1000 Designs a Day: Neural Concept's Thomas von Tschammer on AI-Native Engineering
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Summary
This episode features Thomas von Tschammer of Neural Concept, discussing how physics-aware AI is revolutionizing product engineering. Neural Concept's models accelerate design evaluation from days to minutes, enabling companies like Jaguar Land Rover to test thousands of designs daily and cut development cycles. The conversation explores the shift from traditional simulation bottlenecks to AI-driven workflows, the emergence of engineering copilots, and the potential for AI to generate novel designs and accelerate product innovation, even for highly competitive fields like Formula One racing.
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Hello, and welcome back to the Cognitive Revolution. Today, my guest is Thomas von Schalmer, 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 concepts models can deliver similar results to expensive physics based solvers in minutes, and they now also offer an engineering copilot 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 TikTok 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. Plus, it occasionally produces surprising Move 37 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 handwavy 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 Schummer 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.
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And now on with the show.
Thomas von Schummer, cofounder and managing director of The United States at Neural Concept. Welcome to the Cognitive Revolution.
Thanks very much. Thank you for hosting me.
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 gonna be working through the AI assisted, increasingly AI automated era. But I still am very excited to kinda 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 there we probably can't assume common knowledge in terms of what does the life of an engineer, what does 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?
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.
I can start with the example of the automotive industry. Right? Julienne is a good example, Nathan.
So we've been designing cars essentially for the same way for the past forty years. Right? Forty years ago, before CAN, which is computer assisted design, when you wanted to design a new car, you're essentially building a prototype, right?
And you were crushing that prototype against the wall, making sure that you're 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 a 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 looking before to go to production.
Then about thirty, forty years ago, let's say forty years ago now, we modeled two computer assisted design, right? 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 for what's called numerical simulations, the effect that car crashes crashing against the wall directly on the computer.
Right? So we use for that software that we call FEA software, finite element analysis software, that would predict, simulate the effect on the crash. Since now you don't have to build as many prototypes, you could go to 50, a 100 of different designs of cars a year, which is substantial.
Right? Substantial speed up. 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 engineers several days, maybe one or two days to get that result out, which today is still the main bottleneck when you want to integrate on your design. Right?
And there's been improvements, of course, in the algorithms, compute that we have so that we could spin that up. But essentially, for the past thirty years, we've been using the same CAD tool, and we've been using the same SCA numerical simulation service. That's true for CRASH, but I should say aerodynamics, for thermal management, for every single physics that you need when you build a car.
And now using AI, we're seeing that that same revolution. Right? So from product to canned 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 history designs a year, maybe a 100 designs a year, but not thousands of different objects. And that drastically accelerates your development cycles. That also means that you as an engineer can innovate much further because you have more options that you can explore.
Thanks to these AI 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 comp 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 realized 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. 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 sort of fields to which they apply? What are the unlocks associated with them? And what has Neural Concept's role been in building these models?
Yeah. Great question. So there are many different domains, as you can imagine.
I mean, think about the complexity of the 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 component or sub assembly within the car is being simulated, is being evaluated, and is being evaluated on. Means that we are, as engineers, we are evaluating many different physics on the car.
Aeodynamics is one. It's specifically critical for EVs, on the range, you want to improve the range on your next EV. Crash safety for pedestrians 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. Electromagnetism when we want to build the next generation of electric motors. There is an electromagnetism aspect to it.
And then structural dynamics, generally speaking for the car, durability of the chassis, or different road profiles, and so on and so forth. And just this thing, main categories, of course, as you can imagine, then this is being divided into component sub assemblies. Ultimately, with a company like GM, you have thousands and thousands of engineers that are domain experts on these physics, on those components, so that they can iterate and improve each of these specific estimates.
So those are the big three 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, you 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. What kinda 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. Yeah.
So to the last question, these models, these animals 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're not able to simulate very accurately with traditional solvers.
In that case, what we can do is that if we cannot simulate them, can measure them in wind tunnel or in test loops. Right? And we can gather the data and then train the corresponding animals.
And this is also a part of it. This is what you call hybrid training, essentially, where you combine numerical simulation that can be low off the data because we're 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 a mobile simulation. The same way that we never fully replaced prototypes, we're still doing prototypes today in the industry, right? But we're doing much less prototypes and much later in the process.
Is going to be the same and it's the same for numerical simulation. We're going to fully replace numerical simulations, but we're going 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.
AI, on your own in the development process, will enable you as an engineer to explore a much richer space, to explore the right context, and then narrow down the one that you actually want to simulate to validate before going to prototypes. Today, engineering is such a specific, accurate field that there is not one foundational model that can be used to solve the aerodynamics on every car. There is research definitely going in that direction, and we are sort of the forefront of it at our concepts.
However, it is not yet able to capture the level of agility that you would need, that every car OEM would need to be able to be deployed on 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.
the way that you interact with 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 Cybertruck perhaps with models that we have available.
But once we're in the zone, we kinda know where we're gonna end up. We can refine dramatically, faster because we're able to train these models on all those 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.
Okay.
Exactly. 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 we're 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 them all to match exactly their requirements. 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 your 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?
such that the next development cycle can be even faster in there. Hey. We'll continue our interview in a moment after a word from our sponsors.
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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, maybe you can tell me, like, some of the shortcuts, again, that already exist before we get to the sort of AI copilot 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 kinda manipulating Yeah. 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 gotta 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 when 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 wanna hear too about how with the AI Copilot, like, how is that experience changing for engineers?
How automated is it starting to get? How automated is it likely to get in the naturally 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, JLRG.
I don't know if OEM out there, based in The UK. 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, were using clipped on 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 50 designs evaluated every day.
So 50 designs and iterations between the studio teams, which are responsible for the aesthetic, the design of the car, and the aerodynamicist. 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. AI, and that was what De Palmeki announced a couple of months ago, they went from 50 designs evaluated per day to 1,500 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 in a magical pace to cool the battery, that were able to reduce by 80% the development cycles, essentially becoming 80% faster to develop a new battery called plates. And on top of this, more of the design cycles, they could get better performance.
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. We see examples where the company is cooling 20% better than the battery.
It's 15% lighter. If you think about aerodynamics, I'm pretty sure we also help engineers to find designs that are 2%, 3%, 5% more aerodynamic, which is came change of them every time.
Yeah. So let's dig into that last point. How is that happening?
Again, the math is on to 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. 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? You can imagine a language free way. 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?
Yes. We're seeing is that this AI driven engine workflows are not in the black box. Right?
They are today an assistant to the engineer, so that the engineer can take the right data from design decisions. Right? We are not in the world where we're just sending a spec sheet to AI and expect the AI to get back to us as a black box with the best optimized car.
We are rather in warp roads 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 we'll 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. So you want to have this 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 with your, in the past you were relying on intuition.
Hey, my dad doesn't work, but I think if I tweak it that way manually, it should work out. 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. That means that here in that process, what is very interesting is that the AI is able to interact with the different tools, with CAD.
We talked about CAD. So now the AI is able to interact with CAD, to send your geometries, is able to interact with numerical simulation solvers. Hey, I want to validate this design I came up with, and sends 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, and 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, and using a CAD product. And that 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 wanna 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, Socks.
If you think about what's I think yeah. Jensen mentioned that in later. He's saying today AI has 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.
Today, in the industry, there are many, many companies that are specializing in building that application layer for some very specific application. It should be taking the generic Metos cloud model, operating models, and then bringing the right domain specific skills capabilities, such that these models can have value in very complex environments. This is exactly what we're doing in engineering, essentially.
So we leverage the standard generic models that of the art that you can think of. But we are giving them the right set of tools, and they can work with three d geometries, they can generate new geometries, different set of skills. So they have the context about what a mill 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? Then they can have an impact.
Today, if you just take a plain Nalen, you will not be able to go very far because they have no very accurate three d resulting. 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 extra aerodynamic. Right?
So there is a big gap to bridge here, actually, and that's what we're doing in the company.
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 important parts where these models are able, again, to interact with CAD, even in reference to machine servers, but with also other class of models. If we take a step back and we look at our history at Neuromption 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 deep 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 predict aerodynamics, deformation, temperature, and so on and so forth.
But these are also models that you want as part of your workflow. 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 AIified. 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 Mhmm. 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 kinda 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 kinda reach in, fix that even if we're in production. Obviously, not so with a car.
So am I right that there is much better specs? And does that put models in a really disposition, 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 it remains difficult for AIs in some ways. Good question again.
If you're an automotive supplier, an OEM today, it more than matter. Right? Specific RSQ, as they call it, so request for quotation and specification, and they are 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 speak about a car, which is such a complex problem with an infinite number of dimensions and constraints. Imagine that if you change the thickness of a single component somewhere under the hood, this might impact the overall engine block.
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 talk. 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 engineers, it's because the dimension of the problem is much, much broader, much richer.
You have many more trade offs you need to make, And there's never one way to get to a solution. It's not going to replace engineers anytime soon, but it's going to empower them to be faster. It's going to remove or eliminate the low added value tasks.
That's for sure. That's already happening. Right?
So when engineer needs to manually set up a new simulation, needs to manually go on the CAD and roll 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 demonstrated point.
Are you sure? What what does a bake 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 three d 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. And I think there's some we may not want them to do, where 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 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? What is the part that we're so far off on? I don't know.
So you have a good point.
I strongly believe that with the models and capabilities that we have, can be automated. These models have capabilities to translate any specification into a design, and then simulate that design automatically using IngeConsolvers. 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. Yes, this is being done today for specific products, right? Battery coplays, emotors, 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 card. Right? Because then that's where you really 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 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? It's going to be the ones that are able to deeply ingrain their injury and key into this AI workforce. That's gonna be key.
That's all the good 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 there and potentially also a sort of compute based pricing for simulation execution. I'd just to know where where you've been on that historically.
model, or is the model gonna have to move in another direction? Things essentially will have to be based value. Right?
What's the value we're providing, right? And that's the way you price essentially. The price for a value ratio, right?
The question is how do you monitor value? And 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 on pure value, which we want to deliver for our 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 assist, but now potentially to agents, right?
Pricing will evolve accordingly. And I think it's gonna be the same for every company.
How much disruption do you expect to see in these, like, 100 old 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 forward deployed engineers last week who are doing this for tax Mhmm.
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 to 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?
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 going to widen essentially over the past the next few years for sure. Right? So there's going be a lot of disruption.
What is complicated for companies if you think about traditional OEM? They've been building cars essentially the same way for the past twenty years. Same teams, same tools, same know how.
So you're asking engineers that have worked the same way for a long time now to change their thinking and even the governance of their own company. Think that 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, 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 into Comcast. We talk a lot about these digital native companies, And we work with many of those outside of automotive, that are electronic products, consumer electronic products. Those companies have been building hardware for the past ten years max, maximum.
And you feel that they are able to pick up these new workflows much faster. Right? What do I mean by this?
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 think they are 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.
dynamics and challenges of that. And think about it, some numbers today, Western Europe or US OEM, it takes them between forty to sixty month for a new car developed. 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 sixty to twenty four months.
Right? Because that's what's gonna create the next competitive advantage for that.
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 could 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 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 you mean by that? That means that when I'm saying that building a car is a multi disciplinary and multi amplitude optimization, I also mean that because you want to incorporate design rules, manufacturing constraints as early as possible into your design iterations. Because that was the promise of additive manufacturing fifteen years ago, that you could design freely, because then you could print anything.
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 plants and you have your stamping process, you know what are the manufacturing constraints. You know what you can and cannot do with stamping.
But 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 quickly as possible. So that whenever the model evaluates, explores in the design, it exposes no way that it can be manufactured. 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 reasonable cost. So something in the physics, also manufacturing and costs along the way that you want to bring earlier into the deal.
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 kinda 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'll review.
I tend to think it's more than latter. Right? That much more agile when it's about rolling out the new plants.
Also the way the plant is being operated, it is highly, highly automated there. Now you have engineering executives from Europe, from US, traveling to China to understand how they are setting up their plants, taking lessons from it, and then going back and applying those best practices in their country. This is happening already today, 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. 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 inertia, the history of developments, and they have less processes that are deeply ingrained.
Right? If you don't have processes that were built in the 2000, it's much easier to work in a 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 and much like.
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 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, and trying to bring I say this all the time, so I apologize to listeners that 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, you know, 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 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 this is gonna start with aerodynamics.
Right? This is the lorengine's fruit today because even the physics are complex. It is relatively similar across companies.
It can be relatively easily replicated. So it is going to happen very quickly. And of course, it's a very big difference.
So we are doing actually research in that direction. However, we may not be the first ones to have that foundational model, right? But then, we're And I'm going back to Jensen's five layer cake of AI.
Our role and also our focus is to make sure that whatever the financial 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. So so that you can visualize your designs, you can tweak the geometry as well, leveraging those conditional 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 abilities arrive, everyone will be ready for its message and to leverage the AI.
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 that that are under just this incredible pressure to turn things around quickly. Right? 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?
Yeah. I mean, that that that's super interesting. Right?
So indeed, today, Formula one teams are capped in, to be most distinct, the CPU hours that they can run. So CPU hours is the compute to run external aerodynamic simulations. And what's even more interesting is that depending on your ranking from the previous year, you don't get the same numbers always for the next season.
It's that you wanna try and make it more equal across teams. Right?
race as to, okay, the biggest budget, the biggest compute, so I just went. Essentially. So they're trying to equalize that in some way.
So they handicap. Essentially, if you win, you get less. You get less computers.
A harder schedule. That is a 10. Yeah.
Wow. Because 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 appear like they're now changing it because people have been tanking I can't know.
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.
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 best in Formula One, right? You want to get the best car out there for the next years. So that's a way to balance the 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?
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 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. We've seen Formula one teams. We believe what every single OEM is trying to tend towards, trying to aim for the way they work, the way they iterate, the way they take design decisions.
It's a good way for us essentially to proof test and to stress test our models, our workflows, our platform. 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 saying it, and we're asking those teams to 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.
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 aren't 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 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 does the f one 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 kinda know what the what the old school one looks like.
f one 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 terms 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 gen stick.
We have many, many straight lines, and we're not going to have to overtake much more. So that's the baseline. Then they translate that today into engineering requirements, in terms of how the car improve and the area of car.
Then they are running these AI driven workflows so that are taking those aerodynamic requirements that have information awareness about the geometry, about the three d. And then overnight, the AI driven workflow will generate hundreds, thousands of configurations of design options. We'll evaluate them.
We evaluate the corresponding aerodynamic performances. And on the morning after, the engineer, the aerodynamicist, will have a dashboard, a report interactive. He sees the thousands of points, data points on a dashboard, and he can look at the different trade offs, look at the cross plane design, and pick the one they want they want to move forward for the next race, which is next Saturday.
Right? So token maxing is essentially thousands, tens of thousands of designs being explored and executed overnight, fully automated by these AI models.
Have we seen any surprises come out of that process? Of course, the legendary Move 37. It seems like increasingly with the mythos class Fable models, we're starting to see myth 30 sevens might be strong, but they're definitely stepping outside.
I had a really interesting experience today where I or, like, 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 37 in that it was I've done 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? Yeah.
We have very similar analogies, and I think to be fair, it's one of the favorite part of the jump fuel. That's where it becomes very exciting. We have engineers that are using these workflows, right?
So using AI to explore and asking the AI to explore these 100 thousands of configurations overnight. 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 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. So I need to rethink my intuition because I didn't think it could be that good as a design. So you learn as well from these models, Again, because they explore this much richer space, go out of bone.
They go beyond their intuition, even out to what you are saying, I think. 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.
I didn't get there. How can I learn from it? It's because explored new physics or or new phenomenon that I was not aware of when I was only working with intuition, essentially.
So those are the Is it possible without even trying to reverse engineer the design itself from the AI, which Yeah. Learning in in practice.
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 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?
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 lie the livestream 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 kinda interesting.
you know, it it actually does work. Just try to help me calibrate on how big those surprises are. Yeah.
So you remain drowned in 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 corrupted 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.
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 blender, so you had to move 37. But it actually was not, and it let them rethink the way they were approaching the problem and their intuition around the point.
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, 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 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 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 gonna see people continue to run the neural concept copilot, the engineering copilot with anything less than a fable model? Or is it gonna be like, nah.
have no choice but to do that to stay competitive. Who do think we're gonna land in the short term on this willingness to pay question? It's a very good question, Eliane.
Considering the value is key. And I mean, it's a situation 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 out there.
There are similar specs. 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 with secure more contracts.
If today, let's assume you're building battery cold plates to cool batteries. If you can win one more program per year, that's millions, tens of millions of dollars. Right?
Just one more program on the the three fifty you're winning every year. Right? So that that's very tangible.
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've spent three months, you could allow yourself to spend three months working on the manufacturing process, tuning the details, 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 forty eight to twenty four months, that's for millions of developments, you're selling you're selling for every car. Right?
A car is typically a billion, right, to develop or from scratch. But if you can take even 20% of it, the math is pretty quick.
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 time scale that OpenAI is planning to have a very large chunk taken out of ML research itself in terms of automation. Mhmm. 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, you'd be looking at some core departments, core areas, crash safety, epidemics, 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. That can already lead you to 20%, 30%, 40% speed up on those flagship disciplines.
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 crush, between arrow, 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 really break down and reduce the cycles from 50%, 60%, and that's what we are seeing already.
Today, there is not an OEM that was done it at scale for entire car. But we are seeing this multidisciplinary automated AI driven workflows happening already for some specific disciplines, and we are seeing shift in person person's fear. 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 your 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 what artists are very hostile to the technology, certainly not all, but that's like a pretty common point of view. Mhmm. Especially if it's like, I love doing this.
You know? Why do we wanna 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 see both end of the spectrum.
Right? I do see that engineers are artists in a way. Right?
Yeah. They made their own 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's 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 these type of new workflows, they are sometimes a bit resistant as to, okay, what it actually bring? Right? And they also believe, rightfully 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.
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, ML researcher, are also methods team and machine learning teams within those organization at the forefront of these approaches. And those ones are the first one to adopt it. And those are the first ones that want to explore, want to try new models, tier models, and to benchmark them, experiment.
So we really have, in the same organization, both two extremes. 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 barrier and actually have those engineers hands on and working with the AI models, you see a top of different response. Because then they understand how powerful it can be and how much it can actually empower them to make their job even better, even funnier. 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 engineer. You want to try these things out.
You want test scenarios, test engines. Right? And that's what AI enables you to do today.
As 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 kinda like, you know, really, they look a lot alike.
Let's be honest with our with ourselves. You know, there's been 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 bit push 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 out of your home, it 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. However, before we get there, I do believe in that's the difference 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 workforce. And 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 vehicle less 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 a crazy flywheel going perhaps that that then again, like, leaks out into the rest of the broader industry.
how fast do you think that that kind of stuff could happen? 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 deal that's happening, they take the cheap part.
I pace and they make it. You're given another sand 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, I guess you know about this company on the West Coast. I think the first tagline is that it's not a car. It's a robot actually designed around you, if you recall correctly.
Because they built it fully autonomous minds and first, let's say, and it doesn't look like a car. Even if you think about it, it's it's it's very different from anything we've seen. So I think we the more that the technology becomes in nature, the bigger the ship gonna 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, 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 gonna need some oversight for this. But in terms of, you know, thinking kind of a first principles limit paradigm first, you know, you could have product strategies, you know, virtual market testing.
There's 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, getting into a fast loop that's fed by RL where you have at least a, you know, a sufficiently good, reward model to to steer it in the right direction. Mhmm.
And then that can kinda cascade all the way back perhaps, right, to 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 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, we didn't. I think the capabilities are there. The main question for big companies to implement that is infrastructure, governance mostly, and data, obviously, making 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 going to go even faster. I would say this from tier models that are being developed.
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 talk. Combination of tools that run-in physics. We talked about simulation solvers.
They will be there, and they will remain there. They will be the shield, essentially, for these models in engineering. Right?
But I don't see any fundamental reasons for it, but to happen quickly and actually pretty quickly.
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. How much do you think things like humanoids matter versus just kind of general intelligence that will, you know, also design the machine tools and, you know, kinda bring all the same you can kind of imagine the version that is, like, built on this if if humanoid robots are sort of the analogs of LLMs, 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 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.
will be now critical when we think about manufacturing, the manufacturing aspects and the plants. Now, do I believe those need to be humanoid? I think it's a different story.
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.
I think that's a different topic in itself. However, I think there are still some breakthroughs to be done for robots and humanoids to be actively useful at scale within plants. The first one are the AI models themselves.
Do believe that we need to make much more advancement into the AI models so that those robots can actively interact with the physical world and be efficient. We are far from being there right now in the industry. Then there is another question on the hardware side, About the autonomy of these robots, how do you make sure they don't overheat?
Building a hand, I don't know if you've done it, but there's a lot of research in universities on how do you actually build a hand that is as agile as what we have, that is strong to the brake, 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 of motion.
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 something that, you know, for a 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 to be able to kind of patch things? Yeah. Yeah.
I think I agree. This is definitely the the somewhat so called order.
And thank you, I would say. I think with the first law principle, we can solve a lot of these bottlenecks through a small general automation and intelligence upfront. Right?
The next frontier will be those robots in the plants. Right? But I think to your point, this is definitely.
The future of physical abundance, I think I'm feeling more than perhaps I ever have. I really appreciate you taking the time and and walking me through all this, and the the contribution 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 help me patch up before I let you get back to work today?
No. I think that's we covered a good list 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. Have engineers, you have products that are being designed AI first, and it's true. It's already there, and the acceleration is just starting.
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, they will be led, or they will have an element of AI driven workflows integrations in them.
Thomas von Schummer, co founder of Neural Concept. Thank you for being part of the cognitive revolution.
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