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

Nominal’s cofounders (Cameron McCord, Jason Hoch and Bryce Strauss) realized that the new age of reindustrialization requires a new approach to hardware engineering and testing that’s closer to how software is developed. They founded Nominal with the insight that while SpaceX, Tesla, and Anduril built proprietary internal platforms for hardware testing, the thousands of new hardware entrants can't afford to replicate that work. Nominal serves as the system of record for hardware testing, helping companies move from PDF-based workflows to modern data infrastructure that catalogs telemetry from sensors producing millions of data points per second. The platform enables engineers to author validation logic that follows hardware systems from initial testing through manufacturing and field deployment. We discuss their belief that all hardware companies will become physical AI companies, and why they think Nominal's role as the verification layer will be critical - because unlike a video game, physical products require rigorous validation before they enter the real world. Hosted by: Alfred Lin and Sonya Huang, Sequoia Capital

• 0:00Speaker 00

  We're entering a period where there is going to be 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 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 like, collect this data and actually develop these these models.

• 0:23Speaker 01

  I always think of it as 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. It's 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.

• 0:59Speaker 02

  Cameron, Jason, thank you so much for joining us today.

• 1:01Speaker 00

  Thanks for having us.

• 1:02Speaker 02

  Nominal is the all in one data and AI platform for hardware engineering. You are used by amazing companies from Anduril all the way to the Corvette racing team in industries, including aerospace and defense, robotics, autonomy and more. And I think one notable stat you just shared with me, you're used by four of the top five defense primes in the US. Congratulations on on everything so far, including the the recent race.

• 1:24Speaker 00

  Thanks so much.

• 1:25Speaker 01

  Yeah, thank you.

• 1:26Speaker 02

  Let's jump right in. You've talked about how we're entering a new age of hardware and that, you know, America is rapidly reindustrializing its industrial base. Can you just discuss that?

• 1:38Speaker 00

  Yeah, I think we we talk a lot about there. There are a few, I think, tailwinds, macro tailwinds that that nominal is not really benefiting from now in terms of industrialization and hardware development. Really, when 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 rapidly, particularly in an area where we're very prevalent in is

• 2:19Speaker 00

  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, a VCS. But if you go back and like do a little history, it's like companies that were building pure

• 2:57Speaker 00

  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.

• 3:42Speaker 02

  What do you think is driving the, you know, hardware feels hot again? I have so many hardware companies on my, 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 Anduril, SpaceX, Tesla. Like, what do you think is? Driving, it feels like there's something in the water in the kind of reindustrialization kind of startup community.

• 4:05Speaker 00

  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. I think the positive frame is I think that we're just like, I think humanity is sort of reconciling this, 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 Andro, 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 framing. I think that the opposite framing, I think is, you know,

• 4:50Speaker 00

  we can talk about, you know, AI and how it is impacting many, many worlds here. I think, you know, hardware is still a world where there is defensibility in itself, because hardware is hard, right? I think there's, it's capital intensive, it's difficult to bend metal and steel and electronics and, you know, all of this world is like, it's very difficult. And so I think that, you know, there's, there's people excited about it from an investment perspective as well.

• 5:16Speaker 02

  Awesome.

• 5:17Speaker 03

  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?

• 5:30Speaker 00

  Yeah.

• 5:31Speaker 03

  And how do you make it concrete for them?

• 5:33Speaker 00

  Yeah, I'll start and then I'll, Jason, I'll pass to you as more the expert. But I lived, I lived this problem very, very viscerally at my time at Andro. I got there around sort of 2018, 2019 frame. The company was very early and there was, I think it was very in vogue, particularly then, to try and simulate everything.

• 5:54Speaker 00

  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. Let's like get 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.

• 6:24Speaker 01

  Yeah.

• 6:25Speaker 03

  Physics gets a vote?

• 6:26Speaker 00

  Yeah. Physics gets a vote. I mean, we particularly have started with hardware testing as the like narrow kind of wedge that we've we've built nominal around in these sort of early, 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, we will spread more and more into the simulation and design worlds. But I think being able to like merge those two is actually where the advantage comes from.

• 6:58Speaker 01

  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 were, they developed a model of solving these things in kind of a fragmented way. So the people who are 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, you know, lack of a common data platform or infrastructure starts to really become obvious. So recently, 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 lengths that they've gone to, you know, it's all getting

• 7:42Speaker 01

  disrupted very quickly by the incumbent players like Anduril. And so to move at the. 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 lifecycle can actually take the logic, the validation that they're building in a 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.

• 8:06Speaker 03

  Before you guys started nominal, what did the primes use? What did Androl use? What did SpaceX use to do all this testing and monitoring and learning to change their product?

• 8:19Speaker 01

  Well, 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 on the planet. And they wanted to empower those engineers and they said that the existing software that people use for tests and 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.

• 9:01Speaker 00

  And I'll give, I'll give an example of the many companies that are not SpaceX or Androl or Tesla. I think this sort of status quo in the industry for test data management is, is pretty shocking. It still is an area where for most hardware development, data is almost by 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

• 9:46Speaker 00

  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. And then we were trying to find a mechanism to like post those insights and results back, often via screenshot. So PDFs, PowerPoint engineering is like still the bleeding edge for like many, many of these, these companies. And I think we often talk about like the early days of nominal, we, we were trying to like rip the industry from, you know, 2003 to 2019, 2020.

• 10:29Speaker 00

  And just like good software practices, sound data engineering, like Jason often talks about, you know, what we built today to nominal is, is, you know, 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, of, of coming from 2020, 2021 into the world we're living in today for our users, which is, which is pushing the frontier.

• 10:54Speaker 03

  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? 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?

• 11:16Speaker 01

  Well, as Cameron's 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 have an appetite to work with a partner like us.

• 11:57Speaker 00

  A conversation I, I often have with a 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, 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, cataloged, 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 in

• 12:42Speaker 00

  vogue. 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, for our customers.

• 12:59Speaker 01

  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 wouldn't 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

• 13:43Speaker 01

  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.

• 14:11Speaker 02

  You mentioned the GitHub analogy earlier. If you map out the hardware design lifecycle, so to speak, I'd 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 and production. That's what my simple brain kind of maps it on to. Is that fair?

• 14:28Speaker 00

  Yes, yeah.

• 14:29Speaker 02

  Why start with testing? And you mentioned it's like, it's one of the only category, it's a category that's been served by PDFs, like, you know, design tools, manufacturing, these each have their systems of record. So why has testing been neglected today?

• 14:43Speaker 00

  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 of record. I mean, one way to frame nominal is like, we can be a form of a system of record for testing particularly. I think it's like, 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 result. 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 compound to minutes and hours. And like, that's real value for a customer that's trying to field a product in a competitive market. But there's always this sort of like long

• 15:24Speaker 00

  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 it's sort of like an all hands on deck effort. It can halt programs. And so like, nominal's been able to help customers, 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 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

• 16:08Speaker 00

  good place to start. And then we have this vision in 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 system's 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 in 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 hardware system. And so almost like nominal core, our 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.

• 16:53Speaker 01

  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 as 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 and 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 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.

• 17:32Speaker 02

  Yeah.

• 17:32Speaker 01

  Coming from world like software.

• 17:33Speaker 02

  Totally. And then to your earlier point on physics gets a vote. Testing does seem like, you know, where the rubber meets the road, 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 now have data across 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?

• 18:05Speaker 00

  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 where nominal, where we started to be really valuable for these customers is an interesting insight for us was there's so much, I think that you have to sort of like, what's a good way to say it? 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 are 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?

• 18:49Speaker 00

  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 122nd, 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.

• 19:30Speaker 00

  But yeah, I think we, we're getting more and more involved with that with our customers and think that will be sort of an integral part of that stack. And an 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're sort of, their proprietary IP 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.

• 19:57Speaker 02

  Like you know how in the coding space, there's like the verification agents? It seems to me that you guys can also be like the verification agent that assists in each company's development of its design agent, so to speak.

• 20:09Speaker 01

  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.

• 20:30Speaker 02

  Yeah, makes sense. You brought up the robotic arm example. So I have to ask, do most companies have separate hardware and autonomy teams that you observed today? And then is it separate hardware and autonomy stacks? Do you serve one side of the house only, both sides of the house?

• 20:46Speaker 00

  Yeah, well, we see it, uh, let's pick the robotic arm and keep unpacking it. I think what we see is, um, we kind of see, uh, no pun intended, but, um, we see, uh, we often see three teams that are having, 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, um, it could be, you know, even that digital thread could start at a supplier, you know, and they're onsite, you know, doing the final construction. But there's a manufacturing team. There's normally like an R and D team that does a lot of, uh, prototyping, kind of experimentation, more of what we were talking about, the sort of model development use cases.

• 21:29Speaker 00

  Then there's generally like a customer facing team. So fleet operations, uh, they're trying to observe how the robotic system is performing out in the wild, collecting all of that, you know, onboard telemetry information. So three different teams and three completely different stacks. And so it's been really interesting to come through and, 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, it's a term that we, we speak internally about it nominal and externally. And so being able to have that sort of invisible thread between an anomaly or, or an issue that happened with the robotic system deployed in the field, where that comes back to the R and D team, they can quickly triage it.

• 22:12Speaker 00

  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, um, that's a bigger area where, where nominal plays.

• 22:23Speaker 01

  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 test did it undergo? And the cataloging, and that just gets really, really complex at the scale of systems that our 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.

• 22:54Speaker 03

  So today, you can have basically Claude Code write substantial software. What do you think is needed before we can have an AI system design, manufacture, test, monitor, and sort of come up with new hardware from scratch?

• 23:14Speaker 02

  Trying to vibe code an airplane?

• 23:16Speaker 03

  Maybe the airplane shouldn't be an airplane. It should look something different, especially if we want vertical lift airplanes.

• 23:24Speaker 01

  It's one of the things that I talk 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 being able to vibe code an 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?

• 24:08Speaker 01

  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 where we're focused.

• 24:16Speaker 03

  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 performed, they have data from all the context on how something was made. Shouldn't it be able to sort of learn from all of that?

• 24:32Speaker 01

  Yeah, I think so. Like, I think about, I was talking to 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 a buck wouldn't be there. But now 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, I think over the next couple of years, I'm excited to see what's unlocked by just even having the data asset collected.

• 25:04Speaker 00

  I think one, one, uh, there's a lot of like really frontier work, I think, happening in, uh, a lot of the modeling and simulation side, CFD, um, fluid dynamics. Like people are picking apart. I think the, the testing world is one where, um, it's, uh, I think we're doing it. Like nominal is the one that is going, is going to do it. And, and maybe I'll answer the question too, by giving a vignette of some work that we're doing, some pretty frontier work we're doing with the US Air Force. Um, so we are working with them, um, working with DARPA, the Defense Advanced Research Projects Agency on this really cool effort, um, called, uh, Cipher. It stands for Cyber Physical Systems Executing in Real Time. It wouldn't be the, you know, defense if it wasn't a lot of acronyms.

• 25:49Speaker 00

  But essentially, uh, for, for those kind of listening in, a quick, high level about like what test engineering looks like, um, for, uh, you know, 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, my system needs to be between, you know, this and this value, uh, during this condition. And it's just literally this giant matrix that, that kind of is burned down very sequentially, um, often over, over the course of years. What this effort is, is getting at is actually, um, involving AI agents that, uh, in sort of faster than real time are paired with digital twins and that recommend the next

• 26:33Speaker 00

  best sort of test condition, the sort of like knowledge maximizing, uh, 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. 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 and sort of retraining my my model and and updating

• 27:15Speaker 00

  the digital twin sort of physics informed 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 in sort of the earlier design phases again, but I think it's just been really hard to do in in the test world. But like the fact that we're working, I think, hand in hand with the government on this where like they have access to test ranges and and infrastructure that that make this stuff possible is is really exciting for us.

• 27:41Speaker 03

  How advanced is our defense department on the use of AI? Or not advanced?

• 27:47Speaker 00

  Yeah, it's interesting. I think this administration in particular 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 almost from some contracts, 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 our team will like laugh at and be like, if we had merged that, it would have been really bad for the customer. So I think

• 28:30Speaker 00

  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 the department is like really leaning into more and more experimentation here. The sort of collaborative combat autonomous aircraft platforms are really like pushing the frontier. We have worked closely with Andurall and some other vendors on on that project. So I'm I'm inspired by, no pun intended, the gradient of like where we're going.

• 28:56Speaker 03

  Can I simplify your business to collecting data, visualizing it, analyzing it, iterating it, report on it, and? Isn't that perfect for Agentic AI?

• 29:08Speaker 01

  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

• 29:53Speaker 01

  slide format so that they can disseminate it, or, you know, and literally in some cases, it's like, there's a PDF that I must ship to the, our customer, like, someone who's purchasing this. Like, yes, like, AI can, like, accelerate all of that.

• 30:05Speaker 00

  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, 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

• 30:50Speaker 00

  simple example. But on the building blocks that, that nominal sort of has, like, 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, human insight has been really key, and we're trying to build, one way to look at it 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, is a powerful asset.

• 31:24Speaker 02

  What inning do you think you are in terms of AI in your product? And if you were to zoom out to AI Nirvana for Phenomenal, what does that look like?

• 31:34Speaker 01

  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.

• 31:53Speaker 01

  But I think about like the 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. And I obviously don't watch a lot of sports, but but seriously, like when we talk to our our customers, you know, 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.

• 32:34Speaker 01

  And what that leads to is someone notices something that, you know, when you catch it 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 volumes of data are only going up as these systems get more complex. It makes a lot, 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 skill 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.

• 33:12Speaker 02

  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?

• 33:22Speaker 00

  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 grasp with how little we actually understand about how physical systems operate in the world and how 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 going to be a race to try to like, collect this data and actually develop these models. I think it's very, 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.

• 34:06Speaker 00

  But it's a world where you have, you know, AI 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-train, pre-simulate everything, and then run that sort of like agentic test agent across my physical system and hopefully get 100% 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

• 34:51Speaker 00

  test data and model outputs.

• 34:53Speaker 01

  I always think of it as 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, it's 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 because it's what we live in.

• 35:11Speaker 02

  Yeah. Do you think all hardware companies will become like physical AI companies?

• 35:16Speaker 01

  I think yes. I think like, in the sense that, I mean, at least I hope that, you know, the design, even the generation, the manufacturing, like as all of these things hopefully become accelerated by more sophisticated AI tooling, it will, you know, I hope that people's creativity isn't locked in the physical world in the same way that it is in the software world right now.

• 35:37Speaker 03

  Because most hardware just does one thing and one thing well. It should be a lot more flexible.

• 35:43Speaker 00

  Yes, yeah, I think that's, I think it's a really good point, Alfred. I think like the ability to unlock, yeah, I think more, more versatility. And I'll give like the present day simple example, which is like if you talk to people, they'll often cite, I think it's like the F-18, I'll give another federal example, but the F-18, it's a jet. 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.

• 36:27Speaker 00

  But I think, think about, like, 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.

• 36:40Speaker 03

  That'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, from that F-18?

• 36:52Speaker 00

  Yeah, I mean, I'm sure, I don't actually, I don't know this to be true, but I, I've talked to some pretty emboldened people that I think are, 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 status quo tools are, there are people pushing the frontier there right now, both at, you know, the primes and other places.

• 37:16Speaker 03

  You both graduated from MIT. Why would a person graduating from MIT, why should they join nominal?

• 37:26Speaker 01

  I mean, I think that, you know, Cameron talked about 20 years of SaaSification. And one of the things I'm really passionate about right now is just 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 net, 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 AI is going to go in the next 10 or 20 years.

• 38:09Speaker 01

  Like, a lot of people are going to spend a lot of time thinking about their problems. The nominal is, 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.

• 38:20Speaker 03

  Are you guys ever going to build hardware yourself?

• 38:22Speaker 01

  I think yes. I think, uh, no, I'll just give my take on Karen smiling already, but, uh, we shouldn't like play all of our cards, but, uh, 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 because it will actually, you know, it will shave an hour off your day.

• 39:07Speaker 01

  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, uh, you know, labor, it eventually gets to hardware.

• 39:24Speaker 00

  I was smiling, um, just because I, uh, Jason said, yeah, I don't want to play all the cards, but, um, it's something that I, uh, I think is going to be happening sooner than later, so.

• 39:34Speaker 02

  Our partner Sean would be beaming right now. He constantly reminds us that hardware is the only moat. And not only do you guys sell to hardware companies, uh, it sounds like there might be some interesting things up your sleeve.

• 39:44Speaker 00

  We have a lot of, I think, very unique insights there. Um, and yeah, um, are further along there than, uh, than we might be letting on, so.

• 39:52Speaker 02

  Wonderful. Well, I think it's an incredibly exciting time for the, for hardware, for the physical world, for physical AI. And, um, it's inspiring to see you all, uh, build a company around it and, you know, build the GitHub 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.

• 40:18Speaker 00

  Thanks so much. We say all systems nominal.

• 40:21Speaker 01

  All systems nominal. All systems nominal.

• 40:23Speaker 00

  Thank you.

• 40:23Speaker 02

  Thank you.