1000 Designs a Day: Neural Concept's Thomas von Tschammer on AI-Native Engineering

**Nathan Labenz** (0:00)
Hello, and welcome back to The Cognitive Revolution. Today, my guest is Thomas von Tschammer, co-founder and US managing director of Neural Concept, a Swiss company that uses specialist models for domains like aerodynamics, heat dissipation, and collision safety to help automotive manufacturers and other clients accelerate their product design and engineering processes.
As a Detroit, Michigan native, this topic is of particular interest, because my father actually started his career at General Motors in the drafting department, back when designs and assembly instructions were hand-drawn on paper. And I have vivid memories of watching him use early computer-aided design platforms on Take Your Kid to Work Day, back when I was a young boy. The work at the time was still highly manual and often quite intuitive. But as in so many fields, it's become far more computerized over time. By the time my dad retired, designs were routinely tested via physics-based digital simulations before the physical manufacturing process began. And this increased iteration velocity by an order of magnitude. But still, as we've seen in biological structure and binding prediction, material science, and robotics controls, the compute required to run these simulations often becomes a bottleneck unto itself. Today, as you'll hear, Neural Concepts models can deliver similar results to expensive physics-based solvers in minutes. And they now also offer an engineering copilot product, which can both call these domain-specific prediction models as tools, and actually use the core CAD platforms to make design changes as required. This TikTok combination of agentic optimization and domain-specific validation is the perfect recipe for reinforcement learning. And already today, it allows manufacturers like Jaguar Land Rover to conduct aerodynamic testing on more than 1,000 designs per day. It also frees human engineers to explore much larger regions of design space, and to focus their attention on navigating higher-level trade-offs that involve other parts of the organization. Plus, it occasionally produces surprising, move-37-like designs that actually alert human engineers to new possibilities.
Neural Concept has even found a niche in Formula 1 racing, which, I was surprised to learn, actually limits the amount of compute that teams can use for aerodynamic optimization from one race week to the next.
The bottom line is that we can add engineering to a long list of domains where essentially the same pattern of AI development is working over and over again. What once could only be done manually in the physical world was first digitized, and then dramatically accelerated with specialist models. Today, agentic workflows are accelerating things further, and neural concept is beginning to evolve from training models on a per customer basis to a future of more general purpose foundation models for engineering.
All of which makes it pretty easy for me to imagine a future engineering superintelligence that combines the general purpose design skills with these superhuman intuitions, all in the same set of weights. As we reach that point, and probably even before, we can expect faster and faster product cycles and an explosion of new form factors. All with higher quality and better resource efficiency than we've ever experienced before.
If you've ever felt that promises of AI abundance were a bit too hand-wavy or detached from physical reality, I think this episode should serve to inspire you. And so, I hope you enjoyed this preview of the AI-powered future of engineering, with Thomas von Tschammer of Neural Concept. The Cognitive Revolution is brought to you by Mercury, the fintech that more than 300,000 ambitious companies and individuals trust to run their finances. I've wired AI into nearly every corner of my life. My email, my messages, my calendar. I even gave Mercury virtual cards to my agents, with low limits and category and merchant restrictions, for their autonomous use. But still, my AI's access to my financial data has remained limited. With a normal bank, I might export a bunch of statements and have my assistant process them for me. But for real-time, up-to-date information, and certainly for taking any action, trying to get your agent to use the bank via the browser is just too hard, too slow, and too error-prone to be worth it. And that's why Mercury's new conversational interface, Command, is such a big deal. It's built directly into Mercury, which means you get natural language access to your finances without exposing anything outside of your bank account. No exports, no spreadsheets, no pasting your transactions into third-party tools.
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