Insights

Jonas Ekmark explains why physical AI for driving demands rigorous engineering, hybrid safeguards, representative data, and a safety culture that permeates every decision and release.

AI offers huge promise, but real value comes from practical infrastructure, smart trade-offs, self-reliance, and knowing how to turn ambition into useful results.

Why today’s AI still falls short of human learning: Willem Verbeke on mental simulation, prior knowledge, and why autonomous systems remain stuck in the past.

We built our AI platform around a simple idea: scaling AI works best when teams own their agents, tools, and knowledge.

Listen to Wasil as he breaks down how generative physics and AI speed up simulations, reshape engineering workflows, and unlock faster, more personalized vehicle design.

Professor Amy Loutfi discusses Sweden’s AI leadership, embodied robotics, data and regulation, and how AI-driven scientific discovery could transform our approach to humanity’s toughest challenges.

Explore combining synthetic and real visual data with privacy and security in mind, as Batu and Veronika discuss lossless anonymization and policy on Deeper learning.

Mohammad is focused on one goal: creating software that can handle the full range of real-world traffic – safely, smoothly, and reliably.

Adam is going to Stanford. He’ll trade ideas across fields, test fresh approaches, and come back to Zenseact ready to crack tougher challenges.

Sharing data is the path to a collision-free society. Join Mina and Veronika on Deeper learning to discuss technology’s growing need for data.

What does it take to develop self-driving car technology besides funding, technology, and talent? According to Jonas Ekmark, knowing a little about collisions doesn’t hurt.

Annotation is crucial in the development of autonomous driving, as is geopolitics. Hear Veronika and Kognic CEO Daniel as they explore both topics – and more.