SES AI Corporation (NYSE:SES) Q3 2024 Earnings Call Transcript

SES AI Corporation (NYSE:SES) Q3 2024 Earnings Call Transcript October 31, 2024

SES AI Corporation misses on earnings expectations. Reported EPS is $-0.09 EPS, expectations were $-0.05.

Operator: Good afternoon. Thank you for attending today’s SES AI Third Quarter 2024 Earnings Release and Call. My name is Shana, and I’ll be your moderator for today. [Operator Instructions] I’d now like to turn the conference over to our host, Kyle Pilkington, Chief Legal Officer. Kyle, you may proceed.

Kyle Pilkington: 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.

Qichao Hu: 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 3 AI solutions. So 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.

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 EV A-sample lines to UAM lines. And we’re excited to report that both our Shanghai and Chengzhu EV A-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 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 100mPOWER lithium metal cells developed for our B-Sample 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. GB38031 2020 is a mandatory safety standard encompassing a series of rigorous tests, which include overcharge, over discharge, external short circuit, heating, temperature cycling and crushing. 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 as outlined earlier. 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. 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. 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 and 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 5 projects from 2 companies, 1 electrolyte manufacturer and 1 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. 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 2 lines producing cells from 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, 100% accuracy when we integrate AIF 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.

Jing Nealis: Thank you, Qichao. 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 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.

Q&A Session

Operator: [Operator Instructions] Our first question comes from Shawn Severson with Water Tower Research.

Shawn Severson: Qichao, 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 is the — what form is the revenue going to take?

Qichao Hu: 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 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. So this client, say, an electrolyte company or a battery company can pay us a fee per year as a subscription to use the model. And then when we come out with a new molecule, a new electrolyte product, we will also be manufacturing that electrolyte and then supply that. So a combination of subscription to the model as well as product revenue.

Shawn Severson: And so 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 have a great — it sounds like a pilot — I hate to call it a pilot program, but you’re going to have something that’s actually in use. What is this — 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.

Qichao Hu: So the 3 OEMs that we have been working with, obviously, those will be the first 3 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 is not just for car applications, but also cell phones, robotics, grid storage and then other applications. So we have already been approached by the top 5, I would say, electrolyte 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 9 months to a year per new discovery.

But once we have this arsenal, I mean now we are at about 10 to 7, which is the largest in the world. And then soon we’ll get to 10 to 8. Once we have this arsenal, then we expect to come up with a new molecule for — so we expect to come up with a new electrolyte once every 6 months. So we can expand this very quickly.

Shawn Severson: 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.

Qichao Hu: Yes. I can answer that. And then Jing, you can also jump in. So the B-Sample line, we — 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 will 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 then this is very important is we have to map the property space, which we are doing. That’s basically computation 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 electro 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 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 #1. GPU. I mean we are getting good deals from our partners. But these are the main things.

Jing, if you want to add something?

Jing Nealis: 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 1 B-sample line and converting 2 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.

Operator: [Operator Instructions] Our next question comes from Winnie Dong with Deutsche Bank.

Winnie Dong: Can you guys talk about maybe the revenue potential 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 the conversion to sales?

Qichao Hu: Jing, do you want to address that?

Jing Nealis: 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 contract in the next few weeks, and that’s potentially revenue throughout 2025.

Winnie Dong: 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?

Jing Nealis: 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.

Winnie Dong: Got it. And then I think previously, you guys have talked about 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 your database?

Qichao Hu: Yes, Winnie, so on the molecule, the electrolyte side, that one, 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 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 manufacturing on the same line. So we can actually get the data and then train the model.

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

Shawn Severson: I had a follow-up on the UAM market. I know before you had said that it was developing fast, but it may not be — 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?

Qichao Hu: Yes. 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 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.

Shawn Severson: 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-ion batteries in many of these applications as they go towards commercialization.

Qichao Hu: Yes, 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 the cell level. And then 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 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.

Operator: At this time, I’d like to pass the conference back over to Kyle for more questions. Kyle, you may proceed.

Kyle Pilkington: 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?

Qichao Hu: 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 then 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 1,000x 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.

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

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.

End of Q&A:

Qichao Hu: Yes. 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 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 the request of our OEM partners. And then in addition to this, the complete capabilities that we built, including the molecules, the electro 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.

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