Synopsys, Inc. (SNPS) Earnings Call Transcript & Summary

All right. Well, good morning, everybody, and welcome. My name is Marco Lagos. I run the semiconductor investment banking business for Morgan Stanley in the U.S. Before I introduce our guests and dive into today's conversation, I have to read this preamble -- I'm going to have to read the preamble. So today's discussion may contain forward-looking statements related to Synopsys' current outlook, expectations and beliefs, which are subject to certain risks and uncertainties that could cause actual results to differ. Please refer to the company's most recent SEC filings for a discussion of risk factors that may materially affect in these statements. So with that, I'd love to introduce our speaker today. I'm sitting here with Aart de Geus. Aart, I'll start by saying, in my estimation, he's one of the godfathers of EDA. So I'm pretty honored to be up here with him. He is also an avid musician, has a band called Legally Blues, I believe that he plays with. So we're going to try to riff for you guys up here today, if that's okay with you. All right. And aside from those 2 things, he's cofounded Synopsys in 1986. So quite some time in the industry. He's also the CEO and Chairman of the company today. So with that, Aart, I would love for you to tell us a little bit about the company before we dive on in.

Sure. Well, thank you for having me, and thank you for you being here. Many of you probably know Synopsys pretty well, but just to quickly set the basis. It's actually useful to look at Synopsys as really 3 businesses. EDA, you mentioned it already. It's -- round numbers, not exact numbers, round number 65% of the company. We have 25% of the company is IP blocks. Think of IP blocks as predesigned pieces of chips and the USB on the back of your computer, for example, is for sure designed by Synopsys and provide it. And then we have -- and that is 25% of Synopsys and then 10% is software integrity, which is looking at the security of software. And of course, these 3 things actually a theme that is particularly relevant today because if there's one big destination, and you can see it literally every day in so many things, which is Smart Everything. Everything is going to get some Smart. And with that comes an interesting opportunity space because on one hand, Smart and the electronics behind it have been pushed for many years, the technology development under the label of Moore's Law, which is scale increase of more and more transistors that simultaneously become smaller and smaller. At the same time, what has fundamentally changed is now every vertical market that you can think of has figured out, we have big data, we need to do something with that, and we want to use -- make it Smart. And that, of course, now brings a pool to the market. So you have this technology push up and the pull from the end markets. And the end markets, of course, the minute they see that something can work. And you see, for example, ChatGPT giving a whole new wave of awareness of the potential, they all say, well, give me chips that are faster, that are more powerful that can handle more data 1,000x more. And in order to do that, there are a lot of challenges in our field, but these are precisely the challenges that Synopsys is extremely well equipped for and well weaved into the rest of the ecosystem, the people that are manufacturing, the people that are designing, the people that are testing their chips and so on. And moreover, there is an additional new set of markets that open up such as automotive that only has figured out all smart needs to be not just smart, but it needs to be also secure and really safe because errors could have disastrous impact. So that is why the combination of all of these requirements are so well suited for providing a massive amount of technology, and we're in the midst of that. We ourselves are big consumers or developers of Smart and our own field is making fantastic advances by using AI to automate and get better chip design going. And this is one of the highlights of only of Synopsys from a technology leadership again. And so with that general preamble, I'd like to conclude with the fact that we have given strong guidance for this year, 14% to 15% growth rate in a market that is somewhat wobbly. And every other day, you can see, is it over? Or is it a recession still coming? Well, read the Wall Street Journal every morning and then you will, for sure, not now. And so on that basis, meanwhile, we keep moving forward very rarely, rapidly the technology.

That's fantastic. Thanks for that -- Synopsys. No pun intended. So I guess let's kind of bring it down to investor level, just high level here. So the iShares software index has been down 20% since the start of 2021. [ SoCs ] has been down modally up about 9% over that same period. You guys sit at the beautiful intersection between software and semi. Synopsys has actually achieved a 43% return over the same period stock-wise. What do you think has enabled investors to embrace the company at a time when other companies in software in the sector's overall in software and semis have struggled?

Well, it's always a combination of opportunity space and then execution with that opportunity space. And it's always better to be lucky than smart maybe. But the reality is that we positioned Synopsys now for many years to be at the fulcrum of all of these demands coming from the end markets and the technology developments that are going through some very substantial discontinuities. And at the same time, you have to execute really well. And so our execution really falls fundamentally into 2 or 3 categories. One is a very high attention to the technologies that are going through discontinuity. Just to give you an example, for many, many years, the focus has always been how can you put more on a chip and more on a chip and smaller and faster and so on. And now while that is continuing, it's insufficient. So there's a breakthrough that is heading now to, let's do multiple chips at the same time that are very close together. And out of that, there's 100 to 1,000x opportunity right there. So technology leadership is key. The second thing is the relationship with the customers. And the more there is complexity growing in what we have to do together, the more the world trust actually really matters because trust is built entirely on everything that can go wrong and how do you act at that moment. And that is actually where the team play or just like in a band. If somebody plays a wrong notes, the rest of demand has to make up for that in a second, right? And then the third one is ultimately execution by the company. And in the last few years, I think we have put a high emphasis on driving the products and the capabilities but also enhancing the financials fairly systematically by improving the ops margin and also having a high degree of focus on the growth. And I think that's been very visible as we have relatively quickly passed from $3 billion to $4 billion to $5 billion and outcomes whatever the next one is.

Yes. Look, I think the only thing better than growth with profitability is predictable growth with profitability. Can you talk a little bit about visibility and predictability of your model and your financial model?

Yes. So actually, one of the things that Synopsys pioneered in the software world was this notion of a model where you recognize revenue over time. And so instead of selling software and maybe having a support fee later, what we moved into in the early 2000s is a model where we -- think of it as renting the software for typically 3 years or so and recognizing the revenue as it gets consumed. And that brought a very high degree of stability and also a different negotiation position with the customers because we're both interested in having a stable relationship over a long period of time. Now not all of our business falls in that category, but certainly most of the EDA part, all the software part of EDA, the part of the software integrity but some other parts such as the IP is a little bit more lumpy over time. But in aggregate, it has given us a very high degree of predictability. And of course, the other part of the predictability is to make sure that the relationship with the customers are such that there's an interdependence, we dependent on them and them dependent on us in a fashion that is constructive.

Got it. So kind of taking a step back now to shifting to strategy, what is your sort of long-term vision for the company? And as a second part to that question, I promised your team we talk about AI because you get excited about that. But let's talk about your long-term strategy and your vision first.

Well, the long-term strategy is look at the end market and we actually coined the term Smart Everything already, I think, in 2011 or so because we saw all of these forces gradually coming together that would make it possible. And with many big concepts, it's understood that you wish for something, and it's understood that there's a lot of things to actually do to get it right. And that took a good decade to really materialize. And then suddenly, it's like, well, it's obvious. Everything is going to be smart. How come you're not moving faster. How come you cannot provide another 100x. And actually, for this year -- for this decade, sorry, we have set ourselves the goal to deliver another 1,000x in productivity. And just for the record, in our previous history, Synopsys delivered 10 million x right, in productivity. So we've been somewhat foundational to this entire field. And so we will continue in that direction. Now you mentioned AI. AI, from my perspective, is just another completely different computational way to deal with data. And in somewhat simplistic terms is, traditionally, we have essentially had deductive reasoning if this, then do that; or if that, do this, right? It choices. And that has led to tremendous advances in automation for 50 years. The AI capability is really the ability of that sort of looks like this thing with other words, the pattern matching and pattern matching is something the human is unbelievably good at. When you drove here this morning, most of that [ you there ] completely unconscious because if you're conscious, you would be [ stymied ], you couldn't do it. And yes, by the time you apply AI, it has 2 characteristics. It is not as good as the human in many practical things that we do today, but it learns to optimize gradually and it can see more dimensions than a human. And just take maybe a little walk away example, you're driving here. If you could see the temperature of the tires of all the other cars, you would see some are pretty red. Those are dangerous drivers, [ worst riders ]. Well, it's a little [indiscernible] to example, but this is exactly what we do in domains. We see relationships that before were not visible. Therefore, we can optimize around them. And this is where the computational technology is now sufficient to do this. And in order to be sufficient, it needs to handle a lot of data when we look at the data of a chip. We are quickly talking about 1 trillion or more things to move. And I always ask our own team is our AI so successful because it found one thing that changed the whole picture or now it found 3 billion little things that all improved a little bit. I think the answer is yes. We do both.

So look, I think that addresses sort of AI from the solution for the customer, right, like what you guys are [ saying before ]. But how have you employed it internally to improve your own sort of development?

Well, when I said you quickly have a trillion things to deal with. That's what 1 chip is. It is unbelievable how much is on a chip because the transistors are now moving in size below nanometers, which tend to the minus 9 to Engstrom. Right? And Engstrom is -- well, it contains the word engst, I would like to highlight that with, But other words, it's not so easy to do. And when you have that many details, a single detail can stop things. And this is maybe one of the other characteristics that I would like to highlight for this whole next wave of technology, but also the whole next wave of industrial interaction, which is we are no longer dealing with additive problems. We're dealing with multiplicative problems. So a big set of interactions. And I'd always like to highlight that in English, we often say success. It's the sum of our efforts. It's not. It's the product of our efforts, a single 0, and we all have 0, right? And if you take from the minute parts of the chip all the way to driving a car autonomously without killing somebody, you can see how that multiplication that will be pretty good.

Yes. No, that's great. That's fascinating. As we think about sort of pivoting a little bit to sort of the business composition and business mix, right? You talked a little bit when you opened about what the business is split between EDA, IP and application security, the [ sec ] business, if you will. How do you see that evolving over time given where you're trying to take the company, the mix itself?

Well, I think the mix will evolve a little bit. The smaller businesses tend to always grow a little faster initially. And so that's where the -- we call it the software integrity, application security testing is a good term for it, has the potential to touch many, many things that are actually fairly distanced from what the traditional home of Synopsys is. Although that distance is becoming smaller because the way many new products are conceived, and I'll use the car again as a great example, people are now talking about a car as a software-defined products, right? Well, that software-defined product itself, the software is defined by the workloads. And the workloads may have a lot to do with understanding of traffic and all that. But there's also workloads that want to know, oh, you were looking for McDonald's, and here it is. right? And so there are so many opportunities there. And so the word that ties this together for me is really security. So making sure that things are safe from a being hacked perspective. And that, obviously, is extremely relevant. And then for some markets, you add the word safe because if it can harm human life or impacted, be it medical automation or robots in general or automotive, then they have additional constraints. And I think that, that is, over time, a continuum. And by the way, at the very bottom, we also have security devices in our IP collection that ultimately you have to connect to the safety at top level. Then I'll add 1 more thing, which is a new area of growth, which is what's called silicon life cycle management, and that is to say, well, what can you put in chips so that they can self-diagnose sufficiently so that when you're driving on the highway, and one of the chips inside of the car [ fuel ] say, I'm not feeling so good. How does that chip communicate? I'm not feeling so good to the system, to the dashboard that then says, okay, park the car right now or here's a short prayer because otherwise, it's going to be delayed. It's -- that goes from the most minute physical details all the way to system decisions that have to be very smart to not endanger even the stopping of the car.

Got it. So as I think about that answer, right, it's one thing comes to mind when you talk about the chips not feeling great, right? Data and the explosion of data obviously has a lot of different requirements. You folks have optimized designing chips and helping folks produce chips in a way that maximizes the ability to retain transmit process data. But among many things that you have to manage is heat, right? And the power sync and whether or not that is dangerous to the broader electronics or the chip itself. Why -- can you talk a little bit about your innovations in that regard around power and power management?

Sure. Power really became a topic in the early 2000s, not for the heat reason, but for the battery life cycle reason. And remember, early 2000s really where the mobile phone came about. And while initially was just a phone within a matter of a few years, it was a complete data machine. And so that is where the initial emphasis went on to power. Before that, chips were optimized for performance, how fast and area, how small can you make them because that impacted costs. Power became the third leg on that. And so you're absolutely correct that as we pushed more speed into the chips, with the speed comes a high degree of power utilization. The power is used to switch something but then has to get out in the form of heat. And so heat -- dealing with heat has been a challenge for now quite a number of years, initially on a single chip, now between multiple chips. But there's also a whole different dimension to that, which is, well, is your software going to misuse computation and produce too much heat or can you actually write software that is smarter in the computation. And this is where it's super interesting for us because we have super high-speed simulators. So the things that actually can mock up a design and make -- have the software run on it the ultimate software and tell you, well, this thing really switches a lot here. Can you reduce that? Because all of these switches by the time you turn it into hardware, actually going to produce heat. And so the power problem is not just a horizontal technology issue. It's also a vertical stack all the way to the end applications. And Synopsys is a leader in that area, but it's an area where we also have very strong partners and one of them is ANSYS, for example, as we have some of their source code in our products in a very active participation on driving the state of the art.

Got it. Okay. Well, on the topic of power, I know you love to talk about sort of, obviously, what's next. An exciting thing and semiconductors always is sort of the advent of new materials, right? And as we think about where things are headed now, we've been talking about lasers and photonics for some time. Right now, GaN and silicon carbide are super top of mind for a lot of folks and rightfully so. How are you positioned to sort of exploit that pivot and that trend towards those new materials?

Okay. So one of the things that is not that well known because it's a relatively deep, deep, deep technology is called TCAD, Technology Computer Aided Design. And it is very much predicated on the question of, can you simulate so mock up very, very small physical phenomena, such as a single transistor and understand what happens when you change the materials a little bit, what happens when you change the thickness, the orientation and so on and actually predict the behavior of these. And you're absolutely correct that there is still a wave of new changes initially very much driven by lithography. So how small can you make things also now driven by materials. Can materials learn to align themselves instead of having so many manufacturing steps, but also new materials that are better suited for power devices, for example, and actually a gallium nitride company was just sold to Infineon actually last week or so because they're focusing for automotive on massive power devices. These are things that we can simulate and that we can help optimize. But that is as close to deep physics as you can get.

Absolutely. And put in dumb layman terms, it's enabling the things that enable the things to happen, which I think is...

Yes. Well, you're not that lay and that much of layman -- actually it's enable to enable to enable and then happiness or whatever...

Whatever is results, yes. Well, switching gears a little bit again. you talk about your application security business, the [ sec ] business, Signal Integrity business -- sorry, that the...

Software integrity business.

Software integrity business. It's becoming increasingly important, right? Unfortunately, bad actors, hacks, all sorts of problems with the integrity itself of the software. The company has been pretty aggressive over a pretty long period of time in building that business out. What is the future hold for that business, that market, some would say, remains fragmented in many different ways. Is there a plan to sort of be the company in that regard? Or what's the strategy there?

Well, I think in our area, we're already the largest and we're imminently close to passing $0.5 billion. So it actually starts to matter in the overall P&L, of course. But it also matters in the sense that it's another puzzle piece in a puzzle that looks at security as having many different layers. Just that enablement joke we just made it's actually real, right? And so we see that a lot of opportunities. It's still a fairly young market. You're absolutely right. It's very fragmented. And you're also right to observe that in contrast to physics, which you can view as the enemy or your friend, but its -- physics is actually a pretty neutral thing. Hackers are not. So now you're dealing with human ingenuity that we'll constantly try to find some other breach opportunity. So I think that is a field that will continue to grow in importance and also in sophistication. And the very fact that so many things are becoming interdependent. And it's going to be difficult to verify a complete system to push on having subsystems to be verified and checked out at a much higher degree will continue. So there's an opportunity for growth and execution there. And Synopsys has, at least, the experience of doing that in a couple of fields now over the last few decades. And there are many similarities although it's a much broader ultimate TAM because every software company in the world is potentially part of that. The technology is not dissimilar.

Yes. So aside from a technology similarities and sort of the development core competency you can apply to that. There's a misconception that it's not aligned with your semiconductor business. That's a pretty broad misconception. Can you address that a little bit? Is it linked?

Sure. Well, let's start with the around 2000 misconception that we shouldn't be in the IP business. And I always looked at IP business as a shortcut instead of building everything yourself, you have a catalog of pieces that you can reuse. And initially, people said, "Yes, that's not your business, it's not your job" and so on. But the reality is they get tied over time. Obviously, we have to show over time that there's more synergy between these areas. But we've always invested in technologies that we felt were adjacent, adjacent either by technology or by channel or by end customers. And that would become important over time in the overall equation. And so in that sense, I think that Synopsys has acted for the last 30 years in the notion of multiplications rather than additions. But while it's being viewed as an addition, that doesn't mean you continue to invest. It's just that over time, you understand how the interdependencies will grow.

So you touched on your IP business? It's overlooked sometimes that you guys -- with all the noise around ARM, particularly the ARM NVIDIA deal not that long ago, the e-folks are actually the #2 overall IP player, and you actually have a #1 position in some very important IP categories. Can you talk about some of those around, again, some of the similar themes around IP in data center and AI?

Sure, sure. And actually, I'm glad you mentioned ARM because they're the #1 provider of IP from a revenue point of view. And as everybody knows, looking at going public again. And so -- and also a very good partner of Synopsys. And the way to think about Synopsys is we're everything else, everything around. And of course, when you say around, right there, you see connectivity in so many different ways, right? So I mentioned a simple connectivity, everybody knows USB. It's really old, except no, it's not all at all. There's new versions all the time. And secondly, there's new versions in terms of new silicon technologies. So designing these interfaces in 2-nanometer is very different in 10, which was very different than 135, right? And so over years, this keeps evolving. Now on top of that, you have a massive increase of new needs as the whole configuration of chips is changing from being chips on a board to now being chips literally on other chips, this is called multi-die. And with multi-die comes, the whole question of, well, how fast are the signals between the chips because that's always been the challenge. There's a reason why we like to have as much stuff on a chip as possible. It's really fast inside, right? Whereas if you need to go somewhere else, you have to take the elevator down to the next chip or you have to go around the neighborhood corner. Can we speed that up? And so these interfaces are now a whole next wave. And so there's a little bit of an illustration how all of these things hang together, how you do design, what are the tools to do design with and what are the building blocks. I like to think of them as super sophisticated LEGO blocks. But LEGO blocks people that know LEGO blocks. They know, a, they have grown in sophistication over the years; but secondly, they have been the masters of connectivity. The fact that these plastic things, 40 years later, still work is completely unbelievable from a chemistry point of view right? Well, we do the same, but electronically speaking.

Fantastic. That's a great analogy. And now that we've kind of done the walk-through of your overall business in the 3 sort of business units, I'd like to open it up for any questions from the audience, 1 or 2.

[indiscernible]

That's a -- Yes, please go ahead.

2 questions. RISC-V, what about it. And secondly, quantum computing, do you have a new kitchen in a week. No. So the -- RISC-V, obviously, is yet another processor. There are multiple processes on the market. ARM is the best known one. Actually, the x86 is the best known one, which are have been used massively in general compute, I would say. And so RISC-V is an open standard with all the positives and challenges of open standards because making things work is it's as a technology challenge, but also, ultimately, a responsibility challenge of who do you go to? But there has been certainly an increase of interest in RISC-V and a number of people are trying it out or investing in it. And then some countries that want to be less dependent on the U.S. are looking at that as well. We have supported RISC-V for quite a while, and we have many tools that are optimized for it and so well engaged with that. Quantum computing is a bit more distanced from the Silicon Valley today. Although the interfaces to Quantum are going to be important. And Synopsys had for a number of years, an effort in a cryogenic computation, which is essentially the circuits that work at very low temperatures. Now Quantum is a step below that. But there [ too ], think of it as puzzle pieces that we look at putting in there in time to be of interest. But the other thing I would say is there's already a lot of effort, specifically in the security side, "post-quantum" necessity of protection. And it's based on the assumption that, well, if Quantum suddenly works, all secrecy is gone, well, I don't think it will be. Instead, what's already happening right now is that people are in the traditional mechanisms trying to increase substantially the degree of protection. So there's a lot of reactions around it. It's a field of high interest, and that's sort of my assessment of where we are today.

So you've been talking for a long time about AI. Congratulations. I'm just wondering from your perspective, how much of an inflection is generative AI? Is it big? And if so, how can you facilitate it?

Well, there are 2 inflection points, what you believe and what you do. And what I find so fascinated about ChatGPT right now, is that instantaneously, it became a visualization of, wow, this is cool stuff. Let's see what it can do. And once you start putting the "let see" on it, you'll find that there are many things that there's a lot of learning going, but by everybody asking you learn faster, right? And so this is not any different, I would say, than when the Go game was cracked or when many years earlier, the chess game was cracked was very different approaches. But suddenly, it's -- there's stuff here that could probably be used in many different directions. And so instantaneously, there's a battle royale of all the big guys that say, Well, we better up our search capabilities and our representational capabilities. There's a whole set of openings on can this be applied to different industrial applications or learning or what have you. And of course, every college in the U.S. is probably looking at so those assays where they had really come from. Right? And so change is a spark. And the spark of the perception actually drives the spark of the technology because it accelerates things and there's attention. And this is one of the additional reasons why I'm saying, Smart Everything is upon us. And it certainly feels smarter than previous searches. But there's a lot of questions, intellectual property protection. What if you find a piece of your own code certainly somewhere else. This is a story that has multiple chapters coming.

It sounds like many opportunities, too. So Aart with that, we're out of time. We can talk to you for another week, but I appreciate it.

Thank you, Marco.

Thank You.