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 such as 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 when I was a young boy.
The work, at that 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 to materials science to robotics controls, the compute required to run these simulations often became a bottleneck unto itself.
Today, as you'll hear, Neural Concept’s models can deliver similar results to expensive physics-based solvers in minutes and they now also offer an Engineering Copilot product which can call the domain-specific prediction models as tools, and actually use the core CAD platforms to make design changes as required.
This tick-tock combination of agentic optimization and domain-specific validation is the perfect recipe for Reinforcement Learning, and already, it allows manufacturers like Jaguar Land Rover to conduct aerodynamic testing on more than 1000 designs per day; frees human engineers to explore much larger regions of design space, and to focus their attention on navigating higher-level trade-offs with other parts of the organization; and occasionally produces surprising, Move-37-like designs that outperform human designs and actually alert human engineers to new possibilities.
Neural Concept has even found a niche in Formula 1 racing, which, I was surprised to learn, limits the amount of compute that teams can use for aerodynamic optimization from one 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 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 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 super-human intuition, 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 enjoy this preview of the AI-powered future of engineering, with Thomas von Tschammer of Neural Concept.
Watch now!
Thank you for being part of The Cognitive Revolution,
Nathan Labenz