Sterling Anderson, former chief product officer of Aurora, now leads General Motors' efforts in integrating AI and machine learning into its engineering processes. He described the shift as moving from the second to the third epoch of engineering, where AI-driven probabilistic methods are collapsing traditional silos between design, simulation, and manufacturing. 'Our FEA runs that historically were 15 hours per run? They’re now one minute,' Anderson said. This dramatic reduction in time allows engineers to run a broader set of tests in a fraction of the previous duration, significantly accelerating the development cycle. The use of AI enables parallel processing of multiple virtual simulations, which was previously unattainable with traditional methods. 'When you run this thing in one minute, you’re just pumping through iterations at a much faster clip,' Anderson explained. This transformation is not limited to aerodynamics or structural design but extends to GM's motorsport, energy, defense, and even lunar programs. Jason Fischer, executive director of virtual integration engineering at GM, highlighted the company’s ability to create a virtual environment where hardware and software can be optimized simultaneously, a practice not seen at the scale GM is achieving. 'We’re not using virtual tools just to check our work after we’ve done vehicle design, but we’re actually giving our engineers a virtual environment where they can simultaneously optimize the hardware and the software,' Fischer said. The benefits of these virtualization tools are evident in testing scenarios like Consumer Reports’ avoidance test, where GM now models all the sensors, electronic control units, and domain controllers in a virtual environment. This approach allows for rapid iteration and more robust designs, as engineers can identify and strengthen weak points before physical testing. 'It takes about 15 to 18 hours to run this, depending on complexity,' Fischer said. 'We’re using probabilistic methods, artificial intelligence, and we can get that down to about less than one minute.' *Source: [arstechnica](https://arstechnica.com/cars/2026/06/from-15-hours-to-one-minute-how-ai-ml-is-speeding-up-gms-development/)*