Nathan Labenz: belief

Transcript context

…If you think about it, Formula One engineers are the state of the art of engineering, the most agile teams you can think of. Design of the car is changing between every single race, right? From one week to another. You don't see it because it's very fine details, but the car is actually different, right? They are improving it week over week. So that means that they have to reach an extreme level of automation of design iteration processes 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 design decisions, right? It's a good way for us, essentially, to proof tests and to stress test our models, our 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 saying it, and we're asking those teams to really push the limits of the models and the workflows. to break phase, essentially, because that's when the break phase that we see where we have to focus and what we have to do. 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 not writing code anymore. And then, of course, there's a lot of places where we haven't quite caught up. And so we're maybe still writing called 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 is 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? And I think we kind of know what the old school one looks like. So yeah, what is the token maxing F1 engineer's life look like today? So they will 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 realistic. We have many more straight lines and we're not going to have to overtake much more, right? So that's the baseline. Then they transcend that today into engine requirements in terms of how the car can 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 100, thousands of configurations of design options. We'll evaluate them, we'll evaluate the corresponding aerodynamic performances. And on the morning after, the engineer, the aerodynamicist, will have a dashboard, a report, interactive. He sees the thousands of points, data points on a dashboard, and he can look at the different trade-offs, look at the corresponding 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, 10s of thousands of designs being explored overnight, fully automated by these AI models.…

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