SES AI (SES) Q3 2024 earnings call transcript

Hello everyone, and welcome to our conference call covering our third quarter 2024 results. Joining me today are Chichao Hu, Founder, Chairman, and Chief Executive Officer, and Jing Neelis, Chief Financial Officer. We issued our shareholder letter after market close 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 in 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'll review our business as well as results for the quarter. With that, I'll pass it over to Chichao.

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 immobility and drones. Our UAM lines have completed site acceptance tests at, and we won't sell supply agreements, including with SoftBank. Second, B samples pass EV safety tests. Our 100-empower lithium metal B sample cells successfully pass the GB3A03120, 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 Intumnedro EVA sample to UAM line conversions have completed site acceptance tests at, and achieved ready-to-use part-to-use status.

The new UAM cells manufacture only lines have 30-empowering 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-empower lithium metal cells developed for our B sample JDA's successfully pass the rigorous industry safety test of GB3A03120.

This is the first time in the industry that a large capacity of lithium metal cells successfully pass safety tests required by the global GB3A03120 EV traction battery safety centers. This certification is required by all of SSAI's EVOEM partners, as well as many of the other leading manufacturers as a global standard.

GB3A03120 is a mandatory safety center encompassing a series of rigorous tests, which include overcharge, over discharge, external short circuit, heating, temperature, cycling, and crushing. Achievement of this standard demonstrates that SSAI's 100-empower lithium metal cells can effectively manage safety risk under these stringent test conditions. This is a major milestone towards sea sample and start of production, SOP.

Third, all-in-all AI. Our AI solutions have already borne fruits in advancing our lithium metal plant 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 Elishlai Foundry, formed a partnership with NVIDIA to leverage the latest computing hardware and software, and attracted a team of world-class battery Elishlai scientists, including promoting Dr. Country to our Chief Technology Office at 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, a model development, human domain expertise, the Elishlai Foundry for molecules synthesis and Elishlai formulation, and cell production and validation, from lab to A samples, B samples, and beyond. We expect to enter a pipeline of revenue contracts the first time later this year, including five projects from two companies, one electronic manufacturer and one automaker, to apply our AI accelerated battery material discovery, and 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 Chengju 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 AFSA 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 AFSA 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 achievement in the past quarter. In EV, our 100 MP lithium metal cells successfully passed the rigorous GB3A031 global industry safety test, and industry first for lithium metal, and a major milestone toward commercialization of lithium metal for EV.

In UAM and drone, we now have two lines producing cells for multiple customers, including SoftBank. Powering the success of our lithium metal is our all-in-all AI strategy. We built what we believe to be the world's largest molecular property database using still VR 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 AFSA safety. 100% accuracy when we integrate AFM manufacturing with AFSA safety.

So, thank you for your continuing interest in SDS AI, and now I will turn it over to June for financial updates.

Thank you, Chitau. Today, I will cover our third quarter 2024 financial results and discuss our outputting and capital budgets for full year 2024. In the third quarter, our gap outputting 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 $15 million to $25 million previously, with our reduced and more capital efficient cash usage and expectation that our own 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.

Yeah. Thanks, Sean. So the AI accelerator material discovery, it's really just the beginning. I think I'm 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 nano 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, the electric company or battery company can pay us the fee per year as a subscription to use the model. And then when we come up with a new model to a new electric product, we will also be manufacturing that electric and then supply that. So a combination of subscription to the model as well as product revenue.

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 marketing? 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, 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 worth, but also for this 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 five, I will 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 nine months to a year per new discovery. But once we have this arsenal, I mean, now we're at about 10 to the 7th, which is the largest in the world.

And then soon we'll get a 10 to the 8th. Once we have this arsenal, then we expect to come up with a new model to for. So we try 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 manufacturing development, then obviously there's AI opportunities in front of you.

Yeah. I can answer that. And then too, 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 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 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 really 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 natural foundry is about. We built that earlier this year. And then once we have the molecules synthesized, we formulate the electrical line. 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 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.

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, Talon. Talon is always number one, TPU. I mean, we're getting good deals from our partners.

But these are the main things, if you want to add something.

Yeah, I just want to add a little bit of color on the cash spend. As Chichal mentioned, we are spending money on hiring the best talent and then GPU. But those were in our functional plan, and we reduced our CapEx 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 JDA's plus down payment from revenue contracts that we mentioned earlier.

Sure. Yeah. Well, because of confidentiality with our customers, we currently really can't discuss 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.

All these 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, we actually got 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 these data and then train the model.

Yeah. So 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 amp hour. It's smaller than the 100 amp hour 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.

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 we 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're 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 cargo. It's a big change. And then this means a lot for the economics and the profitability of the UAM and large cargo business.

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 improved cycle life by about 20%. And then that's only after we've mapped 10 to the fifth space, but we're going to map 10 to the eighth. So there's about 1000 times bigger that we can map. So that improved 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 1000 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.

Oh, yeah. I mean, we're quite excited about this new AI accelerated material discovery that we have. And then with the new competition 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 looking metal. And then we're also going to bring this tool to lithium ion. And then this was at a request so far, 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 looking metal. And then next, we're really excited to apply this for lithium ion as well.