SES AI Corporation

Good afternoon. Thank you for attending today's SES AI third quarter 2024 earnings release and call. My name is Jayla, and I'll be your moderator for today. All lines will be muted during the presentation portion of the call with an opportunity for questions and answers at the end. I'd now like to turn the conference over to our host, Kyle Pinkleton, 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 Qi Chaohu, Founder, Chairman, and Chief Executive Officer, and Jing Nilas, 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.

Thanks, Kyle. Good afternoon, and thank you for joining us on our third quarter earnings call. Here we present an update on our progress toward commercialization of our next generation lithium metal batteries and our three AI solutions. So first, early commercial revenue in urban air mobility and drones. Our UAM lines have completed site acceptance tests, SAT, and we won cell supply agreements, including with SoftBank. Second, these samples pass EV safety tests, Our 100 m power lithium metal B sample cells successfully passed the GB3A031-2020, 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. So first, early commercial revenue in UAM and drones. Recently, we signed several commercial agreements to supply lithium metal cells with several customers, including SoftBank. Earlier this year, we converted our EVA sample lines to UAM lines, and we're excited to report that both our Shanghai and Chengdu EVA sample to UAM line conversions have completed site acceptance test, SAT, and achieved ready-to-use, RTU status. The new UAM cells manufactured on these lines have 30 amp power in capacity and are custom designed to meet UAM customer requirements. Second, B-samples pass EV safety tests. As the world's first to enter automotive B-sample joint development for lithium metal, our 100 amp power lithium metal cells developed for our B-sample JDAs successfully pass 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 SSAI's EV OEM partners, as well as many of the other leading manufacturers at the global standards. GB3A031-2020 is a mandatory safety standard encompassing a series of rigorous tests, which include overcharge, overdischarge, external short circuit, heating, temperature cycling, and crushing. Achievement of this standard demonstrates that SES-AI's 100mP 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 our AI. Our AI solutions have already borne fruits in advancing our lifted metal plan as outlined earlier. We're 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. AI accelerated battery material discovery. 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. Kongshui to our Chief Technology Officer, CTO. We achieved remarkable acceleration, which allowed us to compute 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 and electrolyte formulation, and cell production and validation from lab to A samples, 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 electrical 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 system. 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. I'm very proud of our achievements in the past quarter. In EV, our 100 amp power 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 South Bank. 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, 100% accuracy when we integrate AI for manufacturing with AI for safety. So thank you for your continued interest in SES AI. And now I will turn it over to Jing for financial updates.

Thank you, Chi-Chao. Today I will cover our third quarter 2024 financial results and discuss our operating and capital budget for full year 2024. In the third quarter, our GAAP operating expenses were $34.2 million. Cash used in operations were $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 the expectation that our OAN 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 a Q&A section.

We'll now begin our question and answer session. At this time, if you would like to ask a question, please press Start followed by 1 on your telephone keypad. If for any reason you would like to remove that question, please press star followed by two. Again, to ask a question, it is star one. As a reminder, if you're using a speakerphone, please remember to pick up your handset before asking a question. Our first question comes from Sean Severson with the company Watertower Research. Sean, your line is now open.

Thank you. Good afternoon. Keechow, very exciting on the revenue opportunity in the 4Q 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?

Yeah. Thanks, Sean. So, the AI accelerated material discovery It's really just the beginning. I think it's going to change the whole battery industry. We've already demonstrated. So we found a molecule that improved lithium metal cycle by about 20%. That's very exciting. And then now we're going to apply this to silicon lithium ion. And we found silicon anode batteries, in terms of electrolyte, are actually quite similar to lithium metal. But in terms of business model, our goal is a combination of subscription. Client say an electrical company or a battery company can pay us a fee per year as a subscription to use the model. And then when we come up with a new molecule, a new electrical product, we will also be manufacturing that electrical and then supply that. So a combination of subscription to the model as well as a product revenue.

And so when you, When you look at, I guess, growing the number of customers here, can you walk me through how this is going to go to market? I mean, obviously, you'll have a great, sounds like a pilot, I hate to call it a pilot program, but you're going to have something that's actually in use. How does this grow? Do you then add other products to the same OEM, or do you have multiple OEMs you're talking to? How does it develop? I'm just trying to understand the growth process of the AI revenue.

Yeah, yeah. So, so the three OEMs that we have been working with, obviously, those will be the first three customers for this. And then, in addition to them, also, there are electrolyte companies that make electrolytes for not just for other OEMs that we currently don't have JDS with, but also for this, not just for car, car applications, but also cell phones, robotics, grid storage, and then other applications. So we have already been approached by the top five, I would say, electric companies, and then top battery makers, and then the car companies. So we have a few projects ongoing. And then it's not like we have to develop something that's totally new for each one. There is a lot of commonalities among these. And then in the beginning, It will take about nine months to a year per new discovery. But once we have this arsenal, I mean, now we are at about 10 to the 7th, which is the largest in the world, and then soon we'll get it 10 to the 8th. Once we have this arsenal, then we expect to come up with a new monocube for – so we expect to come up with a new electrolyte once every six months. So we can expand this very quickly.

And then my last question is, on the spend, CapEx 10 to 15, 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, you know, if we look back more towards the manufacturing development, but not obviously with AI opportunities in front of you.

Yeah. I mean, I can answer that, and then, Jim, 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 line, the B sample lines, what they are, they are part of this complete end-to-end capability for this AI-accelerated material discovery. A lot of companies, they will do just the AI and recommend molecules, but they don't synthesize and don't test in batteries, so you can't revalidate. But what we're trying to do, and then this is really important, is we have to map the property space, which we're doing. That's basically computation chemistry. We have the best team in the world. And then we filter down and then recommend molecules. But then you have to synthesize the molecules. That's what our electrofoundry is about. We built that earlier this year. And then once we have the molecules synthesized, we formulate the electrolyte, and then we test them in A sample cells, in B sample cells. So all the effort, all the electrical foundry, the teams that we built in the past, now they come together and they're really, I think we're the only one that has this complete end-to-end capability for AI accelerative material discovery in the battery space. I mean, in pharmaceutical, 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 talent. Talent is always number one. GPU, I mean, we're getting good deals from our partners. But these are the main things. Jean, if you want to add something.

Yeah, I just wanted to add a little bit of color on the cash spend. As Chi-Cheng mentioned, we are spending money on hiring the best talent and then GPU. But those were in our original plan, and we reduced our CAPAC to focus on building one B sample line and converting to 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 payment from revenue contracts that we mentioned earlier.

Great. Thank you. That's very helpful.

At this time, there are no more questions registered in the queue. Again, if you would like to ask a question, please press star followed by 1. Our next question comes from Winnie Dong with the company Deutsche Bank. Winnie, your line is now open. Yes, thank you so much.

Can you guys talk about maybe the revenue potentials that are 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, you know, the conversion to sell?

Yeah, June, you want to address that?

Sure. Yeah. 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 contract in the next few weeks, and that's potentially revenue throughout 2025. Got it.

I mean, is there any way you can sort of help us size the potential of that kind of opportunity? It sounds like you know, 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, you know, trajectory into 2025?

Yeah, 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 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.

Okay, got it. And then I think previously you guys have talked about, you know, sharing and pulling 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, you know, your database? Thanks.

Yeah, Winnie. So on the molecule, the electrolyte side, that one we are actually, synthesizing the data so all this data we are computing and then and this is not just hardware but also very advanced new software for computing these properties and then because you have to compute it fast and also accurate so this 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 the manufacturing So 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 manufactured on the same line. So we can actually get these data and then train the model. Thank you.

Very helpful. Thank you so much.

Our next question comes from Sean Severson with the company Water Tower Research. Sean, your line is now open.

Thanks. So to follow up on the UAM market, I know before you had said that it was developing fast, but it's not a huge commercial market immediately in front of you. But I just wanted to get an update on what you're seeing now, if that's changed at all. Obviously, going to market very quickly there. But what do you think today of the commercialization opportunity in UAM?

Yeah, I think if you look at UAM and then that includes both ones that carry passengers as well as like large cargo drones, the market is actually growing quite 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 amp hour. It's smaller than the 100 amp hour EV. And then this size was meant to replace the 2170 lithium ion cells that these UAM companies currently use. And then they asked us, okay, they want this format. this market is actually growing quite fast. And so and then and this is why we opened up this new cell product just for this.

And can you remind me, I know lithium metal has some particularly attractive attributes for UAM. So can you just compare and contrast the competitiveness of lithium metal versus lithium ion? Because it seems like you're displacing

lithium lithium ion batteries in many of these applications as they go towards commercialization yeah sure so for uam because they need high power density a lot of the lithium ions are power cells so they are around 250 watts per kg at a cell level and then for lithium metal the cells that we supplying there are 400 watts per kg. So that means you can fly almost 50% longer, or you can carry 50% more payload, more passengers, or more 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.

Great. Thank you.

At this time, I'd like to pass the conference back over to Cal for more questions. Cal, you may proceed.

Thank you. We do have one question, which was submitted in advance by investors. So the question is for Qiqiao. SES has produced high energy density cells. One of the remaining concerns is related to cycle life requirements by the EVOEMs, as the data is indicating that SES is still working on increasing the cycle life. What are the things that SCS is working on to increase cycle life?

Yeah, 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 then that's only after we've mapped 10 to the fifth 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 publish 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.

Great, thanks. 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 just pass it back to our hosting team for closing remarks.

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