Thomas von Tschammer: evaluation

Transcript context

…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 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 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 want to push design, optimization, 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? means that when I'm saying that building a car is a multi-disciplinary and multi-optic optimization. I also mean that because you want to incorporate design rules, manufacturing constraints as early as possible into your design duration, right? Because that was the promise of additive manufacturing 10 years ago, right? That you could design freely, because then you could print anything, right? But then we quickly realized that this would not work out because it's expensive, it doesn't scale in production. So now let's say you have a manufacturing 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 good as possible. So that whenever the model evaluates evaluate, explores in the design, it explores it, no way 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 for manufacturing at reasonable costs. So starting the physics of some manufacturing and costs along the way that you want to bring earlier into the dream process. 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 of the latter probably, but I'm not really sure how to think about what is contributing to that huge advantage.…

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