Nathan Labenz: belief

Transcript context

…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. One thing I 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 motor sports or engineering sports. I 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 pressure to turn things around quickly, right? So how does that look like right now? And another interesting detail was that apparently Formula 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 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 are we seeing? What should we be learning from the Formula One users? Yeah, I mean, that's super interesting, right? So indeed, today, Formula One teams are capped in, to be most specific, the CPU hours that they can run. So CPU hours is the computes. to run excellent aerodynamic simulations, right? And what's even more interesting is that depending on your RAM key from the previous year, you don't get the same numbers for the next season. The idea is that you want to try and make it more equal across teams, right? And you don't want to make it a race as to, okay, the biggest budget, the biggest compute, so I just win. Essentially, so they're trying to equalize that in some way.…

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