The 65 is on the road here in Las Vegas, Nevada. We are at the Consumer Electronic Show and we are in the Synopsys booth here. Look at these incredible cars. Actually, don't look at the incredible cars yet, but this is pretty awesome. Sassine, it's been a great CES so far, hasn't it?
Yeah, it has. You know, there's so much going on in the world right now. We've got robots, we've got AI, we've got the automotive hall. We're driving rapidly, pun intended, towards autonomy. And we're of course seeing this whole silicon to systems innovation happening at just breakneck pace. I mean, since we've been here Monday, Pat, you know, there's new chip announcements, new driverless and L4 and L5, and we see robots walking around this place. You can't come here for a minute and think, man, the innovation that's happening isn't just incredible right now.
Yeah. You know, it's interesting. CES used to be literally the Consumer Electronic Show. But about 10 years ago, automotive got pulled into the conversation. Why? Because cars became more electrified. They became more intelligent. And here we have ADAS. We have full self-driving and new generative AI algorithms we put in there that make this as complex as probably a hyperscaler rack to put together these days.
And you know what's really interesting? I've had a few great conversations here and there. We're starting to ask the question about the C. It says it's C for consumer electronics, but should it be C for cars? Should it be C for chips? Because it seems like this show is so much more than consumers. And it's really awesome to see all this happening here.
It is one company that's absolutely crushing it and imperative is Synopsys. As these designs get more complex, it's not just chips and it's not just the physical design, it's everything together. Synopsys is leading it hard and therefore we have the CEO of Synopsys, Sassine. Welcome back to the 65.
Thank you. Great to be here.
Yes. I just love the hardware here.
I know. Don't you love it?
Yeah, the there's a Porsche and Synopsys logo right next to it. That's very cool.
It's beautiful. Love it. Didn't expect it.
Yeah, it makes my eyes water. Cars. You want to drive it?
Okay. Well, someday, right? Someday we'll get out there and do that. But I do like to put my foot to the floor every once in a while. But speaking of putting your foot to the floor back here in the automotive section here, there's a lot going on. Synopsys doesn't necessarily always, you know, people don't always synonymously think about it with automotive, but what are the big changes that are going on in the automotive industry that makes this such an important moment and create such a big role for Synopsys to play here?
Yeah, actually if you go back about five, six years ago, CES was all about smart devices, a smart car, a smartphone, smart home, etc. As that world started shifting from smart to intelligent where you have AI driving the device in terms of how it learns, interacts, adapts, reasons, the complexity of building these devices is exponential and it requires multiple levels of optimization across engineering. All these devices have a tremendous number of silicon. You have the actual physical device that you need the sophistication of the mechanical, of the structure, of the fluid, etc. So as we saw the world moving from smart to become more intelligent, we made a very strategic move which is the acquisition of ANSYS. ANSYS is the leader in physics simulation. So almost 90% of the companies you see on that floor, about 100 of the top automotive OEMs, are an ANSYS customer. To that extreme, how pervasive is our technology in designing and verifying these systems? And it only does not apply to automotive because what is an automotive today is a software-defined system.
A robot, software-defined robot, etc. So the same technology required that sophistication from a silicon all the way up to system. So we're thrilled about the opportunity that is expanding for Synopsys with the acquisition of ANSYS.
Yeah, the opportunity is increasing. The complexity is as well. And 10 years ago, Google put a supercomputer in the trunk that barely did self-driving. And now we're to the point where, you know, even in my hometown and I think yours, we have self-driving cars that, okay, they have a few sensors here, a few sensors there, but they have the very extreme electronics inside as well. So with this said, what are your customers sharing with you about some of the biggest challenges that they have to overcome for the cars of the future?
Actually, that's a great example. When you look at the Google example you just mentioned there, you had to validate the autonomy of the car by driving hundreds and thousands of miles in order to validate all the various use cases. Imagine a world where you can virtualize the car itself and the environment and do a lot of that development and validation virtually through a digital twin. Now digital twin would not have been practical five, six, seven years ago because you cannot simulate it. The simulation will just simply take too long. Now there's a big effort on how to accelerate the simulation without losing the fidelity and the accuracy of the simulation. We had recently announced with Nvidia an acceleration using GPU. So that's another level where you can achieve 30, 40, 50x simulation, but you still need to virtualize and create a digital environment of the world around that system. Be it again a car or a robot or on an industrial floor, that representation is a huge opportunity for Synopsys.
But it's really interesting, Sassine, because you're really trying to get three things right at once. You know, you've got the software, a lot of heritage with Synopsys, new capabilities with ANSYS, you've got the electronics right, and then of course you've got physics.
That's right. And so as we think about this, you know, and by the way, I was at the demo over at NVIDIA the other day and they were showing a robot and it was basically showing physics on, physics off, how it learns and it walks through a space and you have physics on and you know it'll bump into something and it learns from it. Then you turn the physics off and it just blows past everything. It's walking down a hill, you put an obstacle in the way, it walks down the obstacle, you know, because you can train it in perfect conditions. And this is really the hard problem with so much of physical AI is physics is very inconsistent whereas a language model or video creation model has much more consistency. How are you learning about how those things all interact together and what is it going to take to get that part right because that's the holy grail.
Yeah. That example you just mentioned, you cannot do it just by validating it through a physical prototype.
No, it's too expensive. It takes forever and you're going to miss many use cases that you did not take into account. So you start at the application level envisioning the use case in a real world environment and how do you create models for it. But that's not sufficient because then you need to take into account the cost of developing that system. How are you taking into account the manufacturing of that system? Because you cannot have a robot or a car that's going to cost a few million dollars and assume it's going to be deployed at scale. So today the way these systems are built, you build a lot of margins between the software layer, the various physics, the mechanical, the structure. If it's a device that will heat, the thermal, what kind of silicon do you need so you're not overkill from a silicon point of view. So how do you take into account the application in a virtual world without having a need to do physical prototyping and optimizing and reducing the margins across every engineering discipline. That's where we're seeing the opportunity. But you cannot build it as Synopsys alone. We have to work with an ecosystem to build it and we have a rich ecosystem partnership across the silicon all the way up to system in order to make it practical. Now is it happening right now today that you can point to a company that's doing it at that level? No. But do I believe two, three years from now in that direction, whichever company is racing to build it is going to be the companies able to take it at scale.
