Good afternoon. Thank you for joining us for the SES AI Third Quarter 2024 Earnings Release and Call. My name is Shana, and I will be your moderator today. I will now hand the conference over to our host, Kyle Pilkington, Chief Legal Officer. Kyle, you may proceed.

Hello, everyone, and welcome to our conference call covering our third 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 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 Qichao.

Thanks, Kyle. Good afternoon, and thank you for joining us on our third quarter earnings call. Here, we present an update on our progress towards commercialization of our next-generation lithium metal batteries and our AI solutions. First, early commercial revenue in urban air mobility and drones. Our UAN lines have completed site acceptance tests (SAT), and we won cell supply agreements, including with SoftBank. Second, B-samples passed EV safety tests. Our 100 mPOWER lithium metal B-sample cells successfully passed the GB38031 2020, a major milestone towards C-samples and start of production (SOP). Third, revenue pipeline from AI accelerated battery material discovery for lithium metal and lithium ion. We expect earlier revenue pipeline from electrolyte projects, including both lithium metal and lithium ion. We recently signed several commercial agreements to supply lithium metal cells with several customers, including SoftBank. Earlier this year, we converted our EV A-sample lines to UAM lines. And we're excited to report that both our Shanghai and Chengdu EV A-sample to UAM line conversions have completed site acceptance tests (SAT) and achieved ready-to-use (RTU) status. The new UAM cells manufactured on these lines have 30% empowering capacity and are custom designed to meet UAM customer requirements. Second, B-samples passed EV safety test. As the world's first to enter automotive B-sample joint development for lithium metal, our 100 mPOWER lithium metal cells developed for our B-Sample joint development agreements (JDAs) successfully passed the rigorous industry safety test of GB38031 2020. This is the first time in the industry that a large capacity lithium metal cell successfully passed safety tests required by the global GB38031-2020 EV traction battery safety standards. This certification is required by all of SES AI's EV OEM partners as well as many of the other leading manufacturers as a global standard. Achievement of the standard demonstrates that SES AI's 100 mPOWER lithium metal cells can effectively manage safety risk under these stringent test conditions. This is a major milestone towards C-sample and start of production (SOP). Third, all in on AI. Our AI solutions have already borne fruits in advancing our lithium metal plan. We are working closely with our OEM partners to broaden the scope of our AI efforts to incorporate other battery chemistry and cell designs. We anticipate that this will enable us to deliver greater value to a broader customer base, better serving our customers using lithium-ion and lithium metal batteries and generating multiple revenue opportunities. Earlier this year, we established our electrolyte foundry, formed a partnership with NVIDIA to leverage the latest computing hardware and software, and attracted a team of world-class battery electrolyte scientists, including promoting Dr. Kang Su to our Chief Technology Officer (CTO). We achieved remarkable acceleration, which allowed us to complete the largest molecular property database in the world. We have the most complete end-to-end capabilities, including molecular property mapping, AI model development, human domain expertise, the electrolyte foundry for molecule synthesis, electrolyte formulation, and cell production and validation from lab to A samples and B samples and beyond. We expect to enter a pipeline of revenue contracts for the first time later this year, including five projects from two companies, one electrolyte manufacturer and one automaker, to apply our AI accelerated battery material discovery end-to-end capability to solve various lithium metal and lithium-ion electrolyte challenges. This is just in the first quarter since we introduced our all-in on AI strategy. AI for manufacturing and safety. Both our Shanghai and Chengdu UAM lines now have AI for manufacturing installed, and we are currently installing AI for manufacturing for the B-sample lines at our OEM partner site. This tool has helped us detect defects that would have escaped using conventional manufacturing quality control. All UAM and drone customers that receive our lithium metal cells and modules will also have our AI for safety embedded in addition to conventional battery management systems to precisely monitor battery health and predict incidents. With AI for safety alone, we have achieved 95% prediction accuracy, achieving the target that we set out earlier this year. And with AI for manufacturing integrated with AI for safety, we can achieve 100% prediction accuracy. These are based on training from 15,000 lithium metal cells. Overall, I'm very proud of our achievements in the past quarter. In EV, our 100 mPOWER lithium metal cells successfully passed the rigorous GB38031 global industry safety test, an industry first for lithium metal and a major milestone toward commercialization of lithium metal for EV. In UAM and drones, we now have two lines producing cells for multiple customers, including SoftBank. Powering the success of our lithium metal is our all-in on AI strategy. We built what we believe to be the world's largest molecular property database using state-of-the-art computing hardware and software, expect to enter a pipeline of revenue contracts for the first time from our AI solutions in the fourth quarter of this year, and achieved our target of 95% prediction accuracy with AI for safety and 100% accuracy when we integrate AI for manufacturing with AI for safety. Thank you for your continued interest in SES AI. And now I will turn it over to Jing for financial updates.

Thank you, Qichao. Today, I will cover our third quarter 2024 financial results and discuss our operating and capital budget for the full year 2024. In the third quarter, our GAAP operating expenses were $34.2 million. Cash used in operations was $22.7 million and capital expenditures were $1.5 million. We ended the third quarter with $274 million in liquidity. As we continue to be very prudent with our cash and management of our expenditures, we updated our full year 2024 guidance. We now expect total cash usage to be in the range of $80 million to $95 million, down from $100 million to $120 million previously. This range is comprised of cash usage from operations of $70 million to $80 million compared with $85 million to $95 million previously, and capital expenditures in the range of $10 million to $15 million compared with $15 million to $25 million previously. With our reduced and more capital-efficient cash usage and expectation that our all-in on AI strategy will start generating revenue in the near future, we expect our strong balance sheet to provide liquidity for the company well into 2028. Now I would like to hand it over to the operator to start the Q&A session.

Our first question comes from Shawn Severson with Water Tower Research.

Qichao, very exciting on the revenue opportunity in the fourth quarter from AI. But can you expand a little bit on what the revenue mechanism is going to be? Is it like a subscription service? Or what form is the revenue going to take?

The AI accelerated material discovery is just the beginning, and it has the potential to transform the entire battery industry. We have already identified a molecule that enhances lithium metal cycling by approximately 20%, which is very promising. Our next step is to apply this discovery to silicon lithium-ion technology. We have determined that silicon anode batteries and lithium metal share similarities in their electrolyte requirements. In terms of our business model, we aim to implement a subscription-based approach. Clients, such as electrolyte or battery companies, will pay an annual subscription fee to utilize our model. Additionally, when we introduce a new molecule or electrolyte product, we will manufacture and supply that product, allowing us to generate revenue from both the subscription model and product sales.

Can you explain how you plan to grow your customer base? It sounds like you have a program in use, which is great. How does this expansion work? Will you be adding more products for the same OEM, or are you engaging with multiple OEMs? I'm trying to grasp the growth process for your AI revenue.

The three OEMs we are currently collaborating with will be our initial customers. Additionally, we are engaging with electrolyte companies that produce electrolytes not only for OEMs with whom we do not have joint development agreements but also for applications beyond automobiles, such as smartphones, robotics, and grid storage. We've been approached by the top five electrolyte manufacturers along with leading battery producers and automotive companies. We are already working on several projects, and fortunately, we don’t need to create entirely new solutions for each partner as there are many similarities in the requirements. Initially, developing a new solution will take around nine months to a year per discovery. Presently, we have about ten million solutions, the largest portfolio globally, and we anticipate expanding to one hundred million. With this extensive portfolio, we aim to introduce a new electrolyte every six months, allowing us to grow rapidly.

And then my last question is on the spend, CapEx, $10 million to $15 million or even from the operations usage. How is the all-in AI fitting into this? I'm just trying to understand where the money is going to now versus if you look back more towards manufacturing development, but obviously, with AI opportunities in front of you.

Yes. I can answer that. And then Jing, you can also jump in. So the B-Sample line, part of the CapEx for this year will be for the B-Sample line. But also the A-sample lines, the B-sample lines, what they are is they are part of this complete end-to-end capability for this AI-accelerated material discovery. A lot of companies that do just the AI and recommend molecules, but they don't synthesize and don't test in batteries. So you can't really validate. But what we're trying to do, and this is very important, is we have to map the property space, which we are doing. That's basically computational chemistry. We have the best team in the world. And then we filter down and recommend molecules. But then you have to synthesize the molecules. That's what our electrolyte foundry is about. We built that earlier this year. And then once we have the molecule synthesized, we formulate the electrolyte and then we test them in A-sample cells in B-sample cells. So all the effort, all the electric foundry, the teams that we built in the past, now they come together. And really, I think we're the only one that has this complete end-to-end capability for AI accelerated material discovery in the battery space. I mean in pharmaceuticals, there are other companies that do this, but in the battery space, this is the only one. And then in terms of CapEx, a lot of the money that we're spending, obviously, is talent. Talent is always number one. GPU. I mean we are getting good deals from our partners. But these are the main things. Jing, if you want to add something?

Yes. I just wanted to add a little bit color on the cash spend. As Qichao mentioned, we are spending money on hiring the best talent and then GPU, but those were in our original plan and where we reduced our CapEx to focus on building one B-sample line and converting two UAM lines. And we have also significantly decreased G&A spending throughout the year. And then also worth mentioning, we are expecting to receive around $7 million in the fourth quarter. It’s a combination of milestone payments from our OEM partners as part of the JDAs plus down payments from revenue contracts that we mentioned earlier.

Our next question comes from Winnie Dong with Deutsche Bank.

Can you guys talk about maybe the revenue potential that is associated with the UAM customer pipeline? And then are there any more details you can potentially share with us on the agreement with SoftBank and when might we see the conversion to sales?

Jing, do you want to address that?

Sure. Yes. So well, because of confidentiality with our customers, we currently really can't disclose too much details about the contract. But the project will kick off in November, and then it will continue until the first half of next year. That's all I can say right now. But for the AI solution revenue, we're expecting to enter into contracts in the next few weeks, and that's potentially revenue throughout 2025.

I mean, is there any way can you sort of help us size the potential of that kind of opportunity? It sounds like there are several ways that you guys can monetize this. Any way to sort of help us with potentially, I don't know, modeling out that kind of trajectory into 2025?

Yes. At this moment, we are not really providing guidance for 2025. But I think what we could say right now is, one, we will put out an announcement when the contract is signed. Two, we are seeing tremendous interest from our existing partners and then new customers and new partners on all these solutions that we're providing. So stay tuned for more information when we announce the deal.

Got it. And then I think previously, you guys have talked about sharing and pooling of cell data from some of your peer battery companies in order to sort of feed into the AI model. Can you maybe talk about where you might be with this and how that can potentially boost your database?

Yes, Winnie. So on the molecule, the electrolyte side, we are actually synthesizing the data. So all the data we are computing. And this is not just hardware, but also very advanced new software for computing these properties. And because you have to compute it fast and also accurately. So we are computing in-house, and then we’re not relying on any data from other companies because also they don’t have that. And then on manufacturing, as part of the B-samples, we actually get data from our OEM partners and then the data are for lithium metal as well as lithium-ion data manufacturing on the same line. So we can actually get the data and then train the model.

Our next question comes from Shawn Severson with Water Tower Research.

I wanted to follow up on the UAM market. Previously, you mentioned that it was developing quickly, but that it may not represent a significant commercial market immediately. I'm interested in any updates on what you're observing now and whether there have been any changes. It seems like there's potential for rapid market entry, so what are your thoughts on the commercialization opportunity in UAM today?

Yes. I think if you look at UAM and then that includes both those that carry passengers as well as large cargo drones, the market is actually growing quite fast, especially in China and then in other parts, this market is growing quickly. And then we actually made a cell that's tailored for UAM, including passenger and large cargo, this 30 mPOWER. It's smaller than the 100 mPOWER EV. And then this size is meant to replace the 2170 lithium-ion cells that these UAM companies currently use. And then they ask us, okay, they want this format. So this market is actually growing quite fast. And that's why we opened up this new cell product just for this.

Can you remind me about the advantages of lithium metal for urban air mobility? Could you compare lithium metal and lithium-ion in terms of competitiveness? It appears that you are replacing lithium-ion batteries in many of these applications as they advance toward commercialization.

Yes, sure. So for UAM, because they need high power density, a lot of the lithium-ion are power cells. So they are around 250 watts per kg at the cell level. For lithium metal, the cells that we are supplying, they are 400 watts per kg. So that means you can fly almost 50% longer or you can carry 50% more payload, more passengers or larger cargoes. It’s a big change. And then this means a lot for the economics and the profitability of the UAM and large cargo business.

At this time, I'd like to pass the conference back over to Kyle for more questions. Kyle, you may proceed. We do have one question, which was submitted in advance by investors. So the question is for Qichao. SES has produced high energy density cells. One of the remaining concerns is related to cycle life requirements by the EV OEMs as the data is indicating that SES is still working on increasing the cycle life. What are the things that SES is working on to increase cycle life?

Yes. So we have a few things that are quite promising. One is this new molecule that we discovered using the AI accelerated, and that improves cycle life by about 20%. And that's only after we've mapped 10 to the 5th space, but we're going to map 10 to the 8th. So there's about 1,000 times bigger that we can map. So that improves cycle life by 20%. And also, on AI for safety, we're also using the new AI tools to be trained on these cycling data, and then we can develop optimized charging protocols that can also improve the cycle life. And also on the website, we published our cycle life under real-world UAM conditions and EV conditions. Especially under the UAM conditions, we can actually get to more than 1,000 cycles. So both at the material level and the charge protocols, both can be improved by our human team as well as the AI accelerated discovery.

With that, I’ll pass it back to the operator.

At this time, there are no more questions registered in the queue. So I'll pass it back to our host for closing remarks.

Yes. I mean, we’re quite excited about this new AI accelerated material discovery that we have. And then with the new computational chemistry algorithms that we develop, we are able to really map all the properties of these molecules very fast and very accurately. And this has helped us identify new molecules for lithium metal. We’re also going to bring this tool to lithium-ion, and this was at the request of our OEM partners. In addition to this, the complete capabilities that we built, including the molecules, the electrolyte foundry and the A-sample, B-samples, we’ve really demonstrated that we can use this for lithium metal. Next, we’re really excited to apply this for lithium-ion as well.

That will conclude today's conference call. Thank you for your participation and enjoy the rest of your day.