The next big leap in AI may not come from systems that generate text, code or images. It may come from AI that understands the physical world.Aparna Pratap, deputy general manager for infotainment at MercedesBenz Research & Development India (MBRDI), says the future of AI in automotive engineering will depend on systems that grasp physics, space and context far more deeply than today’s generative AI models.“In AI research, there are specifically two areas which I follow and which I believe could have immense impact on the automotive side of things, and these relate to AI understanding physics inherently,” she said on our podcast recently.She pointed to work by Yann LeCun on world models and Fei-Fei Li on spatial intelligence. Both lines of research, she said, are trying to give AI an intuitive understanding of the 3D world. That matters deeply in automobiles, where systems must operate in unpredictable real-world environments.Today’s neural networks are trained on millions of data points and videos. But when they encounter edge cases, uncertainty remains. “We are always fingers crossed,” Aparna said. If AI moves towards world modelling and spatial intelligence, “these kinds of predictions become intuitively more intelligent, and also in the real physical space, more natural.”The implication for engineering could be significant. AI models may need less brute-force data training and more intelligent 3D modelling. That could change how autonomous and edgedeployed AI systems are built. A model that understands the physical space inherently could respond better when deployed in vehicles, robots or other physical environments.Mercedes-Benz has already used computer vision and AI in vehicle interfaces. Aparna noted that Mercedes worked on gesture control as early as 2018, using computer vision algorithms that were later deployed in the flagship S-Class in 2020. “You just wave your hand and you’re able to open the sunroof,” she said.But the industry is moving beyond gesture models. “The world is moving towards voice,” she said, adding that carmakers are exploring new modalities that make the user interface more intuitive.Her advice to young engineers is clear: do not chase only the newest tool. Build fundamentals. Core engineering principles, physics, mathematics, especially linear algebra for AI/ML – all of these will remain important.She urged students to participate in hackathons, solve real-world problems, publish work on GitHub, and opensource projects where possible. In agentic AI, she recommended studying open courseware, and courses such as Stanford’s CS336: Language modeling from scratch. “The Stanford course is one of my favourites. You learn how generative AI works, what is the infrastructure required, and then how do you start to deploy it for production grade,” she said.Aparna also recommended a 15-minute daily reading habit. Read anything, and then summarise it without AI.“In today’s world, the students, the workforce, we all talk to two different species – humans and machines,” she said. Engineers must lear n to prompt machines with precision, but must not lose the ability to communicate with humans. That exchange of ideas with humans is so crucial.