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 accelerated. In the past, a “heavy computational method” required 15 hours of computing to complete, Fischer says. Using an AI method based on probabilities, the compute time has been cut to less than one minute. That frees engineers to focus on additional scenarios “that would be difficult, limited, or frankly impractical to re-create with physical vehicles alone,” Fischer says. “Engineers are finding weak spots earlier, and fixing them earlier, to arrive at physical testing with a stronger, more refined vehicle.” “Driver in the loop” simulations add human variables to those of vehicles, plugging in the personas of, say, a Boston man driving in January or a Phoenix woman driving in desert heat. Such simulations are being used to assess people’s responses to heating and cooling in a cabin, as well as in a more hostile environment: the surface of the moon. In contrast to NASA’s original lunar explorations in the 1960s and ’70s, GM can realistically simulate what an astronaut or vehicle will experience after taking that one small step. Fischer says work on its next-gen NASA lunar rover is proving the vast bandwidth of virtualization and simulation tools. “We can alter gravity by adjusting physics in the software,” he says. “Our engineers in a room in Michigan can simulate real-world driving conditions to develop tires for [the lunar] environment.” Anderson, who holds a Ph.D. in mechanical engineering from MIT, where he focused on robotics, notes how these tools are critical for autonomous vehicle development. “We can simulate 100 days of driving in a day, and are approaching roughly 2 million simulation runs per week, which helps us probe edge cases that would be dangerous or impractical to reproduce physically,” he says. “This doesn’t replace road testing, but it makes every real-world mile more valuable and every release decision more informed.” The AI design for the Corvette’s hatch support brackets (in red) is stiffer, lighter, and more durable than the original. General Motors AI is even being deployed in the design of a vehicle as hallowed, and profitable, as the Chevrolet Corvette. Generative physics-based design gets credit for a strut bracket that supports the Corvette’s enormous composite hatch lid, the better to flaunt the sports car’s V-8 engine. For the car’s optional hatch brackets, the unnamed AI designer went back to nature, creating a shape that suggests a tree root and branches and is lighter, stiffer, and more durable than the original. “This is really becoming the new norm for General Motors,” Fischer says.