When we met Sterling Anderson in 2024, he was the chief product officer of Aurora, the self-driving startup he cofounded in 2016 after several years at Tesla. Just over a year ago, though, Anderson decamped from the startup world for something a little more established, taking over as chief product officer at General Motors, the nation’s largest automaker. Since then, he’s had a good view of how GM is entering what he calls the third epoch of engineering and design.
“There was a time when humans looked at birds and were like, ‘OK, those wings seem to work pretty well. Let’s go and design something that looks like them.’” Anderson said, describing the first age of engineering. “And they just kind of iterated their way to something that was marginally feasible.”
The first few hundred years of inventing “was this era of highly empirical iterative design development and engineering,” he said. “And by that I mean humans largely started with what we know or had seen, built prototypes of something that kind of looked like it and maybe tweaked some things, hoping to make it perform better, tested it, iterated, and kind of went through this slow guess-and-check process until we got to something that marginally worked.”
The second age began as computers became powerful enough to do some of the early work. “We started to see virtual development tools, in functionally specific ways, improve the work that people did so they didn’t have to go to empirical prototypical development,” Anderson said.
“For instance, we started to see CFD [computational fluid dynamics] start to inform aero engineers,” he said. “We saw FEA [finite element analysis] inform structural engineers. We saw any number of other virtual tools… but the relay race that was development remained the same, which is to say design passed the baton to aero which passed the baton to structures, just always passed the baton back when they found something that the other guys had to fix.”
But Anderson’s world recently moved into its third epoch, “which is where GM has really been pushing, which is a collapse of those functions into a single broadly informed, largely probabilistic method for design, development and manufacturing of these assets,” he explained. And yes, by probabilistic, he means AI/machine learning.
Probable cause
Using simulation for engineering work like CFD—versus using physical models in a physical wind tunnel—sped up that work, but the complexities of simulation mean it’s very computationally demanding in terms of resources and time. But you can teach a computer how to virtualize that analysis and then run multiple virtualizations in parallel using AI/ML; last month, we reported on just such an example, when IBM and the race car manufacturer Dallara published research showing how the approach produces data that’s well-correlated enough to use.
When we met Sterling Anderson in 2024, he was the chief product officer of Aurora, the self-driving startup he cofounded in 2016 after several years at Tesla. Just over a year ago, though, Anderson decamped from the startup world for something a little more established, taking over as chief product officer at General Motors, the nation’s largest automaker. Since then, he’s had a good view of how GM is entering what he calls the third epoch of engineering and design.
“There was a time when humans looked at birds and were like, ‘OK, those wings seem to work pretty well. Let’s go and design something that looks like them.’” Anderson said, describing the first age of engineering. “And they just kind of iterated their way to something that was marginally feasible.”
When you realize just how much faster these new tools are, it becomes extremely clear why GM is embracing them. “Our FEA runs that historically were 15 hours per run? They’re now one minute,” Anderson told me.
Rather than setting up a simulation to run overnight and hoping nothing goes wrong, “when you run this thing in one minute, you’re just pumping through iterations at a much faster clip and you can run a much broader set of tests than you could ever have done before, just given the time available to you,” Anderson said.
But the reach of these new virtualization tools goes well beyond early engineering analyses and the domains of aerodynamics or structural design, reaching into GM’s other businesses: motorsport, energy and batteries, defense, and even its lunar program.
“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 and inform hardware design or software design or vehicle performance in a way that nobody in the industry is doing, especially at the scale and the speed of what we’re doing,” said Jason Fischer, executive director of virtual integration engineering at GM.
“The beauty of these virtual tools is our collaboration with our motorsports team with NASCAR and Formula One,” Fischer continued. “We co-develop a lot of these tools together and then we independently develop tools depending on who’s got the strength and the bandwidth between the organizations to do that. And as one outpaces the other, we actually sit down and we have a monthly technology transfer between motorsports, and I’ll say the production side of things to ensure that we’re all seeing the latest and greatest technology and using the latest techniques.”
Drive it before you build it
One example Anderson and Fischer walked me through was using virtualization to perform a handling test for a vehicle in development, specifically Consumer Reports’ avoidance test, where a car has to swerve at speed to avoid an obstacle. Instead of connecting all the various subcomponents of a car’s electronics on a test bench to see if they talk to each other without errors, GM now models all the sensors, electronic control units, domain controllers, and so on.
“We actually have IP protection on how we’ve set this system up at General Motors where we can put together the vehicle behavior from a physics perspective,” Fischer said. “So [we can now] run vehicle performance, electronic control units, and software simultaneously in this virtual environment, and we can really open up our design space exploration. This allows us to actually change physical parameters and run thousands of designs of experiments to see how the control logic handles that,” Fischer said.
Since you can easily change conditions like road conditions digitally, it’s simple to iterate through many more variations than was previously possible. “Then you start getting a result that performs well not in this particular maneuver, but it’s actually hardened against [the] real world,” Fischer told me.
Crash performance is improving because engineers can identify weak points and strengthen them well before a physical vehicle ever meets an immovable structure at 40 mph. “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. And it’s not about the time savings in terms of allowing somebody to go home and sleep at night. It’s the fact that one minute later, we know what the answer is, and we can start optimizing that structural performance, and that gives us the ability to look at other things.”
A new vehicle’s HVAC system is another example. Instead of independently designing and optimizing individual components and then connecting and calibrating them, GM can now simultaneously balance airflow and refrigerant behavior with cabin comfort, doing in days or hours what used to take months or weeks. “It really gives our engineers time back to dig deeper and be innovative in their creative designs as opposed to doing repetitive tasks or doing that iterative grind,” Fischer said.
That includes their colleagues who design the factories that build the cars that GM sells to customers—digital twins of new assembly lines are created well in advance of any actual hardware being installed to iron out the bugs.
The first few hundred years of inventing “was this era of highly empirical iterative design development and engineering,” he said. “And by that I mean humans largely started with what we know or had seen, built prototypes of something that kind of looked like it and maybe tweaked some things, hoping to make it perform better, tested it, iterated, and kind of went through this slow guess-and-check process until we got to something that marginally worked.”
The second age began as computers became powerful enough to do some of the early work. “We started to see virtual development tools, in functionally specific ways, improve the work that people did so they didn’t have to go to empirical prototypical development,” Anderson said.
“For instance, we started to see CFD [computational fluid dynamics] start to inform aero engineers,” he said. “We saw FEA [finite element analysis] inform structural engineers. We saw any number of other virtual tools… but the relay race that was development remained the same, which is to say design passed the baton to aero which passed the baton to structures, just always passed the baton back when they found something that the other guys had to fix.”
But Anderson’s world recently moved into its third epoch, “which is where GM has really been pushing, which is a collapse of those functions into a single broadly informed, largely probabilistic method for design, development and manufacturing of these assets,” he explained. And yes, by probabilistic, he means AI/machine learning.
Probable cause
Using simulation for engineering work like CFD—versus using physical models in a physical wind tunnel—sped up that work, but the complexities of simulation mean it’s very computationally demanding in terms of resources and time. But you can teach a computer how to virtualize that analysis and then run multiple virtualizations in parallel using AI/ML; last month, we reported on just such an example, when IBM and the race car manufacturer Dallara published research showing how the approach produces data that’s well-correlated enough to use.
When you realize just how much faster these new tools are, it becomes extremely clear why GM is embracing them. “Our FEA runs that historically were 15 hours per run? They’re now one minute,” Anderson told me.
Rather than setting up a simulation to run overnight and hoping nothing goes wrong, “when you run this thing in one minute, you’re just pumping through iterations at a much faster clip and you can run a much broader set of tests than you could ever have done before, just given the time available to you,” Anderson said.
But the reach of these new virtualization tools goes well beyond early engineering analyses and the domains of aerodynamics or structural design, reaching into GM’s other businesses: motorsport, energy and batteries, defense, and even its lunar program.
“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 and inform hardware design or software design or vehicle performance in a way that nobody in the industry is doing, especially at the scale and the speed of what we’re doing,” said Jason Fischer, executive director of virtual integration engineering at GM.
“The beauty of these virtual tools is our collaboration with our motorsports team with NASCAR and Formula One,” Fischer continued. “We co-develop a lot of these tools together and then we independently develop tools depending on who’s got the strength and the bandwidth between the organizations to do that. And as one outpaces the other, we actually sit down and we have a monthly technology transfer between motorsports, and I’ll say the production side of things to ensure that we’re all seeing the latest and greatest technology and using the latest techniques.”
Drive it before you build it
One example Anderson and Fischer walked me through was using virtualization to perform a handling test for a vehicle in development, specifically Consumer Reports’ avoidance test, where a car has to swerve at speed to avoid an obstacle. Instead of connecting all the various subcomponents of a car’s electronics on a test bench to see if they talk to each other without errors, GM now models all the sensors, electronic control units, domain controllers, and so on.
“We actually have IP protection on how we’ve set this system up at General Motors where we can put together the vehicle behavior from a physics perspective,” Fischer said. “So [we can now] run vehicle performance, electronic control units, and software simultaneously in this virtual environment, and we can really open up our design space exploration. This allows us to actually change physical parameters and run thousands of designs of experiments to see how the control logic handles that,” Fischer said.
Since you can easily change conditions like road conditions digitally, it’s simple to iterate through many more variations than was previously possible. “Then you start getting a result that performs well not in this particular maneuver, but it’s actually hardened against [the] real world,” Fischer told me.
Crash performance is improving because engineers can identify weak points and strengthen them well before a physical vehicle ever meets an immovable structure at 40 mph. “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. And it’s not about the time savings in terms of allowing somebody to go home and sleep at night. It’s the fact that one minute later, we know what the answer is, and we can start optimizing that structural performance, and that gives us the ability to look at other things.”
A new vehicle’s HVAC system is another example. Instead of independently designing and optimizing individual components and then connecting and calibrating them, GM can now simultaneously balance airflow and refrigerant behavior with cabin comfort, doing in days or hours what used to take months or weeks. “It really gives our engineers time back to dig deeper and be innovative in their creative designs as opposed to doing repetitive tasks or doing that iterative grind,” Fischer said.
That includes their colleagues who design the factories that build the cars that GM sells to customers—digital twins of new assembly lines are created well in advance of any actual hardware being installed to iron out the bugs.