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

Okay. All right. So good afternoon, everyone. Welcome to day 3 of the Morgan Stanley TMT Conference. I'm glad to say that I've got Anirudh Devgan on stage with me today, the CEO of Cadence Design. Just before we start, however, a safe harbor statement that need to read out. So today's discussion may contain forward-looking statements, including Cadence's outlook on future business and operating results. So due to risks and uncertainties. Actual results may differ materially from those projected or implied in today's discussion. All forward-looking statements during this meeting are based on estimates and information available as of today and cadence disclaim any obligation to update them. Great. Now let's get back to business, shall we, I welcome again to the stage Cadence. I had to have a look at the share price chart. It's quintupled in 4 years, the share price. It's been a fantastic journey. But for those who maybe just don't know who cadence are, could you maybe reprise what it is you do, where you fit in the EDA space? And how does EDA inform the semiconductor universe.

Good to be here. Thanks, and thanks for attending and your interest in Cadence. So we are basically a software company say computational software, software companies to design -- we sell products, software products to design chips and electronic systems. So almost any chip design in the world today uses some form of cadence software. And then, of course, all this importance of semiconductors in general, last few years and going forward. And I think what you may already know, but what some people don't realize how essential this design software is to design these chips because the chips right now could be like 100 billion transistors in 1 chip. So that's roughly 1 inch by 1 inch. So that's just -- and they're all nonlinear kind of switches, basically these transistors. So to design them, it cannot be done manually, not even close. So for years, they are designed by software. And that's the software we provide to design.

Perfect. So a big part of the design flow here for semis. And what we've heard a lot about recently is obviously the systems design and analysis business that you've moved into. So maybe you could explain how does that fit into EDA? What are the growth drivers? And how should we think of that as an important part of the business going forward?

Yes, absolutely. So the way we look at the world and this -- you probably already know all this, but just to frame what we are trying to do. So the way we look at the world is to convert in 3 concentric circles. So the inner most circle is silicon, then it's the system, which could be a car or a plane or a phone and then the data that surrounds it and this is happening in all verticals, right? And a perfect example is like electric car. So you have all the navigation data, then you actually have the physical car. So that's electrical plus mechanical. So there's a lot of convergence of electrical and mechanical. And it's hardware and software. That's what makes the system. And then the chips that drive the car with cell driving or infotainment. So that's what's happening in the overall customer landscape. And then in terms of our expertise, what Cadence is good at over the last 30 years, is EDA. So what is EDA is basically what I would like to say is computational software. So this is computer science plus math. This is not your regular software. It's very numerical, it's very kind of intense mathematical software. So -- and that's the history of EDA, that's my own background, okay? And what happens is because of Moore's Law and silicon, the complexity of the software has to increase every few years, and this has happened for 30 years. Now if you apply computational software and you overlay it to these 3 kind of circles, so computation software applied to silicon is EDA, which is our core business and which we are the leading provider of EDA software. And then the companies always wants to grow, right, naturally, every company wants to grow, but you have to go into an adjacent area, both in terms of customers like these 3 circles and competence. So if you apply computational software to system and we want to do software. So system software is about what, $50 billion market overall. But about $10 billion of that is computational like simulation, like simulating planes and trains and thermal and data centers, okay? So in 2018, we saw this coming, this convergence between system and silicon. So in 2018, we have a big effort to go into what we call SDA. So EDA is for chips, SDA is for systems like planes and trains and data center. And then if you apply computational software to the third circle, which is data, is, of course, AI because AI Essence is matrix multiply as inference and iterate matrix quantify conjugate grade, that's training, right? So there's a lot of similarities between algorithms in EDA, into SDA and into AI. So our expansion over the last 6, 7 years is in these areas. And of course, these are overlapping circles. So what's happening is the system companies are also doing silicon. This is a big trend. As you know, all the big data center companies, hyperscalers and car companies, phone company has been doing it for a while. So there's overlap. And then we sell software to system companies that are not doing chips like to design like McLaren to design the shape of the car so that it goes fast. And then even AI, we can apply AI to our own products and also do AI in terms of building out the infrastructure -- so SG&A, which is -- we are doing for the last 6, 7 years, growing at more than 20% a year for us and is also a very profitable business. Because even in EDA, in general, we are very profitable and we are very focused on the profitability. But within EDA segment, the most profitable part is simulation because one engineer can launch like 100 [indiscernible] do a lot of exploration. So that's the other thing I like about SDA is not only synergistic from a customer standpoint, from an R&D standpoint, it has a good financial return in terms of profitability.

Maybe just staying on SDA. There was an interesting acquisition, I think you've announced today the beta in the emulation space. It seems -- but interestingly, you've touched on automotive. It looks as though that's a very automotive geared business as well. So maybe if you could walk us through the deal rationale and maybe some of the profitability in a business like this that you can see as a go forward.

Yes. I love the automotive space. And as you know, it's going to go through a lot of transformation, okay? I mean, the 3 big spaces, apart from -- a little bit on the market, a comment on data is -- so -- so silicon business right now, $500 billion, okay? And the system business is about $3 trillion, okay, electronics and all that. And there's a lot of talk about that $500 billion will go to $1 trillion in semiconductors, okay? And of course, a lot of it right now is made up of mobile and consumer and PCs. And I think they will do well. But the 2 new growth areas that we project is data center and automotive. So even for the silicon part, all these projections, at least like $300 billion, $400 billion more in market. And automotive, what is interest -- so the data center part is well understood, right, with AI, and I can talk more, and we are glad to partner with all the leading companies there. But the automotive part -- so one part in automotive is there is electrification of the car. So that is happening, of course. But with that electrification, what is happening is the silicon content is going up -- so right now, I think roughly -- I mean, there are different numbers, but average like $400 per car is the selling of content, okay? And given all this electrification and need for more differentiation because right now, most of the differentiation is in the powertrain. AMG or Turbo or whatever the company is calling. But going forward, it will be more in semiconductors and electronics. So it's projected that each car will have $2,000 to $4,000 of silicon content in the next few years. And there are 100 million cars sold every year. So that's $200 billion to $400 billion more of silicon content. So there will be a lot of growth vectors for semiconductors, but the 2 big ones. And I think it's -- one is slightly delayed than the other because auto takes more time. That's a little more conservative, which they should be. So it will be data center, a few hundred billion, followed by automotive, a few hundred million. And that's in our core business of EDA and silicon design. But there is also an implication of that is in the system part of the business because there's all this overlap between system and silicon. So in the system part of the business, beta has a good presence. They are used by almost all car companies for a structural analysis, which is one of the biggest segment in the simulation part of SDA. So that's why it's a good size. It's about $90 million in annual run rate, and they have a good reach and it completes our portfolio in the system side, but there's also a lot of synergies back to the silicon side given the growth that's going to happen in automotive.

Fascinating space emerging there, it sounds like. And just as I -- maybe I struggle to try and understand all the sort of determinate parts inside systems design analysis never mind the verticals, but it looks as though there's great opportunity in 3D ICs -- and maybe around this space called computational fluid dynamics -- how does that manifest as opportunity for you guys?

Yes. Great question. So I mean, these are 2 big areas. So 3D IC, I mean, you probably know is another way of saying like system in a package, right? If you open up the -- if you look at the Board, you'll see these black things, which are chips, but typically, they're a package. And in the old days, there was only one chip in that black. Now there are multiple chips and they could be stacked. So it's another dimension to extend Moore's law, it's a big trend. And I think it will be for the next 10 years. And Cadence is very well positioned in 3D IC because to do 3D IC well, you need at least 3 components. You need the IC tools, analog or digital design. You need the package tools and you need all the analysis like thermal and all is a big issue for 3D. One of the biggest issue is thermal or mechanical stress and things like that. Because once you're packing these things so densely the thermal profile is huge. You look at like [indiscernible] or all these chips now, they are consuming so much power in a very small package. So we are well positioned because we are the company that has both analog and digital IC tools. We have a leading position in packaging. And then over the last from 2018, we have all the analysis to thermal. So therefore, if you look at the TSMC flow, for example, they launched about 18 months ago, the 3D block flow. So that's primarily based on cadence solutions. And now we're working with all the other major kind of foundries. So 3D IC is almost like a kind of a stapler between chip and system. It's like not full system, but you're moving towards this kind of complicated chiplet-based architectures and most of the industry is going that way. It started with high-performance computing with AI and all these servers. But now in automotive, also they are going through it. And there's a lot of advantages. We can talk about 3D IC for a while. There are a lot of advantages of doing it because like even if you look at some of these big server chips like Amazon has Graviton. it has like 6, 7 chiplets on a package. And when you go to the next generation, you don't have to design or redesign all of them. You just -- some of them can be at more advanced nodes. So there's a lot of reuse and there's a lot of efficiency in that, okay? So I think that's a great area for industry in general and for us. Now one of the big things in 3D IC is thermal, okay? If you talk to these big foundries, they'll say that thermal is a big issue. And then Intel recently announced like backside metal, which is like instead of wiring it goes from the bottom of the chip. And all the other foundries, TSMC, Samsung, they're all looking at backside metal. So what happens when you go backside metal is that the thermal becomes even more critical, because there's less a silicon substrate, which is a very good conductor of heat. So 3D IC and backside metal, which are both going to happen, make the thermal problem much higher. And then at the system level, the thermal problem is huge with all the data centers and how much power they consume, okay? Now to do thermal well, you need both finite element and CFD. So that's the reason we went into CFD a few years ago because finite element is more kind of traditional EDA and CFD is. So to do thermal well, we had to do CMD. Now if you do CFD, then might as well do CFD in general, right? So the last few weeks ago, we launched an exciting new product called Millennium in CFD -- and CFD is a big market. It's computational fluid dynamics, basically airflow, liquid flow, so it has a lot of applications. So one of them is thermal, but it has general applications like playing design, car design, data center design. So it's a big market. And the way we do it is about 2 years ago, because we want to do our -- we are an R&D-driven company technology company. So we want to out innovate right, of course, and everybody says that, but you have to actually do it. So we acquired a company out of Stanford 2 years ago, which has a new form of doing CFD, which is much more accurate. So if you look at the chip industry, we will stimulate about 99% of the ship okay, we like to believe 100%, but you can never get to 100%, but 99.x% because these are NP-complete problems. So when the chips come back, they work first time right. And that's the big thing in the silicon business. But if you look at CFD or designing a plane, for example, or a car, and I talk to a lot of the aerospace companies, they will simulate about 20% of the scenarios. Not that they don't want to simulate the others it's just not accurate enough or fast enough to simulate the others, right? And if you go to biology or something, they will simulate like 1% or 2%, okay? And the reason it's only 20% is that I mean they verify the other scenarios, the remaining, but they verify through physical test like wind tunnels and things like that. So that's, of course, not as efficient as if you do it in the computer. And there is this big trend over towards digital twins and all that, but still, there is a lot of room there. What I'd like to say is in the EDA or the semiconductor business, we don't have digital twins. We don't -- first of all, we never use that term because that was never a semiconductor term. But if I were to use that term, we would have a digital mother because the computer is the golden representation, and we have techniques to -- so that's the value of semiconductor. So we want to bring some of that into this. There's a big opportunity to do that in the system space. Now going to 100% or 99% will take time, but at least we can double our 40%, 50%, 60% coverage. Now why they don't simulate is because it was not accurate enough, okay? If you look at a commercial flight, when it takes off and lands, which is, by the way, the most tricky part, it's to nonlinear and too much turbulence for the traditional CFD to work accurately. So what we did to solve that problem is, first, we acquired this company is 30 years of research out of Stanford. -- which has this high accuracy, they call it LES, a large eddy simulation model, which can cover the whole space. But it's more computationally intensive because it's more accurate. And anyway, speed is a big issue, not just -- I mean, accuracy is #1, but then speed. Now to get speed, these methods, the way they work, are very well suited for GPUs. Okay. traditional CFD only speeds up a little bit on GPU. But this new way out of this company, cascade out of Stanford, you can get like 100 to 1,000 xp on a single GPU because the GPUs are also getting faster and faster. And then you can put AI on top of it to further accelerate it. So this combination of AI plus principal simulation, which is more accurate, plus accelerated computing with GPU can really give a lot of speed up. So one rack of Millennium is equivalent to like 32,000 CPUs. So that's a new kind of disruption in CFD, not just for thermal, which is how we started for general purpose CFD to simulate planes and cars. And so we already have a lot of customers excited. And this is the first time we have done this in silicon, in palladium, which is used to by NVIDIA and a lot of big companies to verify chips. That's how we get 99%. The same philosophy you want to apply to verify systems.

So I'm going to try to summarize that because there's quite a lot you get... So intra-layer you've got quite a lot of BD, the core to a to work there interlayer effectively is where you start to bring in CFD, pipe cleaning it and 3D ICs first. But if you've gone that far, you move to general purpose, so you move into full systems. -- and that includes simulating Navi's stock equation, edits, et cetera. And with that brings you newer, bigger markets to go after. And it's cascade, you said was the name of the company Fantastic.

So it's like 2 years in the making of this product. But of course, 30 years behind that, but also combining it with the GPU acceleration. So it's super exciting. But in general, what I would like to say is, that I talked about this for a while, so I don't know if you heard me before is I think in all markets -- so one thing is the 3 concentric circles. And somebody told me everything with you is 3 things. which is true, by the way, my adviser, my peers we told me the answer to life is e not 2.7. So how many kids you should have 2 or 3 or something like that. So everything is 3, okay. So the other 3 things, apart from the 3 concentric circles is 3 layer in terms of -- and this is going to happen in all markets, okay? What I call it 3 layer cake. And I'll tell you why I call it a cake separate issue, okay? Because when you eat a cake, I don't know anybody who eats it by layer by layer, you have to eat all the 3 layers together, okay? So you consume all of them together. But the 3 layers of that cake is the middle is what we would call principal simulation, basically physical intelligence based on physics, chemistry, bill, the fundamental like differential equations, calculus, And that's what is, like I was saying earlier, that's computational software. That's our core expertise, right? It could be different in some other industries, but I think there's an aspect of physical intelligence. So -- and then the bottom layer of that cake is accelerated computing -- now it used to be CPU, then it used to be cloud and multiple CPUs now is GPUs, FPGAs or domain-specific computing. And then the top layer is AI orchestration. You use AI to do data science, do the not just principal simulation or physical intelligence, use the data intelligence. And that combination of the 3 layer is going to be very profound. And then it has to be verticalized because these are all horizontal technologies in my mind. AI is horizontal technology, principle simulation is horizontal technology, accelerated computers horizontal technology. But the value will be in verticalization of that. So one vertical is, of course, chip design, one could be car and system design. The other could be like drug development or human eye robust or whatever you want. So I think that's what we want to do. We want to verticalize this in critical areas for us, which is SDA, EDA, so on.

That makes perfect sense. If I could change slightly the direction here, Dassault seems to be someone you are increasingly collaborating with. And it sort of makes sense given some of the changes happening in this market. But could you maybe walk us through the types of collaboration you have with them, where the products overlap, which products you use, for instance, and even the markets where you think there might be relevance for that collaboration?

Yes. Dassault is a great company. It's a great partner to Cadence. And we built some partnerships in areas that we have strong partners and areas is not our core expertise. Like, of course, we have great partnership with ARM, for example, over the years. We have a great partnership with NVIDIA. We have a great partnership with TSMC. And then recently, over the last few years, a lot of partnership with Dessault because I mentioned there's 3 concentric circles and our expertise is computational software, which is more simulation heavy in the system space. But there are other kind of implementation platforms, which we don't do or which is not our focus, which Dassault is best-in-class. So like CATIA, all the planes and ships and all the really complicated things are all designed is enterprise class platform called CATIA on the mechanical kind of aspects of it. And then SOLIDWORKS and other industry-leading platform from Dassault for all the consumer devices, and then they are also very good in PLM, which is -- so this is a great partnership from the mechanical side there, the leader, and we believe we are the leader on the electronics side. And we also have a good -- very good strong business in PCB, Alegro, which is a Cadence brand for a long time. widely using the industry. So this electrical mechanical convergence, apart from simulation, it's also true from the design side. So that's the partnership with Dessault to leverage that full solution across both of them.

Perfect. Makes sense. Maybe if I change to IP. Design IP seems to have been a growing part of the business this year. Some of it is looking at that as maybe a function of a growing use of HBM perhaps in AI, but maybe you could help us understand how is that business growing and doing so well?

Yes. I think IP means, of course, a lot of you may know already, but I don't know is not only you sell the software, you sell some premade blocks like on the chips. So like some interface IP like DDR or PCIe or HBM, that the customers don't have to redesign because they're standard-based, good example, of course, for CPU is ARM, right? So we have traditionally not done as much IP. We focus more on the software side. But I think -- and because we have a IP business, which is, I think, 12%, 13% of our revenue, because I wanted to make it more profitable because historically, IP is not that profitable for a lot of reasons. But I think now I'm happy with the profitability of the work on it, it takes several years. And also our scale is getting bigger -- so that's one thing. The second thing is that there is a lot of new -- especially driven by AI and data center and all this disaggregation of chiplets. There are special IPs like UCI, which is connect one chip to the other chip and then HBM, of course, to memory and DDR and PCI. So there's a lot of growth potential there. So I think if you had a good Q4, like you mentioned, and this year also, we expect good growth in that IP business. So it's a combination of what has changed in the market and our own kind of internal operation of the team is much more efficient.

Got you. Okay. Makes sense. Maybe talking about AI, it's not just that you're designing for AI chips. There's a utilization of AI as an enablement of your tools. And we saw that with Serebris as one of your first products out of the gate. That seems to be growing quite nicely through 4Q. But what is your expectations for that whole business line to grow in '24? And where is that being adopted, do you think, in particular?

Yes. I mean that's a huge topic, right? I mean at this point, almost all our customers are using our AI portfolio. So if you look at the 3-layer cake, right, the top layer is a orchestration. So that is there on the EDA side and on the system side. So at this point, we have 5 major platforms in the top layer for kind of generative AI. And -- and AI itself, I think, will have like 3 phases of adoption, just like any new technology, back to 3, right. So the first phase of adoption is like any new technology will be like infrastructure, which is what is happening now, just like what happened with the Internet. And of course, it's a huge. So there, a big portion of our business is selling our regular EDA, IP and FDA to the AI infrastructure companies -- of course, NVIDIA being the remarkable partner, and they are, of course, doing phenomenally then. But they're all these hyperscalers, there's AMD and all the hyperscalers and -- and of course, we work closely, we announced last year with Tesla, which is also kind of a self-driving, AI chip, slightly different and then all the -- like all the other car companies that are doing silicon. So that's a big -- so that's the first thing is the infrastructure build-out of AI, and that we benefit from that -- and we are central to kind of that happening. The second part is what you mentioned is applying AI to our own products... And because of the real value of this kind of generative AI, it can automate things that we could not automate before -- so the question is why didn't you do this before? Because we didn't have the technology to do that. So we have a long history in EDA for automating things that goes back 30 years because these chips are so complicated, they have to be automated. But what we didn't do is we never automated the workflow. So what happens is we have these complicated tools that do a lot of work, but they run for like 1 or 2 days. But the way the user is designing chips, they are not running at one time, right? They run something, then they change something, they run it again, change. This is a natural design process, right? So I was looking at one CPU we're doing for auto company, and they had like -- just to give you an example, had 17 variables that the user is changing. Some of them are process auction, some are tool options, some are design variables -- so that was still done manually still recently. The user knows, okay, I did in the CPU last time in 7-nanometer. Now I'm doing it 3 nanometer. So this is what it should be. And it takes like 6 months to do it, a group of 30, 40, whatever the number is based on their infusion, now you say, why don't you do it mathematically? You're saying you do computational software for 30 years, why didn't you automate that, okay? The problem is if you do it mathematically, traditionally, okay, there is something called design of experiments and statistics to do it. But if you just do exhaustive design of experiments on this, it takes 4 million runs. So there is no way 1 or 2 days, times 4 million. But now with Gen AI and reinforcement learning, we can do it mathematically. And it builds a model and then you do that to traverse the design space. And so in this case, the example I gave you that would take 4 million runs in the traditional kind of brute force design of experiments would take only 200 runs with AI. Now that's to say 200 is still a lot compared to one, but then you can paralyze some of it, so we run it on 10 machines. And then there is a way to make it more efficient because the earlier runs doesn't have -- there are ways to do that. So in the end, on 3, 4x the cost on 10 machines, we can do that. And it gives us better results than -- now, of course, much more efficient. You're taking 1 or 2 weeks versus 3 months. Now they still iterate. That's human nature. It's not that they are done. But the iteration cycle is much, much better. So the productivity could be like 5, 10x better. But what is really more powerful than that, I believe, because productivity is good, but what is even better is that the result is better than what it can be done by the [indiscernible] because it's very difficult to optimize anything in that many dimensions. So in a lot of cases, the result is better than -- now if the designer had a lot of time and -- for each blog, maybe they can do it. But normally, there's a distribution of talent in any organization, right? So we can sometimes. we have never been worse than the best designer, -- most of the time, we are better than the best designer. But then there's a whole distribution. Sometimes we are way better than 10%, 20% better than which is huge, by the way. So if you're going from one process node to another process node spending all this how much they cost, but we can get half of that benefit by better software, better AI. So it's a significant value that is created by this applying AI to chip design because I think chip design is always very developed but never had this workflow optimization. Some of the other industries have to reach that first level of automation and then apply. So I do believe that applying AI to chip design is a very good use case. And we are doing that with our partners like NVIDIA and other companies, right? So they themselves are applying it internally. And all our top 10 customers are using like [indiscernible], we talked about Samsung. We have Intel, we have collaboration with all of these companies. And they have talked at our conferences and publicly disclosed the benefits of. So that, to me, is an incremental value we are providing. And you have to not only get the base tool, let's say, now was, you have to buy the AI copilot or the thing that drives the base too. So that's the second kind of value of AI is applying -- the first is in the infrastructure. The second is applying to our own tools. And the third is like what new markets it will enable. Just like the Internet, in the end, it enables new markets like Facebook or something which were not possible without the Internet. So I think AI will also go through these 3 kind of phases, and we are deep into the first phase. I think we are starting the second phase because it takes a while for these to be deployed, even though the value is huge, normally takes a few years to deploy these new tools. And then the third thing, which may take like 5 to 10 years, 5 to 7 years is a little longer horizon is new applications. And I believe one of the -- I mean there are multiple new applications. But one of the biggest one will be digital biology. And so 2 years ago, we invested in molecular simulation. And of course, this is a little longer-term thing, but I think it's good to invest before it's too late. And I think AI and simulation can play a big role in biology too. So just like chips, we are stimulating 99% systems, we are simulating 20%, 30%. Molecules, we are simulating 1% right now. It's all -- so I think that's the -- but that will take longer cash okay. But that's the sequence in my mind of AI adoption.

But it's fascinated you can still see that scope to capture the value as we go to that third stage and move out to digital biology, as you say, quite interesting. I'm going to open up the floor to see if there's any questions here I think he put his hand up for you guys. Maybe you were quicker. I did'nt see.

Can you talk a bit about the competitive landscape? Of course, Synopsys is acquiring ANSYS? Does it give them capabilities that maybe you currently don't have. And so I'd like to have your opinion on how the competitive landscape is evolving over time?

Yes, sure. I mean we started this journey, like I mentioned in 2018 because of these 3 concentric circles and the SDA, at that time, people asked me, "Hey, what's this? Does it make any sense, EDA companies will do system design and simulation, okay? So I think now you're seeing other people realizing the value of that. That's the way I would like to say. But we were doing pretty well anyway in the market, and we have a pretty complete portfolio. I mean, one area that I wanted was to make sure we have a structural analysis, which is the acquisition we announced today. But overall, I think we are doing well. We have newer products. I feel very confident in the competitive nature. And our SDA market, just to give you example, just by numbers. So we are growing, I think last year in '23, we grew 22% because now this is a significant part of our business. So we can actually -- that was not the case in '18, but now -- so we're growing 22%. And I think last several years, we have grown 20% plus, even though that overall market is growing much less than that. So that's just a proof of that our products are good and the customers are -- we are able to win. And so I feel pretty good. And we always say organic is delicious. So you want the R&D culture of organic innovation. Of course, we do some inorganic -- why not right, if it can turbocharge us. But I think we want to be technology-focused culture with organic innovation. And I think we are well positioned to do that and also good return for the shareholders. To do some inorganic, but if most of the growth can be organic.

We had 2 over here also sticking to this concept of 3. You've been a great organic story. You've been a great compounder of shareholder well -- so thank you on behalf of our unitholders. We've been on a great long-term shareholders. But what are the 3 things that we should worry about longer term for your business?

Well, I feel that if I look compared to like 5, 6 years ago to now, I think we are better positioned than we were. So I think -- I don't know, make sure you have enough allocation because I think sometimes, I would like to say that we are doing R&D software, right, if you think about it. We are doing design software, it's R&D software. And sometimes people say, are you a semiconductor company, but you're like a soft -- are you really a software company like SAS, we are not. So I think sometimes not enough people know like our characters, of course, more people are getting to know. But the value is that we are tied to R&D rather than revenue. So there are some benefits to that. Now the flip side of that is R&D is more -- is always there's not that much fluctuation up and down. So like that's the other thing, like some companies were tied -- directly tied to production could see a much faster impact from AI, which is well deserved, right, like the great partners we have. So we are more -- that way we are more stable, but maybe often a little -- you have to pass it through a low pass filter. We are more -- so you have less variation, so less so -- but I think that to me is a feature, not a bug, but that's one thing the investors should remember is that we like to be compounder of value. So consistent growth and good margin -- that's what I would say. I mean I guess the main thing for us to worry about, I guess, is the main thing I think I worry about it. I think the -- in the earlier days, I would worry about like 10 years ago, what's happening to the semiconductor market because 10 years ago, we were worried that there will be a lot of M&A in our customer base, and that was happening. But now the resurgence of semiconductors is much -- so what we have to worry about actually just being best-in-class, make sure our products are good. We provide value to our customers, focus on R&D, focus on the team. We always say team, technology and customers because everybody -- that's 3 also, okay? And everybody says customers, which is true, by the way, everybody wants to be customer driven. But if you don't focus on the team and the technology, the customers don't like you, right? So we want to make sure that we have the best team and there's a lot of kind of changes in the workplace, make sure we can hire the right people. and there's a lot of changes in technology. So I think as long as we are relevant, we are in a good market to -- so I think that's to ask me to make sure we have the alignment, we say, win with the winners because all the customers are very valuable, but there are some customers that are driving the entire industries like you know, right? So to have partnership with like with NVIDIA or with ARM or with TSMC, with -- on the system side. So to align with the really game-changing customers and a lot of other customers I can't talk about, but they are equally important, okay, and then -- and have the right technology and the right tools.

Could you speak a little bit more about the hardware verification business and the drivers? And does it become compulsory as customers move to smaller nodes and more complex chip architectures?

Yes, yes. Yes. I mean just -- and hardware verification has seen this huge growth over the last few years. And the reason for that is this kind of need for first time right, this 99% or close to 100% verification that is essential for chip design and otherwise too expensive, right -- and the reason -- the way we do that, like in the old days, we would do like silicon design, let's say, this silicon design, and then you would do system or software design, okay? So then silicon design for 3, 4 years, the system part would take 1 year and the chip -- the system would come out in like a mainframe or a server or whatever it is a car. So that's a very long process. But nowadays, like if you look, the silicon comes out and within 3 months, you can get the systems, right? And so the best-in-class company, they will overlap silicon and software development, okay? So that means that you just finish silicon and then the system comes out, right? This is true for phones. It's true for AI chips, it's true for a lot of things, right? Now how is that possible? Is when you're doing software development here, sorry for my. You don't have no chip. So you have to emulate the chip. So we sell these palladium and protium systems that will basically mimic the chip even before it is manufactured. And these are like supercomputers basically. And NVIDIA is a development partner in palladium for the last 15 years, okay? So -- and then a lot of the other big companies. So then what happens is you can, first of all, verify the chip that is correct, but also you can boot windows or Android or whatever your software stack is -- so when the chip comes out, it is functional. So this is a beautiful thing. And we build a custom chip in TSMC, and there are 144 of them in the supercomputer is liquid cooled, okay, to emulate. And it runs 1,000x faster than a typical like a CPU would run for this kind of application. So now as more and more people go to advanced node bigger chips, all these AI chips, it's almost given that you have to use hardware. Otherwise, you cannot do -- first of all, you cannot verify the chip properly and you cannot write the software. And then all these system companies, the big hyperscalers who do chip design. Of course, there are system companies because they have software, right? That's the first definition of system company, along with the hardware. So then all the software can be booted up on these. So that's why over the last few years, our hardware business, which is palladium Protium has done very well. And almost half of our verification business now is hardware assisted because you can accelerate by 1,000x. So that's why we went into CFD this way because what happened in chip design should happen in other areas because hardware acceleration can provide a huge boost from going from simulation to emulation. That 1,000x it changes your use case. You can boot software and you do all kinds of things, which you cannot do on a CPU chip. So we design our own chips. It is a special bullion processor. it's proprietary architecture. That's why we are the leader in this space.

[indiscernible].

Yes. So the reason that -- okay, there's a lot of good questions there. So -- and of course, we are a system company in that way because that thing is not just a chip. It's the whole system, it's a rack and also for the really big chips, they connect like 8 racks together or 16 racks, all connected on InfiniBand, okay? This is super, super high-end liquid cool. I mean this is like if you visit our lab, you'll see this is super duper like -- and then all the IP has to work, and there has a lot of optics also all kinds of stuff. Now those things are -- has certain capacity, billions of gates, okay? So what's happening is when we go from like 3-nanometer to 2-nanometer to 1.4 to 1, okay, it's going to continue for next 10 years, at least, right, all this migration. So whenever -- when you go from 5 to 3 or 3 to 2, necessarily, they may not be getting faster. So this is the whole debt is more slow dead or not, okay? Moore's Law is dead in some ways that it's not classical, they're not scaling that they are getting faster. But one thing is happening is that they are getting more area efficient. So you can pack more things in the chip. That's why we went from 1 CPU to CPU to GPUs to neural engines, right? So the size of the chip is increasing significantly when you go from 5 to 3. So -- and then the amount of verification you run goes up exponentially because if the size doubles, it goes up the combinations. So that's why there is more and more hardware verification needed. So you need more boxes, you need more use cases. So that's a good place to be.

It's a great place to stop. We should carry this on offline, but we're getting into next plant.

Thank you.

Thank you.