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

All right. So welcome, everyone. It's the 51st NASDAQ Conference here in London. And I've got the honor of welcoming Anirudh Devgan, CEO of Cadence to the stage. Maybe just before we start, however, I've got a disclaimer to read. So today's discussion will contain forward-looking statements, including Cadence's outlook on future business and operating results. Due to risks and uncertainties, actual results may differ materially from those projected or implied in today's discussion. Right, Anirudh, welcome to London. Thanks for coming along. Maybe just to kick us off here. So for those who don't know, can you tell us a little bit about Cadence and in particularly Intelligent System Design that's really driven your growth for the last few years?

Yes. Thank you, Lee. Good to be here. It's -- now, I think my third time, right, in London? This is a great conference. So thank you for attending and also such a festive atmosphere. I don't know how many people are in London right now. The streets are packed. So for Cadence, just if you don't know, basically, we have -- we provide software, mostly software, hardware and IP products to design chips and electronic systems. So what we like to say is that almost any chip designing the word today uses some form of cadence software. And then over the years now, about 45% of our customers are what I would call system companies and 55% are semi companies, though there's a merger of system in semi, but system companies would be like car companies or phone companies and semi would be the Broadcom, Qualcomm. So that's the kind of makeup of our customer base. So we are in all regions where chips are designed and electronic systems in all investment in R&D in S&P 500. About 35% of our revenue is R&D and about 15% is application engineering. So that's people working with the customers. And we have about, I think, pretty good financial profile so about 42%, 43% operating margin. So that leaves like 6%, 7% for all the other stuff. So we have been efficient over the years. And I still think there is room to improve margin as we continue to scale because we are a compounder of value and we have done this for the last 10 years. So I just stopped there. And 1 other thing like over the last 6, 7 years, what we have executed, what Lee was saying is what we call Intelligent System Design. So -- and the view of our -- view of the world, which is -- you probably know all this anyway, is that if you look at the word 3 concentric circles. So silicon, system and data, okay? And a perfect example of that is electric car. So you have all the navigation data, then you have the actual physical car, which is mechanical plus electrical hardware plus software and then the silicon that drives the car. And that's true, not just in auto, it's true in all the other verticals, data centers, mobile. And so from a Cadence standpoint, our expertise is computational software, that's computer science plus math. So this is pretty complicated numerical software. So that applied to silicon is EDA, which is chip design software. Applied to system is SDA, which is simulation and system software. And then computational software applied to data is, of course, AI. So our focus last 6, 7 years and going forward is EDA, which is chip design software, SDA, system design software and AI.

Great. maybe we'll come back to some of those levels and the different verticals as well. But let's maybe set the scene by going through again what was said at Q3, it looked like a good set -- a good print momentum into Q4, and particularly there, strong bookings expected in Q4. So I wonder you could frame us out for us how that's happening? What were the key messages coming through?

Yes. This year was a little bit unusual shape. Normally, we are very, very predictable because most of our revenue is ratable. Like this year, is about 82%, 83% is ratable recurring revenue upfront is about 16%, 17%, something like that. But the shape of that was more back-end loaded this year. And so I think Q3 was a good quarter to -- because some investors were concerned why is it more back-end loaded. So there's some of things we did it to ourselves in terms of -- we had a new hardware product, which was launched in Q2. So that pushed revenue into second half. And we had certain IP contracts that kicked in the second half. But I think Q3 was a very strong quarter, and I think it kind of derisk the whole year. And Q4 is also looking good in terms of booking pipeline. We have to close all that. There's still like a couple of weeks left. But overall for the year, we should be more than 13% revenue growth and more than 42% margin. So when we look at rule of 40, like operating margin plus revenue growth, so we have been more than 55 for the last several years, and I expect that hopefully continue going forward. So -- but I think that's -- we always look at that, good growth, which is driven by a lot of thematic things, more and more silicon, more system companies doing silicon and then good operating margin.

Very strong with 55% plus there in the rule of 55. Maybe if we turn to some of your products and AI-enabled products, in particular, Cadence AI. Can you maybe help us understand how is the engagement going with end customers? And what does the rollout of these products now across the whole design flow, it looks like? How do these change things in the medium to long term for Cadence?

Yes, yes. Yes. Of course, AI is picked up today. And to me, AI -- I mean any new technology has like kind of 3 phases to it, and that's definitely true in AI, and we want to make sure that we participate in all the 3 phases. And that's a unique thing about Cadence versus any other software company or semiconductor companies. So the first phase of AI is infrastructure, right, which, of course, whether it's LLMs or more importantly, the chips, the GPUs that are used for training and evaluation. And then the second phase of AI is applying AI to our own products. That's going to happen in all companies. Definitely, we have been working on that for at least 5, 6 years now. And then the third phase of AI is it will create new markets and new products. And I think the third phase is always the biggest in any new technology. But with AI, the first 2 can also be big, especially the first 1 because of the competition. So first thing is anybody designing AI chips is a big Cadence customer and partner. So like NVIDIA, we have been working with them for like 20 years, okay. And they are development partners with us with several of our products, and I think they themselves have said that they can't design these things without Cadence. But that's also true, not just in NVIDIA, which we have a great partnership with all the other data center companies, okay? And most of them have publicly announced that they are doing silicon, whether it's Google, with TPU or Microsoft or Meta or Amazon, and then in other parts of the world. So 1 part is in the infrastructure build-out, which is still going to go on for a while. And then the data center build-out is going to extend, as you know, to like phones and laptops and look at Qualcomm and of course, look at a big phone company. So all those we are central part of their design process, and they use a lot of our products. And all that infrastructure build-out is accelerating, as you know, more and more broader and a faster Cadence. So that's 1 benefit of AI to Cadence, okay? Now the second part, which you mentioned is applying it to our own product, okay? And of course, AI -- one thing with AI is like everybody calls everything AI, so nobody can really figure out what is AI. So I was starting to 1 plumbing company, and they said they are doing AI, so this is the problem, talking about AI these days, okay. But from our standpoint, what is really AI is -- so if you look at -- we have done this kind of numerical software for like 30 years and a lot of automation because some people call automation AI or AI automation. But EDA has done -- EDA stands for electronic design automation. So we have done automation for 30 years. And even if you look at chip design in the late '90s to now last -- or early 2000s to now, the chip design is probably 100x more efficient because of our products and also the foundry ecosystem that has developed. So we have also done automation for a long time. So the question then is what is new that AI can provide to EDA. Because a lot of other industries, they just call automation as AI, which is not really -- I mean, that can be 1 definition of AI. But I think the key thing with EDA, so EDA has very complicated software, okay. Of course, everybody thinks what they do is complicated. But in reality, if you look at this kind of mathematical numerical software, it is designing these chips with 100 billion transistors. So all of these have to be designed by software. But in the history of EDA, what we did was we focused on what I would call like a single run, like you give it some input and it runs. Does a lot of complicated stuff and gives it the output. And it runs for a few days, in some of these kind of implementation platforms, like Innovus, which is used by most of the big companies. But what the customers want, they don't want -- design process is iterative. You don't run it 1 time. Otherwise, the chip will be designed in 2 days, right? So typically, our customers will divide the chip into multiple blocks or CPUs, GPUs, camera systems, all those things. And then you run the design and then you change something and then you run again and change something, you run again, right? This is design process in anything, right? So EDA never provided this kind of workflow automation, because you want -- you have to carry the knowledge from 1 run to the next run to next run, right, to really do workflow automation. And it's not that we didn't want to do that. This is like obvious thing you should do, right, to optimize the whole workflow rather than a single run is because there was no mathematical way to do it, okay? So I'll give you example, like 1 of our automotive customers, that's a big topic, okay. They're designing CPU and it takes like 6 months, maybe each run in a few days. And then the designer has intuition of, okay, I did this last time he or she and this is now a new -- I'm going to 7-nanometer. So this word worked in 10-nanometer and -- so they use their intuition to do that. So that's 1 way to do it, okay? The other way would be like -- and in that example, they were using like 17 different variables that they're changing to design it. So the other classical way, if you remember undergrad or something is design for experiments, okay? You would -- that's a statistical method. So we can automate that, right, as a software provider. But if you do design of experiments, that kind of design will take 4 million runs, okay, to really exhaust the design space. Okay, that doesn't make any sense. Each of these 2 days, 4 million. Now with AI, with reinforcement learning and a lot of these new AI techniques, that can be done in about 200 runs, because AI -- the beautiful thing in AI is that it can create a model of a particular circuit and then you can use that to traverse the search space, okay? So that's a huge thing. And then a lot of things are -- can be done in parallel. There are some sequential parts, but you can run those 200 runs in parallel on 10 or 20 machines. So what used to take in like 1 or 2 days can be done in 1 or 2 weeks. I mean, in terms of 1 run. But that 2 weeks is still much shorter than 6 months that is done in the old way of doing the design project. So that's huge because you say, oh, you are doing in 2 weeks, what it would take in like 3 to 6 months. But that's only part of the benefit because the human nature of design is they would still iterate. It's not -- even the AI does it in 1 or 2 weeks, the designer will still iterate a few times, but less than before. But the real value of AI in this context is that because this is optimization, it's a generative design process. It can give a better answer than a human can do, because you're optimizing in 17 dimensions design process. So not that any of the answers are wrong, but if you are mathematically searching a 17-dimension space, the answer can be better. Not only it's faster. And I think this is the real value of AI is that the answer sometimes can be better by 10%, 15% power which is equivalent to 1 technology node migration, okay? It's huge. I mean some customers will spend months optimizing for 1% or 2% power. And with AI, you're getting like 10%, 8%, 15% savings. Now it depends on how good the design was. And then also, most of these big companies have a wide range of design teams. So what we have found is with our AI products, not only it's not -- it's always better than, but it's always better. It can be slightly better or much better, but better than the best team, but you have a range of teams, so you can uplift the talent of all the teams. So I think this is the main thing with AI because of this knowledge transfer because of this model-based things, we can actually optimize the workflow and the benefit can be significant. Now what does it do to our fan-out customers? So first of all, you can do much bigger chips and all that and which they need to do because we are still in this node migration, right? So we are at 3-nanometer. We'll go to 2. We'll go to 1.4. We will go to 1, okay? This much the industry can see. So that's at least 10 years of migration. Now -- and there are, of course, a lot of companies working beyond 1-nanometer. But at least 10 years, we can see. And then each node, the size of the chip effectively doubles. So even if it's the same size chip, you have to double the number of transistors, okay? So by 2030 -- and right now, the biggest chip is -- you probably know, is Blackwell, right, from NVIDIA. So they have 2 chips in a package. So it's 100 billion, 100 billion, so it's 200 billion. By 2030, it's widely expected the chips will be 1 trillion transistors, okay. So of course, those things are 10x bigger and much harder to design. So the customers themselves need to go higher level to hours of automation, things like AI to be able to do that, okay? So I think that's 1 benefit to our customer. The second benefit is that right now, we are about 11% of R&D is EDA or software, okay? And 89% is headcount, right? So we talked to a lot of customers. If the chip gets 10x bigger and the design complexity gets 30, 40x bigger because bigger chips, you have more things, we have software, all that. So there is no way that our customers can hire 30x more engineers, okay? I mean first of all, they don't want to. Second, there are not that many engineers in the world. So -- but the -- I think the number of engineers will grow maybe 2, 3x, it's not -- because the problem is an exponentially increasing problem. So even if you do a lot of automation, I think the number of engineers will grow by 2, 3x. But there's still this 10x gap in terms of what is needed, where is what can be achieved. And so that can be done with these -- more automation in AI. So at 2030 from now -- and we used to be 7%, 8% of R&D is growing 11%. So the real opportunity is that will the customer will spend more on compute and software, and that portion that 11% can move up versus the overall spending on headcount.

Maybe just touching on all that, there was a lot for us to digest there. But essentially, if you're running a lot of multivariate optimization to raise that bell curve, you're also -- you're transferring cost efficiencies to your customer, both in saving on headcount, but also time to market. How are we going to see cadence capture value on this as you move from 11% to maybe 15% of the R&D budget, how much of that is directly attributed to your LLMs or AI play in these tool sets?

Yes. So we have the base tool, which is in OS, right, which is like what we have always done, which is still a very complicated tool. And then we have these AI things that run on top of it. So I think the customer can see with and without AI, what is the benefit, okay? And we also ran it internally. We do our own design also for Palladium, which is our hardware system. So we also saw like 15% better PPA. And our experience is we are very collaborative. We're working with -- like most of our revenue, like 60%, 70% of our revenue is coming from like top 50 customers. Of course, we have a lot more customers, but these are -- and these are like the top 50 customers. They change over time a little bit. Some new ones come like the hyperscalers come, but these are the like top companies in the world, we would say, okay, in semi and electronic system. So what we find, we have a pretty collaborative relationships. So if we deliver value, normally, the history is that we do get share in that value. And we have all kinds of business models to work with our customers, and you can see that in our history. So our goal is to deliver value, and we are anyway essential to their design process, right? You need to use these tools. And so we price them accordingly. We have different business models, and we see how that progresses.

I'll open up the floor in a second, but I just wanted to ask 1 question on automotive. Clearly it's a vertical, I think you see a lot of opportunity and you made the acquisition of BETA as well. So you see it in both emulation or systems as well as in the compute power. And a lot of work going on in China, it seems as well I wonder if you can help us understand how big could this opportunity be for Cadence?

Well, I think it's going to be huge. I mean, of course, people -- we love the data center opportunity, because that's right here and now and you call it like, I would call it, Horizon 1, all the AI that change the data center. And a lot of people predict the data center opportunity is like -- from a silicon standpoint, is like $200 billion to $400 billion, okay? This is huge. You can take either of those, but it's a big number because the semiconductor market right now is $600 billion. So the data center could increase it by $200 billion to $400 billion. And that's why all this design activity with our big data center companies. So that is right now and that is going to continue for a few -- at least for a few years, if not more. So -- but we're always looking at what is next and what is next after that. And right now, people are a little down on auto because look at the -- some of the revenue or they look at some of the challenges, but what -- when we work with customers, we are looking at what they're designing now. It will come out like 3, 4 years from now, right? So right now, I see as much activity in auto, like I saw in data center a few years ago. And auto right now, there about each car roughly has $400 of semiconductors in each car, okay? And there are about 100 million cars made worldwide every year. So that's about $40 billion of semiconductor content. But if you ask most people, they expect that number to go to anywhere between 2,000 to 4,000, okay? So at 2,000 to 4,000 at 100 million cars, okay, that's $200 billion to $400 billion okay? That is as big as the data center AI opportunities right now, okay? The other thing is like talking about like these 3 phases of AI, so like the infrastructure first and then current products and then new products. So the big question anyway, is what will be the new products? Because in the end, AI has to generate trillions of dollars of value to justify hundreds of billions of dollars of investment, okay. So the $1 million question or $1 trillion question is what new markets will AI generate, okay? And I know I'm sure everybody is thinking about that. Now I do have an opinion on that, okay. Hopefully, I think it will be -- and so what thing with AI -- of course we talk about AI can do like workflow automation and all that, so that's good. So we will capture some of the value. But to generate trillions of dollars of value, what is AI proven to be good at. Of course, a lot of people say it can do reasoning and it can be super intelligent. Okay. Let's hope that's the case. But what has proven to be good at right now is seeing, okay, vision, and talking, right, chatting. So talking and seeing it has proven to be as good as humans, let's say, okay. So to me, the biggest opportunity of applying that and in the beginning, it's the infrastructure phase, but in the real value will be in the verticalization of that, not in the horizontal. It's always the application of -- because AI is a technology, it's like Calculus, it's not the -- a few years later, you won't say, well, nobody said, I'm doing Calculus now because it's like, everybody is -- so it's like in the end, it will be the vertical application of AI. So in my mind, 1 of the biggest will be what I would call physical AI. Because right now, it is not applied to the real world. It's more in software and all, which are great. So the physical AI has to be then -- the cars will be huge if they can be done in more and more self-driving and more AI, which there are signs. Now it has been coming for 10 years. I remember when my daughter was little and say, "Oh, you won't need a drives license." But it seems to be happening, look at what [ Waymo ] is doing, what Tesla is doing, what all the Chinese. So I think in the next few years, it's highly likely that self-driving or more and more driver assist will happen in cars. But the other thing is that the cars also -- there are other autonomous systems, not just cars, which are robots, drones, planes, okay? So I think this is all going to be in physical AI, this also a huge market. And if you look right now, the chips that are used in drones are similar to chips used in cars, look at Tesla or NVIDIA. And these chips will be much more power constrained which -- for robots and cars. So there will be different than data centers. So they have to be custom designed again. And that's what we're seeing right now, like so many -- I just talked to 1 company who's going to do a special chip for a drone. You talk cars, robot. And that is definitely trillions of dollars of economic value, okay? So to me, the horizon 1 is infrastructure or data center, Horizon 2 is physical AI, which is cars, drones, planes. And Horizon 3, now these are shifted -- not everything is going to happen together, but we are here for the long run. So we always track what is happening next. So Horizon 3 to me is sciences AI, because eventually, AI will be applied to do real science, okay? And material science, physical science. But the biggest application will be life sciences, drug discovery and the whole pharmaceutic -- patient process, but also -- so we all invested a few years ago, about 2 years ago in biosimulation and life sciences. Okay. That is maybe 5, 10 years in the making, it's still lots to do, but you don't want to be too late -- you don't want to be too early, but you don't want to be too late. So in my mind, like there's infrastructure AI, then physical AI and then sciences AI. So we want to make sure we are relevant in all these 3 phases. And auto, especially, but then robots and drones and all, I think will be huge in a few years. And so that's why we did BETA acquisition. And also all these companies, I just came back from China, okay? And all these car companies are also designing their own chips. So that silicon system data, which has happened in data center, is, of course, happening in automotive, yes.

Fascinating stuff. I did say I'd open it to the floor. Just wonder if there's any burning questions. Hands go up, maybe at the front here or second row, sorry.

You have a new hardware product cycle coming.

Yes, yes.

Hardware is lumpy. That can be good and bad. Should next year be very good, I guess?

Well, I -- that's a good question. I mean I expect -- I mean, hardware, I've been doing this for some time and hardware has been record year after record year maybe for last 5 years. And we are not talking too much about '25 because we want to see how '24 ends up. We still have to finish our Q4. Even though pipeline is strong, you have to convert the pipeline. But if history is any indication, the '25 should be good for hardware because normally, the first year, there's a lot of transition -- we launched the hardware product in April. So first year, you have to ramp up production and all that. But definitely, second, third year have been historically very good. And we are competitively in a very, very strong position. So just for folks who want to know all the details. So we are the only company that designs our own chips to make this hardware systems, okay? So and these are pretty complicated systems. So when -- for example, when NVIDIA and Jensen talks about that, you used a supercomputer to design the supercomputer, he's talking about Palladium and Cadence [ topography ]. So these things like, for example, Blackwell is designed on 8 racks of Z2. Palladium Z2 is our last generation system because we have to be ahead of the market, right? So that 8 racks of Z2 designed Blackwell, which is 200 billion transistors. And Z3, we can now do 16 racks as Z3. So it can design 1 trillion transistor systems, which the industry will reach by 2030. And by then, we'll have another. So these things are -- and these are designed -- the chips may design by us, made by TSMC. And we can have each rack as we don't disclose exactly, but more than 100 chips, all liquid cooled and 16 of them connected. So there's like 2,000 chips all liquid cool, the biggest chips TSMC makes that emulates a physical -- the chip that has not been created, 1,000x faster than x86. So what customer will use is they will use Palladium, which is a hardware platform to basically emulate a chip, like they will emulate like NVIDIA, for example, because they publicly talked about, they will emulate Blackwell, and they will boot all the software, verify that it works even before you have Blackwell or a phone chip or a car chip. So we have at least -- I mean, we have a huge competitive advantage, and we are winning even in Q3, Q4 because the product was launched in Q2, we are doing very well with all the big customers. And all the AI chip by nature are very, very big and require these kind of -- because software is a big part of bringing up any of these systems. So while the customers are designing the hardware they will use palladium to emulate the chip and then develop software in parallel. So -- and then the demand for hardware is going up, it goes up with the size of the chip. So if the chip gets bigger, you need more hardware. So long story short, I mean, I am pretty optimistic of our hardware positioning and going into the next few years there.

Unfortunately, we are out of time. Private Suite 3. I think they've got meetings there. Anirudh, thanks very much. Thank you.

Thank you.