General Motors Is Cutting Its Development Cycles in Half

For decades, automakers enjoyed a luxury that had nothing to do with the softest leather or the smoothest engines. Their luxury was time, with some popular cars and trucks enduring for a decade or longer before they received a full redesign. The clock is ticking faster now, thanks to China. BYD and other automakers there are speeding EVs and other models from drawing board to showrooms in two years or less. General Motors is among the Western automakers striving to match that blistering pace, by harnessing AI and simulation to dramatically shorten development times. GM’s effort is being spearheaded by Sterling Anderson , the technologist and robotics guru who led development teams for Tesla’s Autopilot and the Model X before cofounding Aurora Innovation , the autonomous trucking company. GM lured Anderson last June as its chief product officer, offering a $40 million package to guide the development of the automaker’s cars, autonomous models, batteries, software, and other tech. How GM Is Accelerating Its Designs In a recent video call, Anderson and Jason Fischer , GM’s executive director of virtual integration engineering, walked me through the company’s latest design processes. But first, Anderson offered a wide-lens view of how AI is transforming everything that came before. Sterling Anderson, robotics guru and former Tesla executive, is pushing AI to accelerate GM’s design process. General Motors Anderson sees design and human ingenuity falling into three main epochs, beginning with thousands of years of empirical design that saw creators largely mimicking nature, building and testing models and advancing from there—slowly, expensively, and narrowly focused. “Flight is a great example,” Anderson says. “Humans looked to birds and said, ‘Hey, those wings seem to work pretty well. Let’s come up with something like it.’” The advent of virtual tools such as CAD and computational fluid dynamics in the 1950s kicked off a second age, he says. Developers had better ways of doing work, but they remained siloed in an inefficient, pass-the-baton process. “Designers still had to toss something over the wall to other engineers, who ultimately had to build that empirical asset anyway,” Anderson says. In automobiles, that meant building prototype vehicles first and then integrating and assessing myriad functions, many of which were developed separately: electrical systems, thermal controls, safety, ride and handling, and so on. Today’s third epoch is characterized by AI and simulation that can collapse those functions into a single virtual development tool, Anderson says. In roughly one minute, a structural engineer can see how a design change might affect a finished vehicle, as opposed to the 15 hours it used to take. The result, he says, “is a dramatically accelerated product development process at GM.” GM is applying this approach to self-driving cars, LMR batteries , Cadillac’s high-profile Formula 1 racing program, military defense systems, and tech for Lunar Outpost’s Pegasus rover , part of NASA’s Artemis mission to land astronauts on the moon in 2028. Fischer says the company’s proprietary environment allows engineers to simultaneously develop and optimize hardware and software, well before the physical prototype stage. A simulated Cadillac performs an emergency avoidance maneuver, with graphs tracking vehicle functions such as brake pressure and steering wheel angle. General Motors In an onscreen demonstration, Fischer runs a digitally rendered Cadillac Escalade IQ through the Consumer Reports avoidance maneuver, which the publication uses to assess a car’s evasive skills. The tricky double lane change is a serious test of the electric SUV’s handling and stability under duress. In the past, physical testing could begin only after an array of systems had been separately developed and stitched together, including the chassis, powertrain, steering, brakes, suspension, sensors, and controls. Engineers would spend months testing and calibrating prototypes in proving grounds and on real-world roads. Now, GM can run detailed, physics-based models of designs across thousands of simulated scenarios—snow and rain, varying road conditions, different suspension setups. “We can do full, virtual calibrations prior to a vehicle ever being built,” Fischer says. “We get a system that performs well not just in ideal conditions, but one that’s been hardened against the real world.” RELATED: AI Models Trained on Physics Are Changing Engineering This approach halved the development time of the electric GMC Hummer, which went from initial designs to showroom in two years, versus a more typical four- to five-year product cycle. GM’s goal is to get a full range of vehicle and tech programs onto that lightning-fast development track. “We’re not there yet, but give us a minute,” Anderson says. Front-end crash simulations have also been a