Chairman and CEO of SES AI. Welcome,Q ichao. Thank you, Brian. As investors may be familiar, SES AI is revolutionizing electric transport and other emerging industries and markets with its pioneering next-generation lithium metal batteries. The company is a leader in AI for materials discovery, using leading-edge AI to augment its R&D. The company is also a leader in AI for battery manufacturing and for battery safety. There's a lot of ground to cover, so I was thinking that we just get started, Qichao. Yeah, sure. My first question: why don't we start with a bit of history and an overview of the company and the emerging markets it serves, which include EVs, UAMs, urban air mobility, drones, robotics, and grid and data centers. Maybe start out with a little bit about how the company was started and tell us about the primary markets you guys are targeting. Yes. So we started focusing on lithium metal batteries, and then the key to making lithium metal battery work has always been electrolyte development. So since 2012, for the past 12 years, we've been focused almost exclusively on electrolyte development. And then so we worked on electrolyte for lithium metal batteries, for drones applications, and then EV applications. And then recently, we found that because the key is in the electrolyte, and then we really want to go one step beyond, and that is to map all the possible materials that could be used for battery electrolyte. And this is not a new idea itself. It's an old idea, but then it was never possible before with the old hardware and the old computing hardware and software. But with the latest GPUs and then GPU-accelerated computational chemistry software, we can actually map all the small molecules, all the physicochemical properties of the small molecules. And then recently, we expanded to electrolytes for any lithium batteries, not just lithium metal, but also lithium ion. So we expanded to beyond just lithium metal for EVs, which we did with Hyundai, with Honda, with GM. And then now we're expanding to also lithium ion for robots, drones, and energy storage. And then we found that this tool itself is actually really powerful. And then we can use AI not only to compute all the physicochemical properties of the molecules, and then we can actually use that to help us screen and then find the interesting molecules. And then so recently, we announced this new battery, 2170. It's actually a lithium ion battery. It's not lithium metal. It's a lithium ion battery. But what's unique about it is the electrolyte. So that really helped us going from just a lithium metal to EV storage to now lithium metal and lithium ion for drones, robots, EV, and stationary storage, a much bigger market. So that's really interesting. I think maybe it would be worthwhile to kind of step back just a little bit and talk through why different battery chemistries are important for these different applications. Yeah. So different applications have different needs for cost, cycle life, safety, energy density, power density, charge rate, discharge rate. It's just different applications. For example, the unmanned applications like drones, robotics, they care less about safety, more about power density, energy density. And then the manned applications like EV, UAM care more about safety, less about power density. Stationary storage care more about longevity, cycle life, less about power density. So the different needs require different chemistry. But all these lithium-based chemistries have one thing in common, and that is lithium plating. So in the case of lithium metal, you get lithium plating every cycle. That's what you do because you do not form a stable SEI, so you always plate lithium. In the case of lithium ion, you also have lithium plating. It's not every cycle. It happens when you do low temperature, fast charge, and also after long cycles. So, but that problem does also exist. For example, LFP for stationary storage after long cycles, silicon-based anodes when you do fast charge and then low temperature, and we found that this electrolyte solvent that we discovered through mapping the Molecular Universe can address lithium plating for both lithium metal and lithium-ion. That's why the application, even though different chemistries for different applications, the electrolyte solvent can be used across the board. Why is plating problematic for a battery, just like in the big picture? Yeah. So plating itself is okay. It's a non-uniform plating. So if you plate uniformly and densely, that's okay. But when you have non-uniform and then local plating, then you accumulate mossy lithium, and that eventually leads to dendrites. So that could either short the battery or in the subsequent cycles, your lithium, your current distribution just is not uniform. So that causes degradation in the battery performance. So I definitely want to get back to AI and how you guys are using AI because I think what you're doing, combining both the modeling and the empirical work with the Electrolyte Foundry, is really interesting. But I wanted to kind of have you walk a little bit through sort of the bigger picture of the battery industry. As investors, I'm sure, are very familiar, the industry today is dominated by several giants, companies from China, from South Korea, from Japan. Could you talk about how SES AI is situated within the industry? And especially given how you see the industry evolving with these new chemistries, with these new applications coming online, how do you think that the industry itself is going to evolve and your position within the industry over the next, let's call it, five years or so? Yeah. I think in terms of the large. I think there's two aspects. One is the large established manufacturing, and then another is the emerging markets. For example, the large established consumer markets, EV and then stationary storage, I think these currently are dominated by Asian players, and then it's really hard for a U.S. company to compete in terms of manufacturing batteries at that scale, at that cost, but we also don't have to because the cost of a battery, I mean, now an LFP lithium ion battery is like $35 per kilowatt hour, right, so it's really low cost, but it's a benefit for the development of AI because that just means your data acquisition is low. It's not so good as a battery company, but it's really good as an AI company. And then another aspect is the emergence of these new markets like drones, robotics for defense applications. And then for all sorts of geopolitical reasons, Asian companies cannot really participate in that market because of defense, all that. But then these are markets that will take off. Drone warfare will be the future warfare. And I think U.S. companies, including SES, will have an advantage there in terms of not just developing the better technology, but also manufacturing that. So I think both for the consumer market, we as an AI company, the cost of data acquisition is low. And then also in the emerging drones and robotics, the market is also high as a battery company. Qichao, I have a question. If I could just jump in. Since the beginning of the business and covered you for quite a while, you've always talked about the manufacturability of batteries as well. So I think investors hear a lot about great stuff being done in the lab, right? There's really interesting things done in solid state, but they don't have the ability to scale to commercial volumes, all kinds of manufacturing issues. And I found part of your story being very unique that when you think about technology and you think about innovation, how manufacturability and design for manufacturability comes into play. Can you tie that back to how you're using AI, what you're doing, your approach overall to technology development and commercialization? Yeah. So from the beginning, we never wanted to change the manufacturing process. So for the consumer site, EV and stationary storage, if you think about what we do as an AI company, we don't change the manufacturing process. We follow the manufacturing quality control process using AI for manufacturing to ensure quality and is defect-free. And then we also collect the data. For example, that 2170 battery that we use, the AI-enhanced 2170, that does not change anything in the manufacturing process. All it changes is just the electrolyte. So before it used to fill with a different liquid electrolyte, now it fills with a new liquid electrolyte. Actually, we have a contract manufacturer that builds the dry cell, and then we just fill in our electrolyte. So that part, we don't change the manufacturing process. Then for the emerging drones and robotics, we also don't change the manufacturing process. It's the same as lithium-ion. Pouch, cylindrical, we do not change that. And then the anode, we change the material. The anode, we use lithium metal. We also now use high-silicon for the anode, but we never change the manufacturing process. I think that has always been the key because one of the most important things about battery is you have to get to that scale. EV, stationary, you obviously need scale. And then drones, robotics, warfare, no one fights a war with two drones, right? You fight with millions of these. So the scale has been quite important for us from the beginning. And that enables you to use existing manufacturing technologies, existing infrastructure. And I translate all that into good cost competitiveness, if I look at it that way. Being able to leverage the existing manufacturing infrastructure and knowledge base out there is key to this because it creates lower cost commercialization, right? Yes. Thanks. So I definitely want to dive into how you guys are using AI for discovery because I think what you guys are doing is a little bit unique. You are using large language models, and you're using it to help mine the scientific literature. But you're also using foundation models that are built specifically for the chemistry using graph neural networks. And then in addition to those two kinds of models, you also have your Electrolyte Foundry. Could you talk a little bit about what each of these types of AI for discovery brings and how, in particular, having that empirical side, having the Electrolyte Foundry then feeds back into what you can do on the modeling side? Yeah. So in terms of AI, there are a few steps. One is first we need to generate the data. And then a lot of the molecule-level material data currently do not exist. So you have to have that data to feed these models. And then if the data don't exist, we have to generate the data. So we start with computational chemistry, not even AI, just high-performance computing. And then we compute, for example, the single molecule energy levels, the solution-level solubility, melting point, boiling point, and then the interface, the properties. First, we compute all these different properties on 10 to the 8th and then 10 to the 9th, and eventually 10 to the 11th, all the small molecules. Why small molecules? Because all the electrolyte, the solvents, all additives, all three components are made of small molecules, less than 20 atoms. So we synthesize the data first. And then, okay, so you have the property data on 10 to the 11th. Obviously, it's not feasible to synthesize, to actually go to the Electrolyte Foundry to make all these molecules and then test them in the batteries. Realistically, you can probably make 1,000 a year, test them in the lab. So how do you go from 10 to the 11th, 100 billion to 1,000? So this screening process requires model development. And then this part, we have the benefit that we have is we have a team of really good human scientists. So we train these models with all the papers that we can get access to. I think we're up to like 90 million papers. Basically, all the battery-related literature, all the organic chemistry-related, all the material science-related papers, we feed the model and then the books. So it has a good understanding, but still not good enough. And then we develop this Asian. And then we have a team of human scientists that basically just, so one, annotate all the papers because you can't just feed the papers. And then you do have to annotate the papers. And then the human scientists will interact with the Asian and then ask a question. For example, what is the interfacial reaction between this type of solvent on this type of anode? What should be the composition of the SEI? And then the agent will provide a response. The human scientist will provide feedback. Okay, this is wrong. This is right. And then it should be this back and forth a lot of times. And then finally, you will have a super intelligent agent that will tell you how to filter from 10 to the 11th down to 1,000. And then we make about 100 molecules per month. So a year, about 1,000. And then we go to the foundry and actually synthesize these solvent molecules. All these are new. And then we formulate them in electrolyte and actually test them in batteries. And then also along the way, for example, when we compute the properties like melting point and the boiling point and the solubility, sometimes we are off. And then sometimes we don't know if that's accurate. So then we actually have to make the molecule and then actually test the solubility, actually test the melting point, and actually provide that feedback. And then we also work with other research labs, try to collect and in some cases buy these data so that we have the real data. And then we can use the real data to train the computation models and then make these adjustments. That's what I personally think is the most interesting about the approach that you guys have. Because over time, what that allows you to do is create a very proprietary set of data that will really sort of augment your discovery and your ability to actually accelerate the commercialization process. Yes. Yes. And this tool, this model is quite powerful. And we have electrolyte scientists who are really experienced, like 15 years, 20 years plus experience in the field. But I mean, and then if you ask them what they go through in terms of thinking process when they have to develop a new electrolyte, they also don't really know. They have these high-level things, but they don't really know. For example, in silicon batteries, a couple of years ago, the industry developed FEC. FEC as a solvent was developed particularly for silicon-based anodes. It wasn't for graphite-based anodes. So how was that discovered? And then so next, how can we discover another new solvent that's similar to FEC but bypass the human thinking process and then rely on this agent? Now, I think on your latest investor presentation, you talked about something like 17 or 18 new potential electrolytes that you've identified in that two-room production. Does that include the new 2170 cell, or is that sort of like an incremental discovery above where you guys have disclosed previously? Yeah. So that was the old one. That was only after we mapped, I think, 10 to the 5th molecules. And then we identified 17 or so new molecules. But since then, we are now on to 10 to the 9th. And then we have a lot more that we need to synthesize and then verify. Is this like over the next year or two years? How long will it actually take you given the throughput of the foundry to map those kind of empirically? Yeah. So mapping the Molecular Universe just in terms of computing the properties, that's pretty fast. The single molecules, like two months, and then the whole Molecular Universe is done. At the solution level, that will take longer. But we expect this year, 2025, we can map the solution, not just the single molecule level, but even the solution-level properties of both organic and inorganic molecules. And then in parallel, come out with do about 100 molecule synthesis per month. Now, one of the things you guys have talked about is how you can apply AI for manufacturing and also how you can apply AI for safety with potentially how that could actually turn into new revenue streams for the company too. Could you talk a little bit about what's happening in both of those and connect the dots a little bit? How does that turn into a potential revenue stream for SES? Yeah. So we've combined AI for Manufacturing and AI for Safety in the sense that AI for Manufacturing helps you identify defect cells. And then so we eliminate potentially defect cells from the beginning. And then that helps AI for Safety during the actual cycling performance. So one way we're going to commercialize that is in energy storage. So for example, we're working with these data centers, crypto mining sites in Texas, Arizona, California. And then one site typically would require, say, 10 megawatt hours, 30 megawatt hours of energy storage. And then we will provide the container, the entire battery solution. And then on top of that, we add the BMS. And then on the BMS, we will add this AI for Safety and then help improve the accuracy of the cell health prediction. A lot of the existing BMS cannot really predict the cell health that accurately. So when you are inaccurate with your battery health prediction, sometimes you will send inaccurate signals, and then you will force the battery to charge or discharge too much. And then that will actually cause a degradation in the battery life. So the battery, for example, if your battery health prediction is inaccurate, then it will only last, say, eight years. But then if you can predict that accurately, then you can actually extend the life to 10 years or in some cases, 15 years, especially if it depends on the use. And then especially in some data centers where you need to draw power from the battery very quickly, then that will actually have a bigger degradation on the battery life. The way we monetize this AI for Safety is we will actually bundle this with the entire battery solution and then provide this pack, but plus this U.S.-made BMS and software to the data center or in some cases, the mining sites. So this is maybe a good point to kind of jump into some questions about the business model in general, maybe how it varies a little bit across some of these various markets. Are you licensing technology to the actual manufacturers? Are you kind of more vertically integrated? Are you going to produce the batteries and own the customer relationship yourself? How should investors think about the business model? Yeah. Yes. I think the business model will likely evolve as we expand the business. In the near term for AI for Science, we're going to sell the batteries. For example, that 2170, we are going to make the 2170 with our electrolyte and then sell the batteries to drones and then robot companies. Down the road, once this electrolyte becomes more widely accepted, I think we can give subscription access to this model, and actually, we've already started doing this with some of the largest electrolyte companies and then let them develop their own. So they will pay annual subscription to this model because by end of this year, we will have completed all the mapping of all the small inorganic and small organic molecules, and then we'll have a fairly capable and intelligent agent. Then we can just give subscription access to electrolyte and battery companies. They can develop whatever. That'll be down the road. So in AI for Science, we go from selling the batteries to selling this model subscription. In AI for Safety, for now, we want to sell the entire solution because the revenue size is big. But down the road, we could also just sell the BMS plus this software to any stationary storage solutions. So I think down the road, we will evolve to subscription model for the AI for Science and then pure software for the AI for Safety. But in the near term, we sell the batteries and we sell the entire solutions. So if you're thinking about licensing this to other manufacturers, to other electrolyte manufacturers, how do you think about what markets are the most interesting in terms of the markets you want to keep for yourself versus what markets you'll license the technology for? So I think we want to keep the U.S. market to ourselves. And then because we have a natural advantage, a lot of the large agent players cannot really effectively compete in the U.S. market, especially defense drones and robotics. And I think for the other markets where the battery margin is low, EV and stationary storage, I think that we can just give license or subscription to other manufacturers. Yeah. That definitely makes sense. I was kind of hoping that we could maybe sort of end the discussion with a little bit of an update on where you guys are with respect to these various markets. So I know that you just introduced the 2170. Could you talk about sort of the go-to-market strategy that you have there, whether or not you guys are already working with specific clients in that space? So last week at the CES, we actually met a few clients in drones and then robotics. And then publicly, we mentioned that as early as Q4, 2024, last year, we already booked revenue. And then we expect this year, 2025, to book even more significant revenue from these various customers. That's definitely exciting. One of the things, if you go back to your last couple of investor presentations, you talk a lot about the joint development agreements that you have with various EV manufacturers and some of the UAM manufacturers. We know that you already have a couple of B-sample lines that are up and running, and you're producing several thousand cells a month, I believe. Could you talk about where you are in the process of setting up a C-sample line, moving batteries into production, and what investors may anticipate in that market for both EVs and for UAMs? For EVs, the B-samples for lithium metal, and recently, the EV OEMs that we have B-sample lithium metal partnerships with, we have also entered into using AI for Science to come up with electrolytes for the lithium-ion, so on lithium metal, we're on track to C-sample second half of 2025, but in addition to lithium metal, we will be accelerated into their already existing lithium-ion programs with this new electrolytes. I think that's exciting because it definitely opens up a far larger addressable market for what you guys are going after. At Battery World, you were talking about the excitement around what you could do with data centers and hyperscalers, especially as we see so much. I mean, I think the estimates are something like $300 billion plus that's going to go into the AI data center infrastructure this year. Could you talk about where you see that market developing and what the opportunity is for you guys there? I think it's immense, and then, for example, this crypto mining side I mentioned, we are already seeing several opportunities with data centers and then crypto sites. Texas is an interesting state because it's deregulated, and then it's more fragmented, the electricity market, and then it's actually easier for us to enter into these 10 megawatt-hours, 30 megawatt-hours projects, so I think in this space, energy storage for data centers and the crypto mining, one, it's fragmented, so it's actually easier for us to do these demo sites, and then two, there is a lot of companies that try to develop these softwares that will do electricity arbitrage, and then this Avatar, this AI for Safety is a key piece of that because all the current AI for electricity arbitrage do not take into account battery health, but this key piece will solve that. And so that would augment the investment that I assume these hyperscalers have into their infrastructure because batteries are obviously a big component of that cost equation. Yeah. Yes. Yes. I could jump in with a question, Brian too. As you look at how the overall industry is evolving, how is what you're doing in AI and creating this data set going to change how battery manufacturing and development evolves? So in other words, I mean, if you become a resource for all of this information and for optimization, how does this spread out and work amongst the other battery manufacturers? I mean, I know you mentioned a little bit, will they just be licensing and buying data from you? I assume this really accelerates the time to manufacturing process and the development process. But if we take a step back and look at a bigger picture of the industry, how does this change how battery materials and batteries are developed a couple of years from now versus what they were a couple of years ago? So for example, if you take a large Asian battery incumbent, what we can do is the AI for Science can replace their material R&D because AI for Science can develop new electrolyte materials and then down the road, new cathode materials much faster than human scientists can. Okay. So that's the materials. That's the first thing upstream. And then they build the batteries manufacturing. And then AI for Manufacturing can speed up the time to development, especially at pilot scale. When any battery company improves the quality and the scalability of a new chemistry, AI for Manufacturing can help accelerate that. And then once the battery is deployed in the field, in the case of a data center, then we get access to all the data. For now, when we sell this complete solution with the BMS and the software on top, we get access to all the data, all the charge, discharge, rest data, and so that can further provide feedback back to the material development, so I think what we will see is in the past, in the semiconductor industry, you have these design companies and then manufacturing companies, and these are separate, but then we never had that in the battery industry because it was so integrated. The manufacturers did their own design because it was hard for pure play design companies, and then it was really hard to make design innovations that fast, but I think once we've mapped all the Molecular Universe and once we have access to all the data, then we can be a pure play design company, and then others can be pure play manufacturing companies. So I think you will see a similar business model emerging in the battery field. Yeah. That's very helpful to each other. Interesting parallel, isn't it, to the development of the industry maturing? Yeah. No, I think that's particularly interesting. I mean, the idea that you could have fabless battery producers and foundries. Unless we have any other questions, Shawn. No, I'm set. This has been very helpful. Thanks, Qichao. Thanks, Brian. Yeah. No, this has been absolutely a fantastic conversation. Thank you again, Qichao, for joining us today. I certainly think that we covered a lot of ground for investors. Let me just wrap up today with our disclaimer. The views expressed in this fireside chat may not necessarily reflect the views of Water Tower Research LLC and are provided for informational purposes only. This fireside chat may not be distributed or reproduced without the written consent of Water Tower Research and should not be considered research nor a recommendation. Water Tower is an investor engagement firm, not a licensed broker, broker dealer, market maker, investment bank, underwriter, or investment advisor. Additional disclaimers can be found at watertowerresearch.com. And with that, I'd like to thank everyone for joining us today. Thanks. And just as a reminder, if you'd like to access this or other additional research or prior fireside chats, please go to the website. We're an open access research platform. So I'd encourage you investors to take a look at watertowerresearch.com and check out all the additional work and background on SES at that site. Thank you. Thank you. Bye. Thanks, everyone. Thank you.