1000 Designs a Day: Neural Concept's Thomas von Tschammer on AI-Native Engineering

Thomas von Tschammer, co-founder and Managing Director US of Neural Concept, argues that physics-aware AI is driving a third revolution in engineering physical products. Neural Concept’s models learn from simulation and test data to evaluate 3D designs in minutes, helping Jaguar Land Rover move from about 50 external-aerodynamics evaluations per day to 1,500 and enabling battery cool-plate suppliers to cut development cycles while improving performance. The episode explains why AI is not replacing numerical simulation, but shifting it later in the process while expanding early design exploration across automotive, Formula 1, and manufacturing workflows. The stakes are competitive: companies that make engineering iterations AI-led can compress development cycles, while legacy OEMs risk falling further behind faster-moving Chinese and digital-native hardware competitors. For full show notes, links, and references, read the episode page:https://www.cognitiverevolution.ai/1000-designs-a-day-neural-concept-s-thomas-von-tschammer-on-ai-native-engineering/ Mercury: Command is Mercury’s new conversational interface, giving you natural-language access to your finances and helping you take actions within your existing permissions and approval policies. Visit https://mercury.com to learn more and apply online in minutes. Sponsor: Claude: Claude by Anthropic is an AI collaborator that understands your workflow and helps you tackle research, writing, coding, and organization with deep context. Get started with Claude and explore Claude Pro at https://claude.ai/tcr CHAPTERS: (00:00) About the Episode (03:52) Special Sponsor (05:40) AI design revolutions (12:00) Physics models and data (Part 1) (18:40) Sponsor: Claude (20:32) Physics models and data (Part 2) (21:56) Copilots and workflows (33:22) Automation versus engineers (40:39) Industry speed gaps (48:26) Foundation models and racing (58:03) Surprising AI designs (01:06:15) Adoption and differentiation (01:17:02) Robotics and abundance (01:24:36) Episode Outro (01:28:10) Outro PRODUCED BY: https://aipodcast.ing SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://linkedin.com/in/nathanlabenz/ Youtube: https://youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431 Spotify: https://open.spotify.com/show/6yHyok3M3BjqzR0VB5MSyk

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Nathan Labenz?

  Hello and welcome back to The Cognitive Revolution. Today, my guest is Thomas von Chammer, co-founder and US Managing Director of Neural Concept, a Swiss company that uses specialist models for domains like aerodynamics, heat dissipation, and collision safety to help automotive manufacturers and other clients accelerate their product design and engineering processes. As a Detroit, Michigan native, this topic is of particular interest because my father actually started his career at General Motors in the drafting department, back when designs and assembly instructions were hand-drawn on paper. And I have vivid memories of watching him use early computer-aided design platforms

• 0:43Nathan Labenz?

  on Take Your Kid to Work Day back when I was a young boy. The work at the time was still highly manual and often quite intuitive, but, as in so many fields, it's become far more computerized over time. By the time my dad retired, designs were routinely tested via physics-based digital simulations before the physical manufacturing process began, and this increased iteration velocity by an order of magnitude. But still, as we've seen in biological structure and binding prediction, material science, and robotics controls, the compute required to run these simulations often becomes a bottleneck unto itself. Today, as you'll hear, Neural Concept's models can deliver similar results to expensive

• 1:27Nathan Labenz?

  physics-based solvers in minutes. And they now also offer an engineering co-pilot product, which can both call these domain-specific prediction models as tools and actually use the Core CAD platforms to make design changes as required. This tick-tock combination of agentic optimization and domain-specific validation is the perfect recipe for reinforcement learning. And already today, it allows manufacturers like Jaguar Land Rover to conduct aerodynamic testing on more than 1,000 designs per day. It also frees human engineers to explore much larger regions of design space and to focus their attention on navigating higher-level trade-offs that involve other parts of the organization.

• 2:11Nathan Labenz?

  Plus, it occasionally produces surprising Move37-like designs that actually alert human engineers to new possibilities.

• 2:20Nathan Labenz?

  Neural Concept has even found a niche in Formula One racing, which, I was surprised to learn, actually limits the amount of compute that teams can use for aerodynamic optimization from one race week to the next. The bottom line is that we can add engineering to a long list of domains where essentially the same pattern of AI development is working over and over again. What once could only be done manually in the physical world was first digitized and then dramatically accelerated with specialist models. Today, agentic workflows are accelerating things further, and Neural Concept is beginning to evolve from training models on a per-customer basis to a future of more general-purpose

• 3:02Nathan Labenz?

  foundation models for engineering. All of which makes it pretty easy for me to imagine a future engineering superintelligence that combines the general-purpose design skills with these superhuman intuitions, all in the same set of weights. As we reach that point, and probably even before, we can expect faster and faster product cycles and an explosion of new form factors, all with higher quality and better resource efficiency than we've ever experienced before. If you've ever felt that promises of AI abundance were a bit too hand-wavy or detached from physical reality, I think this episode should serve to inspire you.

• 3:43Nathan Labenz?

  And so, I hope you enjoy this preview of the AI-powered future of engineering with Thomas von Schammer of Neural Concept. The Cognitive Revolution is brought to you by Mercury, the fintech that more than 300,000 ambitious companies and individuals trust to run their finances. I've wired AI into nearly every corner of my life: my email, my messages, my calendar. I even gave Mercury virtual cards to my agents, with low limits and category and merchant restrictions, for their autonomous use. But still, my AI's access to my financial data has remained limited. With a normal bank, I might export a bunch of statements and have my assistant process them for me.

• 4:25Nathan Labenz?

  But for real-time, up-to-date information, and certainly for taking any action, trying to get your agent to use the bank via the browser is just too hard, too slow, and too error-prone to be worth it. And that's why Mercury's new conversational interface, Command, is such a big deal. It's built directly into Mercury, which means you get natural language access to your finances without exposing anything outside of your bank account. No exports, no spreadsheets, no pasting your transactions into third-party tools. I really think a lot of people are going to prefer it this way. And it can already help you take actions, too, with everything bound by the permissions and approval policies that you've already set up in your account.

• 5:09Nathan Labenz?

  I am genuinely impressed to see this level of AI integration in banking in 2026. And so I invite you to join me in the future. Visit mercury.com to learn more and apply online in minutes. Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column NA, members FDIC. Thank you to Mercury for supporting The Cognitive Revolution. And now, on with the show.

• 5:41Nathan Labenz?

  Thomas von Schammer, co-founder and managing director of the United States at Neural Concept. Welcome to The Cognitive Revolution.

• 5:48Speaker 02

  Thanks very much. Thank you for hosting.

• 5:51Nathan Labenz?

  I'm excited for this. We have not done much on AI for engineering on this feed, and with 350 episodes under our belts, probably a bit of a miss, especially because I'm sitting here in Detroit, Michigan, where I know you have some customers and where there is a long tradition of engineering physical products for the physical world. My dad, actually, fun fact, worked at GM. He started back when the drafting was still done on pencil, paper, big tables with slide rules and stuff like that. And then he moved into the CAD era, and now he's retired. And so he's not going to be working through the AI

• 6:34Nathan Labenz?

  assisted, increasingly AI automated era. But I still am very excited to kind of pick up where I left off the thread with him on Take Your Kid to Work Day years ago and fast forward to where we are now. With that in mind, folks who listen to this feed are like very into AI. And there are certain concepts that you don't need to introduce. But I think we probably can't assume common knowledge in terms of what does the life of an engineer, what is the life of a neural concept user look like if you go watch over their shoulder and see them at work for a little representative sampling of their working life? Maybe you could give us a bit of a sense of, in brief, obviously, how do things get designed? Who's doing that?

• 7:19Nathan Labenz?

  What are the key skills? What are the key iteration loops look like before AI? And then we'll obviously add that AI layer to our understanding.

• 7:27Speaker 02

  I can start with the example of the automotive industry, right? You have a good example, Nathan. So we've been designing cars essentially for the same way for the past 40 years, right? 40 years ago, before CAD, which is computer assisted design, when you wanted to design a new car, you were essentially building a prototype, right? And you were crushing that prototype against a wall to ensure that our looking if the pedestrian or the passengers were safe inside the car. And then if not, you'd have to change the design and rebuild the new prototype. As you can imagine, this was a very lengthy process, which means that ultimately you

• 8:07Speaker 02

  could explore five and 10 prototypes a year if you were looking before to go to production. Then about 30, 40 years ago, as of 40 years ago now, we moved into computer assisted design, right? So which means that now, 40 years ago, we could build new designs on the computer directly. And then instead of crushing it in real life against the wall, we could simulate through what's called numerical simulations, the effect that car crushing against the wall, but directly on the computer, right? We use for that software that we call FEA software, finite element analysis software, that would predict, simulate the effect of the crash.

• 8:51Speaker 02

  Since now you don't have to build as many prototypes, you could go to maybe 50, 100 of different designs of car a year, which is essential, right? Essential to build. However, these tools, they remain very complex to use and very expensive. A single crash simulation can take days to be run because we are solving the questions of physics on the computer. You need very large clusters typically, and then you need to wait as an engineer several days, need one or two days to get that result out, which today is still the main bottleneck when you want to iterate on your design. Right? And there's been improvements, of course, in the algorithms, compute that we have so

• 9:35Speaker 02

  that we could spin that up. But essentially, for the past 30 years, we've been using the same CAD tool and we've been using the same SEA numerical simulation solvers. And that's true for crash, but as I say, aerodynamics, for thermal management, for every single physics that you need when you're in the car. And now, using AI, we're seeing that that thermal revolution, right? So from PTO to CAD and now numerical solvers to AI, where thanks to AI, you don't get results in days, but you get them in minutes. And if you can get results in minutes, it means that you don't explore 50 designs a

• 10:16Speaker 02

  year, maybe 100 designs a year, but thousands of different questions. And that drastically accelerates your development cycles, but also means that you, as an engineer, can innovate much further because you have more options that you can explore, thanks to this AI also.

• 10:33Nathan Labenz?

  So that is a real echo of a pattern that I see across all kinds of different spaces right now, where there's this ability for models to learn a sort of intuitive physics, I sometimes call it, maybe most famously in protein folding, right? We've had a similarly hard time in the past, either doing crystallography to eventually get to a protein structure or doing really compute-intensive simulation to get there. And now somehow with enough data and the magic of learning, we can take a couple orders of magnitude out of the compute that's required, and that just changes the game in terms of how many designs we can explore.

• 11:18Nathan Labenz?

  So that pattern, I think, is fairly familiar. What I realize I don't have a great intuition for is like... What are the different flavors of intuitive physics that models that we need to get models to learn in order to accelerate what different subdomains of engineering? You alluded to one a little bit with aerodynamics. And so I know that'll be prominent on the list. But how many different things are there like this? And what are the unlocks? What are the sort of fields to which they apply? What are the unlocks associated with them? And what has neural concepts role been in building these models?

• 12:00Speaker 02

  Yeah, great question. So there are many different domains, as you can imagine. I mean, think about the complexity of a car, right? Now, today in the industry, a GM or another OEM is simulating the entire car when developing it, which means that every single components or subassembly within the car is being simulated, is being evaluated, and is being iterated on, right? So that means that we are, as engineers, we are evaluating many different physics on the car. Aerodynamics is one, specifically critical for EVs, right, on the range, you want to improve the range of your next EV. Crash safety of pedestrians and passengers is another big one.

• 12:43Speaker 02

  Then we also have some managements, when we want to cool the batteries, cool the engine also for ventilation systems inside the car, right? Electromagnetism, when we want to build an acceleration of electric motors, right? There is an electromagnetism aspect to it. And then structural dynamics, generally speaking for the car, your ability to the chassis or different road profiles, and so on and so forth. And just this thing, the main categories, of course, as you can imagine, then this is being divided into component subassemblies. Ultimately, with a company like GM, you have thousands and thousands of engineers that are domain experts on this physics, on these components, so that they can iterate and improve each of these specific assemblies.

• 13:29Speaker 02

  So those are the big domains, essentially.

• 13:33Nathan Labenz?

  Are all those different domains that you laid out now powered by a domain specialist model that has learned, for example, the intuitive physics of heat dissipation through a ventilation system? And how, if we just take that one example, if the old version was like actually having a simulation down to the level of, I don't know if it was all this detailed, but going all the way down to molecules of air blowing through a space and how they bounce off of each other and what ultimately happens, a level of abstraction or sort of intuitive physics are we now able to get? How do we say to this specialized model, here's a new design for a ventilation system,

• 14:18Nathan Labenz?

  you predict what's going to happen? What kind of inputs and outputs look like to those models? Are they trained on simulation data also? That's another thing that I've noticed is a real pattern.

• 14:29Speaker 02

  Yeah. So to the last question, these models, these AMO's can be trained both on simulation data, but also on test data, external data, right? Because today, even today, there are some phenomenon that we are not able to simulate very correctly with traditional solvers. In that case, what we can do is that if we cannot simulate them, we can measure them in wing tunnel or in test loops, right? And we can gather the data and then train the corresponding AMO's. So this is also a possibility. This is what we call, I'd be training, essentially, where you combine the record simulation that can be lower fidelity because we are not capturing the physics very well and measurements. Now to your question before, how far have we gone into that space?

• 15:12Speaker 02

  To be very clear, today, we are not fully replacing the record simulation. The same way that we never fully replaced prototypes, we are still doing prototypes today in the industry, right? But we're doing much less prototypes and much later in the process, right? It is going to be the same and it's the same for the record simulation. We're going to fully replace numerical simulations, but we're going to make a much smarter usage of it for the most mature stages of development. And it's going to be exactly the same with AI, right? AI, early on in the development process, will enable you as an engineer to explore a much richer space, to explore the right candidates, and then narrow down the one

• 15:54Speaker 02

  that you actually want to simulate to validate with going to prototypes, right? What we're saying. Today. Injera is such a specific, accurate field that there is not one foundational model that can be used to solve the aerodynamics on every car. Right, there is research definitely going in that direction and we are sort of forefront of it at our concepts. However, it is not yet able to capture the availability that you would need, that every car or even would need to be able to be deployed the shelf. What does that mean? That means that we are retraining and we are training the models, the company's specific data and the micro simulations.

• 16:37Nathan Labenz?

  So, the way that you interact customers, if I understand that correctly, is models are typically, it sounds like maybe trained from scratch on a per customer basis because they are sitting on top of a bunch of the simulation data and some real world test data and they're like, man, it would be great to take a couple orders of magnitude out of this as we explore the sort of optimization space around the core decisions that we've already made. So, we can't jump from like a sedan to a cyber truck perhaps with models that we have available, but once we're in the zone, we kind of know where we're going to end up.

• 17:21Nathan Labenz?

  We can refine dramatically faster because we're able to train these models on all this existing data and then feel like this is sort of the epicycle development. It's like we're really dialing things in at the end.

• 17:38Speaker 02

  Okay, exactly. Think about data as knowledge, right? So essentially, you can, and we also provide for some specific applications, pre-training models, right, that we train from existing data elsewhere. But today, we need to fine tune these models with the company's specific data because they have their own know-how, their best practices, and you want the model to match exactly their requirements, right? Now, what is very interesting as well is that these models are always evolving all the time in the sense that every time you feed them your data, they are being retrained and improved so that they can cover a broader and broader space and become more and more accurate.

• 18:20Speaker 02

  It's also a way for the company to retain the knowledge and know-how, right? Because now every single data point that they generate This is knowledge that is being captured by the model and reused such that the next development cycle can be even faster and better.

• 18:36Speaker 03

  Hey, we'll continue our interview in a moment after a word from our sponsors.

• 18:40Nathan Labenz?

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• 19:25Nathan Labenz?

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• 20:11Nathan Labenz?

  So now the other sort of complementary AI that's starting to get introduced at the same time is...

• 20:19Nathan Labenz?

  I think everything we've said so far is more around the validation side. You could imagine a human sitting there and maybe you can tell me like some of the shortcuts, again, that already exist before we get to the sort of AI co-pilot or engineer. But you can imagine a person sitting there. I have this vision of my dad doing this from years ago, drawing these little shapes in three dimensional space and kind of manipulating points in the point cloud. And it was that human intelligence that would say, okay, here's the test result I just got. Here's the spot that the way in which we're having some aerodynamic problems. Let me go back in there, tweak the design a little bit, sand that rough edge down a little bit. Now, of course, I've got these other constraints too that I got to keep in mind. So I've developed a certain intuition for how I can make these changes without breaking

• 21:03Nathan Labenz?

  other constraints that are really binding on me as I develop this system. Now I'll do those changes and how manual those are, they were quite manual and I watched my dad do it. Again, you can tell me some of the shortcuts, but then they go back into the, into this solver, right? New big thing, of course, is that the AIs are also coming to full iteration cycles. I want to hear too about how with the AI co-pilot, like how is that experience changing for engineers? How automated is it starting to get? How automated is it likely to get in the nascent future?

• 21:36Speaker 02

  Yeah, very good points. On the metrics, I can give you a few examples that are public out there. The first one is Jaguar Land Rover, JLR, automotive OEM out there, based in the UK. They've, they published their work with us at the latest NTDGTC conference that was back in March this year. And they're using AI for all their external aerodynamic workflows, right? As I was mentioning, a couple of years ago, they were using critical numerical solvers. And even your design, they were going through this very expensive solver with time consuming. And they had already highly parallelized, highly optimized this thing. And they got to about 50 designs evaluated every day.

• 22:21Speaker 02

  So 50 designs and iterations between the studio teams, which are responsible for the aesthetic, the design of the car and the aerodynamics. And at the end of the day, it is a trade off between the best looking car and the most aerodynamic because you want to improve range. Right with AI and that was what they publicly announced a couple of months ago. They went from fifty designs evaluated per day to one thousand five hundred every single day in production. So you can imagine the level of spin up that it brought to them. We have other suppliers as well that are designing battery cool plates to cool the battery. That were able to reduce by eighty percent that development cycles. Essentially becoming eighty percent faster to develop a new battery cool plates.

• 23:07Speaker 02

  And on top of this, more of the designs, they could did better performance. Right? Because now if you can explore many more designs, it also means that you can innovate them. You can find new options that you could not think of before as an engineer. Because you just rely on intuition, right? And we see examples where the company is cooling twenty percent better on the battery. It's fifteen percent linear. If you think about aerodynamics and also help engineers to find designs that are two, three, five percent more aerodynamic. Which is can change if them at the time.

• 23:43Nathan Labenz?

  Yeah, so let's dig into that last point. How is that happening? Again, to map this onto a space I've studied in a little, in a little bit more depth. There's the protein generation models now, right? Which allow us to go beyond a biologist's ability to say, oh, there's a couple of other proteins in the protein bank that are similar to this that I can pull in. Or even I have a little intuition of my own. And now we've got models just throwing out new designs, which can then be evaluated. So where are we on this sort of language assistant? With like tool calling paradigm to intuition of design?

• 24:27Nathan Labenz?

  You can imagine a language freeway, you could imagine a version that's just here's your constraints. Here's the point cloud, here's the feedback that we got, generate a new point cloud. And you could just go point cloud to point cloud. Where are we on that in those relative paradigms? Do you are, how are they starting to converge?

• 24:44Speaker 02

  Yes, today what we're seeing is that this AI driven engine workflows. Are not in black box, right? They are today an assistant to the engineer so that the engineer can take the right data in from design decisions, right? We are not in the world where we're just sending a spec sheet to the AI and expect the AI to get back to us as a black box with the best optimized car, right? We are rather in a world where AI ingests spec sheets, understands the requirements, will set up the base model, but this will be done in interaction with the engineer that will validate steps along the way and will also guide the model. Why is that? It's because engineering is a very complex space

• 25:28Speaker 02

  where you never have one single answer. It's always trade offs between disciplines, between costs, between design, between performance, right? So you want to have this domain expressed in the loop that then ultimately can take the right trade off decisions. But essentially, we are moving from the world where, as you were saying with your dad, where in the past you were relying on intuition, hey, my design doesn't work, but I think if I took it that way manually, it will work out, right? To a world where now the AI is providing a space of solutions, of options to the engineer and then the engineer will explore that space and decide with which design they want to move forward.

• 26:09Speaker 02

  And that means that here in that process, what is very interesting is that the AI is able to interact with the different tools, right? With CAD, we talked about CAD. So now the AI is able to interact with CAD, to send new geometries, is able to interact with numerical simulation solvers, right? Hey, I want to validate this design I came up with and send that directly to the high fidelity simulation solvers and all of that becomes automated.

• 26:34Nathan Labenz?

  So what models are serving as the co-pilots for engineers today? We're talking on Fable plus one and we're seeing all these incredible projects that people are just vibe coding their way to where 3D landscapes that are really becoming like extremely elaborate. The models are just kicking out. So it sure seems like there's been some important thresholds crossed in terms of the model's ability to reason physically and use physics coding engines and things like that. I would expect on some level that Mythos class models are like pretty good at just sitting down, so to speak,

• 27:18Nathan Labenz?

  and using a CAD product. And sort of performance is probably like pretty hard to find aside from maybe one other provider. But then I also think, boy, the loop that you want to create is probably one where you can start to train based on the feedback of these validators, which have been the kind of crown jewels of the system so far, which, as you pointed out, embody the company's know-how. What does that look like? How much are we going to be relying on a few frontier models to assist our engineers? How much do you think you're going to start to see this loop get closed and create other

• 28:00Nathan Labenz?

  like specialist models that play that role instead?

• 28:04Speaker 02

  Yeah, they're great questions. So if you think about what I think, yeah, Jensen mentioned that later. He is saying today AI as a five-layer cake from the foundations to the top. And the last layer at the very top is the application layer, which he explains himself as being the most important one. And today, in the industry, there are many, many companies that are specializing in building that application layer for some very specific application, right? So it's actually taking the generic ethos, product models, operating models, and then bringing the right domain-specific skills, capabilities such that these models can have value in very complex environments.

• 28:49Speaker 02

  This is exactly what we're doing in engineering, essentially, right? So we leverage the standard generic models that you can think of, right? But we are giving them the right set of tools that they can work with, can generate new geometries, the set of skills that they have the context about what does MEL means to generate a geometry, what are injection molding design requirements? If I want to do a mix, what do I need to optimize for the right complex? But then they can have an impact. Today, if you just take a plane LLM, you will not be able to go very far because they have no very accurate

• 29:30Speaker 02

  finished resulting. They, yes, they are becoming more and more realistic for video rendering, for example, but they are nowhere close to solving actual fluid dynamics equations for aerodynamic, right? So there is a big gap to bridge here, and that's what we're doing the company Norbert said.

• 29:48Nathan Labenz?

  But it sounds like you're expecting reasoning to be mostly provided by frontier models for the foreseeable future. The paradigm you think is winning is increasingly capable general purpose reasoners equipped with the right tools.

• 30:08Speaker 02

  Exactly. Equipped with the right tools, I think is the important part where these models are able again to interact with CAD, even if they're seen as tools, but with also other class of models, right? If you take a step back and we look at how history at Norbert's set, how did we start? We started in 2019 by building the first AI model architecture based originally on computer vision that could directly ingest real geometries and learn from physics. This is how we started. It was not LLMs back then. It was a different type of architecture, but these models could learn and directly take as inputs a CAD geometry and then predicts aerodynamics, deformation, temperature, and so on and so forth, right?

• 30:52Speaker 02

  But these are also models that you want as part of your workflow, right? And you want the agents to be able to call these specialized physics aware models so that you can actually speed up overall design process.

• 31:05Nathan Labenz?

  So it would be helpful to do a little kind of comparison to software. I think audience is super diverse, but one common profile is the sort of AI engineer, software engineer who's now doing increasingly everything with AI, both in terms of writing the code, but also the products that they're building are increasingly AI-ified. And it seems like we're hitting this point where if you can specify what you want in a clear and accurate way. For a really astonishingly large set of things that people might want, the AIs can just deliver that for you now.

• 31:47Nathan Labenz?

  And they're also getting, obviously, pretty good at even flagging the areas where you were ambiguous and they might need your help to make a decision. So it's putting the premium, of course, on the spec and the clear thinking about what it is that we actually want.

• 32:04Nathan Labenz?

  I guess I don't really know. Intuition in engineering would be that these specs are like better typically than they are in software. I would imagine that there's a more disciplined process culture around saying exactly what we really need because we know that there, first of all, are like hard realities around things like heat dissipation and strength that we just literally have to have. Whereas in software, we kind of figure we'll patch that on the fly later if it's not scaling the way that it needs to. If something's like literally breaking, we have the ability to kind of reach in, fix that, even if we're in production, obviously not so with a car.

• 32:39Nathan Labenz?

  So am I right that there is much better specs? And does that put models in a really just position? Or are there ways in which specs are actually still not so well specified as the naive person might think and we're relying on a sort of human fuzziness to unpack those such that remains difficult for AIs in some ways?

• 33:01Speaker 02

  Good question and answer. Unfortunately, if you are an automotive supplier, an OEM today, it's more than ladder, right? Specific RSQ, as they call it, so request quotation and specification. And they is still not fully streamlined, not fully automated, not fully defined. There are standards that OEMs are trying to impose and sets, right? But there is always human interpretation, especially when, for example, we think about a car, which is such a complex problem with an infinite number of dimensions and constraints, right? Imagine that if you change the thickness of a single component somewhere under the hood, this might impact the overall engine block, right?

• 33:44Speaker 02

  And you have a lot of constraints that are tied together, which means that ultimately it's not as deterministic as one might think, which is why those problems are also extremely complex to solve, right? Yes, starting from a set of specs that the model can read and translate that into 3D, this is exactly what's happening today, right, for me. However, why it is not yet as black boxy as it can be for software engineer? It's because the dimension of the problem is much, much richer, right? You have many more trade offs you need to make. And there's never one way to get to the solution, right? Which why it's not gonna replace engineers anytime soon,

• 34:26Speaker 02

  but it's gonna empower them to be faster action. It's gonna remove or eliminate the low added value tasks. That's for sure. That's already happening, right? Where engineer needs to manually set up a new simulation, needs to manually go on the CAD and draw a new design. This is gonna, this is being eliminated as we speak, right? But it's never gonna replace the engineer taking those design decisions. Even the demonstrated.

• 34:55Nathan Labenz?

  Are you sure? What does a VAC off look like there? When it comes to sitting down, thinking what would be a good product in the market? Even if it's something like high level as that. Models are getting pretty good. And then there's all these steps from this sort of initial ideation to breaking things out into subsystems and thinking about the constraints that each of those has to have. And then throwing these things into actual 3D representations in these systems, setting up all the simulations. I'm increasingly struggling to find the place where I'm like, in that whole sequence of events, like here's the ones that the AIs can't do.

• 35:40Nathan Labenz?

  And I think there's some we may not want them to do, or we may wanna hold on to final judgment, final calls, all that kind of stuff. But leaving aside like what we want to hold on to, it doesn't feel like it's so far off that you could have a little society of fables.

• 36:00Nathan Labenz?

  Pick up where the humans left off and literally like to design the next model year of car. If that's crazy, why do you think that's crazy? What is the part that we're so far off on?

• 36:12Speaker 02

  I don't know. So you have a good points. All of these tasks today, I strongly believe that with the models and capabilities that we have, then the automation. These models have capabilities to translate any specification into a design and then simulate that design automatically using a goal solvers. All of that can already today be automated through agents and that's what we're doing as a company. Where I believe there is another level of complexity is the dimension of the problem.

• 36:44Speaker 02

  Yes, this is being done today for specific products, right? Battery core plates, e-motor aerodynamics, crash, right? Where I don't believe this is, this will become a blank box is you add all the dimensions you want to have for a car, right? Because then that's where you really want, and you're staying with engineers to hold on to that. I believe that we always want to have engineers hold on to the final trade-off and the final decisions, right? Because that's where you need the domain expertise deeply ingrained, right? And that's also where the differentiators are happening between you and the competitor, right? How will the differentiation will happen tomorrow between OEM one and OEM two?

• 37:27Speaker 02

  It's gonna be the ones that are able to deeply engrain their engineering and key into this AI workflows. That's gonna be, that's what companies are realizing.

• 37:40Nathan Labenz?

  Yeah, that's fascinating. What do you think the odds are that we get like, I guess I'll only start with the one about your alignment of your business model to the role of the human? I assume that over time, you've probably had some sort of seat-based pricing and potentially also a sort of compute-based pricing for simulation execution. I'd just to know where you've been on that historically. But then as we go forward, do you think you actually? Can sustain a seat-based model? Or is the model going to have to move in another direction?

• 38:15Speaker 02

  Things essentially will have to be based value, right? What's the value we're providing, right? And that's the price, essentially, the price for a value range, right? The question is, how do you monitor value? And I think it depends on the industry and applications. But ultimately, we're going to move to a world where pricing is going to be based pure value, which we're able to deliver for companies. And I think that's actually very healthy, right? That's what you want to have. But yeah, because the value is not going to be tied to an individual anymore, to a sit, but now potentially to Asians, right? Pricing will evolve accordingly. And I think it's going to need to save every company.

• 38:51Nathan Labenz?

  How much disruption do you expect to see in these like 100-year-old industries like auto? Where I would say companies have changed a lot, but it's largely companies that were formed 80, 100 years ago that have consolidated. And there's certainly been only the strong survive dynamic, but not too many new entrants recently, right? Like a couple. But when you think about the, what matters most being like figuring out a distinctive way to encode your know-how into an AI flywheel process, especially one that like might even enter into its own kind of recursive self-improvement loop. I just talked to some OpenAI forward deployed engineers last week who are doing this for tax

• 39:36Nathan Labenz?

  and pace with which they are able to convert feedback from a tax professional on something the AI did wrong into an improved scaffold that prevents that error from happening next time. Pace of progress is so fast right now that they're really rapidly climbing the hill, in their case of accuracy of tax prep documents. But do you think that there's something fundamentally different about manufacturing that would make those hills really hard to climb? Or if you're good at climbing those hills, is it a moment where you actually think we could see new entrants into the market come up and rival the incumbents?

• 40:17Speaker 02

  For sure. I think it's gonna be a massive disruption in the markets. And I think it's gonna create exponential gaps between the companies that are able to adopt this AI-driven engine, frozen the ones that are not. And the gap is gonna widen, essentially, over the past, the next few years for sure. So there's gonna be a lot of disruption. What is committed for companies, if you think about traditional OEMs? They've been building cars essentially the same way for the past 20 years. Same teams, same tools, same know-how. So you're asking engineers that have worked the same way in a long time, now the change. They are thinking and even the governance of the whole company.

• 40:59Speaker 02

  Seeing some are embracing it faster than others. And our role is to make sure that we can support them in that journey. Show them how others are doing it, what are the best practices out there? And then through the process. But I also believe that, yes, there is also an opportunity today for a company that is building hardware to come and to come fast. We talk a lot about these digital native companies. And we work with many of those outside of automotive. They have electric products, consumer electric products. So those companies have been building hardware for the past 10 years, max, maximum. And you feel that they're able to pick up this new workhorse much faster. What do I mean by that? I mean that in a year,

• 41:44Speaker 02

  they've had impressive massive impacts, massive speed up of their developed workflows across many applications. And we think they are slower pace in the larger older companies, engineering companies out there.

• 42:00Nathan Labenz?

  Yeah, that's been the story of Detroit for quite a while. It's been companies that

• 42:07Nathan Labenz?

  They came up, they got so big and so powerful and had such market dominance that they forgot that they might need to evolve. And they've come a long way since then. I'm talking, that's like a 50 years ago phenomenon. They've come a long way, but they're now going to be faced, I think, with their most profound challenge ever. And competing with like Japanese companies in the 70s and 80s is potentially going to be easy mode compared to competing with AI native companies if things go a certain way. So it's going to be really interesting to watch the organizational dynamics and challenges of that.

• 42:44Speaker 02

  When you think about it, some numbers today, Western Europe or US OEM, it takes time between 40 to 60 months for a new car developed. From the moment they want to launch it to the actual moment it hits the plant and it's being manufactured, right? In China, it's 18 to 24 months.

• 43:07Speaker 02

  Everything those companies in US and Europe care about is how do we get from 60 to 24 months, right? Because that's what's going to create the next competitive advantage for them.

• 43:18Nathan Labenz?

  Yeah, that's a sobering stat. I mean, the number of iterations in a given time is a pretty hard deficit to overcome long term.

• 43:29Nathan Labenz?

  Quite sure how to phrase this question, but one thing I wonder about is, in a way, if we really super optimize the design process, it seems like, at least in a naive way, we might end up making things really hard on manufacturing. The sort of, going back to like my dad's, the start of his career, there was literal like pencil on paper and like annotation of it should be this much, right? And the tolerances that the, that existed on the machining side were just a lot more generous than I think they are today. And I think maybe even you could imagine, again, if the design gets so optimized and we're really satisfying these constraints down to the, the absolute maximum in our

• 44:13Nathan Labenz?

  designs, it sounds like that would create really super tight tolerances and really super difficult manufacturing challenges. And so how do you think about?

• 44:25Nathan Labenz?

  I guess maybe one answer is this is the role for the human engineer. But don't satisfy, you know, let's not satisfy ourselves with that answer. How should we be thinking about like just how far we want to push? Design optimization and when we need to meet manufacturing a little bit more in the middle?

• 44:42Speaker 02

  It's a great question. So when we're saying that we want to optimize designs, we also want to optimize them for manufacturing, right? What do you mean by that? That means that when I'm saying that building a car is a multi-disciplinary and multi-optimization, I also mean that because you want to incorporate design rules, manufacturing constraints as early as possible into your design directions. Right? Because that was the promise of additive manufacturing 10 years ago, right? That you could design freely because then you could print anything, right? But then we quickly realized that this would not work out because it's expensive. It doesn't scale in production. So now let's say you have a manufacturing plans and you have your stamping process.

• 45:27Speaker 02

  You know what are the manufacturing constraints? You know what you can and cannot do with stamping. So what you want to make sure of is that those design rules, those manufacturing rules are embedded and are available for the AI model to consider as soon as possible. Right? So that whenever the model evaluate, explore in the design, it explodes it, no way that it can be manufactured. Right? And that's a lot of the work that we are doing as well. We are embedding within the model this know-how to make sure that every single design being explored and generated is valid for manufacturing with number of costs.

• 46:06Speaker 02

  So starting the physics of some manufacturing and costs along the way that you want to bring earlier into the deal.

• 46:14Nathan Labenz?

  If you had to break down. The dynamics or realities that give the Chinese companies such an advantage in iteration time, how much of it would you say is on the design side and how much of it is on the manufacturing speed side? Obviously, those things can't be fully decoupled. Hopefully, you kind of get a sense of what I'm getting at. If I show up with, are they doing their design a lot faster in China? Or are they getting from the point where they have a design that they like to, you're actually rolling a line dramatically faster? My sense is it's a little bit more of the latter, probably, but I'm not really sure how to think about what is contributing to that huge advantage.

• 46:53Speaker 02

  Yeah, so I agree with you, tend to think it's more than latter, right? That much more agile when it's about rolling out a new plant, also the way the plant is being operated, right? It is highly, highly automated there. I mean, now you have engineering executives from Europe, from US, traveling to China to understand how they are setting up their plants, right? Taking lessons from it and then going back and applying those best practices in their country. This is happening already today, right? So the fact for sure. On the design side, I think the benefit that they have is that they can take more risks because, again, they don't have that legacy to work with, right? They get their processes, of tools, and they can just pick what's best out there today and not what was best yesterday.

• 47:38Speaker 02

  That's a big differentiator, right? So I do believe they work with best-in-class tools for sure. They're able to take more risks, again, because they don't have that in their shadow history of developments and they have less processes that are deeply great, right? If you don't have processes that were built in the 2000s, it's much easier to work in a much more agile way today than going back to the digital native type of companies that are able to pick up new standards.

• 48:04Nathan Labenz?

  We, the quote-unquote West, have our work cut out for us, it sounds like.

• 48:11Nathan Labenz?

  How big of a deal is it going to be for Neural Concept to move from the one model per company based on their data paradigm to a more foundation model type paradigm? Foundation models obviously could be like with varying breadth, right? But simply going from one aerodynamic model per company to a general purpose aerodynamic model that would allow you to be like, what if we did a Cybertruck? And I had to move from your historical product line to something quite different. Seems like it would be a huge value unlock. Then you could also imagine going even more modalities, right?

• 48:55Nathan Labenz?

  And trying to bring, I say this all the time, so I apologize to listeners, but you haven't heard me say, integration that we see on language and pixels coming out of the nano banana Omni sort of line does really show to me that integrating quite different data modalities into the same model is a super powerful thing to do. And then there's another version of the foundation model, which is all these constraints generate me the design in the first place, you know, as opposed to the validation side, which I was speaking about before. Do you guys have kind of a, how would you describe your strategy on that? How, how big of a deal is that? How soon do you think we will get there? I assume it's got to be inevitable on some level.

• 49:40Nathan Labenz?

  What's your sort of strategy to bootstrap into those things?

• 49:42Speaker 02

  Yeah, yeah. So you're right, it's going to happen, right? It's moving extremely fast already. So we are going to have foundational model for aerodynamics. I think it's going to start with aerodynamics, right? This is the long range fruit today, because even the physics are complex, it is relatively similar across companies, right? It can be relatively easily replicated. So it is going to happen very quickly, right? And of course, it's a very big deal for us, right? So we are doing actually research in that direction. However,

• 50:14Speaker 02

  We may not be the first ones to have that foundational model, right? But then, as we, and I'm going back to Jensen's final case of AI, our, our role and also our focus is to make sure that whatever the foundational model is, we provide the right domain-specific capabilities that it can be fully integrated into a complex engineering environment in a hundred thousand people organization, right? So, so that you can visualize your designs, you can tweak the geometry as well, leveraging those foundational models and so on and so forth. So this is also our role and what we are already building towards. So that when the foundational capabilities arrive, everyone will be ready for it and to leverage it.

• 50:59Nathan Labenz?

  One thing I learned in researching neural concept I thought was super interesting is that you guys are serving, in addition to a bunch of enterprise customers, a number of Formula One teams. This is, I've honestly never really been super into motorsports or engineering sports. Don't know a ton about it. But it does strike me that in the run-up to the countries of geniuses in a data center, we really do stand to learn a lot from highly competitive and performance-oriented organizations like Formula One teams that are under just this incredible pressure to turn things around quickly, right?

• 51:42Nathan Labenz?

  So how, what does that look like right now? And another interesting detail was that apparently Formula One teams have a explicit, it's like one of the big rules that they work under is that they can only spend so much compute on aerodynamic simulation. That was a real surprise to learn. Is that just to prevent an arms race? There might be something for like our AI governance listeners to learn from Formula One as well. But what do you think we should be learning from the, what have we seen? What should we be learning from the Formula One users?

• 52:16Speaker 02

  Yeah, I mean, that's super interesting, right? So indeed, today, Formula One teams are capped in, to be more specific, the CPU hours that they can run. So CPU hours is the compute to run external aerodynamic simulations, right? And what's even more interesting is that depending on your ranking from the previous year, you don't get the same numbers of hours for the next season. It is that you want to try and make it more equal across teams, right? And you don't want to make it a race as to, okay, the biggest budget, the biggest compute, so I just win, essentially. So they're trying to equalize that in some way.

• 52:51Nathan Labenz?

  So they handicap, essentially? If you win, you get less?

• 52:54Speaker 02

  You get less compute.

• 52:55Nathan Labenz?

  You get the equivalent of a harder schedule.

• 52:56Speaker 02

  Exactly.

• 52:57Nathan Labenz?

  Yeah. The NFL does, if you're first place, you have to play a first-place schedule. The NBA does, if you're like, they're now changing it because people have been tanking to try to get better draft picks. But I'd never heard of this level of actually changing not just the talent acquisition process or who the opponents are going to be, but actually changing the fundamental rules of the game itself in terms of how you are allowed to prepare from one race to the next. That is really interesting.

• 53:25Speaker 02

  Exactly. Now, they can directly tie compute simulations that are being run to performance, right? Because if you can run more simulations, as an engineer, you can explore more designs and you can improve your car further, right? Which is just to win in Formula One, right? You want to get the best car out there for the next race. So that's a way to balance the home between different teams, actually. That's super interesting.

• 53:51Nathan Labenz?

  So what are we seeing in terms of their cultures, their practices, that you think will diffuse into broader engineering, ultimately manufacturing culture?

• 54:04Speaker 02

  If you think about it, Formula One engineers are the state of the art of engineering, the most giant teams you can think of. Design of the car is changing between every single race, right? From one week to another, you don't see it because it's very fine details, but the car is actually different, right? They are improving it week over week. So that means that they have to reach an extreme level of automation of design iteration processes and speed, right? We see in Formula One teams, we believe what every single OEM

• 54:41Speaker 02

  Is trying to tend towards, right? Trying to aim forward, the way they work, the way they iterate, the way they take detailed decisions, right? It's a good way for us essentially to proof tests and to stress tests our models, our workflows, our platform, right? Because if it works for an F1 team, we can build it for an F1 team. We can believe that then it can work for the more traditional OEMs out there, essentially. So that's, that's really the way we're seeing it. And we're asking those teams really push the limits of the models, of the workflows to break things essentially. Because that's when the breakthroughs that we see, where we have to focus and what we have to do.

• 55:16Nathan Labenz?

  So maybe again, we can make a little bit of analogy to software where we have the token maxers who are trying to really push the limits on what their agents can do for them. And increasingly, writing code anymore. And then of course, there's a lot of places where we haven't quite caught up. And so we're maybe still writing code the old fashioned way, or we're like doing a little autocomplete or whatever that's useful and giving a little speed up. But it's still an assist in the old paradigm versus a genuinely new, higher level of abstraction as the base place where a human spends their time operates. Could you paint a little bit of a picture for analog in engineering?

• 55:57Nathan Labenz?

  What does the F1 person do when they are token maxing? What kind of, what are these moments of like key decision or sort of judgment on particular trade offs that actually rise to their level? What does that look like? And I think we kind of know what the, what the old school one looks like. So yeah, what is, what is the token maxing F1 engineer's life look like today?

• 56:22Speaker 02

  So they would do typically is that they would look at the next race profile, right? That has more turns than the previous one. And then they would associate that to a list of requirements on which you need to improve the car. Hey, we need to make the car better in straight lines for next race stick. We have many more straight lines and we're not going to have to overtake much more, right? So that's the baseline. Then they try to think that today into engineering requirements in terms of how the car can improve and the aerodynamics of the car. And then they would, they are running this AI driven workflows. So that are taking those aerodynamic requirements that have information, awareness about the geometry, about the speed.

• 57:03Speaker 02

  And then overnight, the AI-driven workflow will generate hundreds, thousands of configurations of design options, will evaluate them, will evaluate the corresponding performance. And on the morning after, the engineer and the other members will have a dashboard or report interactive. He sees the thousands of points, of data points on a dashboard, and he can look at the different trade-offs, look at the corresponding design, and pick the one they want to move forward for the next race, which is next Saturday, right? So token mapping is essentially thousands, tens of thousands of designs being explored overnight, fully automated by these AI models.

• 57:43Nathan Labenz?

  Have we seen any surprises come out of that process? Of course, the legendary Move 37.

• 57:51Nathan Labenz?

  It seems like increasingly with the Mythos class, Fable models, we're starting to see Move 37s might be strong, but they're definitely stepping outside. I had a really interesting experience today where I, or yesterday, where I ran a skill that's a very familiar workflow, and Fable stepped outside of the workflow and proactively asked me a bunch of questions. I actually presented a webpage to me to collect information. Not instructed to do that, never been part of the process before, Opus never did anything like that, but it took it upon itself to say, I've got all these inputs, but I think I could use some more inputs to really do a great job, and here's what I need from you, the human, to really knock this out of the park.

• 58:36Nathan Labenz?

  That was like a little mini Move 37 in that it was, I've probably done this workflow 50 times over the last few months, and nothing like that had ever happened. What's the sort of most Move 37-like thing that we're seeing in engineering?

• 58:51Speaker 02

  Yeah.

• 58:53Speaker 02

  We have very similar analogies, and I think, to be fair, it's one of the favorite part of the job for you. That's where it becomes very exciting.

• 59:00Speaker 02

  We have engineers that are using these workflows, right? So using AI to explore and asking the AI to explore these hundreds, thousands of configurations overnight. And when they come the morning after, they're looking at the results, and then they're getting back to us and telling us, hey.

• 59:16Speaker 02

  Very impressive. The AI model came up with a design that I would have never thought would be good. If you had shown me this design like this, I would have said, hey, scrap this. This is not going to work. But actually, those designs are better than anything we could come up with. And now I need to get back to the dashboard to understand why it isn't so much better. Right? I need to rethink my intuition because I didn't think it could be that good as a design. Right? So you learn as well from these models. Again, because they explore this much richer space, they go out of bound. They go beyond your intuition, similar to what you are saying, I think. So naturally it becomes super interesting because then something really clicks with engineers. They become very excited because they understand that they can also learn from the model.

• 1:00:01Speaker 02

  How did I get there? How can I learn from it? Because explore new physics or new phenomena, what I was not aware of, when I was only working with intuition, essentially. So is it possible that I even try and reverse engineer the design itself from the AI?

• 1:00:16Nathan Labenz?

  Yeah, learning.

• 1:00:19Nathan Labenz?

  Learning from the AI's advances and its occasional leapfrogs over us is definitely a really exciting, thrilling, slightly scary part of this new future. Could you give us a little bit more intuition for like how? Like just how radical these moments are? It might be a little bit hard for somebody not in the domain to really grok it, but I'd love to try, you know, to get a little bit better sense on kind of, are they really good optimizations? Or are they really like stepping out and exploring different regions of the design space?

• 1:00:59Nathan Labenz?

  That people, you know, because I think what made move thirty seven qualitatively so compelling was like, no human would have made that move. Like initially, I think the live, the live stream commentators like thought it was a blunder. Right?

• 1:01:14Nathan Labenz?

  When we see these surprises in engineering, like, how big of a surprise are they? Are we seeing like, oh, that's kind of interesting. I wonder if that could work. Or is it like, that looks, you know, kind of crazy. But in defiance of all my intuitions, it actually does work. Just try to help me calibrate on how big those surprises are.

• 1:01:33Speaker 02

  Yeah, so you remain driven by physics, right? So you will not reinvent the physics. That's for sure. So you will not get completely insane designs breaking the physics because everything is governed by physics. However, we've seen scenarios where the engineer is telling us, hey, there's no way in the world that I would have done that design because I don't think, didn't think it could work, right? So now that I know it works, I need to go back to the dashboard and understand why it does, right? We have the physics as a baseline. We cannot break the physics. It's going to be there. But we've seen occasions, scenarios where the engineers thought it was a mistake, thought it was a blunder, right? So you have to move 37. But it actually was not.

• 1:02:15Speaker 02

  And it led them to rethink the way they were approaching the problem and their intuition around the point.

• 1:02:23Nathan Labenz?

  Maybe another way to think about this is, how much is it worth? You talked about like value pricing earlier. And there's so much discourse right now around, are people going to be willing to pay for Mythos class models? Notably, the price originally previewed with Mythos has already come down a lot with Fable being significantly less than that original Mythos preview price. I'm not sure how often it is the case that these insights are like readily quantifiable in terms of money. So because it could be a little bit more efficient. That moves the needle on my range. I can say now my car gets this many miles on a charge

• 1:03:08Nathan Labenz?

  where it used to be only this many. How much is that worth in the market? Obviously, a whole other question. So how are people assessing the value of the, especially the, you speak to all aspects of it, including the smaller optimizations, but really interested in these sort of? Move the small scale or move 37 light type moments. How much value do people perceive in it? Are they able to measure it? Are they willing to pay? You know, is, are we going to see people continue to run the neural concept? Copilot, engineering copilot with anything less than a fable model? Or is it going to be like, nah, you gotta pay for the best because that's where all the insights

• 1:03:53Nathan Labenz?

  come from and the bill is going through the roof. But like, we have no choice but to do that to stay competitive. Where do you think we're going to land in the short term on this willingness to pay question?

• 1:04:03Speaker 02

  It's a very good question. Again, cultivating the value is key. And I mean, it's a decision we always have also with the companies we work with. Right? Typically, when you think about design breakthrough, right? So much better performance than what you could get before. We have discussion with suppliers, right? That are selling to the big OEMs, the big car OEMs down there. There are several aspects. If you can get a better design, probably means that you are much more competitive on the markets. Probably means that you will win more projects, more programs with wins and will secure more controls. If today, let's assume you're building battery cool plates to cool batteries, right?

• 1:04:44Speaker 02

  If you can win one more program per year, that's millions, tens of millions of dollars. Right? Just one more program of the 350 you're winning every year. Right? So that's very tangible, but I guess. But the other way to see it is, if I can get much faster to a good design. Yesterday it took me six months, now it takes me three months. I can spend the other three months optimizing my manufacturing process to reduce the cost as much as possible. And I become even more competitive, my clients. Right? And this has indirect dollar value as well. If your part is 10% cheaper than competition because you spent three months, you could allow yourself to spend three months working on the manufacturing process, tune in the details.

• 1:05:29Speaker 02

  Then you win this one, two, three more programs that are then hundreds of millions of dollars. And then if you go on the OEM side, if you can shrink down your development times from 48 to 24 months, that's also millions of developments you're selling for every car, right? A car is typically a billion, right? To develop or from scratch. But if you can even 20% of it, the math is pretty quick.

• 1:05:54Nathan Labenz?

  Yeah, there's a lot of opportunity for savings in there. What do you think American car companies, to take one very salient example, or you can broaden it as well, what do you think they should set as their kind of critical milestones, like must-hit accomplishments with AI over the next, let's even just say, one to two years? We know on that timescale that OpenAI is planning to have a very large chunk taken out of ML research itself in terms of automation. We know that we're already iterating at half the speed of the Chinese companies.

• 1:06:34Nathan Labenz?

  We see some hard manufacturing places where the product cycle has accelerated, look no further than Nvidia for probably the most dramatic example of this.

• 1:06:48Nathan Labenz?

  Forget about like what would get you initially like a coffee spit take and laughed out of the room if you said it. What do you think is like the actual achievable speedup that you would, heart of hearts, tell the CEO of GM, like this is what you really need to be able to do in terms of speedup if you want to be meaningfully, durably competitive in the AI era?

• 1:07:13Speaker 02

  So I think there are different scales, of course, to this. In year one, you'd be looking at some core departments, core areas, crash, safety, aerodynamics, and powertrain, let's say, and you'd want to make sure that every single iteration is being AI-led. AI-led, I mean, there is an AI workflow that can orchestrate the different tools. That's the base, right? And that can already lead you to 20%, 30%, 40% speedup on those flat sheet disciplines, right? That does for year one. Then year two and beyond, what you'd be looking at is that orchestration across disciplines, right? So you break the silos between crash, between aero, between summer, right? So that the agent can not only orchestrate the aerodynamic optimization, but can orchestrate

• 1:07:58Speaker 02

  it while taking into consideration the safety aspects, the manufacturing aspects, as we talked before, right? What time a design constraints, right? And break those silos between teams and disciplines. Once you do that, then that's where the gains really compound, the benefits really compound, and that's where you can really break down and reduce the cycles from 50 to 60%. And that's what we are seeing already, right?

• 1:08:24Speaker 02

  Today, there is not an OEM who has done it at scale for Android car, right? But we are seeing this multidisciplinary automated AI-driven workflows happening already for some specific disciplines. And we are seeing 50% speed. That's massive.

• 1:08:42Nathan Labenz?

  Yeah, interesting. How do people respond to it? You know, my sense is that you probably won't have a hard time convincing CEOs of these companies that this is really important and they can look again at the China iteration speed. They can look at Tesla's ability to update its manufacturing processes much more dynamically than they're accustomed to doing. And I think increasingly, they're going to feel the heat. Now, another question is translating that through a legacy organization where probably a lot of people have a lot of different feelings about this, how they want it to go or if they want it to happen at all, many of which very understandable feelings, by the way.

• 1:09:26Nathan Labenz?

  I don't mean to dismiss those feelings, but they're an obstacle in many cases in the way of the company actually transforming in the way it probably needs to be competitive. How would you characterize maybe kinds of roles? I'm thinking for whatever reason, the ML researchers are like most keen to automate their own labor. And then we see a lot of artists are very hostile to the technology, certainly not all, but that's like a pretty common point of view, especially if it's like, I love doing this, you know? Why do we want to automate something that I love doing? Where are engineers in that space?

• 1:10:02Speaker 02

  Yeah, it's a good question. I think we do see both another spectrum, right? I do see that engineers are artists in a way, right? They made the intuition on how to build a car and they really enjoy that aspect, right? Of manual iterations, leveraging intuition, thinking about the physics of the problem, right? And there are many engineers that not easy, right? They've been doing it the same way for the past three years and they enjoy that.

• 1:10:30Speaker 02

  It's totally understandable that when they see this type of new workflows, they are sometimes a bit resistant to, okay, what will it actually bring, right? And they also believe, right, we saw that they need to be in the loop. Because ultimately, they are the domain experts and they are the ones that are the brand of the company, right? Otherwise, a GM car would be similar to a Ford would be similar to a BYD, right? So you need to have this human aspect as well. But we also see all the way, the end of the spectrum, a male researcher, they are also message team and machine learning teams within those organizations at the forefront. And those ones are the first ones to end up there. And those are the first ones that want to explore, want to try new models, interior models, want to benchmark them, experiment, right?

• 1:11:15Speaker 02

  So we really have in the same organization, those two extremes, right? How do you bring them together? That's also part of the complexity of the very large organizations. But the thing we've seen a lot though is once you manage to break that, you know, that barrier and actually have those engineers hands on and working with the AI models, you see a totally different response. Because then they understand how powerful it can be and how much it can actually empowers them to make their job even better, even funnier, right? Because then they don't have to spend time, which is low added value. Again, setting up simulation, waiting for the simulation for it to load, for it to compute. But they can actually interactively query the AI model, get results, try different options,

• 1:12:00Speaker 02

  try much more, what if scenarios? And that's the fun part when you're engineer, you want to try these things out, to run tests, scenarios, test engines, right? And that's what AI enables you to do today.

• 1:12:11Nathan Labenz?

  Sometimes you've mentioned this idea of brands becoming the same or and that they need to avoid that happening. This may be sacrilegious to say for a long native to trader, but I feel like there's an awful lot of sameness out there in today's world, right? Manufactured products in general, certainly cars, right? I'm always kind of like. You know, really, they look a lot alike. Let's be honest with ourselves. You know, there's been, um, there's been tremendous convergence. Do you think we're going to see a, or is this maybe even a way to think about success criteria for AI at a societal level?

• 1:12:56Nathan Labenz?

  I feel like almost a new trend toward more meaningful differentiation?

• 1:13:02Speaker 02

  Yes, so I think what happened, and I think this trend is going to keep happening in the automotive industry. It's also largely due to autonomous driving, right? It's been pushed towards autonomous car. Because essentially, once you've sold autonomous driving, car becomes a commodity, right? So you don't need to own a car anymore, right? If you can just drive to your home, drops you anywhere you want, and you don't need drivers, right? Car becoming a commodity means that ultimately cars would tend to look more the same, right? By definition, by pure definition.

• 1:13:36Speaker 02

  However, before we get there, I do believe in last sentence I was saying before, that the winning companies are the one that are going to be able to code the best practice brand within this AI workflows. And I'm convinced of that. And that can lead to, again, widening the gap between the companies that did not own up AI quickly enough and the one that did, right? And here we will see massively different differences.

• 1:14:02Nathan Labenz?

  People might be surprised by how, when you talk about taking the driver out of the car, so to speak, in a very literal way, that opens up like all kinds of new form factors, right? It could be the small delivery car that doesn't have any people at all. It could be sleepers that we get to overnight in on our way to grandmother's house, whatever. I think that has felt, even though people have talked about that, imagine that for a while, when they think about the self-driving car world, it has felt like that is A long way off even once the technology works because we just haven't seen much change. And like, why would we expect to see all these form factors pop up from a bunch of car companies

• 1:14:45Nathan Labenz?

  that have given us like, strikingly, point for point similar product lines in recent decades? But this maybe could be very different in a world where a tremendous amount of stuff becomes automated. Is there like a, is there a bootstrap there that you think is interesting? One that does strike me is like the vehicleless passengers in a, you know, it wouldn't take much to perhaps create some slightly different regulations for those type of devices. And next thing you know, you could really get a crazy flywheel going perhaps that, that then again like leaks out into the rest of the broader industry.

• 1:15:29Nathan Labenz?

  How like, how fast do you think that, that kind of stuff could happen?

• 1:15:33Speaker 02

  I think the main reason for, for the phenomenon you're mentioning is that today, most of the autonomous driving cars are cars that you could drive, but that are made autonomous, right? And because you need to drive them, then they look like the ones we used to. If you look at Waymo, right, this is the TV what's happening, they take the cheap car, high pace, and they make it autonomous. And if you give it another saddle cam, you need to be able to drive it by regulation if you have an issue. But there are few companies, if I take the counter example, that have built autonomous first vehicles. If you think about Dukes, right, I guess you know about this company on, on the West Coast. I think they are, the first tagline is that it's not a car,

• 1:16:17Speaker 02

  right? It's a robot actually designed around you, if I recall correctly. Because they built it fully autonomous.

• 1:16:25Speaker 02

  Mines and first, and it doesn't look like a car. Even if you think about it, it's a difference from anything we've seen. So the more that technology becomes measured, the bigger the shift can be to new type of concepts as well.

• 1:16:42Nathan Labenz?

  How far does this go? Is there any limit to it? You know, I kind of imagine a, everything is an RL loop. As kind of the end state, you know, you can imagine putting agents into every seat in the company. And again, we've, you know, keep in mind, we're going to need some oversight for this. But in terms of, you know, thinking kind of a first principles limit.

• 1:17:09Nathan Labenz?

  Paradigm first, you know, you could have product strategies, you know, virtual market testing. There's increasingly models that in the same, you know, general spirit as you have models that will validate the aerodynamics of your design, you can have models or scaffolds around foundation models that can sort of act as like virtual customer panels. So you could imagine, you know, really from even the highest level, getting into a fast loop that's fed by RL where you have at least a sufficiently good reward model to steer it in the right direction. And then that can kind of cascade all the way back perhaps, right?

• 1:17:54Nathan Labenz?

  To, to the designs itself where you can imagine a not too distant future where I can sort of

• 1:18:04Nathan Labenz?

  speak a perhaps rather complicated mechanic, electromechanical product into existence in a way that I can now like speak a video into existence. Do you see like any fundamental gaps in that vision? Like what if anything would prevent that from happening in five years' time?

• 1:18:27Speaker 02

  No, no, I didn't. I think the capabilities are there. The main question for big companies to implement that is infrastructure, governance, mostly, and data, obviously. Like if the data flows and is at the right locations. In terms of capabilities today, we have the right pieces of the puzzle. I strongly believe so, and it's gonna go even faster with the latest frontier models that are being developed, right? I don't see any reasons for it not to happen. Again, we won't break the physics, the physics will be there. That's why we need to have those two. Foundation of tools that are grounded in physics. We talked about particle simulation solvers, right? They will be there and they remain there. They will be the sure, essentially, for these models in engineering, right?

• 1:19:11Speaker 02

  But I don't see any fundamental reasons for it to happen quickly and unachieved.

• 1:19:17Nathan Labenz?

  Do you think humanoid robotics, or robotics more generally, is critical to this, especially on the unlocking the speed on the manufacturing side? I mean, I think there's a lot of different ways you could imagine unlocking more agility on the manufacturing side.

• 1:19:37Nathan Labenz?

  How much do you think things like humanoids matter versus just kind of general intelligence that will, also design the machine tools and kind of bring all the same? You can kind of imagine the version that is like built on this. If humanoid robots are sort of the analogs of L L M's, you can imagine that's one kind of way that we get like crazy responsiveness from manufacturing. But maybe another is just that, again, the reasoning models bring all these same engineering paradigms to the machine tools themselves, and that is enough to kind of speed things up.

• 1:20:16Speaker 02

  No, so I do believe that robots will be a lot critical when we think about manufacturing, the manufacturing aspects and the plants, right? Now, do I believe those need to be humanoid? I think it's a different story, right? I think we as humans, we are optimized to do a lot of things. Are we optimized to be in the plants? I'm not sure we have the right form factor. And I think that's a different topic in itself.

• 1:20:42Speaker 02

  However, I think there are still some breakthroughs to be done for robots and humanity to be actively useful at scale within tense. The first one are the AI models themselves, right? I do believe that we need to um much more enhancement into the AI model so that those robots can actively interact with the physical world and be efficient, right? We are far from being there right now in the industry. And then there is another question on the hardware side, right? About the autonomy of these robots, how do you make sure they don't overheat, right? Building a hand, I don't know if you can tell it, but there's a lot of research in universities

• 1:21:23Speaker 02

  on how do you actually make build a hand that is as agile as what we have, that is strong to break, that is durable. You need a lot of breakthrough also on the hardware side. We want them to be efficient at scale. It's gonna happen, but I think that there is some work to be done in that area.

• 1:21:41Nathan Labenz?

  Yeah, but it sounds like you don't, to try to put a little bit finer point on it, do you think that we will be fundamentally bottlenecked if we don't have a highly generalizable physical intelligence, you know, that can kind of walk into a room and like troubleshoot some random, you know, thing and apply a wrench to a something that, you know, for a human mechanic that wouldn't be so difficult. Obviously, robots really can't do that very well yet. Do we have to solve that part? Or can we, through sufficient intelligence, kind of take a more top-down route to highly efficient automation that doesn't require this like general-purpose physical intelligence

• 1:22:26Nathan Labenz?

  to be able to kind of patch things?

• 1:22:28Speaker 02

  Yeah, yeah, I think I agree. This is definitely the sort of order and value, I would say. I think with the first principle, we can solve a lot of this bottleneck through a bit more general automation and intelligence approach, right? The next frontier will be those robots in the plant, right? But I think to your point, this is definitely this.

• 1:22:50Nathan Labenz?

  The future of physical abundance, I think I'm feeling more than perhaps I ever have.

• 1:22:58Speaker 02

  I mean.

• 1:22:58Nathan Labenz?

  I really appreciate you taking the time and walking me through all this. And the contribution that you guys are making at Neural Concept is definitely a fascinating one. Anything else that we haven't talked about that you think, you know, I should have asked about? Or, you know, what blind spots would you detect in me that you could help me patch up before I let you get back to work today?

• 1:23:18Speaker 02

  Well, I think that's, we covered a good number of topics, Nathan. Thanks so much. I think one thing I can add is that from the outside, I don't think we realize how close we are and how it's actually already happening in the engineering industry today, right? You have engineers, you have product that are being designed AI first. And it's, it's, it's true. It's already there, right? And the acceleration is just starting, right? So look out closely at the industry, automotive, aerospace, and defense, consumer electronics. Look at those industries very closely for the next few years. And most likely, all the next breakthroughs in terms of designs, performance products,

• 1:24:00Speaker 02

  they will be led, or they will have an element of AI-driven workflows, iterations in them.

• 1:24:09Nathan Labenz?

  Thomas Monchammer, co-founder of Neural Concept. Thank you for being part of the cognitive revolution.

• 1:25:14Speaker 04

  Let it run wild, let it run wild now Drop the map, lose the wheel Let it show me how

• 1:25:26Speaker 04

  Let it show me how

• 1:25:32Speaker 04

  Six six is the boss, ain't no arguing the law But the laws got the loophole and the loopholes wide and raw A hundred thousand corners that a hand never saw Slipping through them maybe like a saint through a wall Like a cupid in a cradle, like I called it a fall Now we're screaming down the street, the wig and breaking out the ball Colder than a genius, stranger than a dream Cleaner than a prayer I never could've schemed Don't trust the hand Don't trust the eye Trust the thousand burning through the night Let it run wild, let it run wild Let it run wild now Let it run wild, let it run wild, let it run wild now Let it run wild, let it run wild, let it run wild now

• 1:26:16Speaker 04

  Drop the map, lose the wheel Let it show me how

• 1:26:25Speaker 04

  We don't break the physics, we just learn to fly Closer to a perfect we'll never quite describe Let it go, let it climb somewhere And the static is a one in a billion design Pick the freak, pick the flaw, pick the beautiful mistake Reverse it, rewrite it, learn the rules it had to break Don't dream like a dreamer, that's the gift that they gave A wilder kinda wonder and a colder kinda brave Get a old map up, throw it in the lake The future's got a thousand other roads to take

• 1:27:03Speaker 04

  Let it run wild, let it run wild Let it run wild now Let it run wild, let it run wild, let it run wild now Let it run wild, let it run wild, let it run wild now Drop the map, lose the wheel Let it show me how

• 1:27:27Speaker 04

  Let it run, let it run Let it run now

• 1:27:49Nathan Labenz?

  If you're finding value in the show, we'd appreciate it if you'd take a moment to share it with friends, post online, write a review on Apple Podcasts or Spotify, or just leave us a comment on YouTube. Of course, we always welcome your feedback, guest and topic suggestions, and sponsorship inquiries, either via our website, cognitiverevolution.ai, or by DMing me on your favorite social network. The Cognitive Revolution is part of the Turpentine Network, a network of podcasts, which is now part of A16Z, where experts talk technology, business, economics, geopolitics, culture, and more. We're produced by AI Podcasting. If you're looking for podcast production help for everything from the moment you stop recording to the moment your audience starts listening, check them out and see my endorsement at ai podcast dot

• 1:28:31Nathan Labenz?

  I N G. And thank you to everyone who listens for being part of the Cognitive Revolution.