief executive officer, and Jing Nealis, chief financial officer. We issued our shareholder letter after the 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 scs.ai.
Speaker Change: Hello, everyone, and welcome to our conference call covering our second quarter 2024 results. Joining me today are Qichao Hu, Founder, Chairman and Chief Executive Officer, and Jing Nealis, Chief Financial Officer.
Speaker Change: We issued our shareholder letter after market closed today, which provides a business update as well as our financial results.
Speaker Change: 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 scf.ai.
Kyle Pilkington: 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 filing. This afternoon, we will review our business as well as the results for the quarter. After that, I'll pass this over to Qichao.
Speaker Change: 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.
Speaker Change: 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.
Speaker Change: 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.
Speaker Change: This afternoon, we will review our business as well as results for the quarter. With that, I'll pass it over to Qichao.
Qichao Hu: 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 caused by generative AI and large language models, LLM. 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.
Qichao Hu: Good afternoon, and thank you for joining us on our second quarter earnings call.
Qichao Hu: I want to talk about the seismic shift across any industry by generative AI and large language models, LLMs.
Qichao Hu: AI represents a pivotal development of this decade.
Qichao Hu: This transformative technology is said to disrupt industries, from those seeking the next innovation S-curve to those grappling with shrinking margins.
Speaker Change: The fact is, today's EV battery market is completely different from that of three years ago, or even just one year ago.
Speaker Change: The incumbent battery players now dominate the global market.
Qichao Hu: The next generation battery companies must deliver something completely different and 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 EV1. For next gen batteries to compete with incumbents, we must overcome three hurdles at commercial scale. Quality, Safety, and Future Material Development. The traditional human-based approach simply takes too long.
Speaker Change: The next generation battery companies must deliver something completely different and light years ahead to become relevant. We cannot compete on their terms.
Speaker Change: 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.
Speaker Change: For next-gen batteries to compete with incumbent batteries, we must overcome three hurdles at commercial scale.
Speaker Change: Quality, Safety, and Future Material Development.
Qichao Hu: That's why the introduction of next-gen battery technologies has always been very slow. We are the world leader in lithium metal. We were the world's first to enter into 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 a platform building mindset, we aim to generate both internal and external value.
Speaker Change: The traditional human-based approach simply takes too long. That's why the introduction of next-gen battery technologies has always been very slow.
Speaker Change: 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.
Speaker Change: We have developed very exciting capabilities in materials and manufacturing.
Speaker Change: We have strategically integrated AI into our operations, encompassing design, technology development, manufacturing, and aftermarket support.
Speaker Change: Since embarking on embedding AI into lithium metal, we have realized that the value of AI materializes when it fundamentally reshapes the business model.
Speaker Change: By adopting a thematic approach with platform building mindset, we aim to generate both internal and external value.
Qichao Hu: We've worked diligently to achieve this and are excited to share the preliminary outcomes of our initiative. Today, we're introducing a paradigm shift. Our AI solutions will accelerate the commercialization of all next-gen battery technology; lifting 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 Uwon, 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.
Speaker Change: We've worked diligently to achieve this and are excited to share the preliminary outcomes of our initiatives.
Speaker Change: Today we're introducing a paradigm shift. Our AI solutions will accelerate the commercialization of all next-gen better technologies.
Speaker Change: Lift and Metal represents the forefront of this new approach, but our AI will ultimately be agnostic to any battery technology.
Speaker Change: Let's start with the EV sector.
Speaker Change: Last quarter, we announced our B-Sample joint development partnership with Hyundai to build a line within their electrification center in Uwon, 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.
Qichao Hu: 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 production SLP in 2026. For UAM and drones, we continue to see strong demand. 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 FAT in August, Site Acceptance Test SAT in September, and Start Producing Sales in September.
Speaker Change: This will yield one of the largest capacity lithium metal lines globally, and will manufacture 50 MPa to 100 MPa large automotive lithium metal bead sample cells.
Speaker Change: We continue to work with our automotive OEMs with a goal to reach EVC sample in 2025 and start of production SOP in 2026.
Speaker Change: For UAM and drones, we continue to see strong demands.
Speaker Change: For UAM, we are converting our previous EVA sample lines in South Korea and Shanghai to UAM lines.
Speaker Change: We expect the Korea UAM line to complete Field Acceptance Test, FAT, in August , Site Acceptance Test, SAT, in September , and start producing cells in September .
Qichao Hu: 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 tests for aviation certification. We have already entered into a few cell testing agreements with leading UAM OEMs and expect to enter a few more later this year.
Speaker Change: 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 MPa to 30 MPa medium lithium metal cells and modules.
Speaker Change: We're making great progress testing these lithium metal modules based on the rigorous safety test for aviation certification.
Speaker Change: We have already entered a few cell testing agreements with leading UAM OEMs and expect to enter a few more later this year.
Qichao Hu: 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. That's about 1.8x the $16 billion estimated market size for AR VR goggles in 2023, according to Concentric Intelligence.
Speaker Change: For drones, we're seeing growing demand from both industrial and defense customers, especially for small swarm drones.
Speaker Change: 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 Concentric Intelligence.
Qichao Hu: 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 solution. 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 human engineers define and optimize quality specifications typically take at least a year.
Speaker Change: We have already converted our small cell lines to make 4M power to 6M power small lithium metal cells and modules.
Speaker Change: Now let's talk about our AI solutions. We have three. AI for manufacturing, AI for safety, and AI for science.
Speaker Change: 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.
Qichao Hu: 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 the large-scale commercialization of next-gen battery technology. We believe AI for manufacturing can accelerate this timeline by 10, He 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.
Speaker Change: Battery manufacturing is often more of an art than a science, especially between the good ones and the very best.
Speaker Change: 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.
Speaker Change: We believe AI for manufacturing can accelerate this timeline by 10x.
Speaker Change: He 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.
Qichao Hu: 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 1000 cells per line per month and growing. There are more than 1000 quality checkpoints per cell and growing, including both time series data and images such as CT, x-ray, ultrasound, and vision.
Speaker Change: 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.
Speaker Change: We produce more than 1,000 cells per line per month and growing.
Speaker Change: 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.
Qichao Hu: There are thousands of process steps with complex individual and group relationships. Our AI 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 working 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.
Speaker Change: There are thousands of process steps with complex individual and group relationships.
Speaker Change: Our AI for Manufacturing model has already been pre-trained on more than 15,000 lithium metal cells.
Speaker Change: We're very excited to announce the installation of AI for manufacturing on all of our working 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.
Qichao Hu: This will further accelerate the optimization of manufacturing quality, preparing us for EBC sample and 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 approaches 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 partners and large value manufacturing partners.
Speaker Change: This will further accelerate the optimization of manufacturing quality.
Speaker Change: Preparing us for EBC sample and larger scale UAM and drill manufacturing.
Speaker Change: 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.
Speaker Change: We continue to work with our automotive OEMs with a goal to reach EVC sample in 2025 and SLP in 2026.
Speaker Change: This AI for manufacturing capability allows us to bring enormous value to our auto OEM and large battery manufacturing partners.
Qichao Hu: [inaudible] Traditional vehicle battery health monitoring and safety prediction are based on a set of boundary conditions determined by humans for the space model. These would include, for example, state of health, SOH, state of charge, SOC, capacity, voltage, temperature, current, time, to name a few.
Speaker Change: Second, AI for Safety.
Speaker Change: Traditional vehicle battery health monitoring and safety prediction are based on a set of boundary conditions determined by humans, physics-based models.
Speaker Change: These would include, for example, state of health, SOH, state of charge, SOC, capacity, voltage, temperature, current, time, to name a few.
Qichao Hu: While the boundary conditions are well understood by humans, there are not enough to actually predict battery remaining use for life and infotainment. AI is far more accurate and powerful at detecting anomalies than even the best humans. In AI for Safety, rather than relying solely on human-developed boundary conditions, we have pre-trained our LLM with cell cycling data of more than 15,000 multimetal cells on the various mission profiles, including more than 100 actual flight hours of drones using a lithium metal module. Interestingly, the LLM identifies features that can detect anomalies and send early warning signals far more accurately.
Speaker Change: While the boundary conditions are well understood by humans, there are not enough to actually predict battery remaining useful life and incidence.
Speaker Change: AI is far more accurate and powerful at detecting anomalies than even the best human engineers.
Speaker Change: In AI for Safety, rather than relying solely on human-developed boundary conditions, we have pre-trained our LLM with cell cycling data of more than 15,000 lithium metal cells under various mission profiles.
Speaker Change: including more than 100 actual flight hours of drones using our lithium metal modules.
Speaker Change: Interestingly, the LLM identifies features that can detect anomalies and send early warning signals far more accurately.
Qichao Hu: These AI-developed features work remarkably well, and we are working on improving the explainability of these models. With more vehicle battery data training, we believe that AF 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 continued the cycle test until the actual incident to verify the prediction accuracy.
Speaker Change: These AI-developed features work remarkably.
Speaker Change: And we are working on improving the explainability of these models.
Speaker Change: With more vehicle battery data training, we believe that AF 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.
Speaker Change: In working with our OEM partners, our air for safety model has been able to predict 100% of more than 40 incidents.
Speaker Change: Our model predicted the incidents 10 to 30 cycles earlier than they occurred and sent warning signals.
Speaker Change: We also continue the cycle test until the actual incidence to verify the prediction accuracy.
Speaker Change: In comparison, our human-based models were only able to predict around 80% of the incidents.
Qichao Hu: In comparison, our human-based models were only able to predict around 80% of the Third, AI for Science. Human research and development on battery materials has been the single slowest step in the commercialization of next-gen battery technology. For example, the entire global lithium ion industry spent 30 years studying less than 1000 unique molecules. When they are 100 billion, that's 10 to the 11, unique molecules that could be studied and Shubhie Thakur. On average, it takes human scientists 10 years to introduce a new battery material. We believe AF science can do that in one year.
Speaker Change: Third, AI for Science.
Speaker Change: Human research and development on battery materials has been the single slowest step in commercialization of next-gen battery technologies.
Speaker Change: For example, the entire global lithium ion industry spent 30 years studying less than 1000 unique molecules. When there are 100 billion, that's 10 to the 11th unique molecules that could be studied.
Suge Stadit: and Sugar Studded.
Suge Stadit: On average, it takes human scientists 10 years to introduce a new battery material.
Suge Stadit: We believe AI for Science can do that in one year.
Qichao Hu: Unlike AI for manufacturing and safety, which collects 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 6th molecule.
Suge Stadit: Unlike AI for manufacturing and safety that collect actual data from the lines and vehicles,
Suge Stadit: AFScience requires an enormous molecular property database that currently does not exist.
Suge Stadit: Synthesizing this property database requires massive computing power.
Suge Stadit: 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.
Suge Stadit: Several universities, national labs, and big tech companies have participated in this initiative, and we have already mapped about 10 to the 6th molecules.
Qichao Hu: 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.
Suge Stadit: With more GPUs, we expect to map a large enough molecular universe that our AI model training will reach sufficient accuracy.
Suge Stadit: 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.
Suge Stadit: Power Tools, Automotive, and other applications.
Qichao Hu: That's why we built ElectroFoundry, which has been operational since April this year. This electrical 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. So how do we monetize all this?
Suge Stadit: Most of these molecules are completely new and not commercially available. That's why we built ElectriFoundry, which has been operational since April this year.
Suge Stadit: This electrical foundry employs some of the best organic synthesis chemists in the world.
Suge Stadit: 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.
Suge Stadit: No one in the battery industry has such a complete capability.
Qichao Hu: 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.
Speaker Change: So how do we monetize all this?
Speaker Change: 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.
Speaker Change: In AI for Manufacturing and Safety,
Speaker Change: To truly ensure near 100% safety in the field, manufacturing quality and vehicle safety data must be integrated.
Speaker Change: 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.
Qichao Hu: 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 to be structured as a premium valid for the entire warranty period.
Speaker Change: 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.
Speaker Change: The larger and more diverse the data, the more accurate the models become.
Speaker Change: We expect the pricing could be structured as a premium valid for the entire warranty period.
Qichao Hu: 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 Air Force Science. SESAI has the strongest battery electrolyte development capability. Many battery companies and OEMs do not have the resources to develop good electrolyte material.
Speaker Change: 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.
Speaker Change: In air for science.
Speaker Change: SES AI has the strongest battery electrolyte development capability.
Speaker Change: Many battery companies and OEMs do not have the resource to develop good electrolyte materials.
Qichao Hu: 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 humans. We will then expand to lithium ion applications, such as low temperature performance, 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 rollout.
Speaker Change: We can in-source intelligence and help them solve their challenges.
Speaker Change: We will start by seeking to beat the lithium metal electrolyte Coulombic efficiency record set by human scientists.
Speaker Change: 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.
Speaker Change: 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.
Speaker Change: The pricing structure may be based on a development fee and recurring licensing royalty.
Qichao Hu: We have been applying this to lithium metal material discovery and expect to apply it to lithium ion material discovery. So we're going all in on AI. AI is changing everything. Our AI for manufacturing, AI for safety, and AI for science models are accelerating the commercialization, time to revenue, and profitability of lithium metal for EV, UAM, and drones. But they can also be applied to the broader lithium ion architecture. Having navigated numerous industry cycles, I'm particularly proud of developing a technology from the ground up that many deem impossible. Our collaboration with the
Speaker Change: We have been applying this to lithium metal material discovery and expect to apply it to lithium ion material discovery.
Speaker Change: So we're going all in on AI. AI is changing everything. Our AI for manufacturing, AI for safety, and AI for science models are accelerating the commercialization, time to revenue, and profitability of lithium metal for EV, UAM, and drones.
Speaker Change: But they can also be applied to the broader ListMio applications.
Speaker Change: Having navigated numerous industry cycles, I'm particularly proud of developing a technology from the ground up that many deem impossible. Our collaboration with a diverse portfolio of world-class customers further validates our efforts.
Qichao Hu: This portfolio of world-class customers further validates our efforts. However, I've never been more excited about our business than I am now with
Speaker Change: However, I've never been more excited about our business than I am now with the integration of AI into every aspect of our operations.
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Qichao Hu: I firmly believe this will enable us to drive transformative change on a global scale. I am truly fortunate to be living in this exciting period in transportation, science, and AI. In addition to the vision we have outlined for our three AI solutions, our top priorities for the year remain focusing on capital efficiency, attracting top talent, continuing to make progress on delivering lithium metal cells to our EV, UAM, and drone partners, and leading the AI transformation of the battery industry. Thank you for your continued interest in SES AI. And now I want to turn it over to Jim for financial matters.
Speaker Change: I firmly believe this will enable us to drive transformative change on a global scale. I am truly fortunate to be living in this exciting period in transportation, science, and AI.
Speaker Change: In addition to the vision we have outlined for our three AI solutions,
Speaker Change: Our top priorities for the year remain focusing on capital efficiency, attracting top talent, continuing to make progress on delivering lithium metal cells to our EV, UAM, and drone partners, and leading the AI transformation of the battery industry.
Speaker Change: Thank you for continued interest in SESAI. And now I want to turn it over to Jun for financials.
Jim: Thank you, Qichao. Today I will cover our second quarter 2024 financial results and discuss our operating and capital budget for the full year 2024. In the second quarter, our GAAP operating expenses were $24.6 million, cash used in operations was $22.1 million, and capital expenditures were $3.7 million. We ended the second quarter with $294.7 million in liquidity. As we continue to be very prudent with our cash and management of expenditures, we updated our full year 2024 guidance.
Jun: Thank you, Qichao. Today I will cover our second quarter 2024 financial results and discuss our operating and capital budget for the full year 2024.
Jun: In the second quarter, our GAAP operating expenses were $24.6 million.
Jun: Cash used in operations was $22.1 million and capital expenditures were $3.7 million. We ended the second quarter with $294.7 million in liquidity.
Jun: As we continue to be very prudent with our cash and management of expenditures, we updated our full year 2024 guidance.
Jim: We now expect total cash usage to be in the range of 100 million to 120 million, down from 110 million to 130 million previously. This range is comprised of cash usage from operations of $85 million to $95 million, compared with $90 million to $100 million previously, and capital expenditures in the range of $15 million to $25 million, compared with $20 million to $30 million previously. We expect our strong balance sheet to provide liquidity for the company well into 2027.
Jun: We now expect total cash usage to be in the range of $100 million to $120 million, down from $110 million to $130 million previously.
Jun: This range is comprised of cash usage from operations of $85 million to $95 million, compared with $90 million to $100 million previously.
Jun: and Capital Expenditures in the range of $15 million to $25 million, compared with $20 million to $30 million previously.
Jun: We expect our strong balance sheet to provide liquidity for the company well into 2027.
Jim: Going forward in C-Sample and beyond, we expect to share capacity buildup capital expenditures with our OEM partners. UAM, drones, and our AI solutions could provide potential upside to earlier commercialization. With that, I'll hand the call back to the operator to open up for questions.
Jun: Going forward in CSAMPL and beyond, we expect to share capacity buildup capital expenditures with our OEM partners. UAM, drones, and our AI solutions could provide potential upside to earlier commercialization.
Speaker Change: With that, I'll hand the call back to the operator to open up for questions.
Operator: If you'd like to queue for a question, you can do so by pressing star one on your telephone keypad. If, for any reason, you'd like to remove your question, press star two. Again, to join the question queue, please press star one. Our first question is from Jed Dorsheimer with William Blair. Your line is now open.
Speaker Change: If you'd like to queue for a question you can do so by pressing star 1 on your telephone keypad. If for any reason you'd like to remove your question press star 2. Again to join the question queue please press star 1.
Speaker Change: Our first question is from Jed Dorsheimer with William Blair. Your line is now open.
Mark Haywood Shooter: You have Mark Shooter on for Jed. Qichao, I'd like to hear what incremental data you've seen on AI to really push for this all-in approach. I know you've been working on these AI applications in the background for some time, but what was so incrementally positive here to really push this strategy shift?
Speaker Change: You have Mark Shooter on for Jed.
Qichao Hu: Qichao, I'd like to hear what incremental data you've seen on AI to really push for this all-in approach. I know you've been working on these AI applications in the background for some time, but what was so incrementally positive here to really push this strategy shift?
Qichao Hu: Hey Mark. So really, in all three areas, we started working on these three AIs, AI for Safety, really back in 2017. And then AI for Manufacturing, really towards the end of A sample, beginning B sample. So towards the end of A sample, and now as we begin B sample with more data.
Qichao Hu: Hey, Mark. So really, in all three areas, we started working on these three AIs. AI for Safety really back in 2017, and then AI for Manufacturing really towards the end of A sample, beginning of B sample.
Speaker Change: So towards the end of A sample and now as we begin B sample with more data.
Qichao Hu: And then in manufacturing, we found once we hit about 1000 quality checkpoints per cell and get about 1000 cells per month per line, it's really helpful. And then because when we make a new cell design, the human engineers don't. So when you start with a new cell design, basically, you have no experience; you have no idea what quality specs to use. So the traditional process really is too slow.
Speaker Change: And then in manufacturing, we found once we hit about 1000 quality checkpoints per cell, and get about 1000 cells per month per line,
Speaker Change: It's such a really helpful. And then, because when we make a new cell design, the human engineers don't
Speaker Change: So when you start with a new cell design, basically, you have no experience, you have no idea what quality specs to use. So the traditional
Qichao Hu: And then we started applying AI models. And then first, we collected all this data. And then the model would actually recommend very interesting quality specs. And then we started seeing this, I would say towards the end of last year and then beginning of this year.
Speaker Change: process really is just too slow.
Speaker Change: And then first we collected all this data. And then the model would actually recommend very interesting quality specs. And then we started seeing this, I would say,
Qichao Hu: So instead of human engineers trying lots of experiments and then figuring out, okay, the optimal electrical amount is two grams per amp hour, or the optimal gap between the cathode and the anode is 1.5 millimeters. Actually, this AI model is actually going to rank all the quality issues for you and then tell you, so this one, for example, the pressure during the hot press on the jolly roll has a bigger impact than your ceiling temperature. And then it's actually going to tell you the relationships between all these steps. So that was shocking in a really powerful way.
Speaker Change: towards end of last year and then beginning of this year. So instead of human engineers trying lots of experiments and then figuring out, okay, the optimal electrical amount is two grams per amp power or
Speaker Change: Well, the optimal gap between cathode and anode is 1.5 millimeters. Actually, this AI model is actually going to rank all the quality issues for you, and then tell you, so this one.
Speaker Change: For example, the pressure during hot press on the jolly roll has a bigger impact than your ceiling temperature. And then actually it's going to tell you the relationships between all these steps. So that was like shocking, but in a really powerful way.
Qichao Hu: So instead of the traditional way of improving manufacturing quality, this model was just as out of the world powerful. And it still doesn't replace quality engineers. We still have them.
Speaker Change: So instead of the traditional way of improving manufacturing quality, this model was just like out of the world powerful. And it still doesn't replace quality engineers. We still have
Speaker Change: Good quality engineers from the big lithium ion industries, but it's a really helpful tool to to supplement.
Qichao Hu: [inaudible] Sir, compliment the human engineers. And then on the safety side, so we started training a large language model with all the cycling data, charge, and discharge. And actually, if you look at the charge and discharge curve, it's actually very much like a sentence. So you train a large-language model. And then, we had several examples where, and this is also another case where now we are in the B-sample, and also we're testing against mission profiles for UAMs and then drones. And then the traditional, the OEMs would have nine, sometimes more than a dozen physics-based models, like SOC, SOH, and then set those as boundary conditions. If any of those get triggered, then you have an alarm.
Speaker Change: [inaudible]
Speaker Change: And then actually if you look at the charge and discharge curve, it's actually very much like a sentence, so you train a large language model, and then
Speaker Change: So we had several examples where
Speaker Change: And this is also another case where now we are in B-Sample, and also we're testing against mission profiles for UAMs and then drones. And then the traditional, the OEMs would have nine, sometimes more than a dozen physics-based models, like SOC, SOH, and then set those as boundary conditions. If any of those...
Speaker Change: get triggered, then you have an alarm. But it takes a long time to develop that that
Qichao Hu: But it takes a long time to develop that set of physics-based models that only works for mature chemistries. Again, it's like manufacturing. When you introduce a new cell chemistry, like none of the existing processes, I mean, the existing process is just too slow, but none of the existing set of metrics works. The manufacturing quality specs don't work. The physics-based models, those boundary conditions don't work.
Speaker Change: That sort of thing.
Speaker Change: physics-based models that only works for mature chemistries. Again, protective manufacturing, when you introduce a new
Speaker Change: cell chemistry, like, none of the existing process, I mean, the existing process is just too slow, but none of the existing set of metrics works.
Speaker Change: Manufacturing Quality Specs don't work. The physics-based models, those...
Speaker Change: Foundry conditions don't work. So if you continue to use the traditional process, it will take too long.
Qichao Hu: So if you continue to use the traditional process, it will take too long. So then this large language model, actually, we had an example where the cell actually had an incident on a cycle, like 170 something. And then none of the other physics-based models was able to predict anything before that. But this one AI model, this large language model that actually found this feature, which we cannot explain today, that actually sent a warning on cycle 144, about 30 cycles before that. So that's really powerful.
Speaker Change: So then this, this large language model actually.
Speaker Change: We had an example where the cell actually had an incident on a cycle of 170-something.
Speaker Change: And then none of the other physics-based models was able to predict anything before that. But this one AI model, this one large language model that actually found this feature, which we cannot explain today, that actually sent a warning on cycle 144, about 30 cycles before.
Qichao Hu: And then, so both quality manufacturing and then safety is like when you introduce a new cell design, your experience doesn't work anymore, your existing quality, your existing set of metrics don't work anymore. So an AI model will help you develop that much faster. And then in AI for Science, So we actually expanded our electrical team, both the AI team and the human scientist team. And then just since the end of last year, our AI model was actually able to find 17 new molecules.
Speaker Change: So that's really powerful.
Speaker Change: And then, so both quality manufacturing and then safety, it's like when you introduce a new cell design, your experience doesn't work anymore. Your existing set of metrics don't work anymore. So AI model will help you develop that much faster.
Speaker Change: and then in Air for Science.
Speaker Change: So, we actually hired, we actually expanded our electrical team, both AI team and human scientist team.
Speaker Change: And then just since end of last year, our AI model was actually able to find 17 new molecules.
Qichao Hu: And then we actually synthesized three of them. And then we did the testing. And the performance so far is just as good as the molecules that human scientists came up with in the past 10 years since 2012. And then this is only after having mapped 10 to the 6th, right? If we map 10 to the 8th, and 10 to the 11th, we're pretty confident that we can find something that works better. So I think these three signals that we found towards the end of last year and the beginning of this year just made us convinced, okay, if you want to introduce a new battery chemistry, and then we're doing that at scale, B sample, C sample. Why spend eight years? Why spend 10 years just improving the quality and improving the safety when you can use AI to do things much faster?
Speaker Change: And we actually...
Speaker Change: [inaudible]
Speaker Change: 10 years, since 2012. And then...
Speaker Change: Unknown Speaker 07. I'm going to go ahead and unmute myself. Unknown Speaker 07. Okay. Thank you. Unknown Speaker 07. Okay. Thank you. Unknown Speaker 07. Thank you. Unknown Speaker 07. Thank you. Okay.
Speaker Change: Qichao Hu, Unknown Executive, Jing Nealis
Speaker Change: Unknown Speaker May just convince. Okay.
Speaker Change: If you want to introduce a new battery chemistry, and then we're doing that at scale, B-sample, C-sample.
Speaker Change: Why spend 8 years, why spend 10 years just improving the quality, improving the safety, when you can use AI to do things much faster?
Mark Haywood Shooter: That's great. I appreciate all the color there, Qichao.
Speaker Change: Well, that's great. I appreciate all the color there, Qichao. We hear a lot of times that AI is making software engineers 10x engineers.
Speaker Change: It sounds like you're applying AI to make your material scientists and your quality control engineers.
Speaker Change: CNN, and Eric.
Eric: That's great to hear. I'm particularly interested in what comes out of AI for science in the electrolyte space, because that is such a vast mapping that needs to occur. I agree with you there.
Mark Haywood Shooter: It sounds, you know, we hear a lot of time that AI is making software engineers, you know, 10x engineers, but it sounds like you're applying AI to make your material scientists and your quality control engineers, 10x engineers. So that's great to hear. I'm particularly interested in what comes out of AI for science in the electrolyte space, because that is such a vast mapping that needs to occur.
Speaker Change: One follow up about the OEM partners. I was thinking of, oh, sorry, how are the OEM partners specifically the EV OEM partners, how are they looking at, you know, the, this AI manufacturing and the science, I'm sorry, not the science, the safety?
Speaker Change: Are they looking at it as an attractive bonus that they currently don't have for traditional lithium-ion, or are they looking at it as a necessary proof point to convince them of the safety of a new chemistry that they're not comfortable with?
Mark Haywood Shooter: Yeah, one follow-up question about the OEM partners, I was thinking of, oh, sorry, how are the OEM partners, specifically the EV OEM partners, how are they looking at, you know, this AI for manufacturing and the science, I'm sorry, not for science, for safety? Are they looking at it as an attractive bonus that they currently don't have for traditional lithium ion? Or are they looking at it as a necessary proof point to convince them of the safety of a new chemistry that they're not comfortable with?
Qichao Hu: Yeah, so it's two things. One is it's a necessary approach to convince them of the new battery chemistry at commercial scale. We're not talking about R&D anymore, not A-sample; we're talking about B-sample and then C-sample. Like we're seriously talking about putting tens of thousands of cars with lithium metal batteries in the field with all kinds of users for EVs and UAMs. So at this point, we need a lot of data, a lot of real-world experience, and also AI models to really guarantee safety. Because now we're talking not about safety in the lab, but safety in the field. So, one thing is a necessity.
Speaker Change: Yeah.
Speaker Change: So it's two things. One is it's a it's a
Speaker Change: necessary approach to convince them of a new battery chemistry at commercial scale. We're not talking about R&D anymore, not A-sample. We're talking about B-sample and C-sample. Like we're seriously talking about putting tens of thousands of cars with lithium metal battery in the field with
Speaker Change: with all kinds of users for EVs and UAMs. So at this point, we need a lot of data, a lot of real world experience, and also...
Qichao Hu: Second, a lot of these automakers want to make their own batteries, and so far, they are, they are, they are. So, so far, the power is in the hands of the large battery manufacturers, the CATLs, the LGs of the world. So for the automakers to control their own destiny, they really need to quickly control the battery, and then having access to and having control over battery manufacturing data and battery performance data in the vehicle is very powerful. It allows the automakers to quickly get up to speed, and then get to the same level of proficiency in terms of manufacturing quality and safety compared to the large battery manufacturers. So these two are really important for the OEMs. And then it's both for lithium metal but also for any next-gen lithium ion battery.
Speaker Change: AI model to really guarantee safety, because now we're talking about not safety in the lab, but safety in the field. So one thing is necessity. Second is a lot of these automakers want to make their own batteries. And so far, they are, they are
Mark Haywood Shooter: Thanks, Qichao. I appreciate it.
Speaker Change: They are...
Speaker Change: Qichao Hu, Unknown Executive, Jing Nealis
Speaker Change: So far, the power is in the hands of the large battery manufacturers.
Speaker Change: the CATLs, the LGs of the world. So for the
Speaker Change: The automakers to control their own destiny, they really need to quickly control battery and then having access and having control to battery manufacturing data and
Speaker Change: Get up to speed and then get to the same level of proficiency in terms of
Speaker Change: Manufacturing Quality and Safety compared to the large battery manufacturers. So these two are really important for the OEMs. And then it's both for lithium metal, but also for any next-gen lithium ion.
Speaker Change: Thanks Qichao, I appreciate it.
Operator: As an additional reminder, it is star number one. If you'd like to join the question queue, our next question is from Sean Severson with Watertower Research. Your line is now open. Great, thank you.
Speaker Change: As an additional reminder, it is star one. If you'd like to join the question queue. Our next question is from Sean Severson with Watertower Research. Your line is now open.
Shawn Michael Severson: Thank you. Qichao, I just wanted to go back to the monetization of AI. I mean, I think it's clear that you've simply been able to make a better lithium metal battery, right? With the information you have, and what I'm trying to understand is how does that model expand to, you know, the lithium ion industry, the OEMs, what you were talking about, as far as uses and applications for AI, how does this get monetized outside of your own manufacturing?
Shawn Michael Severson: Great. Thank you.
Shawn Michael Severson: Qichao, I just wanted to go back to the monetization of the AI. I mean, I think it's clear in the pathway for you've simply been able to make a better lithium metal battery, right, with the information you have. And what I'm trying to understand
Speaker Change: is how does that model expand to, you know, the lithium ion industry, the OEMs, what you were talking about, as far as uses and applications for AI, how does this get monetized outside of your own manufacturing? Unknown Speaker
Qichao Hu: Yeah, so once you get an AI, then it becomes very chemistry agnostic. And then actually, in AI for manufacturing and AI for safety, we do train our models with both lithium metal data, more than 15,000 lithium metal cells in house, as well as lithium ion data that we get from our OEM partners, we get from public sources, and the more diverse the larger the data size that you train this model on, the smarter the model becomes.
Speaker Change: Yeah.
Speaker Change: So once you get AI, then it becomes very chemistry agnostic and then actually in AI for manufacturing and AI for safety, we do train our models with both lithium metal data, more than 15,000 lithium metal cells in house.
Speaker Change: [inaudible]
Qichao Hu: So the AI for manufacturing, we could also apply this, for example, say a company wants to commercialize a next-gen silicon lithium-ion battery, and then it also happens to be pouch spec cells. We can apply this model to that manufacturing line because they're also new to that cell design, that cell manufacturing process, and they also don't know what quality to apply because it's new. So we can apply this to that. And also, if an OEM wants to put a silicon anode lithium ion cell or any less proven lithium ion cell in the vehicle, and then also to monitor its health and then predict the remaining useful life and incident, then this large language model trained on the data can also be used for that.
Speaker Change: So the AI for manufacturing, we could also apply this, for example, say, a company wants to
Speaker Change: to commercialize next-gen silicon.
Speaker Change: lithium-ion battery, and then it also happens to be pouch stack cells. We can apply this model to that manufacturing line because
Speaker Change: They're also new to that cell design, that cell manufacturing process, and they also don't know what quality specs to apply because it's new. So we can apply this to that.
Speaker Change: And also if
Speaker Change: and OEM wants to put a silicon anode lithium ion cell or any less proven lithium ion cell in the vehicle and then also to monitor the health and then predict.
Speaker Change: [inaudible]
Shawn Michael Severson: So, they would, in effect, kind of license this from you or license the solution for you; they pay you for the AI.
Speaker Change: So they would in effect kind of license this from you or license the solution from you. They pay you for the for the AI.
Qichao Hu: Yes, yes. So, for example, in the first part, the first phase of engagement, basically, it'll be free. They provide us with data and then allow us to fine-tune our model. And once our model is fine-tuned, then we license that model to them. And then, so for air for safety, it could be a premium per vehicle per month over the eight or 10-year warranty period. And for air for manufacturing, it could also be a fee per line per year.
Speaker Change: Yes, yes. So, for example, in the first...
Speaker Change: part, the first phase of engagement basically it'll be free. They provide us data and then to fine-tune our model and once our model is fine-tuned then we
Speaker Change: [inaudible]
Speaker Change: Also a fee per line per year.
Shawn Michael Severson: Do you expect to own the IP that comes from this, you know, particularly in the field of science, I mean, you come up with a new combination or new chemistry, these things that then you will own, and you will patent and license those things? Or are they going to be specifically used by the OEM for the solution, and they will own it?
Speaker Change: Do you expect to own the IP that comes from this, you know, particularly in the for science? I mean you come up with new a new combination or new chemistry, these things that then you will own and you will you will patent and license those things or are they going to be specifically used by the OEM for the solution and they would own it?
Qichao Hu: Yeah, so the models we definitely own. And then, in some cases, we might open source the models, so the models can be trained faster. But then, especially in the AFS science case, when we actually make the Molecular Universe, the molecule property database that we plan to make open source, and then the model, part of the model will also be open source so that others can develop it, and then this model can become smarter.
Speaker Change: Yeah, so the models we definitely own. And then, in some cases, we might open source the models, so the models can be trained faster. But then, especially in the AFA science case, when we actually
Speaker Change: So the molecular universe, the molecule property database that we plan to make
Speaker Change: Open Source. And then the model, part of the model will also be open source so that others can
Speaker Change: [inaudible]
Qichao Hu: But then once we use that model and then generate a new molecule that has, for example, higher chromic efficiency on lithium metal or can improve low temperature performance, then those molecules, the output, of course, will be our proprietary IP that we're going to be last.
Speaker Change: But then once we use that model, and then generate a new molecule that has, for example, higher chromic efficiency on lithium metal, or can improve low temperature fast charge of silicon lithium ion, then those molecules, the output, of course, will be our proprietary IP that we're going to license. Unknown Speaker
Shawn Michael Severson: Thanks. My last question is, will the AI be proactive and reactive? And by that, what I mean is, let's say there is a problem that is happening, right? Something is happening. Can you then take that data and solve for it? I understand there is a predictive portion of this as well, but can you solve problems that the battery manufacturers and OEMs are experiencing after the fact?
Speaker Change: My last question is, will the AI be proactive and reactive? And by that, what I mean is.
Speaker Change: Let's say there is a problem that is happening, right, something that's occurring.
Speaker Change: Can you then take that data and solve for it? I understand there's a predictive.
Speaker Change: portion of this as well, but can you solve problems that the battery manufacturers and OEMs are experiencing after the fact?
Qichao Hu: Yeah, so in manufacturing, for sure. For example, we can actually blindly manufacture cells, meaning you just manufacture cells, collect data without any initial quality specifications, and then the AI is going to collect all the data and then get trained, and then recommend quality specifications. And actually, it's going to rank them.
Speaker Change: Yeah, so in manufacturing, for sure. For example, we can actually blindly manufacture cells, meaning you just manufacture cells, collect data without any initial quality specs. And then the AI is going to collect all the data and then get trained, and then recommend quality specs. And actually, it's going to rank it.
Qichao Hu: For example, certain steps will have a higher impact on quality than other steps. And then, so that's going to tell you, for example, step number 17, and that's a hot press where you need to lower the pressure to improve the quality. Yes. So in AI for manufacturing, definitely, you can get to a point where you can start with blind manufacturing, and then the AI will tell you where to fix. So yes, in the air for safety on the vehicles.
Speaker Change: For example, certain steps will have higher impact on quality than other steps, and then
Speaker Change: So that's going to tell you, for example, step number 17, and that's
Speaker Change: hot press that you need to lower the pressure to improve the quality. Yes. So in AF manufacturing, definitely, you can get to a point where you can start with blind manufacturing, and then the AI will tell you where to fix.
Speaker Change: So, yes, in air for safety on the vehicles.
Qichao Hu: So the goal is to monitor the health and then predict and then, but not really control it. So whatever prediction we make, we're going to send that back to the OEM, and then what the OEMs do with the signal, that's up to them. Great, I think that was right.
Speaker Change: So the goal is to monitor the health and then predict, but then not really to control it. So whatever prediction we make, we're going to send that back to the OEM. And then what the OEMs do with the signal, that's up to them.
Shawn Michael Severson: Great. That was very helpful. Thanks, Qichao. We have no further questions, so I'll pass the call back to the management team.
Speaker Change: Great. That was very helpful. Thank you, Qichao.
Speaker Change: Thank you, Qichao.
Operator: We have no further questions, so I'll pass the call back to the management team for any closing remarks. That concludes today's call. Thank you all for your participation. You may now disconnect your line.
Speaker Change: We have no further questions, so I'll pass the call back to the management team for any closing remarks.
Speaker Change: Qichao Hu, Unknown Executive, Jing Nealis Qichao Hu, Unknown Executive, Jing Nealis
Speaker Change: Qichao Hu, Unknown Executive, Jing Nealis Qichao Hu, Unknown Executive, Jing Nealis
Speaker Change: Qichao Hu, Unknown Executive, Jing Nealis Qichao Hu, Unknown Executive, Jing Nealis
Speaker Change: That concludes today's call. Thank you all for your participation. You may now disconnect your line.
Speaker Change: Thank you. Thank you. Thank you.
Speaker Change: Qichao Hu, Unknown Executive, Jing Nealis Qichao Hu, Unknown Executive, Jing Nealis