Nathan Labenz: commitment

Transcript context

…Yeah. So I agree with you. I tend to think it's more than latter, right? That much more agile when it's about rolling out a new plant. Also the way the plant is being operated, right? It is highly, highly automated there. I mean, now you have engineering executives from Europe, from US. traveling to China to understand how they are setting up their plans, right, taking lessons from it, and then going back and applying those best practices in their country. This is happening already today, right? So the fact for sure. On the design side, I think the benefit that they have is that they can take more risks because, again, they don't have that legacy to work with, right? 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 are in the history of developments, and they have less processes that are deeply great, right? If you don't have processes that were built in the 2000s, it's much easier to work in a much more agile way today. Then we're going back to the digital native type of companies that are able to pick up new standards. We, 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 I'd move from your historical product line to something quite different? Seems like it would be a huge value on lock. Then you could also imagine going even more modalities, right, and trying to bring, I say this all the time, so I apologize to listeners, but you haven't heard me say, integration that we see on language and pixels coming out of the nano banana omni sort of line does really show to me that integrating quite different data modalities into the same model is a super powerful thing to do. And then there's another version of the foundation model, which is all these constraints generate me the design in the 1st place, you know, as opposed to the validation side, which I was speaking about before. Do you guys have kind of a How would you describe your strategy on that? How big of a deal is that? How soon do you think we will get there? I assume it's got to be inevitable on some level. What's your sort of strategy to bootstrap into those things? Yeah, So you're right, it's going to happen, right? It's moving extremely fast already. So we are going to have foundational model for aerodynamics. I think it's going to start with aerodynamics, right? This is the low hangings fruit today, because even the physics are complex, it is relatively similar. across components. It can be easily replicated. So it is going to happen very quickly, right? And of course, it's a very big difference, right? So we are doing actually research in that direction. However, we may not be the first ones to have that foundational model, right? But then, as we, and I'm going back to Jensen's final cache 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 100,000 people organization, right? 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 it.…

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