Physics Gets a Vote: Nominal Cofounders on Hardware Development in an AI World

We're entering a period where there is going to be much more hardware testing. So I actually think that the pendulum is going to swing back. I think we are coming to grasp with how little we actually understand about how physical systems operate in the world and how lacking we are from a data perspective. It's going to be a race to try to collect this data and actually develop these models. - How I was thinking of it is if you have AGI designing a video game for your child, you might let them play it without it being rigorously tested just a video game. But if you had AGI building a toy for your child, you would really want to make sure that it wasn't physically dangerous. It's like the physical world will just always be different as this is what we're looking for. (upbeat music) - Cameron Jason, thank you so much for joining us today. - Thanks for having us. - Nominal is the all in one data and AI platform for hardware engineering. You are used by amazing companies from Anderill all the way to the Corvette racing team in industries, including aerospace and defense, robotics, autonomy and more. And I think one notable set, you just shared with me, you're used by four of the top five defense primes in the US. Congratulations on everything so far, including the recent race. - Thanks so much, yeah, thank you. - Let's jump right in. - You've talked about how we're entering a new age of hardware and that America is rapidly reindustrializing its industrial base. Can you just discuss that? - Yeah, I think we talk a lot about, there are a few, I think, tailwinds, macro tailwinds that Nominal is not really benefiting from now in terms of industrialization and hardware development. Really, when we think about that, we have this huge compression in timeline. People are trying to build and field hardware products faster than they ever have before. We think hardware testing plays a particular part in that and we can cover that in much more detail. I think reindustrialization more broadly, more money is going into building hardware products really, really rapidly, particularly in an area where we're very prevalent in is in aerospace and defense. And I think really the paradigm of how hardware is being developed is shifting really, really rapidly. I'll give a little sort of vignette, I think of how we kind of think about it. You know, I think if you look back at core software development and you think about what kind of happened over the past two decades, I think one really good way to think about it is actually talking about GitHub as an example. And I'll use that to talk about, you know, GitHub is a version control system, right, of ECS. But if you go back and like do a little history, it's like companies that were building pure software used to, you know, they would locally manage versions of their software that they would develop. They would, you know, eventually they started to centralize that internally, but still within the company, all internally managed. And then eventually it got so good, it became productized and outsourced and, you know, venture dollars pour in. And that was really, I think, the first of, you know, the creation of something like GitHub, but I think all of the sort of CICD and DevOps, you know, tools that we take for granted today and the software testing problem really is a solved problem. But that same luxury does not exist for the hundreds of thousands of hardware engineers that are now at the frontier of software to find hardware, autonomy and robotics. And that's really the space that nominal is playing in. - What do you think is driving the, you know, hardware feels hot again. I have so many hardware companies on my calendar every week. Seems like there's a whole generation of founders that feels regalvanized, they've been trained at the likes of Anderl, SpaceX, Tesla. Like, what do you think is driving, it feels like there's something in the water in the kind of re-industrialization, kind of start up community. - Yeah, I think it's probably, there's probably a positive frame answer to that question and probably like a negative frame answer to the question too. The positive frame is I think that we're just like, I think humanity is sort of reconciling there's like these big oscillations. I think with like a lot of the ambition and a lot of the things we want to exist in the world are in the physical world. And I think people are just sort of coming around, again, two decades of like the sassification of the world. And I think people are just excited to build real things again. And I think particularly companies like SpaceX, like Anderl, like Tesla, I think have proven that if you make investments in the infrastructure and the tools to do this type of hardware development, it's a massive competitive advantage. That's a positive frame. I think that the opposite framing I think is, we can talk about AI and how it is impacting many, many worlds here. I think hardware is still a world where there is defensibility in itself because hardware is hard, right? I think it's capital intensive, it's difficult to ban metal and steel and electronics and all of this world is like, it's very difficult. And so I think that there's people excited about it from an investment perspective as well. - Awesome. So one of the things that we've always observed is that there's a big gap between what works in simulation and what works in real life. What's that gap today? How do you like help founders with that gap? - Yeah. - And how do you make it concrete for them? - Yeah, I'll start and then I'll pass to you as more of the expert. But I lived this problem very viscerally at my time at Anderl. I got there around sort of 2018, 2019 frame. The company was very early and there was, I think it was very invoked particularly then to try and simulate everything. And I think that the real power comes from blending simulation outputs of models with real world telemetry sensor data logs coming off of physical systems. And the advantage is being able to do that continuously and very iteratively. I saw the pendulum swing to let's do everything in simulation. That's like as early as we can in the design lifecycle, like we can solve problems there. But we sort of always joke like physics gets a vote. It still gets a vote. And we have started on, yeah, physics gets a vote. - Yeah, physics gets a vote. I mean, we particularly have started with hardware testing as the like narrow kind of wedge that we've built nominal around in these sort of early years because that is where software defined hardware is like touching reality for the first time. And I think it is where most of the, it's the tip of the spear for how software is going to impact the, you know, physical AI and the development of systems. Eventually, I think we will spread more and more into the simulation in design worlds. But I think being able to like merge those two is actually where the advantage comes from. - Yeah, I was gonna say part of the reason our customers have an appetite to partner with someone like nominal is because, you know, these hardware organizations 25 or 30 years ago where they developed a model of solving these things in kind of a fragmented way. So the people who were building your simulation would be different than the people who were doing the first prototype would be different than the people who are doing the manufacturing. And as it all becomes more connected, the lack of a common data platform or infrastructure starts to really become obvious. So recently, I like, I talked to someone who, you know, for 30 years has become like a specialist, this is at one of the traditional primes, like a specialist in their specific proprietary simulation technology. And while it's amazing, like the length that they've gone to, you know, it's all getting disrupted very quickly by the incumbent players like Anderl. And so to move at the speed that people are kind of expecting nowadays, you have to make sure that the engineer who is maybe involved at the early stage of the life cycle can actually take the logic, the validation that they're building on tool like nominal and apply it, you know, much, much later when something's actually out in the field and they're monitoring something that's a production use case. - Before you guys started nominal, what did the primes use, what did Anderl use, what did SpaceX use to do all this testing and monitoring and learning to change the product? - Well, so SpaceX is really interesting to us because kind of like unlike other players, they decided from the beginning that they had hired some of the most talented, intelligent, hardworking engineers in the planet. And they wanted to empower those engineers. And they said that the existing software that people use for testing, especially like test data analysis wasn't good enough and they started to build something proprietary in-house. And when we were starting the company and kind of studying that we said, like, hey, this is a huge reason for their eventual success. Like it actually led to this acceleration but, you know, a thousand companies that are now being started this year, next year, it doesn't make sense for all of them to build a platform like that. So that's part of the motivation behind nominal. - And I'll give an example of the many companies that are not SpaceX or Andrew or Tesla. I think this sort of status quo in the industry for test data management is pretty shocking. It still is an area where for most hardware development, data is almost like default stored locally. So there's a lot of like network accessible storage. It is still a world where like the cloud is not like common. It is engineers like downloading data from a central drive to their local machine, their laptop, to run their own individual MATLAB or Python or, you know, insert other parsing or analysis, you know, software to come to their individual result. Like I'm an avionics engineer, Jason's a GNC engineer, you're a thermal engineer. Like we're all doing our work independently. Um, and then we are. trying to find a mechanism to like post those insights and results back, often via screenshot. So PDF's PowerPoint engineering is like still the bleeding edge for like many, many of these companies. And I think we often talk about like the early days of nominal. We were trying to like rip the industry from 2003 to 2019, 2020, and just like good software practices, sound data engineering, like Jason often talks about, you know, what we built today to nominal is having to get 11, 10 or 11 really, really hard software problems right to empower our users. And then now we're on a very exciting journey, I think of like coming from 2020 to 2021 into the world we're living in today for our users, which is, which is pushing the frontier. Yeah. How much are you educating incumbents? And like you said, you were working with four out of five defense primes. How how much are they really adopting AI? How much are you educating them or what they need to do to improve their product? It still looks like it takes many, many years for them to make any change to their hardware products. Well, as Cameron saying, like the state of the art here is is kind of behind. And so as we kind of catch them up, that's the, like, necessary first step to using AI. So like as someone who uses AI tools every day, you know, you might think it's natural for a hardware engineer to ask a question, like, Hey, what happened in the last 50 tests that I ran? And is relevant to the tests that I'm looking at now. But that kind of assumes that the data from the last 50 tests even lives in one place. And that's kind of the problem that needs to be solved. And the, you know, the primes are interested in solving that they recognize the value there. And, you know, some of them are getting, I would say, like tired of trying to build it in house themselves and want to, they're having an appetite to work with a partner like us. A conversation I often have with the, you know, chief engineer, CIO, CTO sort of across the table is like this, this concept that they're well aware that there is, there are insights trapped in their hardware systems. So this is the real world of like data acquisition systems, test stands, lab testing, power supplies, instrumentation, like that is their bread and butter for for bringing their hardware, you know, products to life. And such a small percentage of that data is ultimately making it into some central repository where it can be sort of structured with metadata, organized catalogued, just like that basic step. It used to be digital engineering. I think that was sort of the the term of art that was very invoke. And now the conversation is rhyming more with physical AI, but I think the building blocks to getting these organizations ready to build like AI capability and applications on top of that really starts with that sort of semantic layer that nominal provides in a lot of the way that we catalog this this hardware data for our customers. And I'll say that like the ambition of AI here is like gets me really excited because sometimes it's, you know, asking really interesting questions of like, okay, is there something that my team didn't catch when they did all the review of if you have 10,000 sensors that are each producing a million points a second like that's a ton of data that, you know, automation can maybe surface things we want otherwise notice. But we should recognize that some of it's also going to just accelerate the more tedious parts of data ingestion and data review. So right now it might be the case that, you know, one of our hardware engineering users every week they want to automate, hey, this data check should be happening every single time we do a flight test. Even as I'm not becoming involved in it, we're having to do that testing at a remote location. There's a flight operator who's going to be doing it in my place. Oh, but I still want that data check to be happening. Like maybe the friction to them doing that is they don't want to learn, you know, a custom domain specific language for encoding that check. If they could use, you know, in English to code prompt in a tool like nominal, that might be the thing that like unlocks them to actually get that across the line. And then they can focus on the kind of like more creative, more judgment, human aspects of designing hardware systems. You mentioned the GitHub analogy earlier. If you map out the hardware design lifecycle, cycle, so to speak, I imagine there's there's the design of the thing. There's the testing of the thing. There's the manufacturing of the thing. And there's the monitoring of the thing of the production. That's what my simple brain kind of maps it on too. Is that fair? Yes. Yeah. Why start with testing? And you mentioned it's like it's one of the only category that's been served by PDFs. Like, you know, design tools, manufacturing, these each have their systems of record. So why is testing been neglected today? Yeah, I think it's really, I mean, one answer to the question would be like start with it because it has sort of been neglected and it doesn't really have its system record. I mean, one way to frame nominal is like, we can be a form of a system record for testing, particularly. I think it's like, there's a there's a quick business reason I think for starting with it, which is like it is an area where I think demonstrating ROI with speed is just like so clear for a customer results. So being able to there's sort of this like mantra in hardware development where, you know, testing is like it's this function. There's sort of incremental improvements you can make, you know, save seconds that can pound a minute and hours. And like, that's real value value for a customer that's trying to feel the product in a competitive market. But there's always this sort of like long tail of risk that everyone who's been on a major hardware program knows. There's always like something hidden in the data that they can't sort of figure out and that it's sort of like an all hands on deck effort. It can it can hold programs. And so like nominal has been able to help customers or I think surface insights there. I think the other answer though is just that like testing is by definition iterative like that is what testing is. It's sort of the most classic like experimental independent variable like science, right? And so I think it is just it's it's iterative in nature, which is exactly what nominal wants to be aligned with, which is like, how can we drive that sort of like iteration? And I think when you look at the hardware development lifecycle, testing is a really good place to start. And then we have this vision and our customers pull us in this direction already of if I use a software platform data platform in testing, I develop all of the validation logic that governs that that systems performance on a specific test. That should be the exact same set of logic that is easily dispersed in an organization to the production manufacturing sort of end of line quality test, where I am just automatically running and nominal we call them checklist, but like validation logic essentially. And then I should also be able to deploy that to the edge to this, you know, hardware system. And so almost like nominal core or core product becomes like the authoring hub of all logic that governs the performance of physical systems. And I can sort of version control it and deploy it at the edge. And for people in the audience who are software engineers, I just want to clarify because hardware testing is so rich. And it's one of the things that I've come to really appreciate is someone who comes from a more pure software background. You know, when I think of software testing, there's something as basic as like a unit test, which is just like so simple and deterministic and even like richer kind of like end to end or production level testing software. It just pales in comparison. Like if you are building an aircraft and you were performing a flight test, like the test still involves there's a physical machine. There's hundreds of people involved. There's someone like in it who's flying. And so then you might do pre test to make sure that that's safe. It's just, you know, it actually becomes closer to what you might think of as like a quote unquote production use case coming from world like software. And then to your earlier point on physics gets a vote, testing does seem like, you know, where the rubber meets the road and like does the thing behave as expected, which is really all that matters. My AI brain immediately helps to what an interesting data set you're collecting there, right? Because you, you know, have data cross customers on different configurations, different design patterns and like how they actually perform in tests. And so can you talk a little bit about do you have designs about going further into kind of pushing AI research in that space? Yeah, yeah. And I'd say like we are nominal is already in use, you know, with companies that are that are doing, you know, model like sort of physical model development and training these sort of models. And we're nominal where we started to be really valuable for these customers. There's an interesting insight for us was there's so much. I think that you have to sort of like, what's it going to say? You have to be able to sort of like separate out when you are testing the performance of models on hardware systems. And so nominal, it turns out, I think that we were really good at doing is automatically finding anomalies in data. And so for customers that are trying to figure out does am I collecting good data to then inform the development of my model in a robotic system? Let's just take a robotic arm, for example, simple example. There could be issues with the servo issues with the motor issues with the physical performance of that system that are actually going to make all of the data you collect bad. It's a nominal sort of running in the background actually saying, Hey, of the 120 second, you know, test where this robotic arm folded a piece of laundry. Actually, only, you know, this percentage of data, did we have like high fidelity confidence that the actual physical telemetry and components were performing at, you know, within calibration, within standard. Therefore, you can extract those pieces of data to go into sort of like actually training a model. And that's just like, that's the sort of crawl step of this. But yeah, I think we, we, we're getting more and more involved with that with our customers and think that we'll be sort of an integral part of that stack. And in area where they frankly don't see, don't see it as a differentiated capability that they would want to build themselves. It's hard to and they sort of, their proprietary IPs is developing the model itself. But like nominal, I think the ability for us to derive insights across many of those like use cases, I think is going to be helpful for customers to bring them. Like, you know, in the coding space, there's like the the verify. So it seems to me that you guys can almost be like the verification agent that assists in each company's development of its design agent, so it's big. Yeah, I mean, this is the analogy that I'm the most excited about, which is like it would be amazing to have unit testing for hardware. But part of why agents have gotten so good in the world of coding is just because things are verifiable. And so like that learning loop can go really fast and it would be a huge dream to have that for hardware. But I think it's necessary to build, you know, essentially like test and validation infrastructure to get there. Yeah, makes sense. You you brought up the robotic arm example. So I have to ask, do most companies have separate hardware and autonomy teams that you observe today? And then is it separate hardware and autonomy stacks? Do you serve one to the house only both sides of the house? Yeah, well, we see it. Let's pick the robotic arm and keep unpacking it. I think what we see is we kind of see no pun intended. But we see we often see three teams that have that have three different stacks. And depending on if the company actually manufactures its own, you know, robotic systems, but there's a manufacturing stack. And there's a manufacturing team. So the people that are actually assembling the robot, it could be, you know, even that digital thread could start at a supplier, you know, doing the final construction, but there's a manufacturing team. There's normally like an R&D team that does a lot of prototyping kind of experimentation. More of what we were talking about, the sort of model development use cases, then there's generally like a customer facing team. So fleet operations, they're trying to observe how the robotic system is performing out in the wild, collecting all of that, you know, on board telemetry information. So three different teams and three completely different stacks. And so it's really interesting to come through and work with customers to actually find the way that nominal spans all three of those use cases and how powerful that is. We talk a lot about continuous hardware testing like it's a term that we speak internally about it nominal and externally. And so being able to have that sort of invisible thread between an anomaly or an issue that happened with the robotic system deployed in the field, where that comes back to the R&D team. They can quickly triage it. And then if it if it does derive from a physical component, you know, malfunction or something that's out of calibration sort of follow it all the way back, like I think that's a big area where we're normal place. Yeah, I would say that like a word that our users care a ton about is just traceability, like they always want to understand like where did this part come from what tested it undergo. And the cataloging and that just gets really, really complex at the scale of systems that are customers are building. So if you're building an aircraft, you know, it's not the case that you can have every single subsystem go through every single test all the time. It's just too expensive. You don't have enough budget time resources. And so keeping track of that is like fundamental to doing good hardware engineering work. So today you can have basically a club code right substantial software. What do you think is needed before we can have an AI system design manufacturer tests monitor and sort of come up with new hardware from scratch. Which other vibe couldn't airplane maybe the airplane shouldn't be an airplane. It should look something different, especially if we want vertical lift airplanes. It's one of the things that I talked about when I'm like trying to hire a team like when I'm trying to say like, Hey, like if you're a software engineer, like come work on nominal. It's like we've all spent so much time building the internet and the internet works like pretty well, but we're still really far away from beyond the vibe code and airplane. Like I think about like right now I can't I have to assemble IKEA furniture myself at home right like it would be great to have that problem solved and that's like such a microcosm of saying like, Hey, can I design my own IKEA furniture at home. So it just feels like there's like many, many steps between where we are today and being able to like vibe code hardware, but a lot of them come back to whether it's like the feedback loop of like, is this thing working or not. Or even just like how do we even have training data sets to do hardware AI research like a lot of it comes back to the problem of like data collection data cleaning data standardization, which is, you know, again like really we're focused. But if they if a company uses nominal they have if they integrate all the data they have the data from the test they have data from how different designs perform they have data from all the context on how something was made. So shouldn't it be able to sort of learn from all that. Yeah, I think so like I think about, I was talking someone this week about, you know, when a test is happening like even just the audio data of like the operators talking to each other during that test like that's a really valuable data set to collect and start to, you know, incorporate into a platform like nominal. I think before, you know, AI tools like that would seem like a little bit too much effort like the bang for buck wouldn't be there. So it's like, oh, of course we should do that like that should all just kind of like be brought into one place and I think over the next couple of years I'm excited to see what's unlocked by just even having the data as I collected. I think one one there's a lot of like really frontier work I think happening in a lot of the modeling and simulation side CFD fluid dynamics like people are picking apart. The testing world is one where it's I think we're doing it like nominal is the one that is going is going to do it and maybe I'll answer the question to by giving a vignette of some work that we're doing some pretty frontier work we're doing with the US Air Force. So we are working with them working with DARPA the defense defense research projects agency on this really cool effort called cipher it stands for cyber physical systems executing in real time. It wouldn't be the defense if it wasn't a lot of acronyms but essentially for for those kind of listening in quick high level about like what test engineering looks like for a major airplane or or weapon system sort of development. The it's this giant matrix of very deterministic test points that need to be satisfied so my system needs to be between you know this in this value during this condition. It's just literally this giant matrix that that kind of is burned down very sequentially often over over the course of years what this effort is getting at is actually involving AI agents that in sort of faster than real time are paired with digital twins. And that recommend the next best sort of test condition to sort of like knowledge maximizing next test condition extremely quickly so rather than like run a flight go fly collect data see if I met one discrete deterministic test point land look at data say yes do it again. And actually now that especially the systems themselves are autonomous you can have like really high endurance and so in sort of again real time or faster than real time sort of change the paradigm of testing from a matrix where I like. Discreetly go through to a actually just sort of like a gradient curve where I'm sort of like always adjusting my vector extremely quickly and sort of retraining my model and updating the digital twin sort of physics and form surrogate model of like what the world is that's really cool and I think like that is that is the Nirvana that we're like getting towards and I think like it's we're seeing it in sort of the earlier design faces again but I think it's just been really hard to do in the test world but like the fact that we're. Working I think hand in hand with the government on this where they have access to test ranges and infrastructure that that make this stuff possible is really exciting for us how advanced is our defense apartment on the use of AI for not advanced yeah it's interesting I think this administration particularly has been like very forward leaning on AI so it's actually been you know there it used to be AI used to be sort of a disqualifier most from some. Contract some sort of opportunities just because it's it's we talk about nominal is like the epitome of mission critical applications you don't want experimentation Jason sometimes we have a slack channel where we'll post you know we use coding agents and tools as well and. They're really good for a lot of like front end you know react components and different things but some of the recommendations for some of the like back end you know things are team will like laugh at you like if we emerge that it would be really bad for the customer so I think like there's good reason to have some sort of you know skepticism but that's changing quickly so I think like the yeah the department is like really leaning into more and more experimentation here sort of collaborative combat autonomous aircraft platform. Is really like pushing the frontier we worked closely with androle and some other vendors on on that project so I'm inspired by no pun intended the gradient of like where we're going. Simplify your business to collecting data visualizing it analyzing it iterating it report on it and. That's not perfect for a genetic AI yeah I think if you think about the loop that is hardware testing there's a ton of different like every single point in that process could be accelerated so earlier I talked about like there's some tedious aspects of data review. And I would say like one of them is reporting where once you already have the data analyzed if you the electrical engineer who's designing a battery subsystem have already kind of done the like interesting parts that extract from your brain only the things that you know about like okay how do I take these input channels and actually synthesize it into the. Did the system perform what I think it should have performed or not in a way that my team can understand the VP can understand now at this point other people might want to. Ask that question and get into like a certain powerpoint slide format so that they can disseminate it or you know in literally in some cases it's like there's a PDF that I must ship to. our customer, like someone who's purchasing this, like yes, like AI can like accelerate all of that. - Yeah, I think, I mean, I get excited by the shift of the paradigm, we sometimes internally talk about, like it used to be that 50 humans would be involved in the testing and validating of like one physical hardware product. I think right now we're sort of in the like, that's changed from a ratio to like one to one, but like how do we get to a world where one human can sort of be using like agentic tools in this space, using nominal can sort of be doing it in parallel for 50 systems and what does that kind of look like? And so we've already built really, really interesting and powerful things in our system, just where you can have that sort of like chat interface, LLM interface where you're saying things like, hey, plot the kinematics of the drone. And that's just like a really simple example, but on the building blocks that nominal sort of has like, and you know, users eyes sort of light up when that's just a task that is extremely manual that they would have to go through. But there's still these areas where I think like human insight has been really key and we're trying to build, one way to look at is like we're trying to build a massive data set of the human enriched like data, which is I think, you know, mechanical engineering masters, PhDs like enriching this data and doing it in nominal is a powerful asset. Totally. - What inning do you think you are in terms of AI in your product? And if you really, you know, zoom out to AI Nervana for nominal, what does that look like? - I'd say it's like still early innings just like thinking about how much has changed even just the last three months. Like I think I'm someone who's just like 12 months from now, I hope we still think that we're in the early innings because if we don't then we're probably not humble enough about just like what's coming around the corner. But I think about like the features that we've added today and you know, I think we could have twice as many software engineers at nominal building AI capabilities and still like discovering new things that our users might find exciting. So one thing I was like joking about earlier is like do we need like a money ball for hardware testing where it's like if you're watching a sports game there's like always these very obscure stats like oh if this person completes this play then they'll be the third best. I obviously don't watch a lot of sports. But seriously like when we talked to our customers one of the reasons they like nominal is like we're putting more data in front of more eyeballs than they're used to having going on in their organizations and what that leads to is someone notices something that you know when you catch in that moment it only takes you 30 minutes to address something that's going wrong versus if it went unnoticed you know it could lead to something exploding and then it's like two days of like the entire company being shut down from their like most critical test campaign and when you just again like the volume of the data are only going up as these systems get more complex it makes a lot of sense to have agents kind of like monitoring almost as like pair programmers in your control room as you're doing these high-scale tests and saying like hey like you're not looking at this but relative to the last 50 times you've done this it's out of family and like it's probably worth someone investigating. - Yeah got it. I guess if you zoom out to this like AGI future you know hardware company of the future what does the hardware company of the future look like? - I have a thesis that actually I think that we're gonna we're entering a period where there is going to be much more obviously I believe this from a business perspective but much more hardware testing. So I actually think that we are like the pendulum is going to swing back. I think we are coming to grass with how little we actually understand about how physical systems operate in the world and how like lacking we are from a data perspective. And so I think companies are building more and more hardware I think we're like it's gonna be a race to try to like collect this data and actually develop these models. I think it's good for nominal. I think eventually that's going to like come full circle where the best way to build a hardware product is like is minimizing the amount of real world testing but it's a world where you have you know AIA agents working along that very simple sort of the steps you laid out in hardware product development like optimizing each of those steps and then optimizing sort of the steps between those and actually being able to link the design space to the test space with like you know agentic reasoning across like how do I optimize testing of the system in the smallest amount of time possible and only preferably do it once like pre-trained, pre-simulate everything and then run that sort of like agentic test agent across my physical system and hopefully get 100% you know satisfaction but I think we're far away from that and I think to get there there's going to be like this huge explosion of the need for more testing and more fusion of real world test data and model outputs. - How I was thinking of it is like if you have AGI designing like a video game for your child like you might let them play it without it being like rigorously tested just a video game but if you had AGI like building a toy for your child you would like really want to make sure that it wasn't physically dangerous it's like the physical world will just always be different 'cause it's what we live in. - Yeah, do you think all hardware companies will become like physical AI companies? - I think yes, I think like in the sense that I mean at least I hope that you know the design even the generation and the manufacturing like as all of these things hopefully become accelerated by more sophisticated AGI tooling, it will, you know I hope that people's creativity is unlocked in the physical world in the same way that it is in the software world right now. - Because most hardware just does one thing and one thing well, it should be a lot more flexible. - Yes, I think that's, I think it's a really good point offered I think like the ability to unlock yeah I think more versatility and I'll give like the present a simple example which is like if you talk to people that they'll often cite I think it's like the F18, I'll give another federal example but the F18 subject like the limitations and inefficiencies of that vehicle as a result of the process in which it was tested there's like all this extra stuff on it the way that like rear fins are mounted is like any aviator would say it's like a very inefficient sort of vehicle and I think that's just like an interesting example of like what you get when you have the worst test process but I think think about close your eyes and squint like when you have the best test process I think you can actually build in a lot more flexibility and versatility into the end product which will be really really interesting it's fascinating why not just take all that data all the reasons that it became inefficient feed that into an AI model and say let's strip out all the things you don't need from that F18 yeah I mean I'm sure I don't actually, I don't know this to be true but I've talked to some pretty emboldened people that I think are trying to do that type of like work by example I think to showcase and I think that fits you know in line with some of the efforts like we're working with as much as we talked about what the statistical tools are they're people pushing the frontier there right now both the primes and other places you both graduated from MIT what why would a person graduating from MIT why should they join nominal? I mean I think that you know Cameron talked about 20 years of sassification and one of the things I'm really passionate about right now is that you know for our customers use cases like the running of software like you have to think about the laws of physics like physics gets a vote not just in did this hardware system work or not but if you have a scale of data that is too expensive to ship to AWS and that's not like crunching that data is necessary to determine do your physical system work or not you have to just like operate with a set of like software and computing principles that a lot of people have moved away from but you know I think if we're really you know ambitious like sounds like this room is about like where physical a guy is going to go in the next 10 or 20 years like a lot of people are going to spend a lot of time thinking about their problems so nominal you know I think we're on the leading edge of like where you know software engineers are going to disproportionately be spending time in the next decade. Are you guys ever going to build hardware yourself? I think yes I think no I'll just give my take on Cameron smiling already but we shouldn't like play all of our cards but the supply chain of hardware data is like really what we spend a lot of time thinking about so you have the you know the source of the data would be a sensor and then it goes all the way to you know you're crunching it you're giving these reports to people who can actually apply their human judgment to is it safe to launch this satellite. Now how do you get better and better at like managing that supply chain? It's like probably by touching every part of it I always say that we have to like earn the right to capture data like we have to make our users lives better we can't just say like hey you have to use this tool because it gets the data cataloged in the right way. We say like hey you should use this tool 'cause it will actually you know it will shave an hour off your day oh by the way it also catalogs your data in a way that's like organizationally beneficial and when I think about those workflows and like pulling the thread all the way how do you reduce the number of steps involved in this person's you know labor it eventually gets to hardware. I was smiling just because Jason said yeah I don't play all the cards but it's something that I I think is gonna be happening sooner than later. So our partner Sean would be beaming right now he constantly reminds us that hardware is the only mode and not only do you guys sell to hardware companies it sounds like there might be some interesting things up your sleeve. We have a lot of I think very unique insights there and yeah are further along there than we might be letting on so. Wonderful. Well I think it's an incredibly exciting time for hardware, for the physical world, for physical AI. And it's inspiring to see you all build a company around it and build the get hub equivalent that's going to just radically transform the professionalism, the reliability, the speed of all the engineers who are now inspired and galvanized to go after the space. And so congratulations to you all on what you've done. And excited to see what you continue to build. - Thanks so much. We say all systems nominal. - All systems nominal systems nominal. - Thank you. - Thank you. (upbeat music) (upbeat music)