Jaguar Land Rover went from evaluating 50 aerodynamic designs per day with traditional computational solvers to 1,500 designs per day in production using Neural Concept's AI.
Citing JLR's public NVIDIA GTC presentation, von Tschammer says AI adoption took the automaker's daily aerodynamic design throughput from 50 to 1,500 designs, alongside other clients seeing 80% faster development cycles and better-performing parts. ✦ AI generated
Thomas von Tschammer · The Cognitive Revolution · 2026-07-01 · original ↗
starts at this moment · 22:46
“I want to hear too about how with the AI co-pilot, like how is that experience changing for engineers? How automated is it starting to get? How automated is it likely to get in the not-so-distant future?”
With AI, and that was what the company a couple of months ago. They went from 50 designs to validity per day to 1,500. Every single day in production. So, you can imagine the level of speedup that it brought to them.
verbatim transcript · starts at 22:46
22:46the studio teams, which are responsible for the aesthetic, the design of the car, and the aerodynamicists. Right? And at the end of the day, it is a trade-off between the best-looking car and the most aerodynamic, because you want to improve range. Right? With AI, and that was what the company a couple of months ago. They went from 50 designs to validity per day to 1,500. Every single day in production.
23:11So, you can imagine the level of speedup that it brought to them. We have other suppliers as well that are designing medical pace to cool the battery that were able to reduce by 80% their development cycles. Actually, they come in 80% faster to develop a new medical pace. And on top of this, more than design cycles, they could get better performance. Right? Because now, if you can explore many more designs, it also
23:37means that you can innovate there. You can find new options that you could not think of before as an engineer because you just rely on intuition, right? And we see examples where the copy is cooling 20% better on the battery, it's 15% lighter. If you think about aerodynamics, I'm pretty sure it also helped engineers to find designs that are 2 3 5% more aerodynamic, which is game-changing for them at the game.
24:06>> Yeah, so let's dig into that last point. How is that happening? Again, to map this onto a space I've studied in a little in a little bit more depth. There's the protein generation models now, right? Which allow us to go beyond a biologist's ability to say, "Oh, I there's a couple other proteins in the protein bank that are similar to this that I can pull in or even I have a
24:30little intuition of my own." And now we've got models just throwing out new designs, which can then be evaluated. So, where are we on this sort of language assistant um with like tool calling paradigm to intuition of design. You can imagine a language freeway. You could imagine a version that's just Here's your constraints. Here's the point cloud. Here's the feedback that we got. Generate a new point cloud, and you
- ·JLR evaluated 50 designs/day with traditional solvers
- ·Now validates 1,500 designs/day in production
- ·Shift enabled by Neural Concept's AI platform
- ·Cited from JLR's public NVIDIA GTC talk
- ·Other clients see 80% faster development cycles
- ·AI also yields better-performing parts overall
- ·Von Tschammer: speedup scale is dramatic
- ·Other clients report 80% faster development cycles
- ·AI also yields better-performing parts
- ·Signals wider industry shift beyond JLR