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

Hello and welcome back to the cognitive revolution. Today, my guest is Thomas von Tschammer, 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 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 models can deliver similar results to expensive 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 one thousand 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. Plus, it occasionally produces surprising move thirty seven like designs that actually alert human engineers to new possibilities. 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 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.

And so I hope you enjoy this preview of the AI powered future of engineering with Thomas von Tschammer 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.

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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. Thomas von Tschammer, co-founder and managing director of the United States at Neural Concept. Welcome to the cognitive revolution. I'm excited for this. We have not done much on AI for engineering on this feed. Thanks very much. Thank you for listening. I can start with the example of the automotive industry, right?

Nathan is a good example, Nathan. So we've been designing cars essentially for the same way for the past four years, right? Four years ago before CAN, 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 the wall, making sure that are looking if the pedestrian or the passengers were safe inside the car. And then, if not, you'd have to change the design and then rebuild the new prototype.

As you can imagine, this was a very lengthy process, which means that ultimately you could explore five and ten prototypes a year if you were lucky before you had to go to production. Then about thirty forty years ago, let's say forty years ago now, we moved into computer-assisted design, right? So which means that now, four 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 crash it crashing against the wall directly on the computer, right?

We use for that software that we call FEA software, finite elements analysis software, that would predict, simulate the effect of the crush. Since now you don't have to build as many prototypes, you could go through maybe fifty, a hundred of different designs of car a year, which is substantial, right? Substantial speed. However, these tools they remain very complex to use and very expensive, right?

A single crash simulation can take days to be run because we are solving the equations of physics on the computer. You need very large clusters, typically, and then you need to wait as engineers several days, maybe 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 that we could spin them. But essentially, for the past thirty 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, for aerodynamics, for thermal management, for every single physics that you need when you build a car. And now, using AI, we are seeing that that third revolution, right?

So from product 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 fifty designs a year, maybe a hundred designs a year, but now thousands of different options. 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 the A models.

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 com uh 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. 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 that to which they apply? What are the unlocks associated associated with them?

And what has Neural Concept role been in, um, building these models?

yeah, 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 of subassembly within the car is being simulated, is being evaluated, and is being iterated on, right? So that means that we are as an engineers, we are evaluating many different physics on the car.

Aerodynamics is one, specifically critical for EVs, right? For the range, you want to improve the range on your next EV. Crash safety of pedestrian and passengers is another big one. Then you also have thermal management when we want to cool the batteries, cool the engine, but also for ventilation systems inside the car, right? Electromagnetism when we want to build the next generation of electric motors, right? There is a big electromagnetism aspect to it, and then structural dynamics, generally speaking, for the car, durability of the chassis or different load profiles, and so on and so forth.

And just listing the the main categories, of course, as you can imagine, then this is being uh 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 uh each of these uh specific assemblies. So those are the big the big domains, essentially.

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?

Level of abstraction or sort of intuitive physics, are we now able to get? How do we say to this like specialized model, here's a new design for a ventilation system. You like, tell it, predict what's gonna happen, kind of inputs and outputs look like to those models. Are they trained on simulation data? Also, that's another Yes, It's thing that I I've noticed is a real pattern.

yeah. So to the last question, these models, these AMMOs can be trained both on simulation data, but also on test data, ex amount of data, right? Because today, even today, there are some phenomenon that we are not able to simulate very accurately with traditional solvers. In that case, what we can do is that if we cannot simulate them, we can measure them in wind tunnel or in test loops, right? And we can gather that data and then train the corresponding air models.

So this is also a possibility. This is what we call hybrid training, essentially, where you combine numerical simulation that can be low 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? To be very clear, today we are not fully replacing numerical simulation. The same way that we never fully replaced prototypes, we are still doing prototypes today in industry, right? But we're doing much less prototypes and much later in the process, right?

It is gonna be the same and it's the same for numerical simulation. We're gonna fully replace numerical simulations, but we're gonna make a much smarter usage of it for the most mature stages of developments. And it's gonna be exactly the same with AI, right? AI earlier on in the development process. We enable you as an engineer to explore a much richer space, to explore the right candidates, and then narrow down the one that you actually want to simulate to validate before going to prototypes, right?

What we've seen today, engineering is such a specific accurate field that there is not one what 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 the forefront of it at Neural Concept. However, it is not yet able to capture the level of fidelity that you would need that every car OEM 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 specific data and numerical simulations.

So a the way that you interact customers, if I understand that correctly, is models are typically it sounds like maybe pre 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 um optimization space around the core decisions that we've already made.

So we can't jump from like a sedan to a Cybertruck, perhaps with models that we have available. But once we're in the zone, we kind of know where we're gonna end up. We can refine dramatically, uh, faster, because we're able to train these models on all this existing data. And then feel like this is sort of the the epicycle development. It's like we're really dialing things in at the end. Um, Yeah, okay, exactly. So so

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

It's also a way for the company to retain 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.

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That's Claude dot ai slash tcr. Yeah, so I think that's a good thing. I think it's good. So now the other sort of complementary AI that's starting to get introduced at the same time is, 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 the 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 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 could 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 not too distant future?

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 JLAR Automotive OEM out there based in the UK. They've they've published their work with us at the latest Nvidia GTC 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 traditional numerical solvers, and even in your design, they were going through this very expensive solver, was time consuming, and they had already highly parallelized, highly optimized this string, and they got to about fifty designs evaluated every day.

So fifty designs and iterations between the studio teams, which are responsible for the aesthetic design of the car and the aerodynamicists, right? And at the end of the day, 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 1,500 every single day in production.

So you can imagine the level of speed 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 80%. That development cycles, essentially becoming 80% faster to develop a new battery cool plates. And on top of these smaller design cycles, they could get better performance, right? Because now, if you can explore many more designs, it also means that you can innovate better.

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 20% better on the battery, it's 15% lighter. If you think about aerodynamics, and three D also help engineers to find designs that are two, three, 5% more aerodynamic, which is can change if them as we can.

Yeah, so let's dig into that last point. How is that happening again? To Mathis on the 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, I. There's a couple 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, um, with like tool calling paradigm to intuition of design? 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? On in those relative paradigms, do you s? How are they starting to converge?

Yeah, so today, what we've seen is that these AI driven engineering workflows are not a black box, right? They are today an assistant to the engineer, so that the engineer can take the right data inform 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 workflows where AI ingests the spec sheets, understand the requirements, we 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 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 these domain experts in the loop that then ultimately can take the right trade-off decisions. But essentially, we are moving from a world where, as you were saying, with your with your dad, where in the past you were relying on intuition. Hey, my design doesn't work, but I think if I tweak it that way manually, it should 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. 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.

So what models are you 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 three D 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, uh, 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, and using a CAD product. Um, and a 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? Like, how much are we gonna be relying on a few frontier models to assist our engineers? How much do you think you're gonna start to see this loop get closed and create other like specialist models that play that role instead?

Yeah, yeah, great question. So if you think about what's, I think, yeah, Jensen mentioned that during the later GTC, he seemed to the AI as a five layer cake from the foundations to the top, and the lay 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 essentially taking the generic metas pre-trained models of pre-trained models and then bringing the right domain specific scale set of abilities such that these models can have value in very complex environments. This is exactly what we're doing in engineering, essentially, right? So we leverage the standard generic models, state of the art that you can think of, right? But we are giving them the right set of tools that they can work with. Three D geometries, they can generate new geometries, the set of skills, so that they have the context about what a meal 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 context? But then they can have an impact. Today, if you just take a plane, the land, you will not able to go very far because they have no very accurate three D 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 XR aerodynamic, right? So there is a big gap to bridge here, essentially, and that's what we're doing, the company, Neural Concept.

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.

Exactly equipped with the right tools, I think is the is the important part where these models are able again to interact with CAD, even with simulation solvers, but with also other class of models, right? If we take a step back and we look at how history happened from SAP, how did we start? We started in 2019 by building the first AI model architecture based originally on computer vision that could directly ingest three D geometries and learn from physics.

This is how we started, right? So 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 predict aerodynamics, deformation, temperature, and so on, and so forth, right? 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.

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 or 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.

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. 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 and fix that, even if we're in production. Obviously, not so with a car. Um, 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 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.

Good question, again. So, unfortunately, if you're an automotive supplier, an OEM today, it's more than latter, right? Specific RSQ, as they call it, so request for 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? 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 can translate that into three D. This is exactly what's happening today, right, already.

However, why it is not yet as black boxy as it can be for software engineering, it's because the dimension of the problem is much, much broader, much richer, right? You have many more trade-offs you need to make, and there's never one way to get to a solution, right? Which why it's not gonna replace engineers at any time soon, but it's gonna empower them to be faster, actually, it's gonna remove or eliminate the low added value tasks.

That's for sure. That's already happening, right, where the 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, eliminated as we speak, right? But it's never gonna replace the engineer taking those design decisions, sure, even the demonstrator, the

Are you sure? What what does a vacoff 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 the 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 three D representations in these systems, setting up all the simulations.

Um, 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. And I think there are some we may not want them to do, or we may want to 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 pick up where the humans left off and literally, like, design the next model year of car.

If that's crazy, why do you think that's crazy? Or what is the part that we're so far off on?

oh no, so you have a good point. All of these tasks today, I strongly believe that with the models and capabilities that we have can be automated in simulation, right? These models have capabilities to translate any specification into a design and then simulate that design automatically using numerical 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, right?

Yes, this is being done today for specific products, right? Battery cool plates, e-motor, aerodynamics, crash, right? Where I don't believe this is. This will become a black 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 saying we want 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 in the differentiation will happen tomorrow between OEM one and OEM two? It's gonna be the ones that are able to deeply ingrain their engineering IP into these AI workflows. That's gonna be key. That's what the new companies are realizing.

Yeah, that's fascinating. What do you think the odds are that we get? Like, I guess I'll only start with one about your alignment of your business model, 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 be interested to know where 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 gonna have to move in another direction?

I think, essentially, we 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 ratio, right? The question is, how do you monitor value? Then? And I think it depends on the industry and applications. But ultimately, we're gonna move to a world where pricing is gonna be based purely on value you're able to deliver for companies. And I think that's actually very healthy, right? That's what you wanna have.

But yeah, because the value is not gonna be tied to an individual anymore, to a seat, but now potentially to agents, right? Pricing will evolve, according, and I think it's gonna be the same for every company.

How much disruption do you expect to see in these, like hundred year old, uh, industries like auto, where obviously companies have changed a lot? But it's it's largely companies that were formed eighty, hundred 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, four deployed engineers last week who are doing this for tax 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?

Oh, 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 these AI driven engineering workflows and the ones that are not right. And the gap is gonna widen essentially over the past, the next few years, for sure, right? So there's gonna be a lot of disruption. What is convenient for companies if you think about traditional OEM? It's that they've been building cars essentially the same way for the past twenty years, right?

Same teams, same tools, same know-how, right? So you're asking engineers that have worked the same way in some long time now to change. They were thinking at even the governments of the whole company, right? Seeing it, some are embracing it faster than others, right? 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 practice out there, and walk them through the process.

But I also believe that, yes, there is also an opportunity today for a company that is building hardware to come in to come fast, right? We talk a lot about these digital native companies, right? And we work with many of those outside of automotive, right? That are doing electronic products, consuming electronic products, right? So those companies have been building hardware for the past ten years, max maximum, right? And you feel that they are able to pick up these new workflows much faster, right?

What do I mean by that? I mean that in a year, they've had impressive massive impact, massive speed up of their developed workflows across many applications, right? And we're seeing the slower pace in the larger, older companies, engineering companies out there.

Yeah, that's been the story of Detroit for quite a while. It's been companies that 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 fifty years ago phenomenon. They've come a long way, but they're now gonna be faced, I think, with their most profound challenge ever. And competing with, like Japanese companies in the seventies and eighties, is potentially gonna be easy mode compared to competing with AI native companies if things go a certain way.

So it's gonna be really interesting to watch the organizational dynamics and challenges of that.

When you think about it, some numbers today, uh, Western Europe or US OEM, it takes them between forty to sixty month for a new car development from the moment they want to launch it to the actual moment. It hits the plants and it's being manufactured, right? In in China, it's eighteen to twenty four months. Everything those companies in US and Europe care about is how do we get from sixteen to twenty four months, right? Because that's what's gonna create the next competitive advantage for them.

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. 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 can imagine again, if the design gets so optimized and we're really satisfying these constraints down to the the absolute maximum in our designs, it sounds like that would create really super tight tolerances and really super difficult manufacturing challenges. And so how do you think about? I guess maybe one answer is this is the role for the human engineer, but don't satisfy your, you know, let's not satisfy ourselves with that answer.

How should we be thinking about, like, just how far we wanna push design optimization? And and when we need to meet manufacturing a little bit more in the middle?

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 I mean by that? That means that when I'm saying that building a car is a multi disciplinary and multi objective optimization, I also mean that because you want to incorporate design rules, manufacturing constraints as early as possible into your design iterations, right? Because that was the promise of additive manufacturing eighteen 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 so expensive, it doesn't scale in production. So now let's say you have manufacturing plans and you have your stamping process, 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 evaluates, explores in the design, it explores it, knowing 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 valid for manufacturing. The right number of costs, so it's not only the physics, but also manufacturing and costs along the way that you want to bring earlier into the loop.

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, you know, 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 the latter, probably, but I'm not really sure how to think about what is contributing to that huge advantage.

Yeah, yeah, so I'm with you. Tend to think it's more than other, right? They are much more agile when it's about rolling out a new plant, but 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? The key 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? Legacy of processes of tools, and they can just pick what's best out there today and not what was best yesterday. 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 ineradicable history of developments and they have less processes that are deeply ingrained, right? If you don't have processing that were built in the 2,000, it's much easier to work in much more agile way today. And then we're going back to the digital native type of companies that are able to pick up new standards much faster.

We the quote unquote West have our work cut out for us. It sounds like, how big of a deal is it gonna 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 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, 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. Um, you know, as opposed to, you know, to the validation side, um, 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 gotta be inevitable on some level.

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

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

However, we might not be the first ones to have that foundational model, right? But then as we're, and I'm going back to Jensen's five layer cake 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 100,000 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. Essentially, and to leverage, they

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. I 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.

They that are under just this incredible pressure to turn things around quickly, right? So how does that look like right now? And another interesting detail was that apparently, uh, 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?

yeah, I mean that that 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 rank from the previous year, you don't get the same number of hours for the next season. The idea is that you wanna try and make it more equal across teams, right?

And you don't wanna 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.

Wow, so they handicap, essentially, if you win, you get less, you get less compute, or they have a harder schedule.

Yes, you get less compute. Exactly.

Yeah, wow, the NFL does, the NFL does. If you have, 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

And because because if you think about it,

to try to get better draft picks. But the I'd never heard of this level of actually changing, not just the talent acquisition process or who the the opponents are gonna 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.

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 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 competition between different teams. Actually, that's super interesting.

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

Next, if you think about it, Formula One engineers are the state of the art of engineering, the most agile 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 it's very fine details, but the car is actually different, right? They are improving it a week over week. So that means that they have to reach an extreme level of automation of design, iteration processes, and right.

We see in Formula one teams, we believe what every single OEM is trying to tend towards right, trying to aim for the way they work, the way they iterate, the way they take design 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 can work for the more traditional OEMs out there, essentially.

So that's that's really the way we're seeing it, and we are asking those teams, really push the limits of the models and the workflows to break phase, essentially, because that's when the break phase that we see where we have to focus and what we have to develop there.

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 are writing code anymore. Um, 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 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? What is 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, uh, F1 engineer's life look like today?

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. We have many more straight lines and we know, that have to overtake much more, right? Um, so that that's the baseline. Then that you are saying that today into engineering requirements in terms of how the car should improve and the aerodynamics of the car.

And then they would. They are running these AI driven workflows so that are taking those aerodynamic requirements that have information awareness about the geometry about the three. And then overnight, the AI driven workflow will generate hundreds, thousands of configurations of design options. We'll evaluate them, we'll evaluate the corresponding aerodynamic performances, and on the morning after, the engineer, the aerodynamicist will have a dashboard, a report, interactive. It is thousands of points of data points on a dashboard, and it can look at the different trade-offs, look at the corresponding design, and pick the one they want, 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 and evaluated overnight, fully automated by these AI models.

Have we seen any surprises come out of that process? Of course, the legendary move thirty seven, um, it seems like increasingly with the Mythos class fable models, we're starting to see. Move thirty Saturdays might be strong, but they're definitely stepping outside. I had a really interesting experience today, where I, or late 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 web page 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. That was like a little mini move thirty seven in that it was.

I've done probably done this workflow fifty times over the last three months and nothing like that had ever happened. What's the sort of mo most move thirty seven like thing that we're seeing in engineering?

Yeah, we have very similar analogies, and I think, to be fair, it's one of the favorite part of the job. Few, that's where it becomes very exciting. Um, we, we have engineers that are using these workflows, right? So using AI to explore and asking the AI to explore these hundred 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, this is 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 gonna work. But act actually, those designs are better than anything we could come up with. And now I need to get to get back to the dashboard to understand why it is so much better, right? So 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, this much richer space, they go out of bound, they go beyond your intuition. Similar to what you were saying, I think. So that's where 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. How did it get there? How can I learn from it? Because explore new physics or new phenomena that I was not aware of when I was only working with intuition, essentially.

So, those are. Is it possible that Even try reverse engineer the design itself from the AI, which yeah, learning in the process?

learning from the AI's uh, advances and its occasional leapfrogs, uh, 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 um moments are? It may be a little bit hard for somebody not in the domain to 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 that people?

You know? Cause 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? When we see these surprises in engineering, like, how big of a surprise are they? Are we? 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?

Um, but in defiance of all my intuitions, it it actually does work. Just like, try to help me calibrate on how big those surprises are.

Yeah, so you remain grounded by physics, right? So you will not reinvent the physics, that that's for sure, so you will not get completely insane designs breaking the physics, because everything is grounded by physics. However, we've seen scenarios where the engineer is telling us, Hey, there's no way in the world I would have done that design because I don't think I 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 is so much better, right?

So 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, this much richer space, they go out of bound, they go beyond your intuition. Similar to what you were saying, I think so. That's where 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.

How did it get there? How can I learn from it? Because explore new physics or new phenomena that I was not aware of when I was only working with intuition, essentially. Is it possible So that's what I've even tried

so?

reverse engineer the design itself from the AI, which Yeah, learning in the process.

learning from the AI's uh, advances and its occasional leapfrogs, uh, 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 um moments are? It may be a little bit hard for somebody not in the domain to 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 that people?

You know? Cause 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? When we see these surprises in engineering, like, how big of a surprise are they? Are we? 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?

Um, but in defiance of all my intuitions, it it actually does work. Just like, try to help me calibrate on how big those surprises are.

Yeah, so you remain grounded by physics, right? So you will not reinvent the physics, that that's for sure, so you will not get completely insane designs breaking the physics, because everything is grounded by physics. However, we've seen scenarios where the engineer is telling us, Hey, there's no way in the world I would have done that design because I don't think I 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 is so much better, right?

So 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, this much richer space, they go out of bound, they go beyond your intuition. Similar to what you were saying, I think

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 gonna be willing to pay for mythos class models? Notably, the price originally previewed with Mythos has already come down a lot with, uh, 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.

Um, 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, uh, miles on a charge 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 thirty seven like type moments.

How much value do people perceive in it? Are they able to measure it? Are they willing to pay? Um, you know, is are we gonna see people continue to run the neural concept, co-pilot, the engineering co-pilot with anything less than a fable model? Or is it gonna be like, nah, you, you gotta pay for the best, because that's where all the insights come from, and the bill is going through the roof. But, like, we have no choice but to do that to stay competitive.

Who do you think we're gonna land in the short term on this willingness to pay question?

it's a very good question again, costing the value is key, and I mean, it's a discussion we always have also with the companies we work with, right? Typically, when you think about design breakthrough, right? So it's much better performance that what you could get before. We have this discussion with suppliers, right that are setting to the big OEMs, the big car OEMs down there. Um, there are several aspects. If you can get a better design, probably means that you are much more competitive on the market, probably means that you will win more projects, more programs with OEMs, and with secure more contracts.

If today, let's say, assume you're building battery cold plates to cool batteries, right? If you can win one more program per year, that's millions, tens of millions of dollars, right? Just one more program. Out of the the three fifty, you're winning every year, right? So that that's very tangible, dollar values. But the other way to see it is, look, if I can get much faster to a good design. Yesterday, it took me six months, but 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 with my clients, right? And this has a direct dollar value as well. If your part is 10% cheaper than the competition because you spend three months, you could allow yourself to spend three months working on the manufacturing process, tuning the details, then you win these 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 forty eight to twenty four months, that's also millions of developments. You're saving, hence you're selling for every car, right? A car is typically a billion, right to develop, or from scratch. But if you can think even 20% of it, the math is pretty quick.

Yeah, 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. Um, we know that we're already iterating at half the speed of the Chinese companies.

We see some hard manufacturing places where the product cycle has accelerated. Look no further than Nvidia for, you know, probably the most dramatic example of this. Forget about, like, what would get you initially, like, a, you know, coffee spit take and laughed out of the room if you said it. What do you think is like the the actual achievable speed up that you would, you know, heart of hearts, tell the CEO of GM? Like, this is what you really need to to be able to do in terms of speed up.

If you wanna be meaningfully, you know, durably competitive in the AI era,

So I think there are different scales, of course, to this. In year one, they'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% speed up on those flagship disciplines, right?

That that's 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 thermal, right? So that the agent can not only orchestrate the aerodynamic optimization, but can orchestrate it while taking into consideration the safety aspects, the manufacturing aspects, as we talked before, right? Automatic 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.

You can really break down and reduce development cycles from fif to fifty, 60%. And that's what we are seeing already, right? Today, there is not an OEM that was done it at scale for the entire car, right? But we are seeing the multidisciplinary automated AI driven workflows happening already for some specific disciplines, and we are seeing 50%, 60% speed up. That that's massive.

yeah, interesting. How do people respond to it? You know, my 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 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 gonna 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, I don't mean to dismiss those feelings, but they're an 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?

Yeah, it's a it's a good question. I would, I think we do sleep. Both end of the spectrum, right? I do see that engineers are artists in a way, right? They are. They made the intuition on how to build the 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 thirty years, and they enjoy that.

Totally understandable that when they see this type of new workflows, they are sometimes a bit resistant. To okay, what would it actually bring, right? And they also believe, right, we, so 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 these human aspects as well.

But you also see all the way all the end of the spectrum, the ML researcher, because they're also message team and machine learning teams within those organizations at the at the forefront of these workflows. And those ones are the first ones to adopt it, and those are the first ones that want to explore, want to try new models, mature models, and to benchmark them, experiment, right? 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 very large organizations. But thing we've seen a lot, though, is once you manage to break that halo, 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 empower them to make their job even 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, try much more what if scenarios, and that's the fun part. When you're an engineer, you want to try these things out, you want to test scenarios, test entries, right? And that's what AI enables you to do today.

Sometimes you 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 detroiter, 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 our with ourselves, you know, there's been, um, there's been tremendous convergence.

Do you think we're gonna see a, or is this maybe even a way to think about success criteria for AI at a societal level? I feel like almost a new trend toward more meaningful differentiation.

Yes, so I think what happened, and I think this trend is gonna keep happening in the automotive industry. It's also largely due to autonomous driving, right? This big push towards autonomous car, cause 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 to drive it, right? Car becoming a commodity means that ultimately cars would tend to look more the same, right?

By definition, by pure definition. However, before we get there, I do believe, and that's the point, so I was, I was saying before that. The winning companies are the one that are gonna be able to code the the best practice brand within these AI workflows, right? And I I'm convinced of that. And that can lead to again widening the gap between the companies that did not adopt AI quickly enough and the one that did right. And here we will see massive, different differences.

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 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. I 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 a crazy flywheel going, perhaps that that then again, like, leaks out into the rest of, um, the broader industry. How, how, like, how fast do you think that that kind of stuff could happen?

I think the main reason for for the phenomenon you you're mentioning is that today most of the autonomous driving cars are cars that you could drive that are not made autonomous, right? And because you need to drive them, then they look like the ones we used to. It's if you look at Waymo, right? This is actually what's happening. They take the cheap part, high pace, and then make it autonomous. If you're given on the side of camp, you need to be able to drive it by regulation if you have an issue.

But there are a few companies, um, if I take the contra example, that have built autonomous first vehicles. If you think about Jukes, right? I guess you know about this company on on the West Coast. I think they are. The the first tagline is that it's not a car, right? It's a robotaxi designed around you if I recall correctly, because they've built it fully autonomous minds and first, that's it, and and it doesn't look like a car.

Even if you think about it, it's it's a different, anything we've seen. So I think with the more that the technology becomes mature, the bigger the shift can be to new type of concepts as well.

How far does this go? Is there any limit to it? You know, I I kind of imagine a everything is an RL loop, um, as kind of the end state, you know, you can imagine putting agents into every seat in the company. Um, and again, we've, you know, keep in mind, we're gonna need some oversight for this. But in terms of, you know, thinking kind of a first principles limit, um, paradigm first, you know, you could have product strategies, you know, virtual market testing.

There's gonna. 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 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, um, getting into a fast loop that's fed by RL, where you have at least a, you know, a sufficiently good um reward model to to steer it in the right direction.

And then that can kind of cascade all the way back, perhaps, right to um, to the designs itself, where you can imagine a a not too distant future where I can sort of speak a perhaps rather complicated mechanic electromechanical, uh, product into existence in a way that I can now, like, speak a video into existence. Do you see like any fundamental gaps in in that vision? Like, what, if anything, would prevent that from happening in five years time?

No, no, I, I, we didn't. I think the capabilities are there. Um, the main question for big companies to implement that is infrastructure, governance, mostly, right, and data, obviously, making sure the data flows and is at the right locations. In terms of capabilities today, we have the right pieces of the puzzle. I, I strongly believe so, and it's gonna go even faster. Uh, with the latest foundation models that are being developed, right? I don't see any 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 foundation of tools that are grounded in physics. We talked about numerical simulation solvers, right? They will be there, and they will remain there. They will be the short, essentially for these models in engineering, right? But I don't see any fundamental reasons for it not to happen quickly. And actually pretty quickly.

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. Um, how, how much do you think things like humanoids matter versus just kind of general intelligence that will, you know, de also design the machine tools, and, you know, d kind of bring all the same?

You can kind of imagine the version that is like built on this. I if if humanoid robots are sort of the analogs of LLMs, you can imagine, um, 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 the all these same engineering paradigms to the machine tools themselves. And that, you know, is is enough to kind of speed things up.

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

However, I think there are there are still some some breakthroughs to be done for robots and humanoid to be actively useful at scale within plants. The first one are the A I models themselves, right? I I do believe that we need to, um, much more advancement into the A I models 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 looked at it, but there's a lot of research in universities on how do you actually make, build a hand? That is as agile as what we have, uh, that is so don't 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 that area for sure.

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, uh, uh, 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, um, kind of take a more top-down route to highly efficient automation? That doesn't require this, like, general purpose physical intelligence, to be able to kind of patch things.

Yeah, yeah, I think I agree. This is definitely the the sum of so called other, and thank you, I would say. I think with the first law principle, we can solve a lot of these bottlenecks through at least more general automation and intelligence up front, right? The next frontier will be those robots in the plants, right? But I think, to your point, this is definitely

The future of, uh, physical abundance, I think, um, I'm feeling more than perhaps I ever have. I mean, I really appreciate you taking the time and and walking me through all this and the the contribution that you guys are making at 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, what blind spots would you detect in me that you could, uh, help me patch up before I let you get back to work today?

no, I think that's, um, we covered a good the topics, Nathan, thanks a lot. I think one thing I can add is that from the outside, I don't think we realize how close we are and and how it's actually already happening in the engineering industry today, right? You have engineers, you have products 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, uh, look out closely at the industry, automotive, aerospace and defense, consumer and 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, they will be led, or they will have an element of AI driven workflows, iterations in them, going forward.

Thomas von Tschammer, co-founder of Neural Concept, thank you for being part of the cognitive revolution.

Midnight in the middle of the night, I got the mind of his own. I'm making little babies in the middle with the bone, but drawing the same lines. The world was a stone. Now I'm letting go of the wheel, let the wild thing run faster than a beaver, find it in the threat. It's running down the street that I never could have said. Don't ask me how it knows what it knows in the dark. I just feed it to the fire and it hands me back a spark.

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