Nathan Labenz: observation

Transcript context

…So I think there are different scales, of course, to this. In year one, You'd be looking at some core departments, core areas, crash safety, aerodynamics, and powertrain, let's say. And you'd want to make sure that every single iteration is being AI-led. AI-led, I mean, there is an AI workflow that can orchestrate 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 is for year one. Then year 2 and year. What you'd be looking at is that orchestration across disciplines, right? So you break the silos between crash, between arrow, between thermo, right? So that the agent can not only orchestrate the aerodynamic optimization, but can orchestrate it while taking into consideration the safety aspects, the manufacturing aspects, as we talked before, right? Automatic design constraints, right? And break those silos between teams and disciplines. Once you do that, then that's where the gains really compound, the benefits really compound. And that's where you can really break down and reduce the cycles from 50 to 50 to 60 percent, and that's what we are seeing already, right? Today, there is not an OEM that was done at scale for right, but we are seeing this multidisciplinary automated AI-driven workflows happening already for some specific disciplines, and we are seeing 50%, 60%, that's massive. Yeah, interesting. How do people respond to it? 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, many of which very understandable feelings, by the way. I don't mean to dismiss those feelings, but they're an obstacle in many cases in the way of the company actually transforming in the way it probably needs to be competitive. How would you characterize maybe your kinds of roles? I'm thinking for whatever reason, the ML researchers are like most keen to automate their own labor. And then we see a lot of artists are very hostile to the technology, certainly not all, but that's like a pretty common point of view, especially if it's like, I love doing this. You know, why do we want to automate something that I love doing? Where are engineers in that space? Yeah, it's a 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? They made their own intuition on how to build a car and they really enjoy that aspect, right, of manual iterations, leveraging intuition, thinking about the physics of the problem, right? And there are many engineers that's not easy, right? They've been doing it the same way for the past 30 years and they enjoy that. It's 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, right, we saw 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 VYD, right? So you need to have this human aspect as well. But we also see all the way, the end of the spectrum, ML researcher, There are also methods team and machine learning teams within those organizations at the forefront in ZooPro. 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, frontier models, and to benchmark them and experiment. So we really have, in the same organization, those two extremes, right? How do you bring them together? That's also part of the complexity of those very large organizations. But the thing we've seen a lot, though, is Once you manage to break that final, that barrier, and actually have those engineers hands-on and working with the AI models, you see a top-event response. Because then they understand how powerful it can be and how much it can actually empowers 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, 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 this phase out, to test scenarios, test engines, right? And that's what AI enables you to do today.…

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