The AI Agent That Compressed 8 Years of R&D Into 2 Weeks

An AI agent can process several tens of thousands of papers a day and then has perfect memory of all the content. So that can be reduced from a month to all the order of minutes. Instead of human scientists, you have what's called ALAP, autonomous lab. It's basically a high throughput robot that will do 5,000 formulations in one morning. When you give that to an AI model, it will give you about a thousand parameters. We can't really interpret that. It's like a different language, not meant for us human species to understand, but it works. Welcome humans to the Neuron AI podcast. I'm your host Corey Knowles and I'm joined as always by the undefeated champion of one more thing, Grant Harvey. How are you today, Grant? I'm good. I'm good. It's a little rainy out here in Southern California, which is uncommon. So if you hear the pitter-patter of rain, that's what's going on. Then today I win the rent lottery. I'd like to say it's beautiful here. Wow, that's rare. I know, right? What will be joined here in a moment by Dr. Chi-Chao Hu, founder, chairman and CEO of SESAI, a company working on lithium metal batteries and a more transparent EV battery supply chain, with joint development agreements in place already with General Motors Honda Hyundai. And maybe more. We'll find out. Now, if you're wondering why this matters for AI, SESAI actually uses AI agents to discover new battery materials. Their platform, molecular universe, compresses years of material research into minutes. And they also use AI on the manufacturing side to catch defects and predict battery health. It's a great example of AI solving a hard physical world problem, not just a digital one. But first, please take a second to like and subscribe to the channel so we can keep bringing you the most interesting people in tech and AI. And with that, Dr. Hu, welcome to the Neuron. Thank you both for having me. It's great to have you here. We're really excited about it. And I guess for those who haven't thought deeply about batteries in years, because I assume the average person probably doesn't. But there's a lot going on. What problem are you trying to solve at SESAI? A lot. I mean, I think if you look at batteries, it's everywhere. But then it's a simple device. But then it's quite often the simple device that SESAI is most complicated, especially if actually trying to change it. So I would say 10 years ago, the problem that we tried to solve was a better type of battery, a new material for the battery. And then that's evolved to trying to come up with a new way to come up with new materials. Wow. That's really interesting. And you're using AI as part of that process. Like we just talked about very briefly, two of the ways that you're doing that. One of them is molecular universe. And perhaps we could talk a bit more about that. And then the other one is avatar as well. Yeah. Yeah. Yeah. So if you look at the battery applications, some applications you need to have higher energy density. Basically make the batteries lighter. And then in some applications, you need to make it cheaper. In some you have to make it last longer. And then each one takes about 10 years. So if you follow the traditional path, and then it will basically take you about a decade to solve each of these battery materials problems. And that's not a very. Why is it. Yeah, why does it take so long? So there's a couple of things in the battery. And it's similar in life science, in drug discovery. When you have a new material discovery, you go through several phases. Right? Basically first you go through this idea creation phase. Like you have to have an idea. You come up with an idea for this new type of materials. And the second is is idea filtering stage. You have this idea and then you have lots of candidates, candidate materials. And they have the filter. This could be a tent, could be millions and then billions down to hundreds. And then third is validation. So you're down to couple hundred, but they have to test this. And then in drug discovery, you go through trials, clinical phase one, phase two, and then approval. And then for battery, depending on the application, you have to do room temperature cycling, low temperature cycling, high temperature cycling. And in some applications, for example, EV, you buy a car and that battery needs to last at least a year. A lot of times the warranty is for 15 years. So that means you actually test the battery for at least eight years. And there's no good way to accelerate that testing. Testing process just takes a long time. Yeah, that makes sense. And then how does a molecular universe end or avatar help with that process, if at all, or do they solve a different problem? So again, it's still the same three phases. Idea creation, candidate filtering, and then validation. So for each one, idea creation in the traditional human process, that takes about on average a month to come up with a really good idea. And also the horizon of human scientists is limited. On average, a human scientist reads three to five papers, because human scientists also have to eat, have to eat coffee, and then get sick and tired. That's inefficient. I know. And then an AI agent can process several tens of thousands of papers a day and then has perfect memory of all the content. And then second, the filtering stage, that in the traditional process, you have senior scientists, principal scientists coming out with this idea. And then those candidates are sent to the junior scientists, basically in the lab to make these things. And then a junior scientist, for example, can try different, maybe 10 to 20 different formulations a day by hand. So now as part of AI, you have this dry lab, white lab. So the idea creation, think of that as a dry lab, basically compute ideas. And then this idea filtering, this is a white lab. So you go to the lab and then instead of human scientists, you have what's called a lab, autonomous lab. Yeah, instead of life times. Just work for one human. Yeah, yeah, yeah. And then it's like perfect accuracy, no error. So that can reduce the filtering from again, several weeks, another several weeks, even months to just days. And then the last one, probably the biggest band for the buck is the validation. Validation takes a long time, takes several years traditionally. So once you have enough data and you can train these machine learning models, you only need to capture just the first, probably two weeks of testing. And then you will know, you will know it's end of life. So each basically take each phase and then you can shrink what was originally years now to weeks, weeks, if not days. So this does away with the whole idea of having to actually test a battery for eight physical years. It's able to do this in like a controlled, analyzed setting. Yes, yes, yeah, that's amazing. We've seen this technique being developed and deployed in life science a lot. No, of course, so in R&D, when you develop a new material, you have to go through lots of trials and errors. So you go through a hundred times that don't work. And then that one hundred first time works. So that first 100 times, you can really use this process to accelerate. And then that final trial, the last trial that actually gets you the breakthrough, that one of course, you can still take the time, do the full testing, do all that. But just the process before that can be much faster. I guess that explains why batteries essentially went the better part of a century with pretty minimal advancements. Is that in that? Yeah, yeah, almost no. So just put in context the materials that are used in batteries. A lot of times these are small molecules, small organic molecules. Then in the universe, there's about 10 to the 60th, six zero possible small molecules. And then since the 1990s, the last almost 40 years, the battery industry only screened about 10 to the third, different small molecules. So we explored 10 to the third out of 10 to the 60th possible. A lot of room on unexplored. Exactly, yeah. That's amazing. So did you, when you decided to move on lithium metal, was that because you already had a hypothesis that that was just better? Was that just where the industry was moving? And where did you think of coming, like bringing AI into the equation to help speed this up? When did that happen? Yeah, so we knew lithium metal was going to be the best. Because if you look at the periodic table, lithium metal is number three. Then lithium is the lightest metal we have on the periodic table. And then even lighter than that, you have helium and hydrogen. So in terms of portable energy source, you can't get better than lithium because it's already the lightest metal we have. So that's what we focus on that now to make lithium metal safe in the stable for a long time for different applications It's really difficult to to come up with a material called Electro life for the lithium metal so you had to come up with a electrical material that was stable and it's safe on lithium metal So then a lot of the work was coming up with a cocktail a formulation with new small molecules for lithium metal that will make it safe and stable got it got it And then just to follow up on the the AI side of that Did you use AI as part of that process or did the AI come after you already had the initial cocktail and you wanted to you know Do all the testing and all of that almost in parallel So we were really frustrated with how slow and how much work was taken to come up with a To test different cocktails for the lithium metal electrolyte so so this tool molecular universe really came out of them So we wanted to okay say if we have a database if someone just had map all the small molecules that could be used for this battery And have almost like a dictionary there for us and we could just just go to the dictionary find the molecules Then and then also instead of testing here then if we just had a model and then we only give it for example Just the first phase or so and then we can almost predict the end of life and then in self having a human doing the the testing in a very In the facial way if you just had a high-throughput robot So we have all these ideas, but then no one really supplied these these tools to us, so we build these tools That's awesome. Yeah, I love that. He like doesn't exist. Well shoot. Guess we got to make it. Yeah. Yeah From a from a practical perspective like to someone who's maybe you know not as well-versed in this What's the practical advantage of say lithium metal over mainstream lithium ion that we use every day in our phones and such? So it's lighter and then smaller Well that means for example if you were to put that in the pickup truck the range of a pickup truck is depending on the The amount of battery power and also the weight of the truck So if you can make the battery lighter then that truck can go farther or you can put one more passenger You can put more more payloads and then same thing with drones So it's it's able to fly farther or you can add more passengers and then more payloads Wow, okay, so that's really interesting it makes a lot of sense I guess up to now this has really been a balancing act of how much battery can we put in and not Cross this threshold where we're losing range based on the weight of the battery. Yes, right So if you use the so if you keep the technology the same and then you're limited to the same power to weight ratio It's almost like a rocket you cannot just add more battery and then expect that to go farther because In the more battery you add also you're adding more weight. So it's not gonna work So you have to really use a different chemistry. That's when you start looking at how can I make my seats lighter? How can I build lighter dash? How can I make my Motors out of the wheel I guess everywhere you can trim and ounce This is why like the early days of EV in the 90s and 2000s there were these tiny cars, right? Like no one no one liked those cars because we're too small. Yeah, but then then the the EVs after the 2010s They started becoming more more practical like families and then more practical. Yeah, and it's something you'd be more I like to say that you know Tesla kind of brought the cool factor to That was a thing that was really missing was the idea that you know cars always kind of been an extension of yourself in a way and and representative of who you are in And the little bitty smart cars were You know not very representative of most people in a lot of ways I think Was I up for me? Yeah, well that makes me wonder about the current hang up today, right? Because the EV market today is really interesting where if you look at it in the US There's really not an affordable EV that most people can drive and I feel like that's probably the biggest constraint on adoption right now is like they're just too too expensive for that average person I'm wondering feel free to adjust that or correct that but what would you say is the biggest technical constraint right now? Is it safety? Is it cycle life? Manufacturability or actually just cost of the materials so actually if you look at EVs today I mean the US market is a bit unique if you look at Europe if look at Asia the EV markets are actually quite quite different For example if you go to Norway to go to China Basically more than a third of all the new cars being sold are EVs and then and then the cost has come down a lot so quite affordable compared to a regular car and then a lot of these new EVs are being made by By a new wave of car companies and they really stress the interior design. There's TV inside There's a massage chair inside And also you can have a hot pot inside this so so the the utility It's way more enjoyable driving a EV than a regular car Uh, because of all the all the new utilities. I would say the US EV market is unique in the sense The subsidy went away as on September last year. Now it's it's uh Without the subsidy the economics changes and then also US market is a Mooted market in the sense a lot of cars from Asia is hard for those to come in without a tariff So they also uh changes the the market That's not even a battery science constraint. That's just a it's not yeah, yeah, it's political economic everything else can Yeah, basically Yeah Does the supply have an effect? I know that you know, traditionally some of these metals are very Geographically located and and acquiring them is difficult comes with a lot of a lot of struggles as well. Does that play a role in Pushing these things forward or the decision to stay with Lithium I should say I mean it does to a certain extent But now the supply chain is is quite diverse a lot of the Lithian cons from South America Australia They get refined in China and also some in Canada and then they get a simple into batteries So in terms of availability of this is not an issue anymore. Of course sometimes The raw material price fluctuates and that influenced the cost of battery But in terms of availability knows not a limitation. Okay, that's that's good I was curious because I know I was thinking of like with cobalt there were struggles as People were looking in those directions and others and I wasn't sure about Specifically how the supply of Lithium looked so thank you. Yeah, so so cobalt is is used in the foam but then so for example In the foam the cathode is called lithium cobalt oxide it is basically all cobalt so but then in the in the EV there are two types there's a Nickel cobalt magnus where cobalt is it's less than 10% And then the other type is lithium iron phosphates. It's it's actually cobalt free. There's no cobalt In that type so it's it's not a Constraint anymore. That's good enough and lithium was a constraint But it seems like a lot of emphasis went towards making that making more net new minds for lithium and trying to make it very Accessible over the stuff. Yeah, and also also Recycling so for example, but now we have lithium coming out of mines So one example you take lithium coming out mines in Chile and then that gets shipped to China To go process and then simple into a battery and then sold into a vehicle in the US and then this battery in the vehicle gets recycled in the US and then that lithium that nickel that magnus get used for the new battery Yeah, so the Recycling actually allows you to not go back to the mine anymore Yeah, that's awesome and that was like a much needed Aspect of this supply chain that I feel like it's got yarned out recently. Yeah, just great We have not far from here. We had a Alithium battery recycling facility that was dealing in like old EV batteries essentially and stripping those and and preparing the materials to go back Is there a limit to how many times that can be reused or does it stay you know essentially you're looking at I mean there's some loss Some loss less than 10% so each son you lose some but but it's a to a most part is pretty efficient Wow, that's amazing. You said like the battery should last at least eight years, right like so In theory, that's like 80 years worth of potentially 78 years. Yeah. Yeah, exactly. Yeah. Yeah, that's cool I want to go back to molecular universe for a second because you have this great database of all of this chemistry information I guess my question is What would be the next thing that you would want to do with that database like are you just making lithium metal as efficient as possible Are you coming up in new compounds are you exploring other material batteries like what can you do with this now that you have it? Yeah, so it's almost like the Britonica the encyclopedia, right? So And we put a lot of emphasis on the data and then two kinds of data again dry data and the what data So we really want to map the entire universe of materials and all the properties not as batteries But then but then like pesticides detergents cosmetics oil and gas paint basically everything these materials boil down to small molecules and then there is not An incyclopedia of all these materials. So so our goal is to gradually build this database of all the materials and then map it So map a meaning dry data and the what data dry data just use computing horsepower and then compute all the All the all the properties and the what data is basically we have these high throughput robots that should run these experiments 24/7 and then collect the what data and then so we use the what data to calibrate the the computer dry data And then we end up with this this modern day encyclopedia and then this this we can Feed it into into new models that we are developing for the different Abligations. So this goes way beyond just batteries and and the goal is is to apply this to almost any Material R&D. So the other day we have this so one of our employees is working on a Project with a home goods product. It's especially detergent and he's testing different cocktails for detergent Yeah, and that's quite quite similar That's cool That's I was not expecting that yeah that need me neither that is that is really Impressive and I want to call out something that you mentioned so you're using AI at the beginning to do your dry work But you're also using high-powered robots at the tail end of that to handle the wet experiments as well, right? Yes, because if you only do the dry computation, it's not very accurate I give you one example if you just use models to compute for example melting point and boiling point of certain molecules Typically you're off by 30 50 degrees Celsius But if you have you have actual data from the wetland like actual raw data And then you use those to calibrate then that error bar can shrink to maybe plus minus two or five degrees So does that create a feedback loop then where you're like using the you know molecular mapping for the dry data You're then getting wet data to validate and then you can feed that wet data back to your map and create a more like Efficient map or more accurate map. Yeah, so the Dry data really allows you to map a much bigger Universe you can compute for example 10 to the eighth 10 to the ninth pretty quickly Wet data you're talking about 10 to the fourth 10 to the fifth so significantly less than the dry data but but that's enough to calibrate the Dry data okay, so it's like basically yeah, it's like I could would it be equivalent to tuning it to tuning the dry data? Yeah, yeah exactly. Yeah, and I guess just so I understand is this a when we're talking about this map is this a bunch of Text data or is there 3d models involved when we're dealing with chemistry is it a mix of both like what does it actually look like conceptually What the data look like Yeah, then like what is the map of the data actually look like are you dealing with 3d simulation models like like that or is this all just a bunch of like Text of chemical compound combinations. What's kind of consists of it? I guess slow and moderate was it consistent? Okay, so the molecule database consists of just molecules structures And then and the structures are in 3d, but then you can represent the 3d in what's called smiles strings for example The water is htl and then you you just write Oh, so you can represent a 3d structure with with a string of letters c h all those letters and then and then will be compute and will we measure are these properties the properties are just in these numbers for example melting point boiling point energy levels Fiscosity just numbers. So at the end you end up with an excel table of of the of 10 to the 9th 10 to the 11th eventually 10 to the 60th smiles strings and then all the all the numbers all the properties. That's awesome. Do you have a rough idea of how many of those You're running in you know, I don't know what week on the year What's like I feel like there are so many applications for this like you mentioned that it goes so far. Yeah, what's there? You know, I heard yeah, yeah, yeah, not enough 9 enough So for now the And then so the basic basic dry data we're computing is using a technique called density function theory that one if we use Michelin accelerated density function theory we do about 9 million molecules a day just The single molecules level and then once you've got to the cocktail level So that's why you mix three or five different molecules together Right now we can do about 2000 a day, but we need to do way more I assumed it was going to be just a tiny fraction of what your dry is. Yeah, exactly. Yeah So how do you choose which from your dry work is going to go and actually be tested or they're like right? You need to test one from this area or is it uh, is it random? Are you going shotgun? Are you or are you honing in Is it a various? Yeah, so that's where we shall have human scientists coming into Train this so think of the database as just like a dictionary right you still need a person to know okay What what letter what what word do I look up so That's where the intelligence comes in for each domain we have about 50 human scientists to teach the model for example, we we saw use the Frontier models like the GP-D5 and then Gemini and then and then those are not specifically trained in these domains They're very general So we would have about a team of 50 Domain scientists and then teach the Frontier models for example about batteries about about pesticides about cosmetics in each domain Here are the things that you should look for for example in the battery A case to have a high temperature stable cycle life you need the molecule to have this particular structure So when you go through that entire database of molecules look for these structures and then look for For melting point boiling points within certain branch look for energies within certain range So the human scientists would actually teach the frontier model of these Stomach specific knowledge and then this intelligence would go look for the corresponding molecules in that database So is this take the form of like a system prompt? Is this like an agent like instructions that you're giving it? Or are you fine-tuning the model? How are you actually talking to it in this way? So for now we we are using And the genetic LEM and then it's it's a combination of GP-D5 and then Gemini and then so all the domain specific constraints and then the The information we would teach this agent and then the agent will look for in the database So cool that is What are your thoughts on the whole thing now where it's like open AI is pushing this idea that the agents are you know coming up with their own Physical physics theories and and all this stuff would you ever you know do buy into that and would you ever have the AI be the one doing the The what word to look up at some point like are you are you bullish on that idea? Absolutely absolutely So we are I'm totally bullish on that So with the caveat is that I wouldn't trust the explanation I would trust the result. I give you one example So we have lots of the battery Test data charge and discharge the voltage curves and then as a human scientist so we're all trained in the for example Newtonian science, right? School where taught physics chemistry, material science mathematics We're taught these theorems and then you you study the theorems and then you apply these theorems and then the world must follow these Laws the different laws of physics now with AI they go beyond that they use laws that we are not able to comprehend So so for example that voltage and the charge and discharge voltage curve a trained human scientist Would see would characterize that curve maybe with 20 Primeters these are typical parameters you will learn in school in books when you give that to an AM model It will give you about a thousand parameters, but most of these parameters you are not able to explain what they are It's not like the human scientists will see 20 parameters. Okay, this is charged. This is capacity. This is time This is a DQTV you can explain these things The a thousand parameters from the AI you are not able to explain those things It's like a different language not meant for us human species to understand But they are they are they real like I like like or is this a hallucination like how would you know that? Because because we see so when the AM model fits we see a thousand different parameters But then they are in the form of zero once zero once we can't really Interpret that we can't really give them physical meaning But these and then if you were to ask the human scientists to find patterns based on their 20 parameters The patterns are are weaker and not as strong as when you ask the AM model to find patterns based on 1000 plus parameters. Right. So, and then we asked the AMOLED to predict end of life just with a beginning performance is much more accurate. So even though we're not able to assign your physical meanings, it works. It's like a different set of laws that we're not able to comprehend, but it works. That is so cool. It is. It is. Such a just an interesting field to see this happening in. And also a really interesting application of AI. Like so often, Grant and I have these discussions where we're dealing with how to make better models, how to make models understand better and talking about reasoning and inference. And what attracted us to this conversation so much was the idea that this is AI being used in real scientific fields, you know, today. I'm wondering how long have you been taking this approach, if you don't mind me asking. About about three years since on the material side and then and I think going forward, now that the approach really works, we really need to expand this. So a lot of the high throughput robots and the computing we do need to expand those. So we can actually map it much faster. Okay. Yeah, make sense. You're going to need more robots. Absolutely. And actually speaking of robots, unless you have another trade. I just had one little follow up. Go for it. So do you notice a significant difference as the models have improved since you've been doing this over the course of three years? You've obviously seen some pretty monumental leaps in technology over that period. Yeah. It's basic to the, the more data you give it, the smarter it gets. And I will say the biggest difference is once you've reached a sufficient amount of data that you teach it, then the model is able to give you results and forecasts that's that's almost spanner. So then then you can really save a lot of effort. But you really have to teach a sufficient amount of data. My concern with that approach though is that like the current language models, right, they have a limit to their context, right? So are you must be using something else or you can tell me what you think about this? Like if you have this giant database of all this different molecular data, how do you make sure that it's considering absolutely everything when it's going to work here to get what I'm saying? It's considering everything in terms of like basically how do you prevent loss from happening with the context window when you're running an agent through this data? I guess what I'm wondering. So when we have the raw data, we don't really teach that to a large language model. We use a foundation model because the large language models are really good when the data is in a text format, but when it seemed like Excel numbers, it's not as good. So we use that to teach. So those two parallel approach on the database, the raw data from the lab, dry data and what and what data we use that to teach a foundation model, no large language model. And the in parallel to build that intelligence, we take all the books, all the papers about this domain and then we teach that large language model to learn how to search for it. So one is is is a so think of the database as the map and that's not L.A.M. and the think of of the L.A.M. as the search end that is L.A.M. So we don't feed that large database into the L.A.M. we feed that into the map into the foundation model and then to the L.A.M. we only feed a more limited list of properties. And that makes sense because when you're doing a more specific run, you have a more constrained problem space. So you're like, okay, we know we need to focus in this area because we're looking for this chemical property. That makes sense. I guess that was the thing that was starting off is like, I know L.A.M.s have a context limit of a million. You're like, I know there's an answer and I just don't know it yet. Yeah. So you can't necessarily put all the chemical data in the world in L.A.M. and expect to get that. Yeah. Yeah. Yeah. That's cool. Well, I want to talk a little bit more about lithium metal because I think it's really interesting that you all have these three major JDAs in place already with GM Honda Hyundai. And that, what does that look like in practice? Is that actively providing batteries working together toward that? Yeah. So it's really to improve lithium metal and then develop the battery so that it's ready to be deployed in vehicles. And of course, that technology development, that product development can also be used for drones or energy storage for data centers for lots of other applications. Nothing we have seen in the electrification effort. The EV industry has been sort of the pioneer of the technology and product that developed in EV are now used in other industries as part of the electrification. That makes sense. So what are the milestones that OEMs are looking for in something like this? Are they, I assume there are goals you're after? Cost duration. Yeah, so there are technical specs. You have to meet range, high temperature, low temperature performance, safety, a lot of safety. The safety test is no joke. And then also the scale, you do, for example, a thousand sales and then a million and then 10 million and then also at those scales, they are your supply chain, your quality, all the quality process. I love the details in the manufacturing. Okay. What about avatar? Because what I thought was really interesting about avatar, which is the other AI tool that you use is it's actually tracking the battery life cycle and you mentioned safety. So I'm curious if you could talk a little bit about that and why that's a big deal because batteries are living chemistry and being able to track them is really important and other probably. For example, EV and then you really want to track the safety. All the batteries in the same fleet of vehicles have the same chemistry. But once they start entering the manufacturing line, they will have different defects. Maybe this one has some defects in step 70. The other one has some defects in step 400. Typically, you have about 3000 or so steps in the manufacturing. So you'll have different manufacturing defects and the ones they are packed together inside a car and then the driver behavior is going to be different. So the final battery inside the car, the health, the safety, actually is a function of the manufacturing defects and also the driver's unique behaviors. All this you really want to track and monitor so that you can do maintenance. And then you really want to, for example, a regular car, you do oil change once every four or six months. And then with EV, if you can track that, then you want to be able to predict the incidents before it happens. So that's the top goal. But really is to prevent an incident and also predict an incident before it happens. And then once and then if you apply that in energy storage, you can actually use that for illustrously trading. So what that means is we do trading is basically supply and demand. Demand there are these virtual power plants that has to do with weather. You forecast the weather, you forecast any storm, any major sporting events, any if it's a data center, any incoming inferencing, big inferencing jobs. So that's the demand side. And then on the supply side, you have a choice. Do I bid or do I not bid? And then if I participate in the bid, okay, I make some money now. But then I will probably hurt my battery down the road. So I reduce my battery from eight months to from eight years to seven point five years. So I lose five five months, half a year of a revenue down the road. So you know, a very accurate battery health monitoring allows you to optimize the supply side of this trading. And we're seeing this in both data centers and EV. So once we save you, yeah, why for safety? Yeah, it's actually you use a lot for energy trading. Yeah, yeah. Well, especially with the event data centers, I imagine they need to be very, very efficient with their power, right? So this is very helpful. Yeah. A lot of times the data centers cannot predict what's coming down the pipeline. And the data centers are actually quite different from a normal grid because you really have to allow for search in power. So if you have a huge job coming in, then you have to drain the entire battery. In about two minutes and then that kind of super high power density battery, we have not seen it's actually quite, quite new and it's got to be safe enough. And then also it has to have really high power density in the data centers. So the on the supply side is actually quite challenging. So obviously you are developing your. own robots. I'm curious what your thoughts are in terms of whether you are potentially working on something like this or whether you just have general thoughts on the direction, how to actually give robots enough power so that they can be as efficient as possible. It seems like it's a battery problem to me, but I'm curious if that's something you're actively working on or thinking about. So the robots that we're building are more stationary and they're plugged in. So it's not more industrial approach. Yeah, exactly. It's based like a machine with a robotic arm. We don't really build like a battery of power towards humanoid. We don't build that. But I think for batteries, I mean, we've actually, we have some humanoid customers where we supply the battery and then we're getting. So before it was about two to four hour runtime per battery, we're able to extend that to about eight hours. Once you get to eight hours, then it's almost like a human worker, like eight hour shift, right? Like one shift, second shift. So then, then, okay, then, then give the robot a break, right? Yeah. So, here we go to the corner, charge, and then another robot comes in and then, and then do the work. I mean, I think eight hour is doable from a battery perspective. I've seen some companies where they designed the robot. So it can actually swap the battery by itself. Yeah. That's one concept. When it recognizes a certain amount of, like, oh, I'm down at 12%, it's time to plug in. Yeah. Yeah. Or just swap it with a fresh battery. Yeah. Yeah. Smart. Yeah. Some of the, yeah. Yeah. No, that's funny. It's like kind of like how, you know, we we're like, man, I'm getting hungry. I should probably take my lunch break soon, because my work's going to suffer if I do. Yeah. Oh, that's cool. Is there any benefits of trying to get it to like 20 hours or 10 hours? Is that diminishing returns, do you think? I mean, yes, because the robots are expensive. And then you definitely want the, you definitely want the robots to be functioning as much as possible. And then we're so we're seeing swapping. So if the swapping can be done efficiently, then then the batteries, the batteries don't have to last 24 hours. The batteries need to last probably 8, 10 hours and then to swap it. And then the robots will go back to work. Some kind of a charging station where it goes and hooks one and and sucks it on takes the other one then and and puts it back on a charger and you could theoretically just cycle. Yes. Yes. And also, we've seen a robot customer where the humanoid is actually work on the line. And then another humanoid, not humanoid. Another battery pot will actually come to the humanoid and then swap the battery. So the humanoid actually never has to leave the line. Wow. Wow. So just recharge it. It'd be like if you're working the line, so it comes that it feeds you. Yeah. Yeah. Some like that. Yeah. I'm kind of a cord. You can just plug in when you know you're not moving. It could just jack into the line. What about as these things get more intelligent, right? Like let's say like Nvidia comes up with a new GPU that could potentially power the robot and give it twice as much intelligence or four times as much intelligence as they will. But then maybe that requires more power. So then that's a trade off, right? It's like you're trading intelligence and battery life potentially. Yeah. Yeah. If if the GPU runs more than that does consume battery probably. And I don't get it. On board system though, necessarily. You know, if you're running strong enough, I don't know. That's a good question. What do you think your GPU rig be on board or would it be in the cloud or a server room in your house? I think both you will have cloud and also edge. I think both. That makes sense. Yeah. Maybe you have like a local one that's maybe more like for real time and then maybe your thinking power is done on the cloud perhaps. What about you? What do you think of GPU costs? I don't want to have to buy GB 300. What about what about the use case of glasses? Because I know air glasses have been power constrained for for the last couple of years. And that's one of the reasons we haven't seen like consumer grade AR glasses really take off. Meta is obviously making good progress there. But we talked a lot about EV size batteries or drones or robots. But what about making it as small as possible and as high efficient as possible? Yeah. Yeah. It's possible. And a lot of a lot of these these new high energy batteries are very dense and then you can pack them in a small place. But the glasses actually consume a lot of power especially when you have the camera on. It's especially very pale hungry. Yeah. I wear my daily glasses and I can tell you if you're running the camera you're going to run out of time. About everything else it does really smooth and you get good life out of. But if you're running a camera it's going to drain quick. Is that something that is worth pursuing do you think like in terms of some of using a molecular universe and trying to yeah, trying to solve for that? Yeah. We actually have we have some some users that try to use that platform to solve their problem. Wow. That's awesome. That's so cool. Keep us posted if anything exciting happens there. Well, Dr. Hood thank you so much for joining us. It has been just an absolutely enlightening conversation and it's fun to see how real companies, real scientists are using AI out in the real world. And go ahead. I'm sorry. Oh no. No, it's been quite fun to share this with you guys. And actually AI especially AI for science has been used in material science in life science. New drugs coming out, new paint, new batteries. Lots of new things coming out will be discovered by AI. You know, to their human partners. Is there anything on the horizon that you're working on that you'd like to plug in that vein? Anything exciting that you want to touch on the floor. I mean, I mean, love the batteries and battery backup for data center. We're working on it. That's quite interesting. So we have one universe that's powered by a data center and then one universe basically maps the universe and it comes up with these molecules and then we use that to to put them back into batteries and we use the batteries to power these data centers. So it's almost like a loop. Yeah, that's awesome. That's cool. Well, I'm sure you're going to be busy for years with all of the data center projects you're working on. A lot of work to do there. Dr. Who? I'm sorry. I'm sorry. So wait for someone to keep up with what you all are doing and go learn more. Yeah, so we have, they can follow molecular universe is molecular dash universe.com. Yeah. And then also once you a while, we send out these awesome awesome. Awesome. Very cool. All right. Well, thank you so much to everyone who watched today. Please take just a minute out to like, subscribe. We really appreciate it helps us continue to bring you guests that are doing amazing things in the technology and AI space on that note. That's all for us this week. Farewell for now, humans. (upbeat music)