"The Cognitive Revolution" · 2026-07-01 · 1h 29m
Key moments - from our scoring
Substance score
58 / 100
Five dimensions, 20 points each
Neural Concept uses domain-specialist AI models to accelerate engineering design cycles in automotive manufacturing and beyond. Rather than replacing traditional physics-based simulations (FEA solvers, computational fluid dynamics), the company's models - trained on customer-specific simulation and test data - deliver results in minutes instead of days, enabling exploration of thousands of designs rather than dozens. Thomas von Tschammer explains how Jaguar Land Rover scaled from 50 aerodynamic designs evaluated daily to 1,500 using Neural Concept's platform, while battery suppliers reduced development cycles by 80%. The conversation covers the three engineering revolutions: manual prototyping, computer-aided design with numerical solvers, and now AI-accelerated iteration. Neural Concept is evolving toward agentic engineering workflows - AI copilots that call domain models as tools and directly manipulate CAD platforms to propose design changes. This hybrid approach (AI validation plus AI-driven design generation) mirrors patterns seen in protein folding and enables engineers to explore vastly larger design spaces while occasionally discovering non-obvious solutions. The company is also moving from per-customer fine-tuned models toward more general foundation models for engineering, with applications extending to Formula One (where compute budgets for optimization are strictly regulated) and thermal management, structural dynamics, and electromagnetics beyond aerodynamics.
Jaguar Land Rover increased from 50 designs evaluated per day using traditional numerical solvers to 1,500 designs per day using Neural Concept's AI models, a 30x improvement announced at Nvidia GTC in March.
Neural Concept covers aerodynamics, crash safety, thermal management (battery and engine cooling), electromagnetism (for electric motors), and structural dynamics, with models trained on both simulation data and real-world test measurements.
Neural Concept's models do not fully replace traditional solvers; instead, engineers use AI models early in development to explore a much larger design space, then narrow candidates for validation with traditional simulations and prototypes at more mature stages.
Models are trained on customer-specific simulation and test data, then fine-tuned with company data because each OEM has unique know-how, best practices, and requirements; the models continuously retrain as new data is generated, capturing evolving knowledge.
Beyond speed gains, customers report battery cold plates cooling 20% better and 15% lighter, aerodynamic improvements of 2-5%, and development cycles reduced by 80% for some suppliers.
Our reviewer’s read on each dimension, with quotes from the episode.
The episode delivers several genuinely useful datapoints on how AI compresses engineering design cycles, including the workflow mechanics and the F1 compute-cap governance angle, but the signal is diluted by the host's lengthy, meandering preambles and repeated software/protein-folding analogies that consume significant runtime without adding new substance.
from 50 designs evaluated per day to 1,500 every single day in production
these models are always evolving over time in the sense that every time you feed them your data out, they are being retrained and improved so that they can cover a, uh, broader and broader space and become more and more accurate
The core thesis - AI compresses simulation time by orders of magnitude, enabling exponentially more design iterations - is a well-worn pattern in AI podcasting; the guest applies it competently to engineering but adds little truly counterintuitive framing. The most genuinely fresh angle is the Formula One CPU-hour handicapping system as a proto-model for AI compute governance, but even that is not developed into a strong original argument.
depending on your ranking from the previous year, you don't get the same numbers always for the next season. The idea is that you want to try and make it more equal across teams
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
Thomas von Tschammer is a genuine practitioner - co-founder actively deploying these systems at named enterprise clients like JLR and Formula One teams - which distinguishes him from thought-leader guests; however, he is a startup founder/BD figure rather than a chief engineer or simulation scientist, and several answers stay at a strategic level rather than revealing deep technical specifics.
They published their work with us uh, at the latest Nvidia GTC conference that was back in March this year
We started in 2019 by building the first AI model architecture based originally on computer vision that could directly ingest geometries and learn from physics
The episode is anchored by a handful of concrete, named metrics - JLR's 50-to-1,500 designs per day, the 48-60 versus 18-24 month OEM development cycle gap, 80% cycle reduction for battery cold-plate suppliers - and specific named entities (JLR, F1 teams, Nvidia GTC); however, many other claims (2-3-5% aero gains, year-1 and year-2 speedup projections) are stated without sourcing, and supplier examples remain anonymous.
they went from 50 designs evaluated per day to 1,500 every single day in production
Western Europe or US OEM, it takes them between 48 to 60 months for a new car developed... in China it's 18 to 24 months
The host is well-prepared and occasionally lands a genuine push - asking 'Are you sure?' on the question of whether AI will replace engineers and demanding the guest defend the ceiling - but most questions are long, answer-telegraphing monologues that let the guest coast, and there is minimal follow-up pressure when the guest retreats to safe generalities about 'trade-offs' and 'domain experts in the loop.'
Are you sure? What does a Bake off look like there?
If that's crazy, why do you think that's crazy? What is the part that we're so far off on?
Computed from the transcript - who did the talking, and the words that came up most.
Thomas von Tschammer, co-founder and Managing Director US of Neural Concept, argues that physics-aware AI is driving a third revolution in engineering physical products. Neural Concept’s models learn from simulation and test data to evaluate 3D designs in minutes, helping Jaguar Land Rover move from about 50 external-aerodynamics evaluations per day to 1,500 and enabling battery cool-plate suppliers to cut development cycles while improving performance. The episode explains why AI is not replacing numerical simulation, but shifting it later in the process while expanding early design exploration across automotive, Formula 1, and manufacturing workflows. The stakes are competitive: companies that make engineering iterations AI-led can compress development cycles, while legacy OEMs risk falling further behind faster-moving Chinese and digital-native hardware competitors. For full show notes, links, and references, read the episode page: Mercury: Command is Mercury’s new conversational interface, giving you natural-language access to your finances and helping you take actions within your existing permissions and approval policies. Visit to learn more and apply online in minutes.
Transcribed and scored by The B2B Podcast Index.
Speaker A: Hello and welcome back to the Cognitive Revolution. Today my guest is Thomas von Chalmer, 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 um, Take youe 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 CORECAD platforms to make design changes as required. This TikTok combination of agentic optimization and domain specific validation and 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.
Speaker B: What once could only be done manually
Speaker A: 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 enjoyed this preview of the AI powered future of engineering with Thomas von Chalmer of Neural Concept the Cognitive Revolution is brought to you by Mercury, the fintech that more than 300,000ambitious companies and individuals trust to run their finances. I've wired AI into nearly every corner of my life. My email, my messages, my calendar. I even gave Mercury virtual cards to my agents with low limits and category and merchant restrictions for their autonomous use. But still my AI's access to my financial data has remained limited. With a normal bank, I might export a bunch of statements and have my assistant process them for me, but for real time, up to date information, and certainly for taking any action, trying to get your agent to use the bank via the browser is just too hard, too slow and too error prone to be worth it. And that's why Mercury's new conversational interface command is such a big deal. It's built directly into Mercury, which means you get natural language access to your finances without exposing anything outside of your bank account. No exports, no spreadsheets, no pasting your transactions into third party tools. I really think a lot of people are going to prefer it this way and it can already help you take actions too, with everything bound by the permissions and approval policies that you've already
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Speaker B: And now on with the show. Thomas Von Schammer, Co Founder and Managing Director of the United States at Neural Concept welcome to the Cognitive Revolution.
Speaker C: Thanks very much. Thank you for hosting me.
Speaker B: 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
Speaker A: bit of a miss.
Speaker B: Um, 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. Um, my dad actually, fun fact, worked at gm. He started back when the drafting was still done on pencil, paper, big tables with slide rules and stuff like that. And then he moved into the CAD era and now he's retired and so he's not going to be working through the AI assisted, increasingly AI automated era. But I still am, um, very excited to kind of pick up where I left off the thread with him on take your kid to work day years ago and fast forward to where we are now with that in mind. Folks who listen to this feed are like very into AI. Um, and there's certain concepts that you don't need to introduce, but I think we probably can't assume common knowledge in terms of what does the life of an engineer, what is the life of a neural concept user look like if you go watch over their shoulder and see them at work for a little representative sampling of their working life, Maybe you could give us a bit of a sense of, in brief, obviously, how do things get designed? Who's doing that? 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.
Speaker C: I can start with the example of the automotive industry. Right. You should give you as a good example, Nathan. So we've been designing cars essentially for the same way for the past four years, right. Forty years ago, before can, which is computer aesthetic 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, or 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 30, 40 years ago, let's say 40 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 of that car crashing against the wall directly on the computer. Right. We use for that software that we call FEA software, finite element analysis software that would predict, simulate the effect on the crash. Since now you don't have to build as many prototypes, you could go to maybe 50100 of different designs of car per year, which is substantial. Right? Substantial split. 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 questions of physics on the computer. You need very large clusters, typically, and then you need to wait as an engineer 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, um, compute that we have so that we could spin that up. But essentially for the past 30 years we've been using the same CAD tool and we've been using the same sea numerical simulation solvers. And that's true for crash, but that's the same 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 third revolution. So from prototype to can and numerical solvers to AI, where thanks to AI, you don't get results in days, but you get them in minutes. And if you can get results in minutes, it means that you don't explore 50 designs a year, maybe a hundred designs a year, but now thousands on different coffees. 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 aliens.
Speaker B: 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.
Speaker C: Right.
Speaker B: We've had a similarly hard time in the past either doing crystallography to eventually get to a protein structure, or doing really, 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 to which they apply? What are the unlocks associated with them. And what has neural concepts role been in um, building these models?
Speaker C: 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, ah, GM or another OEM is simulating the entire car when developing it. Which means that every single component or subassembly within the car is being simulated, is being evaluated and is being iterated on. Right? So that means that we are, as an engineer, we, we are evaluating many different physics on the car. Aerodynamics is one specifically critical for EVs, right on the range, you want to improve the range on your next ev. Crash safety for pedestrians and passengers is another big one. Then we also have thermal management when we want to cool the batteries, cool the engine, but also for ventilation systems inside the car. Electromagnetism, um, we, when we want to build an acceleration of electric motors, there is a big 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 listing the main categories, of course, as you can imagine then this is being divided, uh, into component sub assemblies. Ultimately with a component gm, you have thousands and thousands of engineers that are domain experts on these physics on these components so that they can iterate and improve, uh, each of these, uh, specific estimates. So those are the big domains essentially.
Speaker B: 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 human 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, uh, 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 going to happen, kind of inputs and outputs look like to those models, are they trained on simulation data also? That's another thing that I've noticed is a real pattern.
Speaker C: Yeah. So to the last question, These models, these AMLs, can be trained both on simulation data, but also on test data.
Speaker D: Right.
Speaker C: Because today, even today, there are some phenomenon that we are not able to simulate very accurately with traditional sources. In that case, what we can do is that if we cannot simulate Them, we can measure them in wind tunnel or in test labs.
Speaker D: Right.
Speaker C: And we can gather that data and then train the corresponding AMLs. So this is also a part of the hybrid training, essentially where you combine numerical simulation that can be lower for the AC 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 Mercury simulation the same way that we never fully replaced prototypes. We are still doing prototypes today in the industry. Right. But we are doing much less prototypes and much later in the process. Right. It is going to be the same and it's the same fundamental simulation. We're not m going to fully replace numerical simulations, but we're going to make a much smarter usage of it for the most mature stages of development and it's going to be exactly the same with AI. AI earlier on in the development process will enable you as an engineer to explore a much richer space to explore the right candidates and then narrow down the one that you actually want to simulate to vanity before going to prototypes.
Speaker D: Right.
Speaker C: What we're saying 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.
Speaker D: Right.
Speaker C: 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 elite 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.
Speaker B: So 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, 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 going to end up. We can refine dramatically faster, uh, because we're able to train these models on all this existing data and then feel like this is sort of the epicycle development. It's like we're really dialing things in at the end. Um, okay, Exactly.
Speaker C: 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 are trained from existing data, um, 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 what 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 out, they are being retrained and improved so that they can cover a, uh, 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. Hey.
Speaker B: We'll continue our interview in a moment after a word from M, our sponsors.
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Speaker B: 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 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 kind of manipulating points in the point cloud. And it was that human intelligence that would say, okay, here's the test result I just got. Here's the spot that the way in which we're having some aerodynamic problems. Let me go back in there, tweak the design a little bit, sand that rough edge down a little bit. Now of course I've got these other constraints too that I got to keep in mind. So I've developed a certain intuition for how I can make these changes without breaking other constraints that are really binding on me. As I develop this system now, I'll do those changes and how manual those are, uh, 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 want to 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 not too distant future?
Speaker C: 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 automotive OEM out there based in the uk. They published their work with us uh, 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 crypto numerical solvers and even in your design they were going through this very expensive solver with 10 Kotume and they had ah, 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 aerodynamicis. 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 with AI. And that was what the public key 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 uh, designing battery cold plates to cool the battery that were able to reduce by 80% their development cycles essentially becoming 80% faster to develop a new battery cold plates. And on top of these smaller design cycles 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. Um, the battery, it's 15% liner. If you think about aerodynamics company, um, we also help engineers to find designs that are 2, 3, 5% more aerodynamic, which is game changer for them as you can. Yeah.
Speaker B: So let's uh, dig into that last point. How is that happening again? To map this onto a space I've studied in a little, in a little bit more depth, there's the protein generation models now, right. Which allow us to go beyond a biologist's ability to say, oh, there's a couple 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 uh, 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 in those relative paradigms? Do you are. How are they starting to converge?
Speaker C: Yes. Today what we're seeing is that this AI driven engine workflows are um, 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 a 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 optics car.
Speaker A: Right.
Speaker C: We are rather in workflows where AI ingests the spec sheets, understand the requirements, will set up the base model, but this will be done in interaction with the engineer that will validate steps along the way and will also guide the model. Why is that? It's because EGM 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 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 Dan, where in the past you were relying on intuition, hey, my Dan doesn't work, but I think if I took it that way manually, it will 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 eventually. 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 uh, interact with CAD to send your geometries, is able to interact with numerical simulation solvers, right? Hey, I want to validate this design I came up with and sends that directly to the high C simulation solvers and all of that becomes automated.
Speaker B: So what models are you serving as the co pilots for engineers? Today we're talking on um, Fable plus one and we're seeing all these incredible projects that people are just vibe coding their way to where 3D landscapes that are really becoming like extremely elaborate, the models are just kicking out. So it sure seems like there's been some important thresholds crossed in terms of the model's ability to reason physically and use physics, 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 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? Uh, how much are we going to be relying on a few frontier models to assist our engineers? How much do you think you're going to start to see this loop get closed and create other like specialist models that play that role instead.
Speaker C: Yeah, yeah, great question. So if you think about what I think. Yeah. Jensen mentioned that during later cheating he seemed to the AI as a five layer cake from the foundations to the top. And the last layer at, ah, 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. So essentially taking the Jerry Nitos cloud models 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. 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. Uh, they can work with 3D geometries, they can handwrite linear geometries specific set of skills so that they have the context about what does mill means to generate good 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 plain nan, you will not be able to go very far because they have no very accurate finish with zoning. 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 external aerodynamics. So there is a big gap to bridge here and that's what we're doing the company normal.
Speaker B: 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.
Speaker C: Exactly. Equipped with the right tools. I think is the important parts where these models are uh, able again to interact with CAD using method simulation solvers, but with also other class of models.
Speaker D: Right.
Speaker C: If we take a step back and we look at our history of the reference 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 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, the formation, 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
Speaker B: be helpful to do a little kind of comparison to software, I think. Audience is super diverse. But one common profile is the sort of AI engineer software, uh, 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 fied. 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, 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 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.
Speaker C: Good question again, unfortunately, if you are an automotive supervision OEM today, it's more than that, right? Specific rsq, as they call it, request for quotation and specification. And they is still not fully streamlined or not fully automated, not fully defined. There are standards that OEMs are trying to impose and set 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 then translate that into 3D. This is exactly what's happening today, right? Already. However, why it is not yet as black boxy as it can be for software engineer, it's because the dimension of the problem is much, much broader, much richer. Right. You have many more trade offs you need to make and there's never one way to get to the solution. Right. Which is why it's not going to replace engineers, uh, anytime soon. But it's going to empower them to be faster. Actually it's going to 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 being eliminated as we speak. Right. But it's never going to replace the engineer taking those design decisions. Even the demonstrator.
Speaker B: Are you sure? 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 3D 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? What is the part that we're so far off on?
Speaker C: I don't know. So you have a good point. All of these tasks today, I strongly believe that with the models and communities that we have can be automated instrument. These models have capabilities to translate any specification into a design and then emulate that design automatically using nical 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. Yes. This is being done today for specific products. Right. Battery core plates, emotor, aerodynamics, crash. Right. Where I don't believe 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. And you are saying we want engineers to hold on to that. I believe that we always want to have engineers all done to the final trade off and the final decisions. Right. Because that's where you need domestic parties deeply ingrained. Right. And that's also where the differentiators are happening between you and your competitor. Right. How when the differentiation will happen tomorrow between OEM 1 and OEM 2, it's going to be the ones that are able to deeply ingrain their engine IP into this AI morphos. That's going to be key. That's what companies are realizing.
Speaker B: Yeah. That's fascinating. What do you think the odds are that we get like. I guess let me 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 you've been on that historically. But then as we go forward, do you think you actually can sustain a seat based model or is the model going to have to move in another direction?
Speaker C: Uh, things essentially will have to be based value. Right. What's the value we're providing. Right. And that's the price, essentially the price for a value ratio. Right. The question is how do you monitor value?
Speaker D: Hm.
Speaker C: And I think it depends on the industry and the applications. But ultimately we're going to move to a world where pricing is going to be based, pure value you're able to deliver for companies. And I think that's actually very healthy. 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 going to be the same for every company.
Speaker B: How much disruption do you expect to see in these like 100-year-old uh, industries like auto, where obviously companies have changed a lot. But it's, it's largely companies that were formed 80, 100 years ago that have consolidated and there's certainly been only the strong survive dynamic. But not uh, too many new entrants recently. Right.
Speaker C: Like a couple.
Speaker B: 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 and pace with which they are able to convert feedback from a tax professional on something the AI did wrong into an improved state 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?
Speaker C: For sure. I think it's going to be a massive disruption in the markets and I think it's going to create exponential gaps between the companies that are able to adopt this AI driven Egyptian workflows and the ones that are not. Right. And the gap is going to widen essentially over the past the next few years for sure. Right. So there's going to be a lot of disruption. What is complicated for companies, if you think about traditional oem is that they've been building cars essentially in the same way for the past 20 years, same teams, same tools, same know how. Right. 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 the whole company. Right. 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 practices 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 and to come fast. Right. We talk a lot about these digital native companies. Right. And we work with many of those outside automotive.
Speaker D: Right.
Speaker C: That are the electronic products, consumer electronic products. Right. So those companies have been building hardware for the past 10 years max, maximum. Right. And you feel that they're 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 development workflows across many applications. Right. And we think the slower pace in the larger older companies, engineering companies out
Speaker B: 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. And I'm talking That's like a 50 years ago phenomenon. They've come a long way, but they're now going to be faced, I think with their most profound challenge ever. And competing with like Japanese companies in the 70s and 80s is potentially going to be easy mode compared to competing with AI native companies if things go a certain way. So it's going to be really interesting to watch the organizational dynamics and challenges of that.
Speaker C: When you think about it, some numbers today, Western Europe or US OEM, it takes them between 48 to 60 months for a new car developed from the moment they want to launch it to the actual moment it hates the plant. And it's being manufactured right in, In China it's 18 to 24 months. Everything those companies in US and Europe care about is how do we get from 16 to 24 months. Right. Because that's what's going to create the next competitive advantage.
Speaker B: 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.
Speaker C: M
Speaker B: 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 want to push design optimization and when we need to meet manufacturing a little bit more in the middle?
Speaker C: 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 multidisciplinary and multi opportunity optimization, I also mean that because you want to incorporate design rules, manufacturing constraints as early as possible into your design terrorist. Because that was the promise of additive manufacturing 15 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 plant 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. Right. So that whenever the model evaluates, explores in the design, it explores it, knowing that it can be manufacturing. 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 reasonable costs. So something the physics also manufacturing and costs along, um, the way that you want to bring earlier into the deal.
Speaker B: 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.
Speaker C: Yeah, yeah. So I agree with. You tend to think it's more than ladder, right. That 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 plans. Right. Taking lessons from it and then going back into 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 inertia 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. Then, um, we're going back to the digital native type of companies that are able to pick up new standards much higher.
Speaker B: We, uh, the quote unquote west have our work cut out for us. It sounds like. How big of a deal is it going to be for neural concept to move from the one model per company based on their data paradigm to a more foundation model type paradigm? Foundation models obviously could be like with varying breadth. Right? But simply going from one aerodynamic model per company to a general purpose aerodynamic model, that would allow you to be like, what if we did a cybertruck? And uh, I'd 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. Um, 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?
Speaker C: 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.
Speaker D: Right.
Speaker C: This is the Lorenz fruit today. Because even the physicstar complex 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. Right. So we are doing active research in that direction. However, we might not be the first ones to have that foundational model. Right. But then going back to Jensen's final case of AI, our role and also our focus is to make sure that whatever the foundational model is, we provide the right domain specific capabilities that it can be fully integrated into a complex engineering environment in a hundred thousand people organization 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 capabilities arrive, everyone will be ready for it. And to leverage today, one thing I
Speaker B: learned in researching neural concept, I thought was super interesting is that you guys are serving in addition to a bunch of enterprise customers, a number of Formula one teams. This is, I've honestly never really been super into motorsports or engineering sports, don't know a ton about it. But it does strike me that in the run up to the countries of geniuses in a data center, we really do stand to learn a lot from highly competitive and performance oriented organizations like Formula one teams that are under just this incredible, uh, pressure to turn things around quickly. Right. So how does that look like right now? And another interesting detail was that apparently Formula uh 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?
Speaker C: Yeah, I mean that's super interesting. Right? So indeed today Formula one teams are capped in, to be more specific, the CPU hours that they can run. So CPU hours is the compute to run external aerodynamic simulations. Right. And what's even more interesting is that depending on your ranking from the previous year, you don't get the same numbers always for the next season. The idea is that you want to try and make it more equal across teams.
Speaker D: Right.
Speaker C: And you don't want to make it a 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.
Speaker B: So they handicap essentially if you win, you get less, you get less confused. Schedule.
Speaker A: Yeah.
Speaker B: Because the NFL does Uh, 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 to try to get better draft picks. But I'd never heard of this level of actually changing not just the talent acquisition process or who the opponents are going to be, but actually changing the fundamental rules of the game itself in terms of how you are allowed to prepare from one race to the next. That is really interesting.
Speaker C: Now they can directly tie compute stimulations 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, which is the story 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 Hubble between different teams. M actually that's super interesting.
Speaker B: So what are we seeing in terms of their cultures, their practices, their that you think will diffuse into broader engineering, ultimately manufacturing culture.
Speaker C: 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.
Speaker D: Right.
Speaker C: They are improving it week over week. So that means that they have to reach an extreme level of automation of design, iteration, processes, uh, and speed. 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 detailed decisions. Right. It's a good way for us essentially to proof tests and to stress tests, um, our models, our ah, workflows, our platform. Right? Because if it works for an F1 team, we can build it for an F1 team. We can believe that then it can work for the more traditional OEMs out there, essentially. So that's really the way we're seeing it. And we are asking those teams really push the limits of the models on 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.
Speaker B: So maybe again we can make a little bit of analogy to software where we have the token maxers who are trying to really push the limits on what their agents can do for them and increasingly writing code anymore. 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 cold the old fashioned way, or we're like doing a little autocomplete or whatever that's useful and giving a little speed up, but it's still an assist in the old paradigm versus a genuinely new higher level of abstraction as the base place where a human spends their time operates. Could you paint a little bit of a picture for analog in engineering? What does the F1 person do when they are token maxing? What kind of, what are these moments of like key decision or sort of judgment on particular trade offs that actually rise to their level? What does that look like? Uh, and I think we kind of know what the old school one looks like.
Speaker D: So.
Speaker B: Yeah. What does the token maxing, uh, F1 engineer's life look like today?
Speaker C: 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 they need to improve the car. Hey, we need to make the car better in straight lines for next rail stick. We have many more freight times and we're not going to have to overtake much more. Right. Um, so that's the baseline. Then they translate that today into engineering requirements in terms of how the car improve and the aerodynamics of the car. And then they are running these AI driven workflows so that are taking those aerodynamic requirements that have information, awareness about the geometry, about the 3D and then overnight the AI driven workflow will generate hundred 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. It sees the thousands of points of data points on a dashboard and he can look at the different trade offs, look at the cross planning design and pick the one 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 evicted overnight, fully automated by these AI models.
Speaker B: Have we seen any surprises come out of that process? Of course, the legendary move 37 um, seems like increasingly with the Mythos class Fable models we're starting to see move 37s might be strong, but they're definitely stepping outside. I had a really interesting experience today where I or yesterday where I ran a skill that's a very familiar workflow, um, and Fable stepped outside of the workflow and proactively asked me a bunch of questions, actually presented a webpage to me to collect information not Instructed to do that. Never been part of the process before. OPUS never did anything like that. But it took it upon itself to say, I've got all these inputs, but I think I could use some more inputs to really do a great job. And here's what I need from you, the human, to really knock this out of the park. That was like a little mini move 37 in that. It was. I've probably done this workflow 50 times over the last few months and nothing like that had ever happened. What's the sort of most move 37 like thing that we're seeing in engineering?
Speaker C: Yeah, we have very similar analogies and I think to be fair, it's one of the favorite part of the job. 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 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, very impressive. The AI model came up with a design that I would have uh, never thought would be good. If you had shown me this design like this, I would have said, hey, scrap this, this is not going to work. But actually those designs are better than anything we could come up with. And now I need to get back to the dashboard to understand why it isn't so much better. Right. 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 much richer space. They go out of bound, they go beyond your intuition, similar to what you are 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 explored new physics or new phenomenon? What I was not aware of when I was only working with intuition, essentially. So is it possible that even trying to reverse engineer the design themselves from the AI, which yeah, learning,
Speaker B: learning from the AI's advances, uh, 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 might be a little bit hard for somebody not in the domain to, to really grock it, but I'd love to try, you know, to get a little bit better sense on kind of, uh, 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 37 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. Um, 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 actually does work. Just try to help me calibrate on how big those surprises are. Yeah.
Speaker C: So you remain drawn in by physics. Right. So you will not reinvent the physics, that's for sure. So you will not get completely insane design breaking the physics. Because everything is crowded by physics. However, we've seen scenarios where the engineer is telling us, hey, there's no way in the world that I would have done that design, because I don't think. Didn't think it could work. Right. So now that I know it works, I need to go back to the dashboard and understand why it does. Right. We have the physics as a baseline. We cannot break the physics. It's going to be there. But we've seen occasions, scenarios where the engineers thought it was a mistake, thought it was a blender. Right. So you had to move 37, but it actually was not. And it led them to rethink the way they were approaching the problem and their intuition around the problem.
Speaker B: Maybe another way to think about this is how much is it worth? You talked about, like, value pricing earlier, and there's so much discourse right now around, are people going to be willing to pay for Mythos class models? Notably, the price originally previewed with Mythos has already come down a lot with, 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 37 light 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 going to see people continue to run the neural concept co pilot, the engineering co pilot with anything less than a fable model, or is it going to be like, nah, you got to 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. What do you think we're going to land in the short term on this willingness to pay questions?
Speaker C: It's a very good question. Again, quantifying the value is key. And I mean it's a decision we always have also with the companies we work with. Right. Typically when you think about design breakthrough, right. So much better performances than what you could get before we have discussion with suppliers that are selling to the big OEMs, the big car OEMs out there, they are similar space. 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 wins and with secure more contracts. If today let's assume you're building battery cool plates to cool batteries. If you can win one more program per year, that's millions, tens of millions of dollars. Just one more program of the 350 you're winning every year. So that's very tangible dollar values. 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. And this has indirect dollar value as well. If your part is 10% cheaper than competition because you spent three months, you could allow yourself to spend three months working on the manufacturing process, tune in the details. Then you win these 1, 2, 3 more programs that are then hundreds of millions of dollars. And then if you go on the OEM side, if you can shrink down your development times from 48 to 24 months, that's also millions of developments you're, uh, selling for every car. A car is typically a billion, right. To develop or from scratch. But if you can even m20% of it, the math is pretty quick.
Speaker B: Yeah, a lot of opportunity for savings in there.
Speaker C: What do you think?
Speaker B: American car companies, to take one very salient example, or 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, uh, 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 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 want to be meaningfully, you know, durably competitive in the AI era.
Speaker C: So I think there are different scales of course to this. In year one you'd be looking at uh, some core departments, core areas, crash safety, dynamics and power chain, 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 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's for year one, then year two and beyond. What you'll be looking at is that orchestration across disciplines, right? So you break the silos between crash between aero, between thermo, so that the agent can um, not only orchestrate the aerodynamic optimization, but can orchestrate it while taking into consideration the safety aspects, the manufacturing aspects, as we talked before.
Speaker D: Right.
Speaker C: Automatic design constraints. Right. And break those time offs 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 to 50, 60%. And that's what we are seeing already, right. Today there is not an OEM who has done it at scale for the entire car. Right. But we are seeing this multidisciplinary automated AI driven workflows happening already for some specific disciplines. And we are seeing 50%, 60% speed. That's massive.
Speaker B: Yeah, interesting. How do people respond to it? Uh, my sense is that you probably won't have a hard time convincing CEOs of these companies that this is really important. And they can look again at the China iteration speed. They can look at Tesla's ability to update its manufacturing processes much more dynamically than they're accustomed to doing. And I think increasingly they're going to feel the heat. Now another question is translating that through a legacy organization where probably a lot of people have a lot of different feelings about this, how they want it to go or if they want it to happen at all. Um, many of which very understandable feeling feelings, by the way. I don't mean to dismiss those feelings, but they're, 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?
Speaker C: Yeah, 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 the intuition on how to build a car and they really enjoy that aspect, right. Of manual iterations, leveraging intuition, thinking about the physics of the problem.
Speaker A: Right.
Speaker C: And there are many engineers. That's not easy.
Speaker D: Right.
Speaker C: They've been doing it the same way for the past 30 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 would it actually bring. Right. And they also believe, rightly 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 this human aspect as well. But we also see all the way on the end of the spectrum ML research here. There are also methods, team and machine learning teams within those organizations that are at the forefront in Zoe proofs. 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, anti air models and to benchmark them, experiment. So we really have in the same organization, both two extremes, right. How do you bring them together? That's also part of the complexity of those very large organizations. But thing we've seen a lot though is that once you manage to break that High load that barrier and actually have those engineers hands on and working with the AI models, you see a ton 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. 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 or 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 those things out your own test scenarios, test engines, Right? And that's what AI enables you to do today.
Speaker B: 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, uh, 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 ourselves. You know, there's been, um, there's been tremendous convergence. Do you think we're going to see a. Or is this maybe even a way to think about success criteria for AI at a societal level? I feel like almost a new trend toward more meaningful differentiation.
Speaker C: Yes. So I think what happened, and I think this trend is going to keep happening in the automotive industry. It's also largely due to autonomous driving, this big push towards autonomous car. Because essentially once you've sold autonomous driving, car becomes a commodity, right. You don't need to own a car anymore. Right. If you can just drive to your home, drops you anywhere you want and you don't need drivers. Right. Car becoming a commodity means that ultimately cars would tend to look more the same by definition, by pure definition. However, before we get there, I do believe in that statement I was saying before that the winning companies are the ones that are going to be able to code the best practice brand within these AI workflows. 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 massively different differences.
Speaker B: People might be surprised by how, uh, 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 uh, 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 passengers in a, uh, you know, it wouldn't take much to perhaps create some slightly different regulations for those type of devices. And next thing you know you could really get a crazy flywheel going perhaps that, that then again like leaks out into the rest of um, the broader industry. Uh, how, like how fast do you think that that kind of stuff could happen?
Speaker C: 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 are not meant autonomous. Right. And because you need to drive them then they look like the ones we used to. If you look at way more right, this is exactly what's happening. They take the garb I pace and they make it autonomous. Um, if you give it on the side cam, 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 counter example, Tara builds autonomous first vehicles. If you think about Dukes, right. I guess you know about this company on the west coast. I think the first tagline is that it's not a car, right. It's a robotaxi 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 if you think about it. It's very different from anything we've seen. So I think the more that topology becomes major, the bigger the shift going to be to new type of concepts as well.
Speaker B: How far does this go? Is there any limit to it? I kind of imagine everything is an RL loop, um, as kind of the end state. You can imagine putting agents into every seat in the company. And um, again we've, you know, keep in mind we're going to need some oversight for this but in terms of you know, thinking kind of a first principles limit, um, paradigm first, you know, you could have product strategies, you know, virtual market testing. There's um, there's increasingly models that in the same, you know, general spirit as you have models that will validate the aerodynamics of your design, you can have models or scaffolds around foundation models that can sort of act as like virtual customer panels. So you could imagine really from even the highest level, um, getting into a fast loop that's fed by rl, where you have at least a sufficiently good um, reward model 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 not too distant future where I can sort of speak a perhaps rather complicated mechanical electromechanical product, uh, 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?
Speaker C: No, no, I uh, didn't. I think the capabilities are there. The main question for big companies to implement that is infrastructure governance mostly.
Speaker D: Right.
Speaker C: 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 deposit. I strongly believe so. And it's going to go even faster with the latest frontier models that are being developed. Right. Um, I don't see any reasons for it not to happen again. We won't break the physics. The physics will be there. That's why we need to have those foundation of tools that ground in physics. We talked about chemical signature solvers. They will be there and they will remain there. There will be the fuel essentially for these models in engineering. Right. But I don't see any fundamental reasons for it but to happen quickly.
Speaker B: And actually do you think humanoid robotics or robotics more generally, um, 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 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, kind of bring all the same. You can kind of imagine the version that is like built on this. If humanoid robots are sort of the analogs of LLMs, you can imagine, um, that's one kind of way that we get crazy responsiveness from manufacturing. But maybe another is just that again the reasoning models bring all these Same engineering paradigms to the machine tools themselves. And that is enough to kind of speed things up.
Speaker C: No. So I do believe that robots will be now critical when we think about manufacturing, the manufacturing aspects and the plants right now, do I believe those need to be humanoid? I think it's a different story. Right. I think we as human we are optimized to do a lot of things. Are we optimized to be in the plant? I'm not sure we have the right form factor and 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. Right. I do believe that we need to much more advancements into the AI 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 universities on how do you actually build a hand that is as agile as what we have? Uh, that is great, that is durable. You need a lot of breakthrough. Also on the hardware side, we want them to be efficient at scale. It's going to happen. But I think that there is uh, some work to be done uh, in that area.
Speaker B: 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, something that, you know, for a human mechanic that wouldn't be so difficult. Obviously robots really can't do that very well yet. Do we have to solve that part or can we through sufficient intelligence kind um, 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?
Speaker C: Yeah, yeah, I think I agree. This is definitely the somewhat second order value I would say. I think with the first principle we can solve a lot of these bottlenecks through at least more general automation and intelligence upfront. Right. Um, the next frontier will be those robots in the plants. Right. But I think to your point, this
Speaker B: is definitely so the future of physical uh, abundance, I think, um, I'm feeling more than perhaps I ever have. I mean, I really appreciate you taking the time and walking me through all this, and, uh, the contribution that you guys are making at Neural Concept is definitely a fascinating one. Anything else that we haven't talked about that you think, you know, I should have asked about or, you know, what, 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?
Speaker C: No, I think that's, um. We covered a good topic, 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 how it's actually already happening in the engineering industry today, right? You have engineers, you have product that are being design AI first. And it's true, it's already there, right? And the acceleration is just starting, right? So look, uh, 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, uh, they will have an element of AI driven workflows, iterations in them.
Speaker B: M. Thomas Von Chalmer co founder of Neural Concept thank you for being part of the cognitive revolution.
Speaker D: Hey, hey, hey, hey Midnight in the middle Got the mind of his own I'm making little babies in the middle when the bone been drawing the same line since the world was a stone Now I'm letting go the wheel Let the wild thing wrong Faster than the people Finer than the thread it's running down the shape 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 don't you trust the hand don't trust the eye Trust the thousand Burning through the night Let it run wild Let it run wild Let it run wild now Let it run wild Let it run wild Let it run wild now Let it run wild Let it run wild Let it run wild now Drop the map lose the wheel wheel Let it show me how uh Let it show me how. Arguing the law but the law's got the loophole and the loopholes widening raw 100,000 corners that a hand never saw uh slipping through them maybe like a saint to go wild like a K in the cradle like I called it up all now we're screaming down the straight away and breaking up the the ball Colder than a genius Stranger than a dream Cleaner than a bread I never could have scheme don't trust the hand don't trust the hand don't trust the eye Trust the thousand burning through the night Let it run wild, let it run wild Let it run wild now Let it run wild Let it run wild Let it run wild now Let it run oh wow Let it run wow Let it run wow now Drop the map, lose the wheel Let it show me how uh m
Speaker A: We
Speaker D: don't break the physics we just learn to fly closer to a perfect we'll never quite describe Let it go, let it go Let it climb well and the static is one in a um billion desire Pick the freak Pick the flaw Pick the beautiful mistake Reverse it rewrite it Learn the rules they had to break don't dream like I dreaming that's the gift that they gave or why they kind of wondering the code that kind of break te old map up throw it in the lake the future's got a thousand other roads to take. Let it roll wild let it run wild Woo. Let it run wild now Let it run wild Let it run wild Let it run wild now Let it run wild Let it run wild let it run wild now Drop the map, lose the wheel Let it show me how. Let it run, let it run Let it run now.
Speaker A: If you're finding value in the show, we'd appreciate it if you'd take a moment to share it with friends, post online, write a review on Apple Podcasts or Spotify, or just leave us a comment on YouTube. Of course, we always welcome your feedback, guest and topic suggestions and sponsorship inquiries either via our website Cognitiverevolution AI or by DMing me on your favorite social network. The Cognitive Revolution is part of the Turpentine Network, a, uh, network of podcasts which is now part of a 16Z where experts talk technology, business, economics, geopolitics, culture and more. We're produced by AI Podcasting. If you're looking for podcast production help for everything from the moment you stop recording to the moment your audience starts listening, check them out and see my endorsement@aipodcast.ing M. And thank you to everyone who listens for being part of the Cognitive Revolution.
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