Cadence Design Systems, Inc. (CDNS) Earnings Call Transcript & Summary

Yes. And can you say something about sort of like what is -- so if you think about the monetization of it in terms of revenue terms, what is different about the genetic flow that's actually driving accelerating recurring revenue growth today versus the past things like [indiscernible] other AI features, which were maybe in your core offering, where we didn't see that kind of like acceleration in revenue?

Yes, that's a very good question. And of course, we always did a lot of good work, but what is new with this agentic AI. And we always wanted to do it. Just going back decades is we wanted to automate -- more automate the running of our tools. Our tools are fairly complex. And typically, what happens is they run for a few days, this is not like it doesn't run for 5 minutes, right? If you're doing some block, it will run for a few days to do all kinds of optimization. But what the customers are doing is they run at one time and design is naturally iterative. So they have a RTL. They would change it and then they will run it again and they change it and they run it again, okay? And typically, a user would do like 3 or 4 experiments at a time because that's what typically a human will do. But if an agent is running it, first of all, agentic is much more meaningful to us than GenAI because some people said, well, GenAI has been around for 4 years, why did it not have a big impact on chip design. Because GenAI helps like improve the IO of the tool, you can talk to the -- look up documentation or whatever. But that's not -- okay, that's useful, but that's not shattering, okay? What is interesting in agentic AI that you can define a workflow for a graph or like you do a, you do B, you do C, if you get stuck, you do -- and this is all relatively new with Claude Code and all like about a year ago or a little more than a year ago. So this kind of workflow, combined with our base tools can give a lot more productivity. And then when the agent runs it, it runs like 100 experiments. It's not running 3 or 4 experiments. But this kind of workflow is the new thing. So that's why I'm so confident that our agentic solutions will have a real impact versus GenAI a few years ago. And Cerebrus and all were good, but now with agentic and the base, but it calls more of the base than less of the base. And then this kind of productivity, this 5, 10x productivity or at least several is possible, will help meet the exponential demand of our customers. And this is -- the demand for all these -- we have engaged with all the top companies with all our agentic solutions. And of course, the usage of the base tools is also going up like we see in our results.

Yes. So -- if you think about that acceleration, sort of where do you think came to getting the most competitive traction today? And sort of what are the product areas that represent like the most remaining market share opportunity for the company over the next few years.

I mean, right now, we are doing well in almost all of our products, which is great. Normally, you always want to see that, but it doesn't happen that often. But right now, I think we are hitting in all cylinders. And we are not dependent on one critical area, but right now, all of them are firing EDA, anyway, we have the broadest portfolio for chip design. I don't know how familiar -- we not only do digital design, we do analog, memory, mixed signal, packaging, PCB. So Cadence has always had the most complete portfolio, and it's -- and then we work closely with TSMC for a long time with ARM for a long time and now with Intel and Samsung. So core EDA is as strong as it has ever been. And then we put all the agenting on top of that, right? And I think we are definitely leading in agentic. And then hardware, which is like hardware acceleration, which can run things like 100x faster, we are the only company that designs our own chip actually at TSMC. If you look at our hardware systems, these are as complex as the latest GPU or XPU system. So these are liquid cool fully optically connected rack and then we have like a 10-, 15-year lead in designing our own. So that's hardware. And the demand for hardware is going up because, first of all, more people are designing chips, but hardware is used in proportion to the size of the chip. If the size is going to go up by 48x in the next 5 years, so that's a systematic improvement. And then IP was the weak point of cadence historically, and I didn't invest as much in IP because it's not as profitable as EDA. But now I think, especially with AI and 3D IC, there is more opportunities in IP. So if you look at IP, our business is up 30% this year. Was up, I think, 30% last year, probably. So last 3 years, it has grown much, much higher than the market. And I feel that IP can still continue to grow well with all this Intel and Samsung and of course, with TSMC so I feel all these 3 major areas, 3 or 4 end system business is going pretty well. So we are in a good position. And the main thing is our customers are growing. So if the customers are growing, they want to do more and more innovation.

Yes. I want to get back to IP, but first to just close out a little on hardware for a second. I mean you've talked about demand being supply-constrained, I think. What's structurally driving that demand for hardware I mean, is it the skill, the designs, which you mentioned? Or is it also kind of like your customers shifting towards emulation to more of a strategic capability rather than sort of a project level.

Yes. I mean, one thing -- I don't know how familiar with this is like give me a few minutes to explain what the hardware systems do. I mean we call it hardware, but it's hardware plus software. People would call it like full stack basically. But basically what happens is that at this point, you cannot design any complicated chip without this. It's not possible. And there are multiple reasons for it. What these systems will do is even before, let's say, you're designing a chip for like 9 months or 12 months, whatever it is, 6 to 12 months typically is the design time. You want to verify the chip in your environment, whether it's a software environment, it's like Windows or Cuda or iOS or whatever. So we can have a chip behave like a chip, RTL, we can make it behave like a chip even before it comes back from TSMC or any foundry. And that is used to not only develop software, but also verify the functionality of the chip Because if you can boot some OS on top of your chip and run your application correctly, then, of course, you know the chip is correct. And that's only possible with this kind of palladium kind of systems. So then they become like irreplaceable. Otherwise, what will happen is you would do the design and then you would check and then if you redo the design and take few iteration, which is the old way of doing it. And only a few companies are doing -- most of them have moved to hardware-assisted design process. And the second reason they are popular is not only you can verify the chip, you can write your software because you can emulate the chip, and these are custom chips that emulate the chip like 1,000x faster than CPUs. I mean they're still slower than real life but much, much faster than anything else. So you can develop all your software. So all these system companies, the hyperscalers, they are developing chips, of course, they have software to develop. So for those two reasons, it became irreplaceable. And then the amount of hardware you buy is proportional to the size of the chip, which is going up. So one, it became irreplaceable, to, there are more chip design, the their size of the chip going up. So it has been a record year for, I don't know, last 6 years. I don't think that's going to slow down.

Yes. Very good. IP. Let's come back to that one for a second. With your market -- in terms of your market position there, you've got a very wide product breadth across a bunch of areas, including TDR, SerDes, PCI, even process or course to some extent. Maybe talk about sort of the diversity of the IP offerings and like in what are the specific areas where you feel like you have most competitive advantage?

I think IP, the interesting part is, of course, we focus on lower nodes and HPC IP, which is exactly what is of course, growing the most. Because we didn't want to do all parts of IP because it's not as profitable. And also we want to do, of course, where the work is going. And we focus on like 5 or 6 critical pieces of IP. Some of it we developed, some of them we acquired. So like this is like the SerDes IP, the PCIe, UCI, which is chip-to-chip HBM connection to memory -- so these are -- in terms of design IP, these are the critical IP that a lot of customers want. And then the other key thing that happened is our team is much better than before. I mean, in the end, right, these are standard based IP, so the customer will buy if the PPA is good. In the end, it's not just having the IP just like in anything is how good is your IP. So our team is -- we -- anyway, I personally believe all the leaders should be highly technical and engineering background. So that's true for all my GMs. And I think we have -- this is one thing that has changed in the last few years. Our our EDA teams is always world-class, okay? -- hardware teams, world-class -- now our IP team is world class in terms of design capabilities. And they can also use AI to further accelerate their own -- so then the output of the IPs are very competitive at TSMC and other foundries. And then the third thing that happened is these other foundries also want to get it, but we need to develop IP for them. So whether it's Samsung, Intel, Rapids, along with TSMC. So I think these three things, our focus is correct in terms of the market segment. Our team is much better and PPA is much better PPAs power performance area of IPs. And then the market is naturally growing with newer foundries.

Got it. Okay. I want to move on to your last segment, system design analysis. My personal interest is like, I think, is the most interesting segment you have in terms of the evolution. You've talked about FDA enabling companies like aerospace and defense OEMs to simulate the whole system. How different is the product strategy when you're selling to somebody like a Boeing relative to somebody like NVIDIA or AMD, I mean, do they want the same physics models? Or do you have to take a fundamentally different approach to R&D for that?

No, it's similar. That's why I did it. By the way, I don't know if you know the history. I'm the one who started it in 2017 and people thought this was preposterous, like why would EDA and SDA be together because they were not together, okay? And there were multiple reasons for it. I don't know how much time I have to explain the reasons. But at the highest level, first of all, the math is very simple. R&D is very similar. And SDA is easier than EDA. Of course, the FDA guys don't like it when I say that. But EDA algorithms are much more complex than SDA algorithm. Electromagnetics is much simpler than circuit simulation. But they're in the same direction. They are also mathematical software. So all companies want to expand, but you want to expand in your core strength. So what is -- because they asked me like, okay, Anirudh, you're going to be CEO. I became President, he going to be CEO. So what is your strategy okay? So the strategy is that go amplify your core trend. What is our core strength in Cadence or my background or all the EDF is nemesis, computation software. This is not -- like I said in the beginning, this is not like some database software or look up a website. This is mathematically deep like as deep as you can get. Of course, everybody thinks what they do is hard. But you can look at what we do. It's like the most difficult CS plus mats plus physics. So that could be applied to two systems. And then the question is, why do you apply to systems. Because if you look at the market, so that's our core strength, mathematical software. If you look at the market, I always thought the market will evolve into these three concentric circles. Again, this is obvious now, but the silicon is in the middle than system and then data, okay? A perfect example is like a car, right, or self-driving. You have all the navigation data, then you have the car, which is mechanical plus electrical, hardware plus software and silicon that drives the car. And this is going to happen in all markets. So if you take those three concentric circles and overlay the strength of ours, which is computational software. Of course, competitional software applied to silicon is EDA, chip design, EDA and IP. And that was always our core, always wanted to make sure that we are #1 in EDA because the other mistake people move as they expand into other markets but lose focus on the core market. So our always focus from the beginning is EDA should be #1. That's why over invested in EDA versus IP, even though IP is interesting now. But in EDA, we have the broadest portfolio. We are clearly -- the company to work with -- but then if you apply computational software to systems, that's SDA. And we want to do things which are synergistic to chip design, so which is like thermal and alter magnetic analysis, which are 3D IC, which are closer -- and then computational software applied to data is, of course, AI. By the way, the AI is even simpler than SDA, okay? AI people don't like that either, okay. It's just linear algebra, okay? That's like I took 7 courses in Algebra under grad, okay? So don' forget you when grade...

Forgot about the Kindergarten on...

Yes. So AI is just -- the algorithms AIs are even simpler than but it's a good -- I mean it has a lot of application, but it is same kind of computational software apply to chip design, which is the most complex and, of course, growing exponentially, then systems and then data.

Great. Just a minute or two left, but I wanted to quickly ask you about physical AI. So how should we think about sort of physical AI being a long-term opportunity for Cadence and sort of sort of where are companies seeing practical value in those applications today or your capabilities today? And how should we kind of think about physical AI being in terms of magnitude of revenue contribution over time for Cadence?

Yes. I'm super excited about physical and have been for some time. And not that we are, of course, excited about the current trends of data center. I mean, those are huge but also physical AI will be a very big application. I mean, if you talk about the cake in the beginning, the 3-layer cake, also for 5 years, talk to about three slices of the cake. These are vertical slices. Because in the end, of course, the value will be vertical, right, not horizontal. So the big slice right now is data center and infrastructure. And I think we are very well positioned. We are working with all the MAG 7 like we discussed, you can see it in our results. But the other thing in strategic direction is you want to make sure you don't miss any of the other big things. One thing is you have to grow in your core trends, number one. So I'd explain like competition software. Number two, you have to grow with the market, so then chip companies are becoming system companies and AI companies, which is obvious now look what NAD is doing or Broadcom and Google and Apple. And number three, you don't want to miss any big trends, okay? So we always over invest ahead of it, not too much, but always ahead of the big trend. So then what are the big trends? If the three layers are horizontal. The three vertical sites are data center first, we are very well positioned. And I believe physical AI will be huge because these are all trillion-dollar markets. See if AI is good enough to reason and talk and see -- imagine what could happen in cars and robots and drones. And these are trillions, trillions of dollars of market. And then the third slide I always believed is signed to the AI, which is life sciences and other deep sciences. I think what happens is people confuse that all these three are happening at the same time. And to some extent, they are, but they have a peak of each cycle. So I think data center is -- I think physically may peak in the next 3 to 7 years. And then life sciences and all will maybe 5 to 10 years from now because that's another important thing. It's very difficult. So we want to invest in all these 3 slices. So we, of course, do life sciences, as you know. And physically, IV did acquisition in Hexagon to get the best kind of middle layer for that. It's the best robotic simulator. So the opportunity for the physical AI is not just -- the AI model will be different. It will be a word model, right? If you go back to the 3 layers of the cake and put the physical AI slice, -- the top of the slice is different because it's a word model, not LLM, -- and there is no data for the word model. You have to do a lot more simulation. So therefore, we invested in Hexagon D&E business for simulation. But also, it will drive a lot of silicon. So our traditional business and the silicon and physical AI will be more mixed signal silicon for cars and drones, which is anyway cadence is traditional strength. And you can see that in Tesla, Rivian or BYD or Xiaomi, I mean I just came back from China, it's amazing what's happening in Xiaomi and BYD and Neo, and they're all designing chips, they're all our customers. Same thing with the U.S. -- some of the U.S. companies like Tesla, what they're doing is remarkable. Rivian, some of the traditional companies are because the criticism has been, oh, this is a very slow-moving market. But I think this self-driving is completely going to change that and then drones and all. So it doesn't mean that we don't love data center. Of course, we love data center, but we just want to make sure we are ready for physical AI ready for science there.

Great. It's a great place to end it. Unfortunately, we're out of time. Anirudh, thanks for being here with us here.

Thank you.