Material Abundance: Radical AI’s Closed-Loop Lab Automates Scientific Discovery

Hello, and welcome back to The Cognitive Revolution.

Today, my guests are Joseph Kraus and Jorge Calindras, co-founders of Radical AI, a company that's building a materials flywheel, an integrated system combining frontier AI models with almost fully autonomous laboratories to dramatically accelerate the discovery and production of new materials. As someone who spent a year as an undergraduate research assistant weighing out small amounts of fine powders and running very tedious experiments, I have long dreamed of the day when science would start to be automated. That day, I'm pleased to say, is now arriving. Radical AI's lab can run 100 experiments per day, roughly doubling both what I did in a full year as an undergrad and what Joseph did in a full year at the Army Research Lab. And they're using that unprecedented throughput to tackle some of humanity's most important materials challenges. The upside here is truly enormous. As Joseph and Jorge explain, advanced materials are the foundation of nearly every major modern industry, from semiconductors and aerospace to energy and defense. Yet developing new materials remains painfully slow and expensive, typically requiring over $100 million and more than a decade to go from discovery to commercialization. This creates a so-called valley of death between academic research, which focuses on fundamental understanding, and corporate R&D, which for the most part focuses on incremental improvements. In that gap lie potentially transformative materials that could enable futuristic technology, such as floating trains, interplanetary travel, and hypersonic flight, but which nobody has successfully brought to market. Joseph and Jorge walk us through their entire closed-loop system, how they use property-driven optimization to navigate the vast space of possible materials, How their AI engine incorporates multimodal data from papers to microscopic images, how their robotic lab conducts synthesis and characterization, why they believe that capturing experimental data at scale is the key unlock, and crucially, how they're vertically integrating all the way through to manufacturing. Along the way, we also discuss why materials science has become more expensive over time, the LK-99 saga and what it reveals about the challenges of synthesis and reproducibility, How the Radical AI system learns from both successes and failures, ultimately building toward a sort of scientific intuition, and their recent Air Force contract to develop high-entropy alloys for hypersonic applications. What struck me most about this conversation is the scale of Radical AI's ambition. This is not a company trying to make paint a bit more durable. On the contrary, they are trying to create the materials that will define One quick note before we get started. For those who want more on the machine learning of materials science, my previous episodes with Tim Deignan and Jonathan Godwin of Orbital Materials could be great complements to this conversation. Today, we focus primarily on the discovery engine that Radical AI is building at a system level, but those earlier episodes do go much deeper into the technical details of how AI models learn to simulate molecular dynamics and ultimately predict material properties. Of course, while the 2 companies could naively be viewed as competitors, I think this conversation makes it abundantly clear that there is functionally unlimited opportunity for better living through materials science. And it's certainly my hope that both companies will go on to massive success. With that, I hope you enjoyed this conversation about accelerating materials discovery with a vertically integrated combination of AI and automation. Thank you, Nathan. with Joseph Kraus and Jorge Calindras, co-founders of Radical AI.

Joseph Kraus and Jorge Calindras, co-founders of Radical AI, welcome to The Cognitive Revolution.

Thank you.

Thanks for having us. Excited to be here.

Yeah, likewise.

So you guys are doing some really interesting frontier work in the application of AI to materials science, and there's a lot to unpack. We were just joking beforehand that automating science is something that, as an undergrad research assistant, I dreamed of for many hours as I was sitting there doing the tedious work of weighing out small amounts of fine powder. And that is, I was told at the time that that was very difficult to automate and probably wouldn't happen in my lifetime. So the fact that it is starting to happen as, you know, obviously as part of a bigger vision gets me excited right off the bat. For starters, you know, our audience is very interested in AI by definition. probably mostly doesn't know a ton about material science broadly. So maybe you could kind of just set us like a baseline. Why is material science hard? What's going on? One of the observations from kind of doing my homework on the company is that the cost of material science breakthroughs seems to be going up. So what's the kind of lay of the land today that you guys are entering into with a new paradigm?

Yeah, well, luckily for you, Nathan, your dreams are about to become, if not already, a reality in today's world. And so it's going to be an incredibly exciting, really, next 5 to 10 years. And we think and fundamentally believe as a company, science will never look the same as it's going to look over the next decade. And so we're very excited to be helping leading that charge and building that. You know, if you think about why materials are so exciting, and I'm sure we'll get into this in the founding story of the company. But they really impact so many different industries in the world. Automotive and aerospace, manufacturing and defense, climate, energy, semiconductors, electronics, the most important industries in the world are all a direct result from materials R&D. But we always see really 3 problems. First, cost, as you called out, north of $100 million to develop a new system from that computational framework through to really scaling that process. Second is time. It takes a long time to do a novel discovery. On average, we see typically 10+ years to really push a novel discovery into commercialization. And sometimes it can be much longer, spanning 20, 25. And then 3, fragmentation, probably one of the biggest problems that contributes both to the cost and time that I just alluded to. In materials science, we always describe it as a company as 2 different paradigms. On one end, you kind of have this very fundamental approach to science, what we call the academic pursuit of science. It drives your understanding of science, very fundamental in nature, and usually not focused on commercial application. There can be some tie-in, of course, but that's not the objective of academic research. The objective is to drive your understanding of, of what you're studying. And then on the other paradigm is really commercialized R&D or corporate R&D, and they're entirely focused on commercialization, which is great, entirely focused on optimization as well. And so they are driving to 1 to 5% performance gains so that they can change their margin and report that to Wall Street. They are not focused on discovering something like an RTAP superconductor, which I'm sure we'll get into. Uh, and this is the problem. In the middle is this wide open white space to do this novel fundamental discovery, but entirely focused on commercial applications. And then of course, that's exactly what we at Radical AI are trying to do. So that's where Material sits today and how we view the landscape and the opportunity that's sitting before us.

Was it always that way? I mean, the obvious analogy that comes to mind for this sort of discovery is drug discovery. And broadly there, the story, I guess, seems to be kind of 2 parts that people typically tell. One is like, for one thing, it used to be easier because there was lower hanging fruit, and maybe that low hanging fruit has sort of been picked. And then the other side is like, well, clinical trials are really expensive. And so even once you have something that's really great, you still got a long way to go before you can actually get it to market. And rightly or wrongly, obviously we want to be somewhat safe with that stuff at least. But safe to say that the costs have grown dramatically. Is it kind of similar in material science? Would you tell a low-hanging fruit depletion story? And is there something analogous to the clinical trial process that makes things harder than it would naively seem like it should be?

Yeah, absolutely. There are very strong similarities and then, of course, very core differences as well. One important thing about materials is that we don't go into the human body. Well, most don't go into the human body. And so there is a big delineation there. There are some that actually do, and there are classes of materials that of course have to be bio-friendly. But there's a lot of similarities on search space, the size of the search space. Now, things like proteins or other things in bio are bigger, but materials are quite literally impossible to move through as well. So they're both large. And then there is this challenge on going the full scale, what we call vertically integrating across. So actually going from that novel discovery and pushing that all the way through to producing that. We have different steps in that process from bio or drug discovery versus materials, but they both equally take around the same amount of time. Whereas bio might be going through clinical trials, materials are going through scale-up and processing. And so how do you actually take this small reaction that you've run in the lab and figure out how to make tons of of that material so it can be used in commercial application or industry. Again, different use cases, different processes to do that approach, but you still get this high cost, you still get this long timeline, and you still get this fragmentation, candidly, where different people are doing different parts of that process. Just like in drug discovery, you might have a company focused on discovery of novel drugs, And then a big pharma really thinking about, I'll pay for the clinical trial, I'll go through that process. And so you put all this process together, whether you're going through clinical trials or you're worrying about optimizing the microstructure for a novel alloy, both of these are time-consuming. They are incredibly expensive to operate and go through, and they lead to this really value gap that doesn't allow a lot of discoveries to proliferate into real commercial application. And so There's a strong corollary between drug discovery and materials, and the differences we think are actually less explored inside materials today. It's the exact opportunity for a company like Radical.

It's interesting because I would think that we'd be getting better at this sort of scale-up and process over time, right? Whereas on the drug discovery side, the clinical trial, you can sort of tell yourself a story of like, well, we're becoming ever more safetyist, ever more bureaucratic. Everybody's got even more boxes to check on all the steps of that process. And again, maybe to some extent for good reason. But on the material side, it seems like we should be getting better. Is there a warp speed? If there was a sufficiently great breakthrough or sufficiently great market need, do we have the ability to be like, we're going to do this in a year? Or is there just some reason that kind of focus and energy can't be brought to bear on the material scale-up in the same way it was for COVID vaccines?

Whether there's an answer to that question or not, I can assure you that Jorge makes the entire team aware that it needs to be done sub-a-year and our technology needs to be going much faster than that. No, to your point, we— this is where we think autonomous experimentation is incredibly important. And we'll get into this in deep detail. Jorge will walk through why experimental results are so important to the AI framework and why our flywheel is so differentiated. But it's not just about changing the way we do discovery from an AI lens, meaning how do we recommend more things that are actually more attuned to the application you're trying to solve for. It is those things. But we actually can just do more experimentation faster with self-driving labs today. And as we're doing that high-throughput experimentation, we are not just running something in high throughput repeatedly over and over. We are using an active learning approach so that every run that we do is incorporating the learnings from the last one, just like a human scientist would do today. And so in the lab that we are building at Radical AI, this full self-driving lab, we are targeting 100 experiments per day right now. I probably did 50 experiments per year when I was at the Army Research Lab as a material scientist. And in some extreme cases, directed programs from agencies in the government, you might push up 400 or 500 experiments a year, like the Mach program, for example. But that's a week of research for us at Radical. And so it's this compounding effect of speeding up experimentation and then this huge unlock of the experimental data. And Jorge, I can kick that to you and kind of how relevant that becomes on the other side of the engine.

Yeah, I mean, experimental data is really the big unlock, we think, to all of the big materials problems. But really, it goes even just beyond experiments. It actually, to your question, Nathan, goes all the way down into scale-up, into processing, and then ultimately into manufacturing. And so to answer your question around where's this big challenge, it is quite literally in scale-up. You start with, at the experimental scale, a material that's the size of a quarter, and then you're trying to create in a 400-pound piece of material from that thing, and at the same time retain all of the properties, even though you're changing it in so many different ways. That's actually really, really hard to do and a different kind of challenge than what exists in drugs, for example. And so a big part of our belief is that we wanna be able to capture experimental data at the research level, right? At that quarter-sized level, we call them buttons. And then we wanna be able to thread that all the way through as we continue to scale up the material to make sure that we are retaining the properties that we want. And if we're not, we're understanding why did we lose those properties? What did we introduce environmentally, chemically, or so on and so forth to change those properties? And what can we do about it in order to not change them in the future? That's a big part of the unlock that we want to go after.

So do we then have sort of the equivalent of orphan drugs in the materials world? We have a bunch of orphan materials that sort of haven't been created at this small scale, but just have not been Ever scaled up for all these kind of just very practical difficulties?

I'll let Joseph answer that as the scientist.

Yes, I would say that's probably a bulk of the work that we do. And there's different lenses of that, right? I mean, if you look back to some of our, the biggest materials that have come out, they just might have taken a longer time, like a transistor, right? And the use of silicon in semiconductors. Obviously one of the most talked about, most impactful, most used material discoveries today. It also took a very long time for that to really push through and continues to exponentially increase today, right? With the node sizes that we're pushing into in semiconductors. And then on the complete flip side of that, you have something like an RTAP superconductor. It's not a new idea. People have been talking about it for decades. It is one of the more exciting material discoveries you could probably ever make. The applications of that are really incredible, but we struggle to understand the synthesis process of that. In the most recent thought, our TAP superconductor LK-99, we failed to be able to make it experimentally. And, and that was even just going from theory and one experiment to replication. Now take exactly what Jorge said. And not only be able to reproduce it at, you know, a quarter size, but then go make, in, in that case, millions of tons, because the world is going to want that, um, at large scale. That is unbelievably complex. And so there are a lot of these things that, from everything from computational discovery to just making it in the lab repeatedly, go to die. And that's not even stepping through the long process of manufacturing scale up, and then really putting it into industry. What product does that material end up in? How is it used? It's a very complex process. And to Jorge's point, when you can capture all that information and use it to design better materials, better compositions, better structures from those compositions, we think we can really 10x, 100x, in some cases 300x, 400x the speed of discovery today.

This customer aspect of it is huge. you know, in our minds. And it really does get back to that white space that Joseph was talking about a couple minutes ago. And honestly, one of the prime examples that we always point to is Gorilla Glass, which a lot of people don't know actually originated as a product for Corning in the '50s. And so Corning developed something called Chemcor. That was in the '50s, maybe the early '60s, and it sat on a shelf for half a century. And it wasn't until about 2007, 2006 when a guy named Steve Jobs walked over to the offices in upstate New York and said, hey, I'm developing a phone. It's going to completely change the world. All of our phones right now have a plastic screen. And I'm Apple. I care about design. I don't want that. I want something that feels better, that looks better. It just has these properties that I'm looking for. And Corning said, you know what, I've got a product and it's been sitting on the shelf for a while. And eventually they took Chemcor and they turned it into Gorilla Glass. And that sits in every Apple product. That they've created over the last 2 decades. And it also sits in the products of Samsung, LG, and I think it's probably the biggest revenue driver for Corning at this point. And so the reason we love this story is because it really alludes to this sort of concept of, number one, we don't really understand the materials we make a lot of the time. We don't really know what their good properties are. And number two, we actually need a customer to tell us what they're looking for in order to map up those properties to customer expectations and where value can exist. So for us, a big part of it is, again, we wanna bring in those customer expectations, we wanna bring in those value drivers very early into the process so that as we're designing materials, as we're experimenting and doing R&D, and then ultimately scaling these up and manufacturing them, we're always keeping the customer in mind and we're always satisfying their needs. That's a really important part for us.

Hey, we'll continue our interview in a moment after a word from our sponsors. I'm very eager to get into the flywheel, but just a couple more kind of foundational understanding questions before we do. One, on the LK-99 saga, it almost sounded like you were saying that was maybe real, but we just don't know how to reproduce it. I had sort of come to the conclusion that it was a mistake, like some measurement error, that they never actually had anything. How would you describe the field's best understanding of where That kind of landed.

No, I think you're correct. I think, you know, the most recent thing I read was accidental creation of an impurity. Copper sulfide, I think, was one of the things that played out from that. However, there, there is this notion around, we didn't even know how to reproduce it. Things like the temperature ranges, as well as even like, what were we making it in, the container? Because of course that's going to affect the, uh, affect experimental results were unknown, were undisclosed. And so. It really gets into the importance of synthesis, how challenging synthesis is. And I think the best example is to think about a cook in a kitchen, right? If I give 2 people the same ingredients to make a loaf of bread, and one scores the bread and lets starter rise and puts the right temperature and bake time, they may have a really good loaf of bread at the end of it. And if someone rushes the process in 30 minutes, At 1,000 degrees to speed it up. Well, they're not going to make a good loaf of bread. I think it's going to be a rock and it's going to be pretty bad to eat. And so same exact ingredients, same exact process. I'm going to heat it. I'm going to score it, right? But even just changing the time variable there and the temperature, entirely different output from that. So whether LK-99 is real or not, I don't know. I don't think I have enough experimental results that we've run to determine that. What I do know is that the proposed method of synthesis and the lacking details from that method don't quantify as a new discovery because a new discovery needs to be reproduced. You don't have a new material until you can make it in a lab, and you don't have a new commercial material until you can take what you made in a lab and scale that up. And so I think we have not done that yet in RTAP superconductors. Whether that material exists or not, I think time will tell.

Fascinating. And has the field just kind of moved on from that for the moment? Like we're all just doing other stuff now. I mean, it seems like we would want to run that to ground, uh, collectively, right?

The field might have moved on, but I can tell you that we think about that problem quite frequently. And when we think about the vision for Radical AI, look, we have a beachhead that we can get into. We have to, otherwise you'll boil the ocean with material science. It impacts every industry. As an early-stage startup, you cannot do that. You must be laser-focused and really drive towards execution, then scale. But when we think about what our technology can really do, our materials flywheel is attempting to remove materials as the biggest bottleneck to our most important industries. We wanna build a world where you go from a human-driven process to an AI and autonomy-driven process. And this approach allows us to create what we call enabling. materials. We actually create the industries that we sell into. We don't just optimize the current industries today. Just like Jobs created the touch glass for smartphone and other products today, that's the exact type of applications that we want to enable in the long run as a north star for the company. And clearly, RTAP superconductors with their immense applications, are somewhere situated on that roadmap.

Yeah, one of the things you should understand about the company and why we founded the company is it's not because we thought there was a big opportunity in materials. I mean, that's definitely true, and it's a big part of what we're going after, but we seriously wanted to create a company that was going to move humanity steps forward. We want to live in a world where we can ride on a floating train. We want to live in a world where we can actually travel to a different planet We want to live in a world where you can do some of the things that we think should be basic, like being able to drive a Tesla from New York to Boston without having to recharge it. Like those are the kinds of things that we feel like they should be available to us and within our generation. And so that's the whole point of Radical AI. We want to make those things happen, but we can't because materials are constantly getting in the way. So that's the real driver for the company and a superconductor. Is probably, from a materials perspective, the epitome of an enabler. It will create so many different things. And we like to think about the second and third order effects of what will come out of the materials we create, in the sense that we can't actually predict what will come out of a superconductor. We won't know what sorts of things it will allow. We won't know what sort of products will come out of it. We won't actually know what the world will look like when we have room temperature superconductors. But what we do know is that it will change the world. And it will make us able to do things and approach technology in an unprecedented fashion. That's the whole point of radical AI.

Could you maybe give a little bit more of a range of like, and I'm conscious that you just said, don't know exactly how the world will look given certain breakthroughs. Obviously it is, you know, nobody would have immediately, classic example from like the smartphone glass, right? It's like people didn't immediately know we were going to have Uber and DoorDash as soon as we could touch the screen. So. Certainly I get that it's going to take time for these things to play out. I get the sense that obviously, okay, room temperature superconductor is one. How many of these sort of canonical things do we have where we're like, if we could just get this property that we don't have today, it would be like a massive step change unlock? I guess I have kind of a couple of layers to this question. What is the range of those things? specific examples are kind of forefront in mind. A layer down is like, how precisely do those get articulated today? Is it like this thing needs to be 4% lighter and then we cross some threshold, or is it like lighter is better in general? And then I always think back to the Elon mass begets mass. I'm sure he didn't coin that, but I heard him kind of go on about that. So I'm also kind of interested in general mental models. If people just want to think like, about the material world, about them and around them in a more sophisticated way. What other things are there that are along the lines of mass begets mass, where you can just develop your intuition for like why materials matter and how they're getting better translates to material progress? So a lot there, but take your time.

So there are a lot of ways to think about stepwise innovation, right? There are a couple of classes of materials that I would say Everyone is attuned to our tap superconductors, high entropy alloys, ultra high temperature ceramics, CMCs, semiconductor based materials. These are people have been thinking about these industries and the novel materials that will drive fundamental impact in all of them if you discover. And it really, if you notice from that answer, comes down to the end application. And we think this is so important. Jorge alluded to this a little bit earlier. We don't make materials in a vacuum at Radical AI, because if we do, then we actually don't know the problems that we're trying to solve. And if you're an academic research group or a national lab or a nonprofit, that could be a great approach to do that. We want to make materials that leave a fundamental impact on the human race by directly solving a huge problem that exists in industry today. or helps enable a new industry in the future. And so if you're gonna take that perspective, a lot of the things that we think we can do, like the examples I just gave, are grounded in, well, what do you want to come from that? I'll give you a perfect example of the versatility to your second question. How versatile are these materials? So something like a high entropy alloy, which is a core area of focus for Radical AI today. These materials have really incredible performance across mechanical properties, thermal properties, and then really their ability to blend them together, meaning you get a really high strength ratio while still being effective at high temperature. It's not one or the other, and that's important. And these materials, these high entropy alloys, can be used in something like a hypersonic application where you are flying at speeds above Mach 5, very high temperature ranges, very high pressure, dealing with things like oxidation inside the atmosphere that are really challenging for a material to solve. We have struggled to solve that problem as a nation today. And then in the exact same material can actually be used in a nuclear reactor, nuclear fusion reactor that is actually getting bombarded with radiation from this power source. And our current materials today, like tungsten, can't withstand That radiation and erosion over long periods of time. And so these high entropy alloys are really one of the options, amorphous metal being another option. That's a subcategory of a high entropy alloy that can try to solve these problems. And so the versatility is very big because the properties are so desirable. And these are, again, what we call these enabling technologies, right? These are unlocking not a 5% improvement gain, right? Or even a 50% improvement gain. These are 100x better, 50x better than what exists today. And this is what we get really excited about. And to go come to your third question and think about the way that these can, I guess you could say, extrapolate out and get very defined. Every industry in the world can define a materials problem. If you go into the athletics industry and clothing industry, well, they're looking for novel materials to wick away sweat and reduce odor. And there are silver nanoparticles inside Lululemon workout shirts today. So that's a real application, and they're trying to optimize there. And if you go to the exact other side of the dimension and look at advanced hypersonic aircraft or space travel, they're looking for intense advanced materials that can block extreme heat, block radiation, withstand immense pressure, all while doing that in an atmosphere that is quite corrosive to the material system. Those are different problems, different defining mechanisms from them, but really important to be able to straddle all those constraints. And so in summary, the real core factor that you have to think about with optimization is what are the most important properties? And when we're defining materials today, rarely is it one property. There are probably a few examples you could think about if you wanted to that would be a single property. We always get asked from customers, yeah, I need it to have this mechanical performance in this environment for temperature and pressure, and at this cost with this supply chain. For an example, inside the defense or aerospace industry. And so you're not just optimizing on thermal expansion, you're not just optimizing on its resistance to oxidation, you're actually optimizing on, well, what does that cost of that solution? And then what does the supply chain build and employ? That solution with where these raw elements are coming from, and so we are always seeing this optimization across a multitude of different properties. And because of that, there becomes so many different combinations, so many different industries, so many different applications in those industries that these materials, like a high entropy alloy, can be useful. And so, in our opinion, there is an endless. amount of problems to solve inside of material science. And you can go very specific, like optimizing the silver nanoparticles in a workout shirt, and you can go very large, like building radiation-blocking shields. So when we land on the surface of Mars, that doesn't become a problem for communications and electronics. Everything in between there is very, very dense and why we think it's one of the biggest opportunities and markets in the world.

Hey, We'll continue our interview in a moment after a word from our sponsors. So maybe that's a great jumping off point to talk about the flywheel. If a customer comes to you with one of these problems, we want to make a Mach 5 aircraft. And to do that, it's going to have to be no heavier or maybe even lighter and stronger and able to handle heat and maybe able to handle radiation.

Who knows?

Maybe this thing's going out into the outer reaches of space. I guess, take me through the loop that you guys get into to work your way up to actually achieving that. Maybe starting also with just like, how often is there that Steve Jobs opportunity where something maybe does exist out there? How would you even go and search? And then if it does exist, where does that allow you to shortcut into the flywheel versus if no such thing exists and you have to come up with your hypotheses of never-before-seen materials and work from that point.

Yeah, so the flywheel consists of, at a high level, 2 different buckets. You have an AI engine on one end and then this fully robotic self-driving lab on the other, and those work together. I'll let Jorge start on the AI front, what that looks like. It gets into a lot of how we do property-driven optimization directly to your question, and we can talk about the experiments after that.

Yeah, so when you think of the way scientists kind of do science today, there's really a lot of different things that come into the picture. You're looking at images out of a microscope. You're reading a bunch of papers. You're using your intuition from previous experiments. You may be doing some computational modeling. That's really all of the tools in the toolset that a scientist uses to eventually come to a confidence level where they want to go out and run it. Experiment. And so we try to figure out how to create, um, an AI engine that incorporates all of that, all of the different modalities from a data perspective that come into play there, all of the different architectures that we need to use at a modeling level, uh, to come into play there. And then this sort of scientific intuition aspect is another part of it that we try to capture. So we bring all of those things together into one piece of technology that we then kind of throw at these problems. And the way we do it is, as Joseph said, by doing what we call property-driven optimization, which is we start with the properties that we're looking for. Those typically come from a customer. So it's the things that you mentioned, right? I would like to use these sets of elements. I would like to have it hit this oxidation level. I would like to have it hit this ductility or this strength. And then we give all of that to the AI engine and we prompt it and we give it some input as well. And we let it go to work. And we let it design experiments that we think are going to be successful against those properties. That's when it then hands it off to the robotic lab, which I'll let Joseph talk about.

Yeah, and this is where, to Jorge's point, is really important. I love the way he said, kind of, how do you use the tools at your disposal? What is an experimental scientist's job? Well, you come up with a bunch of hypotheses. You think about the ways you're going to test them. Like Jorge just mentioned, all of that's happening on the AI side. And then you go and run experiments. You make things in the lab, you test the things that you made in the lab, and you look at all the results. And that is all happening inside our self-driving lab today. So our lab will receive through our operating system a design of experiment approach. Here's the composition. Here's how we're going to make it. Here's what we're looking for. And it will start executing on said experiment. And so we will make a material, that material will come out, it'll go through material characterization. We do a bunch of different characterization techniques to understand what did we make and really what does it look like, which is important to microstructure and then therefore mechanical properties. And then we'll actually run property-driven tests. How strong is the material? What is the stress-strain curve of the material? How does it handle under creep or crack propagation? How hard is the material? And we will do those property tests inside the lab as well. When we collect all of this data from both the material characterization and the property characterization, we actually use machine learning to analyze all of this data in real time. So we are taking this information in, we train kind of these submodels, which Jorge can talk about, to learn how to analyze just like a scientist would. We start to build this intuition. Start to build this understanding of, if I'm optimizing for strength, then I need to pay attention to grain growth and microstructure formation. I'm going to actually look at that evolve in a scanning electron microscope or in a tensile test that I actually do on said alloy. And so, as we analyze all this information, we pull out the insight from that and then feed that as active learning approach. Back to the AI engine where you can think about the scientist takes back the results and says, oh, that was what the outcome was of experiment number 1. Let me now run experiment number 2, updating or tweaking that process based on the results that I just saw. And if you think about what I just described to you, that's what a human scientist does. Not some insane new changing the way we do The scientific method, but we are changing the way we kind of extract information from the scientific method. When I ran that process, I did all of that in a serial-based approach. I looked at one XRD scan, one SEM scan, one tensile test, thought about those, and then went back and did the next thing. And I might have remembered there was a paper I read. I might look into a patent database. Or I might not. When our AI scientist looks at all that information, it is analyzing and comparing those results to millions of XRD scans. And it might be pulling from publications, it might have done past experimental results in our lab, it might have context on other experimental datasets that exist today, you know, and then using all of that to, to make a new informed hypothesis. That operates on a dimension candidly, the human brain just cannot do. And this is where the power of the flywheel becomes so impactful. It's not just an AI scientist or an AI engine, as where I described. And it's not just a self-driving lab, as I just described. It is the combination of these techniques together that build this lucrative approach to not only developing and designing, but then making and testing novel materials. That is how we can actually take a step forward in discovery. And in the future, scaling these materials to larger systems.

So, yeah, I guess I want to dig into this on a bunch of different levels. Maybe for starters, when you're developing the AIs, you kind of mentioned submodels. I'd be interested to understand the range of models that you have. We're seeing increasingly stuff posted from GPT-5 on Twitter that's like, this thing helped me think through a frontier science problem. So I wonder to what degree those sorts of things are in this flow. And then obviously there's going to be more specialised models, and there I can imagine a lot of different approaches. Obviously, we've seen many things that are sort of graph neural network structures that have some sort of radius around particular atoms. What are they connected to? And kind of model things in a more literally physical way. I've also seen interesting structures where it's sort of a bunch of different things, and then you kind of mask out different tokens and ask the model to sort of fill in. Right. here's the chemical composition and here's certain properties and here's how we synthesise it and mask those things out and kind of train the model to fill in those details. So, yeah, can you outline the sort of family of different kinds of models that you're using and what the inputs and outputs are to those?

Yeah, so we use a bunch of different models. We use GANs, like you mentioned, to do atomistic modelling. We have generative models. We use language models, including the GPT family of models, to do Everything from understanding and extracting information out of scientific literature, even generating recommendations. We use computer vision in the lab. We use a bunch of different things in a bunch of different ways. And obviously the inputs and outputs of all those models are slightly different depending on the use case and the model itself. But I think the core thing for us is that there are so many different tasks and things that have to be done from a material science perspective that we actually have to use different architectures and different models in different ways. But I'm happy to go into more detail on any of those.

Yeah, please do. I mean, I'm not even sure exactly what you would consider to be the most core one or a few tasks. I mean, obviously, you know, anything there's, at least in my experience, there's always sort of one or a very small number of tasks where you're like, if we could get this working super well, we know we'd like be successful from there. And then there's a lot of auxiliary things that are sort of, you're more confident it's going to work and it's more about kind of efficiency and ironing out details. What are the really core ones that are sort of the biggest challenges? What are the inputs and outputs of those?

Yeah, so the one where we're most confident it will work is on the computational side. So that's using something like a GNN. In the material science world, we call them MLIPs. There's a lot of MLIPs. They're all fairly successful in different ways and they work in different manners. We know that that's a core part of the strategy. We use them. A lot of people use them, have been using them for a long time. But where we would want to place our bets, where we really think there's going to be a big differentiator, is actually on the language side. We think that really what we're trying to do is understand the vast space of scientific knowledge that's out there and then reason about that, incorporate different modalities and different tools in order to really come about good conclusions so that we can generate hypotheses, design new experiments, send those down into the lab, learn from the success and failures of those experiments, and iterate and adapt over time in order to improve the recommendations that we send down there. We do think that at the core, language models is really what's going to drive a lot of this scientific intuition, this reasoning about what makes a good experiment, what makes a not so great experiment over the long run. Where we think the other models will come into play is sort of bolster those models. You can imagine a GNN being tapped by an LLM, for example, in order to run a computational workload because it just needs to reaffirm some uncertainty or fill in a gap. around some knowledge that it doesn't quite have, even though it's scanned thousands of papers. The same thing could be said for some analysis coming out of the lab, right? We may want to look at a bunch of SEM images and do some computer vision in order to update our intuition, update our understanding of what kinds of experiments we're running in the lab and how are those performing at the sort of microstructure level. So that's sort of how we use all the different models. But again, if we were to say there's one thing we have to nail, It's this sort of replication of scientific intuition and the designing of what would ultimately become an experiment.

As an aside, what's the best anyone has ever been at the human intuition on this? What is the sort of ceiling of human ability? People are very obviously enamoured with John von Neumann. Is there a John von Neumann? I also remember this story of— I don't know if this is even true, but supposedly the structure of benzene came to someone originally in a dream. as they envisioned, like, a snake eating its tail or something, and they were like, oh, it's a ring structure. Okay, we've moved a lot past that, and we've got lots more concrete knowledge to base our intuitions on. But are there people in the world that are just like an obvious cut above everybody else in terms of intuiting what material is going to have what property or how to make it successfully when nobody else knows how How to do it? What does that human landscape look like?

Yeah, it's a great question. Um, the answer I think is, is yes. And one of the best examples I could point to is actually our 3rd co-founder, Vern Seder. He's, you know, he wouldn't say this, but I think we could go out and say this. He's probably one of the world's leading material scientists in the top 3, if not the very best. And when you speak to him, you just get this feeling of, this guy just knows stuff. that the rest of us don't know. He can just understand things and connect dots that are difficult for the average person, even a great scientist. And so absolutely, there are people who have this intuition. But the funny thing about Hurd is when you talk to him and you sort of really probe at where does this intuition come from, he breaks it down pretty simply for you. He says, look, I've been doing this for 40 years. And so I have run a bunch of experiments. Over my career, or I have had a bunch of students run those experiments and I have learned alongside them, or I have read papers from all of my colleagues and the experiments that they have run. And so this scientific intuition, yes, I'm sure there's a part of it that is innate and there are some people who are just talented at this stuff and they can become better than the rest of us, but a lot of it is actually learned. It is learned from going about and doing these things and updating your understanding of what works, what doesn't work, And that really is that scientific intuition that we're trying to capture. Joseph, you probably have some thoughts on this as well.

Well said. There's so much info in the scientific process scientists will use that is from learned experience, whether that's at the beginning of your career or even taking something you've worked on for 20, 30, 40 years and applying that to a new industry. How do they like to test that material? What properties do they care about? Is there a different way that they need to consider Manufacturing. You know, I had spent the better part of my undergraduate and early PhD days on 2D materials for optoelectronic sensors. And then I ended up getting a fellowship at the Army Research Lab and went there and was working on neuromorphic computing. And to your boy, von Neumann, I was working on getting rid of that architecture inside semiconductors.

But—

Hello. You know, the testing requirements, the saturation current, the on-off ratio, the conductivity, the resistance in the memory states that we were building, All those were learned things. And after a year at being in ARL, you know, I was better than probably 99% of other scientists in thinking around how to design new semiconductor materials that were directly at neuromorphic computing, that were directly using ion-gated channels. There was a group of people who were experts in that as well, all from intuition and experience. But exactly to Jorge's point, it comes from this learned process through experimental research. And that is very, very hard to capture. Actually, we don't capture. Of the year of work I did at ARL, 90% of the stuff I did didn't work, quote unquote. It was those negative results we always talk about. But there's no recollection of that. There is no capture of that. There's no like dataset that exists of here are all the e-beam and, and PVD and ALD experiments Joseph ran over the last 12 months. There was just one resulting material, which we pushed out and shared with people. And so if you can't capture that, and the only place it exists is up here, and there's actually like no framework in place today to build this intuition except with exactly what Jorge just really walked through. And that's what gets us so excited. So to Jorge's point around Heard, you know, that's why one, he is such an important part of what we're doing and why when we met him, said we have to build this company together. But two, Has this really unique ability to— how do you replicate that? How do you build scientific intelligence? Intelligence that is not just general from like a, I know the English language and I know what science is and I know the periodic table and electron orbitals and the way we actually make new elements or compositions. But how do I go deeper than that and build this intuition, this scientific intelligence around Yeah, I'm gonna use these 4 elements specifically because I am targeting an X property and that property requires that. And I just know that. How do you teach an AI that? That's quite complex to do.

Yeah. And I think there's sort of 2 layers of this. There's like the easy aspect of it, which is, okay, yeah, science is really hard to do. It's super slow. It's very manual. We don't record it. And so we should automate that and we should learn how to do that with AI. That's not a big leap for people to take. That's an obvious thing. And we obviously do that. I think the bigger leap here, and really the one that we're after, is that— is this sort of understanding that there is an exoscale set of data that's out there, but is virtually impossible to capture. At least it has been in the past. And the only way to get at it is through a really, really inefficient process, which is being a 40-year academic. And that's why we are bottlenecked by all these different things. And that's why we don't have materials that solve some of the biggest problems, right? Because not only are you stuck in doing it over a 4 or 5 decade time span, but in that 4 to 5 decade time span, you can really only focus on one area, maybe 2 areas. And so you're just limited by the amount of things that you can do on top of it being an inefficient process. And yet, like Joseph talked about, we know the data is out there. And so a big part of our belief is if we can build a system that can actually tap into that data and figure out how to use it in a good and efficient way, we can couple that with all of the advancements that we've had in machine learning to really drive at this problem that takes 40 to 50 years and shrink that down so that it's sub-a-decade. And of course, that will translate to solving materials problems to sub-a-decade as well. That's a big part of our belief.

For the people that have this intuition, how sort of language grounded is it? How much can they articulate it versus how much does it live in a sort of fundamentally different space than language? Because I could imagine somebody might say, I can't really tell you why I think this is going to work. I just think that if we do it this way, it might work. Or you could have obviously a very mechanistic and clearly articulatable theory of why it's going to work. In my brief chemistry experience, it was honestly a little bit more of the former from what I mean. And I had a limited time there and a limited view. But the one reaction that I worked on developing for a year, we never really had a clear mechanism. Like the sort of— it was a catalysed palladium catalysed hydrocarbon oxidation at room temperature or slightly warmer, but like mild conditions, whatever. Anyway, who cares? But the point was we never really knew what was going on at the reaction centre. We had kind of vague sense of that. And so it was a very kind of, maybe we'll try this, maybe we'll try that, but without any precise articulation. How common is that? And what does that imply for whether GPT-5 is going to be able to sort of chain of thought its way into making these things happen versus whether there's just kind of different modalities of data that have to be You know, collected and learned from.

Yeah, you took the— one of the answers there, which is modalities of data. It's very important. And we'll talk about— JC can go into deep detail around where that comes into the AI engine and why it's very, very important. Language is an important piece. It's not just language, as you just alluded to. Scientists learn a lot from the different data streams that they're pulling inside their experimental process. There's this one other thing that is a form of data, but is not maybe what you call structured data from an experimental tool or from an output in the lab. And that is this, I guess, intuition, for lack of a better word, or experience you have in running experiments. I'll give you a perfect example. When I was working on these 2D semiconductor materials, we would always look for the transparency of the film. And the technical reason why that we later came to figure out was they were monolayer 2D TMD materials, not multilayer 2D TMD materials, and that drove way better conductivity, et cetera. We didn't know that up front. And so you could see this in an optical microscope. We weren't looking for this in an optical microscope. We weren't taking pictures of transparency and, hmm, you know, what were the— what are the changes in transparency? Until we kind of We're racking our brain around, like, why are we getting such better conductivity in these materials? They're the same material and we're making them the same way. And it actually came from an expert in the 2D material field who said, oh, those are monolayer films. And we were like, what? Like, we know what monolayer films are. I worked on monolayer films for 3 years. But never in that setting have we thought about looking for monolayer-based films. And so this randomness, this accidental procedure that you hear all about in famous science stories is really important to capture this negative result-based process. And that is almost like a separate form of data where we deeply believe it's not just making the things that work. It's not just using the intuition from multimodal datasets that actually allow you to predict new things. It is looking at, exploring and capturing the one-off, random, unexpected outcomes that might have a key insight in that a human scientist will look right past, but an AI engine would never look past. There's a very important piece of what is, I guess you could call intuition as well. But on the multimodal data, that is also equally as important. And there's a huge kind of approach we take on that side.

Yeah, the multimodal aspect of it is just enormous because it gets at this idea that the data is out there. It's just in so many different ways and forms and shapes and is so unorganized. And if you can bring a little bit of order to it and a little bit of cleanliness to it, then you can start to really enrich your representations and really get to better predictions and generations over time. But I do want to go back to what Joseph talked about because This is, I think, something that's actually quite hard to do with machine learning, right? When you have models that effectively learn out of a distribution of data, it becomes very, very challenging to have it do things that are out of distribution, which is effectively what Joseph is talking about. How do you go about this sort of like randomness when the entire purpose of the model is to learn from a distribution where those random things are generally just ignored by the model, right? Or, and they're sort of said like, now that's out of distribution and I'm not going to veer in that direction. And so we think about being very purposeful with that. We think about saying, look, in science, it's a little bit different. We have to do the sort of normal thing that we always do when we have to learn from a distribution of data. And if we can improve that data over time and better enrich the modalities of data that come into that, we'll be good. But that's not entirely true. We have to also design systems that are effectively prized towards novelty and surprise, because that is really where some of the biggest discoveries in science come from. They come from some weird corner of the chemical universe that everyone thought was kind of ugly and they didn't want to look at. And lo and behold, here's this amazing scientific discovery, or pure accident sometimes. And so we have to design systems that not only learn really well from good distributions of data, but also figure out how to basically go hunch hunting, right? How to develop hunches and then say, You know what, I'm going to go out and pursue that thing. I'm actually going to go deeper here. And there's a bunch of different ways we think about doing that. You could do some sort of randomization or diversification, which we do through Bayesian approaches and Monte Carlo and stuff like that. You can do noise sampling. Active learning is a huge part of this for us. But then there's another side of it, which is, again, the sort of underlying design of the system itself really has to be geared towards capturing some scientific intuition at the end of the day, because that is such an important part of it. And so it's what would drive a scientist to pursue something that is a little bit weird, but just like Joseph said, at 10 or 20x or even 100x the scale. Um, that, that's really, really important to us. And so, um, we have a bunch of techniques that we use for that as well— reinforcement learning, uh, and things like that. Um, human in the loop is a big part of it as well, uh, in the early days for us in sort of capturing this uncapturable data, like Joseph was talking about too. So there's a bunch of different techniques that we think are really, really important that go beyond just the normal, hey, train a model on a good distribution of data.

Yeah, that's really fascinating. I mean, that's also basically the core problem in all of AI, I would say, in some sense, right? Is like, for better or worse, you know, are we going to take these things past the point where they model the past And into the point where, into the realm where they can sort of figure out the future. Yeah, I mean, I'd love to hear more about that in kind of any way you want to go deeper on it. But one, to try to map a little bit of what you have experimented with and learned onto the fully general problem, how do you see base models, so to speak, versus RLHF versus just pure success signal RL? kind of fitting into that? Because it seems like people are— that middle ground of RLHF is pretty good for the human assistant. And there's part of me that is concerned with big picture, let's say, AI safety questions. It's like, man, as much as we can get the things to imitate us, I'd really like that, because I do worry about if we just go super hard on RL and give nothing but reward signal, what are these things going to learn and what reward hacks might we find ourselves encountering? But the flip side, I would guess, is that if you just sort of imitate humans too much or just get the thumbs up from the human evaluator too much, you maybe sand down a lot of these rough edges. That certainly seems to be what we see from the chatbots, right? People are like, the base model is more creative. I like that more in some ways, especially if I want to get kind of outside the box ideas. The RLHF kind of gets boring and then the RL again gets interesting, but sometimes in Kind of scary ways, depending on exactly, you know, what sort of problem you've put in front of it. How would you compare and contrast your learning against that stylized story from the chatbot space?

You know, I think a lot of that is definitely true. For us, a big part of it is, do you have good evals? At the end of the day, if you can develop good evals, then that helps you go about this process because a lot of it is undiscovered and a lot of it is true research on the ML side. Trying to understand how to bring these things together and move them in a direction that you want to over time with, like you said, not forgetting about safety and some of the other things that come into play as well, which obviously in material science and in chemistry is an important thing. So evals is hugely important. And for us, there honestly is no better eval than the lab. I mean, that is as ground truth as it gets, right? If you're looking for a good benchmark, a scientific lab is pretty good as a benchmark. It's a really good way to understand Well, did my model actually successfully do what it was supposed to do? And did it do so in a way that didn't come at the expense or the sacrifice of some of these other key things that I really wanted to hold? So that's a really, really important part of it for us. And it brings us back to, again, some of these active learning loops that we were talking about earlier and how do we smartly incorporate that so that we can update these models, not just kind of like at the weight level because it needs to go beyond that. It needs to be a much more dynamic process than, oh, we're just going to retrain the model. There really needs to be a sort of like surrogate or agentic process that sits on top of it and says, yeah, I'm going to use my model to kind of get things going. And hopefully, we're going to improve that model over the long run. But in the meantime, I need to be able to be adaptive and responsive to the new learnings that are coming back out of, again, what is a best-in-class benchmark or a best-in-class eval, aka the scientific lab. So that's a big part of it for us. And I think the other thing that I would bring up is that— We do truly believe in trying to understand how these models work. We're making a ton of investments in mechanistic interpretability, for example, because we think that's going to drive a lot of performance and a lot of gain and the correct steering of these models towards the objectives that we have. And again, if you think about what Radical AI is trying to do and the core purpose of the company, it's to solve really big materials problems for people. And so if our models are just not going in the direction we need them to, We need to be able to move them in that direction. If they're doing things that are against the expectations or the wishes of an end application or an end customer, then we need to be able to curtail those things too. And so it goes a lot further for us than just, well, give it to the model and let it do its thing. And, you know, it's okay if it's a black box and we'll just learn how to improve that black box over time. We really need to be able to use it as a tool over the long run if we want to solve some of these big hard problems.

Especially going back to one of the things that you brought up earlier, like this is the nexus or this is the one of the big problems in AI, you know, coupling on top of that, that we don't have historical datasets to tap into. Like even in the English language, you can like digitalize, you know, old books, right? And bring them in and learn about how Shakespeare wrote and, and kind of build intuition or, or knowledge off of that. We don't have Newton's notebook, and even if we did, it'd be scratches of all the things that they tried to do. There wouldn't be a written text that we can directly learn and, and transcribe. And so coupling on top of that problem is this kind of idea that not only do we have to know where they need to go, but like how, what can we bring in so that they know the different directions that they can go? Because we don't have a different text of the way we write books today. And the way that Shakespeare wrote books back in the day. So if you don't have that intuition, you don't even have that understanding, it becomes really hard to think about how to index that. And I think you've seen this in software, right? Like where a lot of the best software is private. So a lot of the coding agents use what's open source to train on. And that's gotten really good because there is an open source community that pushes really good software.

Yeah.

And so it can learn what really good software looks like and then utilize that data in an effective way. There is no open source science community. There's open source science from publication. I mean that there is no multi-million data point experimental set of, oh, these are all the scientists who have subscribed into, who have opted into publishing their lab notebooks. We don't even have that, let alone structured data like pulling a GitHub repo. And being able to learn from, okay, here's how professional engineers write code, here's how it's organized. We can't do that. So if you layer that on top of exactly what Jorge just talked about, it is really important to know which way our models are going so that we can get in there and kind of like bring a scientist in to try and to drive them to better discoveries. Because exactly like Jorge said, our objective is better discoveries that fundamentally impact humanity. It's not just about, you know, producing or recommending new things that are marginally an improvement. That's not the North Star for the company.

So I definitely want to get into the mechanistic side in a second. Uh, shout out to Eric Ho, CEO of GoodFire, who originally introduced us. But maybe first on data. You've said a couple of times, like, data is the limiting factor. And, you know, the big 3, the holy trinity, right, of AI progress is data, compute, and algorithms. I was struck in looking at some of the specialised models by just how small they are, like 30-some million parameters in some cases, like orders of magnitude smaller, 4 orders of magnitude smaller than some of the real big ones, even 5 maybe. That's a lot of orders of magnitude. So that would reflect, I guess, obviously, that data must be scarce, otherwise I would assume you'd have a bigger model. Where does that future data come from? I mean, you said sort of like, we don't have the history. I was kind of wondering, and I think of an analogy a little bit also to the computer use agents. I've remarked many times and still nobody has come forward to pay me to watch me use my computer. So I'm like, why is nobody just offering me $100 a month or whatever to install some observer on my computer? And I kind of wonder, and there could be different explanations, maybe it's too messy or whatever. It's maybe not worth the trouble. So sources of data I had in mind for you guys were One, literally going and paying grad students to let you digitise their notebooks. I don't know what the going rate for that would be. Grad students would probably be interested in the money, I would think. I don't know if there's IP concerns there or whatever. So has that happened? Why not? Then there's just the straight simulation data, which I understand is at the core of a lot of sort of at least filling in gaps for some of these small kind of force prediction, what's the next time step in evolution of a collection of atoms kind of model. That would be one way you could invest heavily in compute and get data, but I'm not sure what gaps that may leave or where that falls short. And then obviously the lab is kind of the final stop. And maybe I'm even missing categories. What do you think the portfolio is of contribution from those different types of data going forward? Are you ultimately kind of like, our progress is going to be rate limited by how many of these autonomous factories we can build and how many experiments we can run per day? Or is there any other way to convert financial resources into progress that doesn't go through that sort of factory buildout?

Yeah, I mean, data is always going to be the limiting factor, certainly in our field where we're so data limited. But I would even argue, generally speaking, across all areas of machine learning, data's gonna be your biggest driver of success. And if you can figure out a way to get over the hurdle on the data side, then you're gonna be in a good spot. Yeah, improvements in models will help, and certainly access to compute and being able to do more with compute is a big part of it too. But if you asked anyone to choose between each of those 3, they would almost universally say, yeah, gimme data, gimme data at the end of the day. And the same would be said for us. Joseph can probably answer the mix of data and, and where we think that will come into play for us.

Yeah. It's even harder than just a mix of data too, because it's all unstructured. It's not even labeled actually. Like if we could get access to every lab notebook in North America today, that would be great. We'll take it, by the way, if anyone would offer it. We will take it because to Jorge's exact point, we'll always take data. And then we probably have to go hire an entire team. to sit down with everyone who wrote those notes and understand what they wrote and what is it in reference to. I mean, even generalized simple things like running an X-ray diffraction tool, like the peaks mean things, and people might interpret them differently for their research than we interpret them. When we're doing amorphous-based materials, we're using XRD as a confirmation that there is none. crystal structure, we're actually not even looking for relevant data. So, if you gave that to someone who had never worked in amorphous materials, they'd probably be like, there's no peaks here. I don't get it. This is irrelevant to me. I can't train a model on learning XRD. But you can train one on learning amorphous behavior because there isn't this crystal structure. So, maybe that's a very technical example, but it's very important. It's like, even if we had access to all of this, It's not labeled. It's not organized. It's not structured. It's interpreted in wildly different ways based on the discipline that you're working within, and most of it is like only a piece of the data. Now, for example, I used to always write down the timestamp of halfway through my reaction because it didn't matter when I started, and I would always wind down temperature. I would like turn the temp. knob off at halfway, and then I couldn't control the cooling. So however long it took to cool to the end of experiment was irrelevant. So I gave you a timestamp, 1 minute, 1 minute and 6 seconds, 1 minute and 4 seconds, 58 seconds. What does that mean for us doing the same experiments in our lab? Now, those might be relevant metrics, they might not. If I sit there and tell you, oh, that's the halfway point, and I cut temperature exactly at that point, well, now yes, of course, that data becomes more relevant to what we're trying to do. But so there's just this, this challenge in materials is not only is it, uh, multimodal, not only is it not captured anywhere, not only is it really important to, as Jorge said, evals, ground truth benchmarks, we don't even have it structured and, and annotated to be able to use it, even if all 3 of those things I just mentioned existed. And so this data problem, I think, will actually spur up an entire industry. We have a lot of ideas on the way to get around that, which we can dive into. And from a scientist side of understanding the data output I produce as a material scientist, that in and of itself is, is, is quite a big challenge inside ML. Yeah.

On the computer use side, I think maybe one of the reasons that it hasn't been done is sort of analogous to what you're describing, which is like at any given time, it's not, immediately clear what I'm trying to even do as I'm using my computer. So there would have to be a sort of, or probably would need to be, you could guess obviously, but you would be greatly benefited if you could also capture the sort of, you know, internal monologue that is going on when the person's like, exactly, going tab to tab or whatever. Like, what is it that you're even trying to do here? So I take the point that that is generally, and I can think back to my own, you know, not super, uh, well documented or super tidy or thorough notebook notes myself.

And how do you judge quality there, right? If my grandmother records her use of a computer and you record your computer, I hope they're going to be different. I think they're going to be different. How do you delineate that? What sets the bar of not great computer user and great computer user? Now bring that into a subjective field of science. Was I a good scientist or bad? Well, my boss and the people I used to work with think I was great. Other scientists in the field might think I was terrible. I would have not tried those experiments. I would have went a way different direction, would have never used molybdenum, would have used tungsten instead, or whatever the property is. So who sets that bar? Who determines how it moves? How do you even begin to build it when there are so many different fields inside material science? So every single bar or benchmark is different. There's like these deep problems that come up in even just annotating the data that Are really hard to answer. And I, and like in the computer example, I don't know how I would answer that. I, I would generally start to say, well, someone who's very proficient at moving around and understands where all their applications are and, you know, can get their tasks done quickly. But what if someone goes to email and, and opens a new email, pops it out into a new tab, goes to somewhere, writes it in a Notes app, copies that, and then pastes it in Gmail's email box? It's like, okay, well, I wouldn't do that. That's not the most efficient way to write an email. So how do you delineate that? There's probably someone who would argue it is. Oh, I make too many errors when I send emails. I have to write it in Notes with Grammarly installed and check it first. Who's right? So this is so much ambiguity inside classifying, or I guess you'd say labeling the data too. It just becomes really an exponential problem.

Yeah, I think what we're hitting on is that it's pretty abundantly clear that the highest value data comes out of an experimental setting, out of a lab.

Yeah.

And yet it's also where the biggest challenge lies, right? Just getting access to the data and doing it in a way that's orchestrated and clean is really, really challenging, not to mention expensive. And that's why we just, we don't have the data. We have not seen people really build these datasets out because it is so hard to do. And actually, if you flip to the other side of the equation, the computational side, well, that's actually a lot more straightforward. And we've seen major, major push to develop datasets there. But our big thought is, look, that only solves like 10% of the problem. You're not there. And it's because of this intuition again. Well, we know that the highest value is in the lab anyways. It's just really hard to get to. And so yes, computational data is important and it's helpful to us and we're gonna do that too. But we're kind of missing the real big picture here, which is let's figure out the lab. Let's figure out how to get experimental data and incorporate it alongside the computational stuff that we're already getting pretty good at. And that is really going to push things forward. That's really what we're trying to do here.

And if you do do that, worth adding on, you actually control the problems I just brought up. It's no longer unstructured. It no longer lacks context. It's no longer unlabeled. All of those things become a reality. And so not only are we building the dataset, but we actually can use the dataset. And those 2 things are incredibly effective in our opinion at really developing new materials to Jorge's point.

Yeah, that's really interesting. You mentioned earlier the search space is vast. Could you maybe help me develop my intuition for the vastness of the search space? Like how many, you know, different dimensions or are there sort of big branches in it that are kind of just critical to understand forks in the road as you're going down a thought process or ultimately, obviously, a material development process? And then how much of that, in terms of, as you build out the capability of the lab automation, how much of that space can you effectively explore today? Are there parts of it that are like, we can't automate that yet? That's still kind of— because we can't automate plumbing in my 100-year-old house, right? And again, I remember doing chemistry stuff. One of the things I observed in my grad student mentor was like, this dude just knows how to do certain things in a very intuitive way that I don't know, and that certainly at that time no robot had. So as you think about pushing the frontier of what you can automate experimentally, some of it is just horizontal build out at some point, right, to do more throughput. But presumably there's also frontiers of certain kinds of experiments or reactions or what have you that are just not yet automatable that become automatable. So again, take as long as you want. How would you describe the vastness of this space and what parts of it can we explore or can't we explore? What frontiers do you think you'll be pushing the automation forward to actually be able to get into and properly explore?

Yes, it is an incredibly vast search space. It is impossible. It is improbable to think you're going to test every single potential combination. We always refer to, when we're talking about the company, to Eddington's number. I believe it's called 10 to the 80 different observable atoms in the universe. And that is the number we use for the potential combinations. If you go into something like alloys specifically, which is of course our area, you know, there are 60 or so, give or take, elements on the periodic table that you're going to use in novel alloy development. And so if you just, you know, and we, when we talk about high entropy alloys, we're doing 5 or 6 element systems. They're called multi-component alloy systems. If you just take 5 elements and, and, you know, use that, it's like 5 and around 5 million and change in potential combinations that you can develop. And that's if they're all equally balanced. So meaning 20, 20, 20, 20, 20, uh, percentages inside that alloy. No alloy to my knowledge is, is that equally balanced. And so. Within each individual element, of course, you have a 0 to 100%, and then even that many decimal places you want to go into optimize. And so it is massive. The way we always describe it, it is a single grain of sand on an incredibly large beach, a beach that's the size of the Earth. That's what you're really trying to identify and find and looking for a new Alloy or a new material. Now, there are spaces that can be a little bit bigger than that. I think proteins are one to call out that everyone tracks. Everyone thinks about how big they are. I think it's something like 10 to the 130, I think, um, that the search space is there. That's probably just a single grain across multiple Earths or multiple universes even, maybe. That's huge. We think that's a huge advantage for materials. It's actually more likely that we'll find hopefully something in our beach versus the bigger beach, but it is really big. And so when we think about, okay, how do we take that big number and then we actually start testing things and actually get to, okay, how do you drive towards discovery now? Well, the first is indexing the search space and all of that sits within the AI engine. And how do we actually do property-driven optimization to weed down and filter out the best materials that we want to try? And then when we think about the challenge on the experimental side, it's really the material tools themselves. It is not robotics or different automation systems like our rover track that we build that is our materials handling system. It's the tooling themselves that we need to conduct scientific experiments. Those companies have been around for a very long time, you know, some 25, some 50, some 75, 100 years. They have been human operated for decades. So they're built entirely around a human scientist with no automation in consideration. And the ability to then drive those tools with software are, in some cases, nonexistent, or in the cases that it is existent, there's some type of interface, very, very limited in the actuation you can drive. You might be able to, like, get outputs from the tool, but you can't control. You can't control the turbopump. You can't control the heat source. You can't control the vacuum chamber and, like, the pressure that's inside the chamber, therefore rendering an effective tool from an automation standpoint. So a lot of our time on the automation front is spent on how do we take existing tooling infrastructure and make them autonomous, make the tool itself be able to operate in autonomous fashion. We do a lot of custom development ourselves. We have multiple tools in the lab that are entirely custom designed around the material tool to make it automated. We work with companies. We have multiple ongoing conversations with companies where we wanna build a custom tool together. So that's taking that outside automation and bringing it into the real engineering and design of the tool. And then there are some tools that We think will probably be pushed out from its lack of automation or from the ground up by companies because the way that they were built was just entirely human-driven. And we have examples in our lab today where, you know, they're built in a very, very mechanical way, meaning humans flip switches and move knobs and turn dials. Those things we think will be replaced over the next 10 years in, in these new tools that come out. And so, From an experimental setting, a lot of our effort is spent in taking the existing tools today and making them automated. Remember, Radical AI is not a material tooling company. We don't wanna sell XRDs. We wanna sell materials. And so we work with then companies and vendors and do a lot of that work ourselves. So that's how we do it on the experimental side. On the AI side, the search question is challenging because we do not do a combinatorial-based approach. We really don't think combinatorial-based science has driven enough value. Again, we move towards this property-driven optimization that has this active learning component and is using a bunch of different tools in generating there. Jorge can kind of go into details if you want there, but we really kind of take a novel approach, in our opinion, to generating new systems to try.

I mean, the big aspect of it is definitely the active learning side of it. But even in this sort of universe of, hey, we have dozens of elements that we might want to use and how do we go about doing that? Some of that just goes away, right? Because for example, if I really, really care about weight, I'm not going to use aluminum. So you could just remove aluminum. So some of these things do just start to naturally shrink down. You still end up with a very, very, very large number. And this is why you need to have multiple simple machine learning models that can solve multiple problems kind of all along that trajectory so that by the time you get to the active learning loop, you're kind of sending really good stuff down into the lab. And yes, you're learning how to make them in the right way, but hopefully the things that you send down to the lab are already good to begin with.

I guess one more brief follow-up on that, and then there's so many different interesting directions to go. Are there things right now where, like, you have to either patch with a human. It sounds like, I know Emerald Cloud Lab, from what I understand, has certain aspects of the process where it's like a person is told, go to this machine, pick up this file, move it to this other machine, put it in, and they're almost sort of Chinese rooming it to a degree with a person who doesn't necessarily even have to understand what's going on, but is just following very point-level instructions.

Yep.

Are there things like that that you are kind of bridging missing tools? And are there still certain kinds of things that you just can't get to that a purely artisanal process could do that are just outside the bounds of what you can bring into the closed loop as it exists today?

The answer to that is yes today, meaning there are some things we don't have fully automated. So we do have scientists in the lab who help orchestrate experiments. But the answer is also no in that while they end at that solution, that's our starting point. We are actually beginning our learning process from there. And of all the tools we have in the lab, everyone starts at the place I just described, human scientists coming in, showing you how you operate it, running it, and moves to fully autonomous, no human in the loop inside running that tool specifically. And so for all of our tools, We will reach that place. And if a tool cannot, or we think the value is not there to go through, you know, making it automated, we will go work with someone else to custom build a tool specifically to our needs. Now, I don't mean custom build the science part of the tool. Actually, we usually don't touch the science part of the tool, how the tool operates. We don't get into that. We are touching the loading chamber. The control of the vacuum, the control of when the laser turns on and when it turns off, the kind of shuffling of samples and prevention of contamination if there's multiple samples inside a chamber. All mechanically driven, optimization-focused, or I guess you could say autonomy-focused functions of the tool. We are not redesigning How to do X-ray diffraction spectroscopy? That's not we don't do that as a company. We don't want to do that again. We're not a material tools company. We are material discovery and then therefore material manufacturing company, and that's what we want to move towards. The way that we can get there kind of can look different ways, and we are going through that process with all of our tools today. But no, we don't have every single tool we've ever bought fully autonomous today. There are some tools that are going through that custom design process right now and retrofitting process right now that aren't just fully automated yet.

Cool.

Hari, you mentioned the idea of hopefully what you send down to the lab is good. A couple of questions on that. One, is there a sort of inference time scaling law that you can tap into where you're sort of like, if we run 10 times more experiments in silico, 10 times more, and I guess a lot of different kind of ways to think about it. Instead of, we have this desired property, we have this sort of constraint around inputs, generate me a bunch of protocols or generate me the extra, what am I going to dope it with or whatever, right? You could have a lot of different guesses. Is there a sort of known relationship between 10x more of the use of the models, 10x more compute translates to certain higher hit rate. And how does that relate to the sort of loss landscape, if you will, of materials? I'm not sure if my intuition should be, if I come up with a material that has pretty good but not quite properties, does that mean I'm close where I can explore the very local space around that particular thing and expect that there's going to be something better that I can kind of gradient descent my way into? Or I could also imagine that there could be just like a ton of weird discontinuities in the material space such that, you know, I might take one step in a particular direction and be like in a totally different property regime.

Yeah. I mean, there's always this explore-exploit conversation that's coming into play regardless of what stage you're at. You could be at the purely atomistic level where you're again talking about trillions and trillions of different atomic systems that you might want to look at, and you're going to want to explore-exploit there. Right. You're going to want to explore the vastness of it. And then when you find something that feels pretty good, you're going to want to narrow down and go deep there. And as you kind of go down the ladder and eventually get yourself into the lab, it's going to be the same question over and over again. And a big part of it is at each layer, you need to do that slightly differently because the types of questions you're trying to answer in order to understand where you should be exploiting are actually just different. What you are trying to answer at the atomistic level is very, very different in terms of, well, what should I be synthesizing in the lab? Are they related? 100%. They are absolutely related, but they are a different set of questions and you need to have different techniques in order to answer those robustly and soundly. And so that's just something that we're constantly doing over and over again. In terms of inference time and all of that kind of stuff, I mean, generally the way I like to think about it is if you actually just Radical AI, you forget machine learning, you forget automation. You just say, hey, as a scientist, would it make sense for me to think about a bunch of things kind of in my head, come up with a bunch of hypotheses, narrow those down in my own head, maybe on some scratch paper, maybe do a little bit of modeling, and then walk into the lab? Or should I just say, you know what, I'm going straight into the lab? Which one is likely to yield the best result? Well, it's probably the former. And I think that same thing holds holds and applies. Again, you do have to always keep in mind that science doesn't always work in a logical, rational way. There is just a natural degree of randomness and counterintuition that has to come into play, and you cannot forget that. We have to design every single system with this idea in mind. We have to abide by the general rules, but also understand that we need to be able to break them too. And so everything is designed around that core thought process. But Yeah, I mean, to answer your question, if we can offload some of these things in simulation, that naturally makes a lot of sense, and we will do that. You just have to do it in different ways at different stages along the process.

Can you talk a little bit more about the active learning? I mean, we've got these sort of like few-shot prompting type things. Again, there's test time training. There's a bunch of different paradigms where people are trying to push this, but it seems like, again, this is one of the core questions is like, can we really get them over the hump to where they can take on this new data that is genuinely out of distribution and like do an effective update on that? What more can you tell us about like how you're getting that to work?

Yeah. We do active learning again kind of in every state. So, at the atomistic level, we do active learning. And that's different active learning than what we do down in the lab. But we do do it all the time to improve our recommendations, to improve the likelihood that as we kind of move into the next phase, it gets better and better. So at the atomistic level, we might be doing some active learning around uncertainty. And so we might be trying to generate these recommendations around forces. And if we're not feeling pretty good, we might run some DFT and then kind of update our understanding of how we're making our predictions at the atomistic level. In the lab, that's a totally different thing. we're still doing active learning. We might run an experiment and realize, ah, you know what, even though we scanned a bunch of literature, even though we've done all of these calculations, we didn't actually realize that the pressure and temperature is just a little bit different today. And so we actually need to update it for that next experimental run. And so we do do active learning all of the time, and that's mostly because science is not necessarily predictable to a perfect T. It actually does change in a bunch of different ways. And it kind of comes back to this core thing around 2 main aspects, which is number one, we just don't have the data. It's out there, but we don't really have it. And the more of it we can get and the more of it we can get in a good fashion, the more likely we are to move successfully through that process. But then number 2, never forget that there always has to be some degree of randomness that we have to lean into in order to open ourselves up to the greater discoveries. And so we do do active learning kind of all along the way. We think it's just an important part of the process that You know, naturally happens in science. And if we're trying to replicate and improve and upscale the way that we do science, we have to include that into it as well.

Yeah, that's the core stuff these days. So we've made it pretty far in terms of learning the past distribution. So this, how to take a surprise on board and really do the right thing in response to that. That's, yeah, it's central in all areas. In terms of interpretability, a bit earlier, Joseph, you kind of told the story of the grizzled vet who was like, oh, that's a monolayer film you've got there. That's why that's happening. To sort of tell a little hackneyed story there, it seemed like you had gradient descended your way into a heuristic, I guess. If I understand correctly, you had sort of realised without a theory that when they look more transparent, that seems good.

Right.

And it seems like a lot of models today, certainly thinking at the language model level at least, kind of operate in this sort of heuristic zone where they've learned the patterns, but they haven't really grokked the underlying laws. And then you've got this veteran that comes in and says, oh, well, I've grokked that situation, and I can give you not just an I can actually tell you why your intuition has any value with something that's rooted in the actual physics of the question. I basically think that a lot of that is going on in models, as far as I can tell. I always think back to the original Grokking paper from OpenAI, where you get heuristics really fast, you can memorise the training set really fast. It takes orders of magnitude longer in that original experiment to actually do the Grokking and really implement an algorithm. that does the modular division. And then it took interpretability to even know what's going on. Is it still just guessing, or does it have some actual right way that we can trust to solve this problem? Can we be confident that it will actually work for all unseen cases? So where are you in the end? I guess, where do you think the models are, and to what degree have you been able to do the interpretability at this point? to have a sense for where the models are on these Grokking curves? How much of the time are you getting stuff out that's still heuristic and not really reflecting any real understanding versus are there things that you can point to and say demonstrably, we see real understanding here that is principled in any particular model, in any particular area?

So I wouldn't say that we are in a place where we can demonstrably point to a very clear and definitive sense of understanding. What I would say is that depending on the models that we are working with, you see varying levels of this. And it, it's almost like in the inverse where you see sort of signs of memorization, you see signs of obviously hallucination and that kind of stuff as well. And so I think the fact that you can kind of see the inverse means that, you know, There should be a way to get to the opposite end of it as well. And this is why we are so bullish on interpretability really playing a big role here is because we think that there is a realistic world where these models can actually develop a rich representation of a materials problem and that we can then therefore sort of like learn how to exploit that model and steer it in the right direction. But I wouldn't say that we have any proof or anything like that just yet.

And the one thing I would add on from a scientist perspective is, and this is one of the reasons we got excited about working with Eric and the GoodFire team, is this potential ability to try to unearth things like this is— we actually— I don't know if we know. Actually, I do know that we don't know what—

I know that we know.

Yes. Of what we might want to look at. Um, to help impact our discovery, like the monolayer film on an optical microscope and seeing the transparency of that. If you had asked me before, I, I would never have said to even look for that. And so, like, out of the machine learning world, like, at a base level, like, at a scientific level, we— I don't even think we fully know, like, which areas we can look into, what heuristics we can try to capture. That might be impactful and in some cases might not. Like there, there's probably a world where the transparency of something does not matter to the end resulting property that you're going after. So there's the, for it's not effective, but maybe it's transferable, right? And maybe there is something that you can actually learn from that process that comes into a different heuristic that you use. And I think at a scientific level, as a human scientist, This is why GoodFire and interpretability and this concept to me is so exciting. This ability for AI to maybe teach you what those heuristics are, for lack of a very simplistic phrase, and actually use those to drive different results that you would never be able to insinuate. They're numbers, they're weights and biases in a way. That's really cool to me because you remove the human bias, the human perspective from that. And you— and it's just math and, and, uh, on the underlying models. That's a really cool concept. I'm not sure we've had too much of that ever in science per se. Maybe much more in theory where you can kind of have this optionality and do different types of simulations, but certainly limited in experimental. And I think that's a really, really cool perspective to bring into it from purely the scientific perspective, not deep in the machine learning weeds and what does it mean and how does it impact models. from a scientist perspective of what is the output of those models? What does interpretability give me? I just think that's such a cool concept that is very infant in its exploration. And we believe, as Jorge said, will be a really important field for science at large over the next, you know, 2 to 5 years.

Have there been any like Move 37s in the material science domain and Or the alternative being like, there's a lot of just doing kind of grinding work a lot faster and at a scale that just couldn't happen otherwise, I guess. Have there been— would you be confident if one— would you immediately know it if there was one? And if there haven't been any yet, is that a sort of 2 to 5 year timeline in your mind as you just scale out data? Eventually you think you'll get there? What's the— The outlook for material science move 37?

Great question. I think we're moving towards that. I'm not sure if there was an earth-shattering one yet. I mean, like, like an R-type superconductor, like we just use an example we talked about earlier where everyone in the world is going to realize and then feel the direct result of that discovery that comes out of a lab. I can't say we've had a move 37 there yet. There probably are. Examples of materials that are being optimized, that are being discovered in the computational realm, in the AI realm. I think it's very lacking still on the experimental side, hence why we think our flywheel is imperative in doing materials discovery. But I think one cool thing, we always tell the AlphaGo story. We think it's a really good story about indexing information. How do you bring that to science? It had learned Go games, right? I forget the exact millions of Go games or whatever the exact number was and made 1 in 10,000 move. And then the subsequent versions of that AI didn't need to learn Go games, right? It actually made similar moves without having done millions of, of, of learnings in Go. I'm excited for us to get to that place as well. And I think we are building the technology to do the first, uh, version of AlphaGo today. That infamous move 37, everyone watching, AI made a mistake. It was a 1 in 10,000 move. We didn't see it coming. I think we'll have something like that. I think Radical AI will have something like that in the next 12 to 24 months if we follow and hit our objectives in our roadmap. I think once those become par for the course, become more normal, then moving into the— we didn't look at experimental data, or we used experimental data from alloys and start making discovery in room temperature superconductors. That's That's what we think is going to happen. We're going to amass the largest experimental dataset ever built in human history. That's what we think. And so if we do that, we are very, very excited about the learnings that come from that. And then the other areas that we can make Move 37s in that don't need millions of trained examples exactly in that specific material space. To us, that is the holy grail of material science. That is removing materials as the biggest bottleneck to our most important industries. That is enabling a world that is not limited by material, but only limited by imagination and the laws of physics. That's what we want to build. And we are so excited to watch that proliferate and candidly very excited to be leading the charge on the intersection of AI in discovery and fully robotic self-driving labs to supercharge and inform that discovery. We think that flywheel together will really craft a world that, as we always say to the team, you can't imagine today. The amount of Ubers and Airbnbs and Facebooks and even things like Nvidias that come from this world that we can enable, we think it'll be one of the most important companies to ever exist because of that. And that's the world that we're excited to live in.

In many podcasts, I would say that's a great place to end it. But in this one, I have a couple more questions because that is an inspiring vision. And we can link to the careers page in the show notes if you'd like. How about the future of the integration of modalities? This is kind of a hobbyhorse question of mine, but I do, to be very real about it, lower impact work. But I started this company, Waymark. We help small businesses create video content for local advertising. We had basically a pre-AI version of this, and then we've tried to infuse AI into everything that we do. And one of the biggest things that I've observed is that the integration of language and visual understanding has been a real step change when you no longer had to go through a language bottleneck to say, okay, I have this image, I want to change it to this other image, or I just want an image But if I have to say all that in a 100-word prompt, and even if I train a language model, which I've done, to prompt effectively, it's just such a lossy thing, right? And what has made some of the more recent models just go viral and just amaze people so much more is that they clearly have this joint understanding where now I can turn my image into anime, whatever, but it still retains the me-ness of it while layering on this other kind of coat of paint, whatever, right? But that depth of understanding where it knows what you're asking for in language, but it also really clearly gets the image itself at a pixel level, that has seemingly created a major step change. What I understand today about the system that you guys have built is like, there's a lot of models that are doing a lot of kind of subparts. Do you think that this kind of goes the Tesla route eventually, or the Gemini 2.5 flash image out, whatever route, where it's like there's one joint embedding space and kind of language and a sort of intuitive physics to potentially even Grok physics of materials all exist together? Or is that too fanciful to expect at this point?

I don't think it's fanciful at It's, it's probably going too far to say that we'll only ever need that one model to do everything. But I mean, internally, we are developing something like what you described. And so it will be natively multimodal in nature. We'll have encoders for all of the different modalities that, that we want to work with. And we will run it through this model and we'll be able to have things output from it that are quite useful to us. So no, I don't, I don't think it's fanciful at all to say that we'll have something like that. In fact, I think, you know, in the next 6 months, we'll be using that internally here at Radical AI. And certainly within the next 6 to 12 months, we will likely publish and release something along those lines.

The interpretability on that is going to start to get super interesting.

That is one of Eric's prized things. We have been talking about it for a while, if we could work on something, something like that.

Yeah, cool. And that sounds like it's not super far off. How do you think about the IP associated with this? I mean, you're going to accumulate the world's biggest experimental dataset, as you said. And then I don't know if you meant open source necessarily the model that you're creating there when you talk about publish it. And I also just don't know a lot about the IP that exists in the materials science space. So how do you guys think about what data and what value you want to kind of keep internal to the company versus what you can share? Again, I don't even know, is it possible to patent an alloy, especially something amorphous? Would you be patenting the process that creates that? I mean, you can't really patent something that's amorphous, right, and has no repeating structure. I would think that would be hard. Maybe the process to create it. So, yeah, I guess what is the sort of outlook for What is going to be core IP not shared and what can be shared? How do we diffuse the benefits of this while still giving you guys the ROI needed to scale out the autonomous systems to actually scale up the flywheel, the dataset, and eventually get those Move 37s?

Yeah, I mean, it's an awesome question. And when we started the company, we spent a lot of time thinking, okay, what do we want to build? And Not just what do we want to build, what is needed if we are trying to solve the material science problem? Not what's the easy low-hanging fruit, not what's a business model that we know VCs will get behind. What is needed to build a company that does the vision you've heard us articulate today? And what we netted at was, well, if you just sell software, you're never going to make raw materials. And so that can't be it. So we're not going to do that. And there's been a bunch of companies who have tried and failed doing that. If you try to license every material you make, that can be a business model. But the problem is then you need to be creating new discoveries multiple a year for decades to keep up with and continue to drive revenue. Because of course, reverse engineering and expiration of patents and all that becomes a serious problem when you're just licensing. And so then we looked at what the largest material companies do in the world. What does 3M, Dow, Applied Materials, BASF, Materion, et cetera, what do they do? They sell materials at scale. That's what their business models are, except they're doing it in areas that, frankly, to be completely blunt, don't impact society. Look, I understand the need to have a new Sherwin-Williams paint color every year. My wife is a massive fan. I, I know I'm a customer. Um, but that's not why we're building Radical AI. Honestly, they can continue to handle that. We are trying to make the species interplanetary, multiplanetary. We're trying to drive lossless nuclear energy. We're trying to rethink transportation with floating bullet trains. Like, we're trying to create a world that we want to live in. And so if all of that is true, then Not only do you need to sell materials at scale, but you need to push the materials that you're selling at scale into enabling the industries and creating the industries of the future. And so when we do that, our focus is on the IP of making it. And in material science, yes, you get patents on composition, but it's really— the IP is really around the trade secret on how do you make it at scale and for the lowest cost at scale. It's all what we call processing in material science. all in that process. It's where all of the real meat of it is. You know, if I tell you a high entropy alloy that we have today, like the exact composition, you're not really gonna be able to make it at scale per se, right? And so there's this really important piece of this. And so if that is the business model, then that changes the way we think about the AI models. And I'll let Jorge articulate that specifically.

No, I think Joseph hit it on the head. I mean, the machine learning models will change. They will. They're going to get better and better over time. We're going to create some of them. Other people will create some of them as well. We may release some of them. Some of those we probably won't release, to be entirely honest with you. But the one thing that Radical AI will never not do is go after the hardest problems that we think are going to be needle movers for the world. At the end of the day, that's the whole point of the company. And so that will stay core to us through and through.

Love it. You want to talk a little bit about the— I know we're just about We're running out of time here, and I really appreciate all the time you've shared with us. Just yesterday, you put out a news item about working with the US Air Force. Do you want to tell a little bit about that?

Yeah, SuperX is very, very excited about this. It is in high-entropy alloys for hypersonic applications. We are conducting a lot of really hot, not just high-throughput experimentation, but pushing into how to test and optimize these materials for hypersonic applications. Materials again are one of the biggest bottlenecks in the most important industries. And in the hypersonic industry, China, Russia both field hypersonic systems today. They do that because they never stop investing in material science. Actually, it's worth mentioning China, when they make a new discovery in the material, will set up an entire manufacturing hub around said material just to figure out how to scale it. It is an unbelievable emphasis on putting materials at the bedrock of all their most important innovations. And we have struggled with that in the US and other places, and we are trying to drive that nexus back through. And so we spend a lot of time in DC, a lot of time with the Office of Science Technology Policy, the National Science Foundation, Department of Defense, Department of Energy, even going to the Hill and telling people in government, look, public-private partnership and a focus on materials is imperative. Whether you care about hypersonics, you care about nuclear fusion, We care about the datasets that are going to go inside the ML models to generate a bunch of other new materials. That needs to be driven from a government perspective, and you need to rely on private enterprise to do exactly that, which is where we fit in. The Direct-to-Phase 2 is, in our opinion, kind of our belief in trying to push this forward. And separately to that whole plan I just mentioned, really an ability to execute on the first material system that we've chosen. You know, we had a thesis that high entropy alloys can be very impactful for hypersonic technology. They're very hard to discover. It's a very large search space. There aren't a lot of experimental results. And our work with the Air Force is directly around trying to solve that problem and really generate these new materials for them to then go look at exploring putting into hypersonic systems. So that is what we've been working on. And you can find a lot more information in our press release and more information on that. But We're really excited about that first project, which is again specific to the Air Force and hypersonics, but not just stopping there. We have a lot of other things in the works with the US government that we are trying to push forward so that we can identify, look, materials are not a subsector of industry or innovation. Materials are at the cornerstone of innovation, whether it's the Stone Age to the Bronze Age to the Silicon Age. Every modern technological wave has come from novel material advancement, and the future will be exactly the same. And that's kind of the perspective that we're trying to push inside government and inside policy and inside why materials are really important as a nation and really as a society, as the human race.

That's great. That could be the right place to end on. Anything we didn't touch on that you would want to make sure people know about before we go?

One thing we call out, so we are really intense about our culture at Radical AI because we're trying to solve some of the hardest problems in the world. And so you have to have people that have really a ridiculous curiosity, that this question, this ability to go to first principle and ask why, but are also unbelievably relentless. We fail Every single day, and we will continue to fail purposefully every single day for the next decade and beyond that. And that's actually where the unlocks come from. It's kind of building on those failures, of course, to reach success, as cliché it is. So if there are people out there listening who really have an appetite to challenge everything down to the laws of physics, And want to do that in a way that is trying to tackle our most important problems, recognizing that there's going to be a lot of failures along the way, but in the chance that you succeed, you leave a fundamental stamp on the human race of impact and innovation, then please reach out. We are actively recruiting right now, and we are looking for not only the best people in what they do, but people that are willing to dedicate their life's work to a mission. If you're looking for a job, this is not the place for you. You should not apply to Radical AI. You will not fit in here. If you are looking to work on a mission, then please reach out. We'd love to talk to you. And Jorge, anything you wanna add on top of that?

No, I think that was perfectly well said. Nothing to add.

Amazing.

Well, this has been an excellent conversation. I've learned a lot from it, and I think people are gonna find it fascinating. So obviously wish you guys nothing but success in terms of breakthrough after breakthrough, and look forward to chatting changing material reality around us as we unlock one thing after another. Joseph Kraus and Jorge Calendras, co-founders of Radical AI, thank you for being part of the Cognitive Revolution.

Thank you.

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