Thanks to AI, engineers get simulation results in minutes instead of days, which means exploring thousands of design options a year instead of just tens or hundreds.
Von Tschammer traces automotive design from physical prototype-crashing, to CAD/FEA numerical simulation, to AI, noting each shift cut turnaround time and multiplied the number of designs engineers could explore. ✦ AI generated
Thomas von Tschammer · The Cognitive Revolution · 2026-07-01 · original ↗
starts at this moment · 10:07
“What are the key iteration loops look like before AI and then we'll obviously add that AI layer to our understanding.”
thanks to AI, you don't get results in days, but you get them in minutes. And if you do get results in minutes, it means that you don't explore 60 designs a year, maybe 100 designs a year, but now thousands of different options. And that drastically accelerates your development cycles.
verbatim transcript · starts at 10:07
10:07you don't get results in days, but you get them in minutes. And if you do get results in minutes, it means that you don't explore 60 designs a year, maybe 100 designs a year, but now thousands of different options. And that drastically accelerates your development cycles. It also means that you you as an engineer can innovate much further because you have more options that you can explore thanks to these
10:31AIs. >> So, that is a real echo of a pattern that I see across all kinds of different spaces right now, where there's this ability for models to learn a sort of intuitive physics, I sometimes call it. Maybe most famously in protein folding, right? Where you've had a similarly hard time in the past either doing crystallography to eventually get to a protein structure or doing really com-
11:01uh compute-intensive simulation to get there. And now, somehow with enough data and the magic of learning, we can take a couple orders of magnitude out of the compute that's required. And that just changes the game in terms of how many designs we can explore. So, that pattern I think is fairly familiar. What I realize I don't have a great intuition for is like what are the different flavors of
11:29intuitive physics that models that we need to get models to learn in order to accelerate what different subdomains of engineering alluded to one a little bit with aerodynamics. And so I know that'll be prominent on the list. But how many different things are there like this and what are the unlocks what are the sort of fields that to which they apply? What are the unlocks associated associated
11:54with them? And what has neural concepts role been in building these models? >> Yeah. Yeah, great question. So there are many different domains as you can imagine. I mean think about the complexity of a car, right? Now today in the industry a GM or another OEM is simulating the entire car when developing it. Which means that every single components or sub-assembly within the car is being simulated. Is