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

16:22Jason Hoch: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.

16:53Cameron McCord: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.

17:24Cameron McCord: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 coming from world-like software.

17:33Bryce Strauss:Totally. And then to your earlier point on physics gets a vote, testing does seem like where the rubber meets the road. Does the thing behave as expected, which is really all that matters. My AI brain immediately hops to what an interesting data set you're collecting there. Because you now have data across customers on different configurations, different design patterns and how they actually perform in tests. And so can you talk a little bit about Now, do you have designs about going further into kind of pushing AI research in that space? Yeah.

18:05Jason Hoch: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, was 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, a thing that we are really good at doing is automatically finding anomalies in data.

18:42Jason Hoch:And so for customers that are trying to figure out, 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 122nd test where this robotic arm folded a piece of laundry, actually only this percentage of data did we have high fidelity confidence that the actual physical telemetry and components were performing within calibration, within standard.

19:24Jason Hoch: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'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 in an area where they frankly don't see it as a differentiated capability that they would want to build themselves. It's hard to. And their proprietary IP is developing the model itself. But nominal, I think the ability for us to derive insights across many of those use cases I think is going to be helpful for customers to bring them.

19:57Bryce Strauss:Like, you know how in the coding space, there's like the verification agents. 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 to speak.

20:09Jason Hoch:Yeah.

20:09Cameron McCord: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.

20:30Bryce Strauss: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 observe today? And then is it separate hardware and autonomy stacks? Do you serve one side of the house only, both sides of the house?

20:46Jason Hoch: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 often see three teams that have three different stacks, and depending on if the company actually manufactures its own 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 even that digital thread could start at a supplier, and they're on site 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.

21:29Then there's generally like a customer facing team.

21:32Jason Hoch:So fleet operations, 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 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. It's a term that we speak internally about at 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.

22:12Jason Hoch:And then if it does derive from a physical component malfunction or something that's out of calibration and sort of follow it all the way back, I think that's a big area where nominal plays.

22:23Cameron McCord:Yeah, I would say that a word that our users care a ton about is just traceability. They always want to understand where did this part come from, what tests did it undergo, and the cataloging of that just gets really, really complex at the scale of systems that our customers are building. So if you're building an aircraft, 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 fundamental to doing good hardware engineering work.

22:54Bryce Strauss:So today you can have basically cloud 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? Trying to vibe code an airplane? Maybe the airplane shouldn't be an airplane. It should look something different, especially if we want vertical lift airplanes.

23:24Cameron McCord:It's one of the things that I talk about when I'm 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 on the vibe code in 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.

23:57Cameron McCord: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 where we're focused.

24:16Bryce Strauss: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 contexts on how something was made, shouldn't it be able to sort of learn from all of that? Yeah, I think so.

24:33Cameron McCord:I was talking to someone this week about when a test is happening, even just the audio data of the operators talking to each other during that test, that's a really valuable data set to collect and start to incorporate into a platform like Nominal. I think before AI tools, that would seem like a little bit too much effort. The bang for a buck wouldn't be there. But now it's like, oh, of course we should do that. that should all just kind of like be brought into one place. And I think over the next couple years, I'm excited to see what's unlocked by just even having the data asset collected.

25:04Jason Hoch:I think 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. I think the testing world is one where it's, I think we're doing it, like nominal is the one that is going to do it. 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 U.S. Air Force. So we are working with them, working with DARPA, the Defense Advanced Research Projects Agency, on this really cool effort called CIFR. It stands for Cyber Physical Systems Executing in Real Time.

25:46Jason Hoch:It wouldn't be the defense if it wasn't a lot of acronyms. But essentially, for those kind of listening in, quick high level about what test engineering looks like for a major airplane or weapon system sort of development. It's this giant matrix of very deterministic test points that need to be satisfied. So my system needs to be between this and this value during this condition. And it's just literally this giant matrix that kind of is burned down very sequentially. often 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, the sort of like knowledge maximizing next test condition extremely quickly.

26:40Jason Hoch: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 sort of retraining my model and updating the digital twin sort of physics-informed surrogate model of what the world is.

27:19Jason Hoch:That's really cool. And I think that is the nirvana that we're getting towards. And I think we're seeing it in sort of the earlier design phases again, but I think it's just been really hard to do in the test world. But 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 make this stuff possible is really exciting for us.

27:41Bryce Strauss:How advanced is our defense department on the use of AI or not advanced?

27:47Jason Hoch:Yeah, it's interesting. I think this administration particularly has been like very forward leaning on AI. So it's actually been, you know, it used to be AI used to be sort of a disqualifier almost from some contracts, some sort of opportunities. Just because 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.

28:29Jason Hoch:So I think like there's good reason to have some sort of skepticism, but that's changing quickly. So I think like the, yeah, 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 Andrel and some other vendors on that project. So I'm inspired by, no pun intended, the gradient of like where we're going.

28:56Bryce Strauss: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?

29:08Cameron McCord: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, and literally in some cases it's like there's a PDF that I must ship to our customer, like someone who's purchasing this.

30:02Cameron McCord:Like, yes, like AI can like accelerate all of that.

30:05Jason Hoch: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.

30:49Jason Hoch:And that's just like a really simple example. But on the building blocks that Nominal sort of has, like, you know, user's 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 it is like we're trying to build a massive data set of the human enriched like data which is I think um you know mechanical engineering masters PhDs like enriching this data um and doing it in nominal is um is a powerful asset

31:23Bryce Strauss:totally what inning do you think you are in terms of AI in your product and if you were to you know zoom out to AI nirvana for phenomenal what does that look like I'd say it's like still early

31:35Cameron McCord:innings just like thinking about how much has changed even just the last three months like i i think um 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 um but i think about like the the features that we've added today and uh 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 um so one thing i was like uh 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 uh but but seriously like um 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 and what that leads to is someone notices something that uh 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 um and when you just again like the volumes of 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 of 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.

33:12Bryce Strauss: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?

33:22Jason Hoch:I have a thesis that actually I think that we're going to, 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 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 going to be a race to try to like collect this data and actually develop these models.

33:55Jason Hoch:I think it's very, it's good, 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, 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.

34:40Jason Hoch: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 uh of the need for more uh testing and more you know fusion of real world test data

34:51Cameron McCord:and and model outputs 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.

35:12Bryce Strauss:Yeah. Do you think all hardware companies will become like physical AI companies?

35:16Cameron McCord: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 is unlocked in the physical world in the same way that it is in the software world right now.

35:37Bryce Strauss:Because most hardware just does one thing and one thing well. So it should be a lot more flexible.

35:42Jason Hoch: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.

36:20Jason Hoch: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. That's fascinating.

36:41Bryce Strauss: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.

36:52Jason Hoch: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 that 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 the primes and other places.

37:16Bryce Strauss:You both graduated from MIT. Why would a person graduating from MIT, why should they join nominal?

37:26Cameron McCord:I mean, I think that Cameron talked about 20 years of SaaSification. And one of the things I'm really passionate about right now is just that for our customers' use cases, like the running of software, 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 crunching that data is necessary to determine, do your physical system work or not, you have to just operate with a set of software and computing principles that a lot of people have moved away from.

38:01Cameron McCord:But I think if we're really ambitious, it sounds like this room is, about where physical AI is going to go in the next 10 or 20 years, a lot of people are going to spend a lot of time thinking about their problems. So nominals, 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.

38:20Bryce Strauss:Are you guys ever going to build hardware yourself?

38:22Cameron McCord: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 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'll shave an hour off your day oh by the way it also catalogs your data in a way that's like organizationally beneficial.

39:12Cameron McCord: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 labor? It eventually gets to hardware.

39:24Jason Hoch:I was smiling just because Jason said, yeah, I don't want to play all the cards, but it's something that I think is going to be happening sooner than later.

39:33Bryce Strauss: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, it sounds like there might be some interesting things up your sleeve.

39:44Jason Hoch:We have a lot of, I think, very unique insights there. And yeah, are further along there than we might be letting on.

39:53Bryce Strauss: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 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 off to the space. And so congratulations to you all on what you've done and excited to see what you continue to build.

40:18Jason Hoch:Thanks so much. We say all systems nominal. All systems nominal.

40:22Cameron McCord:All systems nominal. Thank you.

40:23Bryce Strauss:Thank you.

40:35Thank you.