SES AI Corporation (SES) Earnings Call Transcript & Summary

So hi, everyone. Thanks for joining us for this last session but certainly not the least with SES AI as part of Deutsche Bank's Global Auto Conference. My name is Winnie Dong and I'm a senior associate at DB's U.S. Auto and Auto Parts research team. SES is looking to develop its next-gen lithium metal battery technology by combining the high energy density of lithium metal with the cost-effective large-scale manufacturing of conventional lithium ion batteries. The company is currently in JDAs, partnership with several OEMs, including GM, Honda and Hyundai and has announced B-sample agreements with 2 out of the 3, which are Honda and Hyundai. Very pleased to be joined by Dr. Qichao Hu, Founder and CEO of SES AI for a fireside chat and Q&A. So Qichao, thanks for being with us. I will hand the floor over to you for some opening remarks and some slides that you want to go through.

Sure. Thanks, Winnie. And I'm Qichao, I'm the Founder and CEO of SES AI. And then we work on lithium metal batteries, the main for EV and UAM applications. Those are some slides. And then there are 3 parts to our story, and that include EV, UAM, and AI. And then in terms of vertical integration, we are very integrated in terms of intelligence. So we start with molecular discovery, and that is a key part to our material. We also go from molecular discovery for the electrolytes to electrolyte formulation and into battery development. And that includes all the A-samples that we have with GM, Hyundai, Honda, and now the B-samples that we have for both EV and UAM applications. And then we also cover Avatar, which is this AI tool that we have to predict public safety. And then in terms of global presence, currently, we are headquartered in Boston. That's where we do all the materials, all the AI development, and all the A-sample lines and the B-sample lines, we have split between Shanghai and Korea. And then, as we mentioned earlier, we announced 3 A-sample JDAs, and now we have 2 B-samples, and then we announced 3 items of JDAs with GM, Hyundai, Honda, and then also 2 B-samples [Technical Difficulty].

Maybe before we go into some of the operational and milestone updates, I do want to sort of, like, take a step back, because this is a pretty sort of like long journey in terms of getting to that commercialization time line. So if we just, like, to shrink the time line into maybe like the past 4 months or so, what would you say is the latest update in terms of competitive landscape and what would be beneficial for us to understand, to set the stage a little bit?

I think if you look at the landscape in terms of EV and UAM and OEMs in general are becoming more serious to lithium metal because of what lithium metal can offer. And then if you look at EV market, of course, lithium metal provides longer range. And then lithium metal, especially when it's paired with lithium with LFP capital, you can actually lower the cost for the same thing. So it's quite appealing, both from a range perspective as well as a cost perspective, to EV OEMs, and then to UAM OEMs, then it's quite obvious, it's unlikely that UAM OEMs will ever reach profitability with lithium ion bodies. So lithium metal is almost a key enabler to UAM OEMs to even get to profitability. And also in the last 12 months to 18 months, more UAM companies are getting certified. So it's not the dream anymore. It's actually becoming very real. And then also, a lot of the OEMs, both EV and UAM, are waking up to the need for AI for manufacturing, AI for safety, and then especially for next-gen batteries. It's not enough to just have a new chemistry, but also use new tools to predict safety and to improve manufacturing quality. I think all these trends and then more OEMs are waking up to a new trend and also investment in lithium metal and also AI for manufacturing. AI for safety actually increase in the past 18 to 12 [Technical Difficulty].

So, yes, I guess you're essentially characterizing with the metal as this end game for [indiscernible]. Maybe if you can just remind us, what is SES AI's approach to developing within this? Some of your competition is sitting on the audience today. How would you characterize as SES AI differentiated in that approach?

Also, we've always wanted to develop lithium metal batteries using very standard lithium ion manufacturing process. And that to us is important. It's scalable. So the key differentiators that we have, one is on the material side. We go really deep in terms of electrolyte development. So the formula is not just the formulation, but the molecular development, identifying new molecules. And right now, we have one of the highest efficiencies for any electrode on lithium metal. And that's not because we use an off the shelf molecule, solid molecule, it's because we actually develop a new molecule, and then we are going to develop even more new molecules. So we go really deep in the electrolyte development. And that electrolyte is one of the most important materials in lithium metal battery in terms of safety and performance. And another thing is the manufacturing. So lithium metal manufacturing is quite similar to lithium ion, but then there are lots of small details that we actually learned and accumulated through the A-sample and the B-sample practice. And the third, I would say, is AI for safety and AI for manufacturing, because lithium metal is new compared to lithium ion, and the OEMs don't have that level of comfort with a new chemistry, we really have to basically get to that level of comfort by collecting a lot of data and then using AI to accelerate that learning instead of just spending 10, 20 years.

Yes, I did ask this question in my prior session, but I don't want to skip it for you, because I do think it's really important to address, which is we are seeing a slower penetration, basically, in terms of EV at least, versus expectation. Maybe it's still growing, but does that change any sort of urgency from your customers specifically?

So I think the slowdown in penetration is more for U.S. EV using lithium ion. But then if you look at foreign car companies, and then we have 2 JDAs with foreign car companies, I think they are not really slowing down even. And Hyundai recently became the second largest producer and seller of EV in the U.S., [indiscernible] Tesla. And also, we're now working on lithium ion. We're working on next-gen. And I think quite contrary, if you look at next-gen batteries, and especially the use of AI for manufacturing and AI for safety, that is actually accelerating. And the more OEMs are beginning to realize the importance of next-gen batteries and also the use of AI to help predict safety and quality.

Let's talk about maybe your cell design that's unique to SES. Can you touch on the functionalities and capabilities without giving away too much of your secret sauce? And then from a technical perspective, where are you today versus 12 months to 18 months ago?

So, in terms of cell design for EV, we have this long pouch cell, which has on the opposite side. And then for EV application, the width is around 500 millimeters to 600 millimeters wide, depending on the OEM, 50 amp hour to 100 amp hours. For UAM, it's a narrower design, about 30 amp hour. And a year ago, we were still in A-sample, and we're still just finalizing fast cell design for EV. We didn't have a UAM cell design. Now the EV cell design we are in B-sample, is actually being designed to fit into demo cards as part of the B-sample. And now we have a UAM cell design.

So, yes, we now do have 2 JDA partnerships that are in transition to B-sample, which you mentioned sample-B this year. Maybe talk through how those 2 relationships look like today, one with Honda, one with Hyundai, which you recently announced, right. And with Honda, it's been a much longer relationship. Can you maybe compare and contrast the 2? And then potentially when could we expect a third B-sample agreement, which is presumably with GM as that last part?

So in terms of the 2 B-samples that we have, they include obviously a new line, a B-sample line that's dedicated to the B-sample. Also very extensive, just collaboration between our team, our material team, cell engineering team, and also their material and self engineering team. Very deep. And then in terms of thirds, we had a few in the pipeline, but now we're not that focused on getting a third JDA. I think the number of JDAs to us is less important than actually how deep we go with the 2 that we have. So for now, we are just focused on the 2 that we have.

Maybe talk more about the depth of the relationship, of the depth of the collaboration [Technical Difficulty]. You have maybe teams in the OEM partners facility or vice versa. How do you engage the 2 teams?

Yes, so this year, and then we announced this as part of the B-sample JDA with Hyundai, we want to be the first time building a line in their facility. And then this is actually quite significant because down the road, when we are in C-sample and then our commercial or EV applications, we are not going to build a line by ourselves. We are going to do this joint venture, likely a 3-way joint venture. So we have to figure out how to build a line, operate a line and build cells in someone else's facility. And then this is actually a really good practice. And then one example and it actually helps lower the cost, instead of us paying for the whole line, paying for the whole facility, driving, and then utility, all that stuff. The OEM budget pay for a lot of that. We still operate the line. And the one thing that's really important is we make sure we install Avatar. For the first time, we are actually installing Avatar, this AI for manufacturing software in someone else's line, in someone else's facility. And then this actually opens up the possibility of a new source of revenue down the road. So whoever that builds lithium metal lines, B-sample, C-sample, we make sure we operate and also we make sure we install Avatar AI for manufacturing software on that line.

Yes, maybe talk about the rest of the manufacturing lines that are in place, because you do have some that are in South Korea, you have some that are in China. What are the plans for those where you're doing this for now? And then maybe talk about, at what rate are you actually producing cells in? Why is it significant?

Yes, so the 2 B-sample lines we're still building, and then the B-sample lines will be more automated than the A-sample lines. A-sample lines are like islands of automation. B-sample lines are more peninsulas of automation and basically much more automated. And then right now, for the A-sample lines, we are keeping 1 A-sample line for continuous development, because while we wait for the B-sample line to be built, we're not waiting for that line. We're building B-samples on the A-sample line, and then another A-sample line we're actually converting to make UAM cells. So the A-sample lines, we can make about 30 to 50 cells a day with 1 shift. And currently, and these are the 30 amp hours, 50 amp hours, and even 100 amp hour cells. And then, so that transits to 500 to 1000 cells per month with 1 shift. And so that's basically the A-sample lines. We're converting 1 for UAM, and then it's actually really meaningful, because not only we can build a cell to supply to the UAM OEMs, we are also building long cells to collect data to train the Avatar, both AI for safety and also AI for manufacturing. It could have a line that's dedicated to building cells to collect data to train Avatar.

Yes. So it seems like data is a very important part of your process. Maybe talk a bit more about what do you do with the data collection? You mentioned Avatar. Can you go a bit more into that? And how does it reinforce your testing, your validation, maybe how does it help you reach your specific goals?

Yes, so in Avatar, we have 2 parts. One is the manufacturing quality. One is basically performance testing. So AI for manufacturing and AI for safety. And then if you think about what impacts the safety of a battery, a big part of that is manufacturing quality. We're saying in the battery industry that quality equals safety. And actually, in lithium ion, most of the recalls the industry had in the past were due to manufacturing defects. So how the Avatar is paying off the manufacturing, that's really important. And then, for example, so we hire a team of very good quality engineers from [indiscernible] Panasonic, LG. But that's not enough because they have experience from lithium ion. But on top of that, we add Avatar. We collect all the manufacturing data, and then we train this model. And what this model does is actually really important. For example, it's going to tell you the 0.1 millimeter in the electrode misalignment has a greater impact on quality and safety than, let's say, 0.1 gram of electrolyte in the filling process. So things like this that no one has the knowledge of, because no one has made lithium metal cells at scale, but this model can actually train them. This is only after just making a couple hundred cells per month. And then in A-sample, over the past 2 years, if we can make more cells, several thousand cells per month in B-sample with EV, to EVs, and also other UAMs, they will have a lot more data to train this Avatar. So, in looking my own in the [indiscernible], so it took lithium ion industry probably 20 years to develop all the quality experience. We can do all that for lithium metal in maybe 18 months with several thousand cells per month with that amount of data. And then for the performance testing, AI for safety, then also when you put a lithium metal battery inside EV or UAM, and then you cycle this on the different mission profile, that also has an impact on the safety. And then, so, in the lab, most of the cyclings are pretty nice cyclings, in the sense that you go from 0% to 100%, and then you charge, rest, discharge, rest, charge. Very structured. But then, in real life, no one does that. So we also train these cells under a variety of mission profiles, both lab's mission profiles, as well as real world EV, UAM mission profiles that we get from the OEMs, that we can integrate the manufacturing data with the actual performance testing data on the different mission profiles, so that the final goal is to really be able to predict incident. And then, so, for example, end of last year, our accuracy was around 92%, meaning all the cells that we had incidenced, we could predict 90%. This year, our goal is to get to 95%. Basically, of all the cells that we will have incident, we want to predict 95%.

Any predictability of incident, does it necessarily prevent the incident? Is that 2 different concepts that we should think about or?

Yes. So, once you can predict it, say, 5 cycles or 3 cycles before, then you can stop it. So, predictability does lead to prevention, because you are predicting it before it happens. So you can stop it before it happens.

Are you talking to your customers about unit economics right now? Like, is this the right stage to talk about that, understanding that there is still a lot of testing and validation that's going back and forth. You're building online in the facility, et cetera. You're building Avatar by [Technical Difficulty]. When is that point where you talk about unit economics with the customer?

Yes. So we have been talking about the structure of the cost. Not the cost, the exact number, but the structure of the cost. Meaning you put the COGS, basically you have the bomb, okay, exploding the bomb capital [indiscernible] what's out there bomb, you have assembly cost, you have all that. And then as part of A-sample, we actually started doing this, making sure that our annual cost is actually reasonable at scale. And now B-samples, especially now that we are operating this B-sample line at an OEM facility, then basically they can see the OGS minus the bomb, and then within the bomb, we can see the capital, because actually the OEMs actually source the capital for us. So capital is transparent. COGS minus the bomb is also transparent. And then what's new is basically electrode cost and anode cost. So these 2, and then we demonstrate our electrolyte is made using mature industrial chemical process that's scalable. And then the only thing that we should have to prove is the anode cost, but also anode cost we start with [indiscernible], we primarily use extrusion. So cost of thin lithium foil. At the end of the day, the final cell in terms of dollar per kilowatt hour will be quite similar to a lithium ion cell using the same cathode and then also using the same assembly process.

Yes. You alluded to Avatars and cell predictability. Can you maybe talk about the potentials of it in a future stage? Like, is it something that in addition to internal quality control, something that you can actually monetize as sort of like an adjacent with it?

Yes, I think so. And then now we're focused on EV, and then UAM is a natural adjacent market to this, the 2 parts of Avatar AI for manufacturing and AI for safety. AI for safety, there are already UAM customers that have asked us, can you apply your Avatar not just to your lithium metal module, can you apply that to lithium ion module alone using the same mission profile? Which makes sense, because the Avatar for safety is agnostic to the chemistry. Basically, the data that used to train it can be lithium ion, can be lithium metal, and the model, of course, will be trained and then based on the data. So, yes, we are already in conversation with several UAM customers about applying Avatar AI for safety to lithium ion modules on the same mission profile. And then for AI for manufacturing as part of B-samples, we are already installing AI for manufacturing in this line [indiscernible] and whoever is going to assemble within our batteries, not only we have to operate, we also have to license AI for manufacturing to that. But yes, the possibility to license AI for manufacturing, AI for safety, yes, we are in conversations with both EV and UAM customers beyond just supplying with normal cells.

So I understand that maybe sourcing may not be like top of mind right now in the current state. But just in light of, like, the recent [ EV tarot ] development, in terms of, like, inflows of raw materials, what is your overall reaction to that? You know, from SES perspective, is there any sort of, like, longer time implications, when you do reach that commercialization state, when you do need to start sourcing math volumes, that is sort of something that you think about.

Yes. Obviously, we consider this together with the OEMs, and then the 3 OEMs that we work with have a global presence. And then, for example, today we source lithium ingots up until lithium ingots, and then we do everything else in house, and then we do that. Basically, we have mirrored image of the whole process, starting from lithium ingots in both Shanghai and Korea. So the goal is Shanghai will serve the China market, Korea will serve basically rest of the world market. And then also you're seeing that in terms of sourcing, oil companies in Korea are doing that, serving as a platform for rest of the world market. Yes. And then in terms of intelligence. So several EV OEMs are also talking about insourcing intelligence, better intelligence. In the past, they had better companies, manufactured the batteries, but they want to not only insource manufacturing, but also insourcing intelligence. So we are actually more vertically integrated in terms of intelligence than we are in terms of materials. In terms of intelligence, we saw from molecules all the way to the final vehicle testing, we don't outsource anything.

Yes. Can you talk about, like, the relationship with GM a bit currently still sort of like in that A-sample stage? If we rewind some of the transcripts, I often hear Mary Barra talking about their exploration of alternatives, chemistries, et cetera. So what is it going to take, I guess, to push them to that B-sample stage? Is it something that, from their conversation with you, not necessarily the immediate near term focus?

And I think GM has their plans, and then we're still working with them. And for us, we're focused on the 2 B-samples that we have and the specs, the performance actually quite similar. So once we hit the 2 B-samples, not only GM, but other European OEMs, the target specs are basically the same. So we are focused on the 2 B-samples that we have and also the UAM segment.

Okay, maybe talk about your vertical integration strategy. I think at some point you have moved the production of lithium metal anode in house because you want to maybe like, mandate control, quality, et cetera. But overall, how much are you looking to do, like, in house versus, like, upstream? It will be upstream for you..

Yes. So I think our goal is to ensure quality and then making sure we have complete data to train Avatar. So if it's a material or process that we can outsource and still get the same quality, same data, okay, fine. But then if it's a material process that we have to insource together the quality and the data, then we'll just insource. And then lithium foil, especially really thin and white with lithium foil, that was one thing that we used to buy from other companies, but then the quality wasn't good. We couldn't figure out what was wrong with it, and we also didn't have complete data to improve that, we decided to insource that.

What are the capital [Technical Difficulty] do that because presumably in order to ensure that you would have to invest some money upfront to do that. Like, what was that sort of diversity process, like, between, like, the value proposition today?

So, for A-sample and the B-sample scale lithium, lithium metal anode foil production, It's roughly less than $5 million, so not that much. And, and then down the road, if we're in C-sample or SOP, if we need to scale that up, well, first of all, in the SOP, the CapEx is going to be shared between us and the OEMs, but we always want to make sure we can control politics and get full access to data.

Yes. So you've talked about UAM many, many times now, maybe help us sort of like, frame the TAM a little bit in that. But for those of us who might not be as familiar with it as we are with auto applications, right. How might the economics be different for UAM? And is the process easier or harder in terms of time line to reaching that commercialization time line target?

Yes. So, couple benefits of UAM compared to EV, and we're so focused on EV. But then, UAM is a more near term opportunity for us, because, 1, if we look at the current status EV industry, cost of battery is less than $100 per kilowatt hour, and therefore, LFP is like $60 or even less. But for UAM, just current market, it's about $400 per kilowatt hour. Even with the [ Maya ], that's the current price. And then also, volume isn't as high for EV. You're not going to get any contract from these large OEMs if you don't show, like, 10 gigawatt hour capacity. But then for UAM, a lot of the leading UAM customers, we're talking about 1 or 2 aircrafts per month. And then our current A-sample lines sell 1000 cells. That's actually 2 aircrafts worth of batteries per month. We can supply modules to them at a higher price now without building a new line. And that's the one. And then #2, if look at UAM, most UAMs operate as a fleet business model. So like Uber, basically dollar per passenger per mile. And then so Uber, United, Delta will want to operate in the U.S. and also other operators in other countries. And if you can make the battery lighter, then the dollar per passenger per mile actually decreases because now you can add more passengers, you can add more suitcases, and the aircrafts will fly much farther. So this benefit is actually really important to UAM companies. Then the time line to commercialization, UAMs don't really have a A-sample, B-sample, C-sample. So from a technology perspective, once you get to B-sample stage with an EV, then you're sort of similar to commercial or UAM. Now of course, you have to get through all the supplemental type certification with FAA, with [indiscernible] with all the different agencies, but that's easier and faster than the cells going through A-sample, B-sample.

You were previously talking about the 3-way joint venture, sort of like business model. Is that something that's sort of been, that you're thinking about that's been set in stone? Because I do recall that in our past conversations, it was in a wholly owned facility or licensing or the street venture setup. It seems like you are sort of. Is that like a set position that's been contemplated or is that still sort of up in the air and you're still exploring [indiscernible].

[indiscernible] open. We are leaning towards this model strongly just because we do want to participate in the manufacturing of lithium metal. And we talked about for EV applications, we do want to participate in the manufacturer. But then that could mean the OEMs put up the CapEx for the line and the facility. But then our people go in to operate and then we also license this Avatar AI for manufacturing to that line. So we collect all the data and then we're still responsible for operating the line and also the quality.

Yes. Who are the most probable sort of partners among your 4 JDA partners?

I think it's with the 2 that we have B-samples with.

We have just under 2 minutes. Anyone have any questions in the audience?

Just really quick one. I'm just curious, do you actually plan to expand your global supply chain across ASEAN, South America, for example? Because within this battery energy driven sector we've been seeing, many companies are looking for sort of rich originations and mitigating their supply chain. I don't know if that's necessary your strategic as well. Or do you see yourself in the maybe filter 3 to 5 years to strategize your global supply chain?

So the question is, do we plan to source material from South America?

How do you see yourself in regards of the global supply chain game? Like, do you see, I saw the map of your locations. So South Korea, China, manufacturing, do you actually foresee yourself into source external resources by mitigating your manufacturing to maybe, I don't know, Southeast Asia or Latin America, because those are generally labor cost effective regions and resource rich.

Yes, we currently don't buy straight from the resource, the mines, we buy, for example, high purity battery grades within ingot. There are companies in Asia and North America that sell this. So we still focus primarily on sell authentic.

Anyone else?

Just a quick question around cash position. If you just talk about that at a high level. And then secondly, maybe any other product milestones that you foresee as being an imperative, maybe in the next 6 months and heading into 2025.

So I think in terms of cash, so in Q1 this year, we still have about $200 million left. And then our guidance for this year is $90 million to $100 million. So we're good. And then we talk about achieving revenue from [indiscernible] first half next year, '25, and then also from EV, second half of next year. So we could see sample and then that's okay. And then in terms of product milestones. So for EV, we are absolutely focused on B-samples and then making sure the lines are operational this year. And then we can start collecting data from the app that we install on these lines. And then for UAM, we do want to get you a supply agreement with an actual UAM as well.

With that, thank you so much.

Thank you.