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

Today's program is being recorded and webcast live. Please welcome Tushar Jain, Vice President, Synopsys Investor Relations.

Thank you. Thank you everyone. Good afternoon, and welcome to Synopsys' Investor Day. We're so glad to have you here. It's great to see so many familiar faces in person rather than on Zoom. It's a very exciting day for all of us here at Synopsys, and we thank you for joining us in person and for those joining us online. Before I go any further, I need to read a short legal disclosure. Synopsys will discuss forecasts, targets and other forward-looking statements during today's presentation. While these statements represent our best judgment as of today, they are subject to risks and uncertainties that could cause actual results to differ materially. Important factors that may affect our future results are described in our most recent SEC filings. We will refer to certain non-GAAP financial measures throughout today's presentation and reconciliations to their most directly comparable GAAP financial measures can be found in the appendix section. A replay of today's event and the presentation materials will be available on our website at www.synopsys.com. All right. With that out of the way, as I said, we have an exciting agenda lined up today. Sassine is going to cover Synopsys' next phase of growth. You're going to hear from many of our customers, and then Shankar is going to join him on stage and go deeper into our AI platform. Following that session, we'll take a short break. And then Sheila is going to come on stage and put all of that in the context of our financial model. And we'll end the day with a Q&A session. With that, let's get started.

Hello, and welcome to Synopsys Investor Day. I cannot be more excited to be here in New York City with our shareholders and many who are joining us online. Before I get started, I want to send a special shout out to our employees. I know you've been anticipating this day as much as our shareholders. And I cannot thank you enough for the trust in the strategy, the agility and courage to act and shape the future of our company and delivering with excellence. So thank you. I cannot be more humbled to be part of this company. Now in my presentation today, I want to talk about what is changing around us and the why and how Synopsys is positioned to maximize this opportunity. This year is a special year for Synopsys. It's our 40th year anniversary. And it's very hard for companies for 40 years to start with a disruptive technology, synthesis and maintain the leadership position all anchored. We're staying on the leading edge of innovation and delivering the technology that our customers must have in order to deliver the best product that they are designing. The other important milestone for us this year is the year 1 of the new synopsis of the fully integrated ANSYS and Synopsys. So we're very thrilled to celebrate our 40th year anniversary as well as our new company. Over the last 40 years, we were able to deliver to our customers with different eras of technology disruption, innovation that was necessary and we became mission-critical to our customers' success in delivering these differentiated products. In the era of pervasive intelligence, the need to go beyond the silicon innovation to silicon to system is a necessity because the optimization cannot happen at 1 level of the stack, it has to happen at the entire stack from silicon to systems. Over the last 5 years, we've been on a mission to transform the company. Five years ago, Aart entrusted me as the President and CEO to lead the next chapter of Synopsys. During this time, with discipline and courage, we've been able to make a number of portfolio decisions with divestitures of assets that we did not feel they were needed to deliver from silicon to system or they were not the right asset for Synopsys to hold. At the same time, we opened up our balance sheet and acquired one of the most essential assets for the physical AI era. And when I say physical AI, that same asset, Ansys is necessary to advance the chip design process as electronics and physics are merging. When we talk about Synopsys as the leader in engineering solutions from silicon to systems is very rare for companies to have 3 parts of their portfolio, and each part holds the #1 leadership position in its market segment. We are the #1 in EDA in silicon IP and simulation and analysis or, as we call it, F&A. These assets are becoming critical as we envision the world, as we envision the future, the future products as digital AI and physical AI are converging. The one common thing with or to drive the digital AI and physical AI is advanced silicon. You're going to hear me talk repeatedly and more and more about purpose-built silicon because the world of just a generic merchant off-the-shelf silicon is not going to deliver the efficiency required and needed in order for the workload and the applications to be optimized all the way along the stack. That optimization is needed and necessary in order to drive the right cost, the right competitiveness of the product. So when we think about silicon to system and the increased complexity, pace and cost of these designs, they need to co-design becomes a must. What does codesign mean in engineering? It means you're optimizing in one domain and having the adjacent domain taken into account, so you're not building too much margin in your product. And that's what Synopsys is thriving to do. That's what we call we are reengineering in this era of pervasive intelligence. How do we bring the portfolio together to enable our customers to build the most differentiated product with the lowest cost on time, high-fidelity product with our portfolio. So when we talk about core design and digital twinning and modeling of the end product, is all to deliver better, faster, cheaper products for our customers. We serve a global R&D spend of $1.7 trillion. If you look at the various industries on this chart and the dollar they invest in R&D in order to build their products. This is where Synopsys opportunity comes in. About 90% of that $1.7 trillion leans on traditional way of building a product, physical prototyping. Only 10% of that $1.7 trillion is using technology in order to build their differentiated product. The trend over the last 5 years, more money is coming towards the technology and that 10% portion of the pie is growing, just simply driven by the complexity of these products. You cannot build an EV or a robot or a drone or a chip by having a physical prototype. You need to virtualize, model, design, simulate before you build and test. So that's the opportunity we have, and we truly cannot be more excited about having our strategy as AI is transforming engineering from silicon to systems and expand our opportunity. At the silicon level, more chips are required to drive this intelligence and the compute for this intelligence. These chips are increasingly becoming application optimized silicon, custom silicon. And is evident by the number of OEM and system companies are trying to invest in building their own silicon. And the reason I'm saying trying to invest is not easy to build the most advanced silicon to support the AI and the system requirements. At the system level, these systems are becoming more intelligent AI driven, a lot of software. The increased demand for simulation and analysis before you spend the money to build the physical prototype is becoming as well a necessity and more important. The physics away have codesign crosses silicon and system. On the chip level, the need to have physic simulation with electronics and physics can be fluid, structure, thermal into electronics is already happening. At the system level, having the representation of physics as your end product operating in the real world is another driver as we move into physical AI. The one thing that we are very excited about is the opportunity that AI is bringing. On Monday, and Shankar will talk about it more, we announced our AI platform where we have in multiple areas and domains an agent engineer that is able to call many of the subagents and the tools to perform tasks autonomously. And that is only possible if you can trust the results of what the agents are producing. First-time right product is essential. In order to build the first time right product you need a trust in physics as these agents are generating output and outcomes. Now let's jump into the actual business, and given the tailwinds, how is Synopsys capturing these opportunities. I'll go over the EDA, SNA and design IP. And as you know, we have two segments. Sheila will talk more about the segments that we have, which is design automation and design IP. I'm going to start with IP. The reason I want to start with -- there is no better place to describe the market than our IP position. Our IP position, when our customers are even thinking before even committing to design a chip, they come to Synopsys and they ask about maturity, readiness of a node or a foundry about the connectivity to connect these chips together and how is the ecosystem is thinking about them. So our IP position gives us the best view of what's happening in the market. So I'm going to start with IP for that reason. What is Synopsys IP portfolio? We have what is called an interface IP, which is an IP that connects a chip-to-chip or chip to a system, and we have the leading position in interface we have the leadership position and foundation IP. What is a foundation IP. Think of foundation IP as the bridge between a foundry process technology to design. It's the library when you're a foundry, and you're building the next process technology, the way to represent it in design is through the foundation IP, and we have that leadership position in Foundation IP. Talking about foundries, we have more than 10 foundry support in our IP business. but 80 process nodes and about 3,000-plus IP products in our portfolio. As we think of data center, and you're hearing many of the leading silicon companies starting to position themselves as silicon to system companies. Why? Because when you think of data center, you cannot think of the chip in isolation. You have to think of the data center itself, the whole system. Now you need to take it from the data center to the rack and how to optimize and design the rack as a whole system. At the rack level, then you go to the blade, inside the blade, there is the compute, the networking, the memory, then there you can double-click into the chip itself. I want to spend some time on this picture right here because it's very representative of what do I mean by a general-purpose merchant chip? And how are our customers differentiating because the chip itself from an architecture point of view, they all look the same. You need an AI accelerator, you need the CPU, you need a memory, you need the networking. You need a bunch of interfaces to move data. Now if the architecture, you cannot be too innovative or creative with the architecture itself, where the innovation comes in is the workload down to the architecture down to the implementation of the silicon. So when you look at such a picture, everything you see in purple in here are our interface. So big part of the system is coming from Synopsys when you're designing that advanced SoC or advanced system. The interface IP has multiple standards, and I'm going to emphasize standards. The reason those tenders are important, if you are a CPU supplier and you're building your own accelerator, it's important for you as a customer to have optionality and make sure that these different components can connect together. That's where Synopsys comes in with the interface IP portfolio. We build based on a standard and we ensure interoperability from the host to the other part, if it's a chip-to-chip or a chip to system type of an integration. The reason customization is becoming very important. Many times, if you buy a merchant chip, as I'm showing you in here, sometimes the bottleneck can be the interface. That's where you're unable to move enough data. Sometimes you don't need that expensive accelerator or CPU to be idle for your specific workload. Therefore, as a system company, you're trying to optimize based on your architecture. Now the challenge of that -- the interface IP business, when I say it's been built on a standard, the traditional process for a standard, there are standard bodies, they decide what will it next CIE, CXL, PCIe will look like. Once the protocol definition is done, the ecosystem gets enabled start developing, then the IP is available. That's the business we've been in now for 28 years with our IP business. Standards get defined, you build to this standard, you validate, you provide it to your customer. The AI leaders, they are not waiting for a standard, yet they want the interoperability of the standard. They know their workload requirements and they get started. The workload requirement defined the system architecture, then they're expecting a Synopsys IP to be available way before the standard is defined. You can look at this as a massive opportunity for Synopsys or a nightmare of how to manage to deliver on a standard and customized way before this standard is defined. Now the custom silicon opportunity, I'm sure you have your own numbers, absolutely increasing. And these are the forecast by 2030, which is 6x. The drivers are supplier optionality, cost, workload efficiency. Again, that's why our customers are building and heading towards purpose-built silicon is to address these exact challenges. Now I'm sure you follow, you see, your read, all hyperscalers are building their own silicon. And if you see the words that they're using in here, it's all about strategic flexibility and supply chain leverage. The cost of ownership from Andy from Satya is optimizing the architecture. So that trend, we've seen it. We've been playing in that trend. What we have, and some of you reminded me earlier decided to do is about a year ago, we said we need to adapt our business model because that's an amazing opportunity. And Synopsys is truly the enabler of all these customers and more to build their own silicon. So as customers are buying merchant, doing ASIC building their own, that build your own silicon cannot happen without Synopsys customization of that IP. So what we have decided to do and have been communicating in that language Factory I, Factory II. Factory III I think of it as our standard based IP where you wait for this standard, you build it once, you sell it many times. That's a fantastic business for Synopsys. We will continue on feeding and investing in that business because this is beyond just data center, automotive, industrial, mobile, consumer, all these chips need a standard. So the focus is not only on the data center opportunity. There's the rest of the market, which is fairly significant that requires that Factory I build once, sell many times. So I don't want any confusion we'll continue investing in this factory and leading with our IP portfolio in this factory. Then we start talking about Factory II where we build an application optimized IP, OIP, and application optimized IP is we build it for a specific customer requirements. And I'll describe in a little bit more details, what does that mean to build an IP in Factory I, build an IP and Factor II from an engineering point of view. The business model, the first one, Factory I is your license ones for a program. And if there is any NRE will charge based on an NRE. In Factory II, there's the license per program. There is customization fee, and you see here is different than an NRE and there's a royalty. The reason there's a customization fee and not an NRE, the scarcity of our resources needs to be put and placed on the highest opportunity as we open up Factory II. Factory II, the reason we can do it is the scale that we have with our IP business. We have a massive investment position in the market that is giving us the opportunity to be able to support both a factory I and a factory II. What's the difference from a customer engagement? And again, we'll maintain both a factory will start with the standard being defined, the IP gets developed. This silicon gets validated with test chips, and we provide it to the customer and we licensed it broadly. In a factory to model, we work with the customer very early in understanding their workloads. We become part of their system definition. Based on the workload, we codesigned the IP with the customer, we'll be part of the IP integration with the customer and the system validation and production. This is not only an IP opportunity for Synopsys. This is IP, EDA and SNA opportunity for Synopsys because as we build the IP and they're doing the system validation, we're taking into account thermal, packaging, stress, how to cool off the system. So it's an excellent opportunity to embed ourselves inside our customer workflow and deliver to this opportunity. So that's the Factory two. I remember as well when we talked about it, many doubters how will you ever change a business model that's been established for 3 decades. We don't see it. We don't get it. There is no way the customer will pay for this. Do you have the skills to do it I'm so glad and happy to report today, we have committed agreements with compute leaders, with ASIC leaders, with connectivity leaders. The press release you saw this morning, it's not a onetime one customer end of story. Typically, back to interoperability. A system company or a leading system company, they want their ecosystem to be on the same IP. So as you're working with the system company, they pull you with their other supplier be it an ASIC or a connectivity to ensure they are working with you and your IP is interoperable with what they are using. This is what we released this morning. This has been a work of many, many months not focused on the dollar and cent focused on how you would do it inside my engineering stack. How will Synopsys team deliver with high level of confidence to my chips? I know just in the brief few minutes I mingled with you many questions. What's the duration? How much is? How about this? How about that? I cannot share many of the terms of this agreement, and I hope you respect that because in terms of an agreement between us and the customer are confidential terms. But I can tell you the following. This is a multiyear agreement. It's a multi-generation agreement, the $1 billion that you saw, it's a license fee. Remember, there are three layers. There's the license fee, then there is the customization fee and then there's the royalty. The $1 billion is a license fee for multiple generations of Graviton, Tranium and Nitro. The reason all three of them because each one of them has a need to connect with another chip in the ecosystem. The other agreements we closed are part of that ecosystem for Amazon. We'll talk more as we wrap up this session around how meaningful that is for Synopsys and leading into the AIP domain. Now the other driver including system OEMs like Amazon and other is foundry optionality. It's very important for customers, especially now more than ever before, given the shortage of supply chain, the shortage of silicon. And as you start optimizing at the system level, the system is multi-die advanced package or chiplet or 3D IC. It gives you a great opportunity to have optionality in the ecosystem. But in order to drive that optionality, you need a company like Synopsys to have the IP ready, available, tested at all the foundry advanced foundry leaders. When we say we are the on-ramp to foundry, we are the on-ramp to foundry, back to foundation IP, which is the bridge and the interface IP that is needed to connect the chip to chip or the chip to the system. Obviously, TSMC is the acknowledged leader in advanced process technology. This is a quote from Kevin emphasizing the importance of Synopsys interface IP, along with the foundation IP and of course, the relationship with TSMC or any foundries not only but IP, it's about IP and EDA enablement in order to drive that innovation moving forward. The other questions that I've gotten from you in an intense fashion over the last year, you've lost Intel. Are you at Intel? Are you on 14A? Are you doing 18? I remember my answer, you cannot be in the foundry business without having Synopsys IP I don't care what others are saying. We have actually next year will be a 20th year anniversary when Synopsys and Intel got married. We became a primary partner. And the depth of the engagements, the breadth of the engagements are very well acknowledged by Intel and by synopsis, the importance of both companies. Now I hope the video I'm about to share with you will reduce your anxiety and hopefully, the questions will be less about are you working with Intel to why we are excited about the relationship and continuation of what you started 20 years ago.

Synopsys is an important partner in Intel's success. We have worked together for many years across our products and foundry business from IP and EDA to simulation to analysis. For Intel 18A and Intel 18AP by giving customers a trusted path to advanced node adoptions. The silicon-proven IP certify EDF and solutions based on our PDs in without going people, to optimizing silicon, IP, AI-driven design flows and advanced packaging for the next generation of and physical AI systems. AI is also how chips are designed. Synopsys is helping engineers shorter design cycles and spend more time innovating. And with ANSYS, Synopsys brings design and multi-physics signed up closer together which is important for advanced packaging and multi-die systems. Together, Intel and Synopsys are helping customers design better products.

Before we go to Samsung, I hope you heard the breadth from ANSYS physics to the AI portfolio to EDA, to package to IP. That's the breadth and depth of the engagements we have with Intel product and Intel Foundry. The other foundry, Samsung back to optionality and most advanced silicon is another outstanding relationship we have in the ecosystem with Samsung. Similar to Intel, similar to what we do with other foundry is how to engage early through DTC design technology co-optimization, to develop, validate the process technology, then the IP, then the IP ramp. With that, let's hear from Jin Man.

Hello, I'm Jin Man Han, President of Samsung Foundry. Investor Days are usually about numbers, road maps and the future. But before getting into any of that, I would simply like to say congratulations to our good friends at synopsis on this exciting occasion. As AI continues to expand across industries from data centers in automotive to physical AI, Samsung Foundry has evolved beyond just being a way for supplier to become a strategic partner, providing comprehensive system solutions to the customers. At the heart of this transformation, our deep IP collaboration with Synopsys Samsung Foundry has secured a robust portfolio of Synopsys IP across all processes from mainstream to leading-edge processes who recently to provide optimized solutions for AI applications, we have been deepening our collaboration beyond standard IP to develop customized IT. By combining Synopsys proven design IP EDA tools with Samsung Advanced process and packaging technologies, our AI platform collaboration provides end-to-end solutions that support complex custom SoC designs, including HPC and 3DIC. This enables our customers to bring optimized products to market faster and with greater confidence across the wide range of industries. The best partnership, much like the best chips are built layer by layer. We are proud of what Samsung Synopsis have built together and even more excited about the many layers of innovation still ahead of us. Once again, congratulations to the entire Synopsis team. We look forward to continuing this exciting journey together. Thank you.

All right. Now to wrap up our IP section. We will be raising our long-term guide from the mid-teens to high teens for IP, driven by more design starts OIP expansion, this factory is firing up and accepting and ramping up on customers, the multi-foundry enablement. The projected growth for AIP by 2030 will be $1 billion based on. So this is a line of sight based on the current contracts and commitments that we have with customers. The ambition is to have that business where royalty revenue is greater than the license revenue. I know a number of you asked as well Will you take a dip in your revenue as you're building up the royalty over time. Of course, royalty will ramp up over time as our customers go into production, but there is no dip we're raising from mid-teens to high teens with OIP factory delivering to a $1 billion by 2030 based on the current customer engagements that we have. EDA. Now I set up the stage for and the need for customization, application-specific chips, et cetera. That chip cannot happen without a fast innovation in EDA. The complexity of these chips. The complexity of these systems, they need to implement whatever technology to be with or ahead of the customers as they're architecting their next system with a lot of our investment and leadership in EDA is coming in. I'm sure you've seen many versions of this slide. If you look at the days of AlexNet or AlphaGo to Astra and the massive requirement need for the compute in order to deliver to that intelligence and reasoning, the chips that deliver to it say the TPU of 20 billion transistors to right now, a heterogeneous multi-die system with hundreds of billions of transistors in order for that to happen, you need EDA to lead and deliver on multiple vectors. As you think of an advanced multi-die system, we talked about AIP. We talked about the memory customization that is required, the whole advanced packaging requirement. Physics becomes essential. The big challenge, not architecting the system is manufacturing that system with reliability. What happens is when that system is operating in the field with the intense workloads, that system overheats that system will fail, unless you're taking all these design into account, the physics impact into account during the design stage. Our position in EDA is truly unique. Starting from the core. The core EDA platform, which is the leading franchise of what we call a hyperconvergent flow for the best PPA. Multi-physics fusion. This is the expansion with the ANSYS portfolio. Hardware-assisted verification is more important than ever in order to validate this complex system. And will the AI workload software work when you bring that silicon back. And I don't believe the pace of innovation and shorter design cycle that we talked about in IP, which applies here, is possible without bringing more and more sophistication with AI. Let me click through each. In the core EDA franchise, the innovation vectors are the agentic AI automation PPA leadership is where customers make the final decision. You cannot have a good enough performance and expect you're going to invest many hundreds of millions of dollars to manufacture the chip. You need to deliver the best power, the best performance, the best area of the chip, how to optimize the system with a multi-die advanced package. The fusion of physics, our leadership position, #1 position with Fusion Compiler 3D IC compiler prime time for timing sign-off. VCS Verdi for functional simulation and debug. PrimeSem, which is a transistor level simulator, Red Hawk the industry standard sign-off for thermal about maybe 1.5 months ago, when Halapino was announced, there was this simplistic extrapolation that if a model can build software Therefore, the model can build a Fusion Compiler or a prime or VCS, and EDA is doomed because the model can create because all you do synopsis is build software. The model is going to build that software. That simplistic extrapolation cannot be more far off than reality of what customers need using the power of the model, but the essentialness of the physics and what we generate and what we sign off before you go to manufacturing is more needed than ever. Same thing, here's a quote from OpenAI. Richard Ho, who is the Head of Hardware, where he emphasized the essentialness of the Synopsys EDA in building that chip. So not only that chip did not happen through magic. There was a lot of effort in bringing EDA through the design to generate and to sign off before in this case, handing it over to Broadcom as the back-end ASIC partner for them to implement the chip and Broadcom as well long-standing relationship across the portfolio, and it was used throughout that particular chip. Now what got the attention of many is the time to design the chip, and I'll talk more about the AI contribution to the time to design the chip. But what went under the hood as a foundation to all of it is the EDA, the hardware and the IP to deliver such a system. Multiphysics fusion. The whole pieces with ANSYS was, at some point, the monolithic chip, the scale will not necessarily hit the wall that you cannot innovate further, but you need to look at it from a system level. So the scale complexity, while it's continuing to advance node to node to node, the architecture of the system moving from a 2D to 2.5 to 3 is what kept pace in order to deliver these AI HPC chips. So both the scale and systemic complexity. The moment you think systemic complexity, you're talking about physics challenges, stress, warpage, thermal, photonics, electromagnetics, power integrity, signal integrity. This is where Synopsys have seen this trend a decade ago. If we go back to the 2015, 2016 era, we have the complete stack the way our customers use them is through connecting them through their own CAD and workflow. Then it moved to a fusion architecture, where you start using engines from sign off into design. So you have a convergent flow. You're not getting surprised later and you iterate. In 2018, this is where our partnership with ANSYS started. We took the electromagnetic and voltage drop engine into Fusion Compiler to address that challenge. Then of course, later, as we are now integrating ANSYS and in the first wave of product releases, that technology, the multiphysics technology of ANSYS now is fused inside the digital implementation platform. There is no 1 in the EDA industry can claim this. This is unique to Synopsys. This is the differentiation of our platform. And the key is not only the design is the multiphysics timing, power sign-off engines that are embedded and fused inside the platform. I started with thanking you for being here and thanking our employees. When we said we're going to release the first wave of products 9 months after closing the deal, there was a lot of doubt because it's a massive effort, massive effort. And we did release the first wave of products. And as you read what is the value, better PPA convergence with sign-off, better outcomes. So it's better, faster, better, faster, et cetera, to achieve the outcomes that you're trying to get to. Since then, last earnings call, I mentioned that we have 5 customers in early deployment of the technology. It expanded to that broader list of customers since then. Why? They are seeing benefits, 10x faster photonic simulation, 3x higher fidelity for 3D geometry, and you click through with better PPA, better turnaround time. With that, numbers are going to show that traction. In 2027, we will see revenue synergy from that first wave of integration between ANSYS and Synopsys. Recall, we talked about EUR 400 million synergy by 2029. And Sheila will talk in more details what is it that we'll see in '27? And how does it build our confidence towards the $400 million by 2029? Given the first wave of customer engagement and early deployment in production in two we know we'll be able to achieve revenue growth from that solution, which is the synergy between Synopsys and ANSYS in '27. Hardware-assisted verification, with hardware-assisted verification, the complexity of these systems, remember, the reason our customers, they build a customized silicon is to optimize between their software, their AI workload and the chip itself. The vehicle to do so is emulation and prototyping. You prototype the chip before the chip is there, and you start running workloads. In order to do so, you need a system that has a very high capacity. Remember those chips are massive and can run at a high speed that you can actually bring up the software and validate it. A cool example here, actually, there is the start-up that came out of stealth mode a few months ago. And I got many questions from investors like, hey, this is this company etch, do you guys work with them? We worked with them at day 1 as they're thinking about their own company and how to build their silicon using our EDA, using our IP and how to validate it. And here, what's exciting was they were big users of our Zibo platform. They were able to bring up their workloads in weeks and weeks in here was about 44 days. In 44 days after the silicon came, they were able to bring up their software. That's the process it would have took about 6, 7 months. By the time you bring the chip, you start running a lot of the software and tuning and that took 44 days. Why? Because they started in parallel they started writing the software, validating the software before the chip was there. So that's a great example of customers using both technology together. Our portfolio span from the emulation to prototyping to what we call EP, which is a hybrid emulation prototyping system, that to provide our customer flexibility. Capacity is our differentiation. We differentiate on capacity and system-level performance. That's the use case that is the sweet spot for Synopsys. That's where we differentiate. That's why our customers buy our system. The software-defined HAV at CONVERGE, I explained, our customers make massive investment in dollar to buy these systems. We're innovating at the software level. So the customer does not have to refresh their hardware with every cycle. They'll buy the software that optimizes and increase the performance while maintaining the same system. That's a high, high value to our customers. Now we monetize at the software and the actual hardware that we ship for the customer. And the need there, the demand for more and more expansion of the hardware cannot really be more insatiable than it is right now, given the complexity of the software that you're building on the chip. While I'm not announcing officially the next hardware system, but coming soon, first half of 2017, the code name is ARTEMIS. It's our next Zibu system. And what's the expectation of the next system larger capacity, higher performance and a reliable and best TCO for our customers. Now I mentioned the complexity of delivering on these chips and multiple chips in a system hasn't really been as complex and as intense as it has been now. How do we deliver to it. Our customers are looking for every opportunity possible to introduce AI into their workflow and lean on synopsis on how to automate further because they are what you're dealing with, not only complexity as more system companies designing chips, the scarcity of resources and people that they know how to design these chips are not readily available. With that, let me bring Shankar to go through our AI platform and strategy. Shankar?

Thank you, Sassine, and thank you all for joining us here today. I wanted to start by going back to a theme that Sassine spoke about earlier. We are in this incredible era of intelligent systems where silicon engineering and system engineering are coming together much closer than ever before. You cannot build a die without thinking about the package in which it will reside. You cannot build a package without thinking about the blade in which it will reside. And you can't build a blade without thinking about the rack in which it will design. So this type of co-optimization that needs to happen all the way from the silicon die level up all the way through a system-level design like a rack design is really opening up tremendous opportunities for Synopsys because of the core design that is needed. And with the portfolio we have of EDA software, our hardware solutions, our simulation and analysis solutions and our IP solutions we believe we are uniquely positioned to deliver the silicon to systems continuum. Now the designers of these intelligence systems are struggling with multiple challenges. The complexity of both silicon design and system design has to be tamed because it's compounding generation over generation. The speed at which these intelligent systems need to be delivered it's accelerating because market windows are shrinking. We talked about building these chips and systems in 3-year cycles, just a few years ago. And now we are talking about building them in 12 months with a strong desire to build them in 9 months. And last but not least, the cost of a failure, the cost of a mistake is incredibly high because most likely, you will miss a market window. So the need for getting first-time right silicon and system and software all at the same time. The stakes have never been higher. Now on top of all these challenges that design teams are facing, with complexity, with cost and schedule, there's another huge challenge, which is the engineering resources needed to build these systems. There are multiple reports that talk about the engineering shortage and a recent one from Goldman Sachs further highlighted this, that at the top line, the number of companies building intelligence systems, hyperscalers, system companies reaching deep into silicon design is growing. But the growth of the human capital to meet this design is not growing at the same rate. And therefore, there's a significant gap between what the human capital and capacity we have in terms of engineering. And the top line resource requirements to continue this incredible build-out that we are all experiencing. And this gap is really going to be closed by two things: more automation, and more AI. And with the recent advances we see in Agentic AI and all the frontier models, we are very confident that we now see a path of how the current human capital can apply these technologies and really meet these stringent requirements in terms of engineering resources. I want to take you through a little journey of Synopsys' AI story, which started almost 2 years ago with respect to generative AI. At that time, models were good, but they didn't have a whole lot of reasoning capabilities. They didn't have much orchestration capabilities. And what we could do with these models essentially is build useful co-pilots with heavy context and prompt engineering, we provided useful assistance for engineers. We were able to even provide some level of automation for specific tasks which were repetitive tasks. While these were appreciated by customers, none of this really changed the engineering workflow in any significant way and thereby did not really add significantly to the capacity of an engineering team that was trying to get more and more done with less. But as models have evolved dramatically over the past 2 years in terms of the ability to launch and orchestrate subagents, the ability to do extraordinary reasoning and problem solving. We are extremely excited about what the next wave of innovation is, and it's taking us towards an era of autonomous engineering. We are now able to now move to a much higher level and closer level objectives where models can parse those objectives and then orchestrate a collection of agents to execute it. And with the most recent advances in models in terms of reasoning, we are at the cusp of really enabling autonomous engineering with what we call as long horizon agents. These are agents that are able to just take a desired outcome with the necessary guardrails and the necessary constraints specified by an engineer and then able to decompose plan and then orchestrate a very sophisticated set of tooling vocations, task level agent invocations using all the proprietary knowledge assets and knowledge graph that we have built around our tools. Proprietary APIs and data we have built around into our tools and almost -- and most importantly, anchored by the ground truth engines and solvers, which are really fundamental to everything that we are doing. So while the AI models will reason and explore the generation of the circuits the generation of and the validation of the designs and silicon and systems are basically done by the portfolio that we have across the silicon to system spectrum. So really, the future is long horizon agent workflows, orchestrating across multiple tools and delivering outcomes, thereby ushering the era of autonomous engineering and helping us close that gap we talked about earlier. So let me take a moment and walk you through the Synopsys Agentic AI portfolio. Because of the strength of our portfolio, as Sasin talked about earlier, all the way from silicon architecture all the way to manufacturing TCAD, OPC and so on. And then our systems analysis portfolio ranging from structures and fluids, all the way to electromagnetics and optics. Synopsys essentially is in a unique position to deliver the broadest and deepest identic portfolio across engineering software. What we are doing in every domain is delivering poor domain, a set of long horizon agents that are able to take very course tasks and execute them through a complex orchestration of tools and task level agents that are essentially able to do specific things. For example, let's take the verification domain. A verification agent engineer is able to take an outcome or an objective like here is a spec 200, 300, 500 pages long and give me a verified RTL and test ventures compared to the spec. Or here is a design that I'm trying to improve the coverage on and I have about 30%, 40% coverage take this and make this into a 90% coverage situation. An implementation agent engineer is now able to specify an outcome like here is a collection of blocks that I want you to run an autonomous place and route closure, run sign off, do the ECOs after sign-off and essentially close this block for me. That's an example of a long horizon agent in implementation. Moving to analog design. The nature of the task is no longer, let me click these 10 buttons to get this transistor moved from point A to point B or to connect this device to this other device. The nature of the task is here is a spec of a circuit that I want to build go through the design of the schematic, the layout and the simulation of that resulting design and meet these objectives. That's what we mean by long horizon agents, and these agents are essentially invoking a strong library of task level agents provided by Synopsys per domain. And of course, everything is anchored by the ground truth, physics solvers and engines that we have built across decades across the entire spectrum of silicon to systems. The story doesn't end just in silicon. We've also now extended the same philosophy over the simulation and analysis. So a CFD engineer can essentially provide a geometry side describe the kind of objectives that they are looking for and the entire setup and meshing of the CFD, the execution of Fluent and then the results analysis of the Fluent CFP simulation, and then the next steps in order to further improve that all gets handled by a long horizon agent. So this is really something that we believe is going to bring a significant level of autonomy into engineering workflows and thereby increase the capacity. Now all these capabilities, the long horizon agents the task agents that the long horizon agents can invoke and then all the invocation of the tools are all driven by the Synopsys Agentic AI platform, which we call AutoPilot. It is the broadest and deepest portfolio, as I mentioned, across the entire engineering spectrum from silicon to systems. And so a couple of things that are unique about this platform first and foremost, interoperability and openness. In this platform, our customers can onboard their agents to benefit from all the assets on the platform. like our knowledge graphs, proprietary APIs, proprietary data to write powerful agents on our platform. At the same time, we want to meet our customers where they are at. And so if they are down their Agenetic journey and they want to integrate our agents into their agentic workflow, we also connect to our customers' agentic environments through protocols like MCP and A2A to essentially enable that as well. Last but not least, the compute and LLM optionality. It is, essentially, today, we are supporting the entire spectrum of proprietary foundation models all the way through open source foundation models. And really, the performance of the Agentic capability regardless of domain is heavily dependent on the quality of the foundation model and the ability of reasoning that it's able to do, the ability of orchestration it's able to do. And as you will hear later, there's a tremendous opportunity here as well in terms of how to really work with foundation models that understand semiconductors or physics much better and thereby get even better results. So a picture conveys a thousand words, but I believe a demo can weigh 100,000 words. So let me give you an example of how our 3DIC agent engineer is able to go from a very core spec of what an AI advanced package looks like, almost a napkin diagram and take that and work through the entire process of advanced package design and simulation. This is an area which transcends multiple domains, the design, which is now anchored in Synopsys 3DIC Compiler all the multiphysics analysis steps that are anchored in the ANSYS technologies of HFSS and Red Hawk and many others. And what you will see here is how the long horizon agent is able to decompose the objective and execute task agents, execute the ground truth tools at the base of it and really run an end-to-end flow to design an advanced 3DIC. [Presentation]

I hope you can see why we are so excited about how these long horizon agent engineers can really change the way in which engineering is being done and really increased capacity in very stretched engineering teams. Let me now give you an example from the simulation and analysis area. And here, we're going to look at electromagnetic coupling and electromagnetic interference, which is very important for any electronics design. And what you're going to see here essentially is how an engineer essentially is using the Synopsys Agentic autopilot platform to really execute a EM analysis of a design, find specific signals where they think there might be problems do some sweep simulation sweeps to determine whether or not the simulation results are meeting some industry standard requirements, which is encoded in something called [ SYSPR22 ]. So let's take a look at ANSYS HFSS and ANSYS Electronics desktop embedded within the Synopsys Autopilot platform and enabling an agent engineer flow. So here, what you can see is look at the objective that is been provided here. And again, it's all natural language interface, right? There's no clicking of buttons and no pulling down menus. And essentially, the directive was given to run the EMI scanner on this PCB and then the necessary tools are getting set up and invoked. The results from those tools are being summarized, analyzed. And now the engineer says zoom into the clock net because I think that's where the problem is. And again, the right 2-level invocations are happening underneath. And then the next query is, hey, do me a sweep across a range of frequencies and show me what the fields look like. And again, this is what I mean by specifying outcomes and objectives rather than having to know the intricacies of the tool and how to drive the tool to get specific results. And in this case, you see the radiated emissions, and then the next request here is plot the [ SIwave ] results and then compare it to SYSPR22, which is a standard for EM interference. And again, here, it's basically opening up the SYSPR22 PDF, understanding what the requirements are, firing a plot, taking these results and essentially generating the comparison to the SYSPR22 standards, which is basically illustrated by -- and in fact, it doesn't find the file, it goes and locates it correctly. And then it basically plots the final graph comparing the field values from the EM simulation against what the guidelines are with respect to the standards. So this is kind of really illustrating how the nature of design is changing with agent KI and how, in this case, the red line is the Cisco 22 requirements, and you can see the plot is basically satisfying those requirements. And so natural language interfaces all the tool calling is being handled by the agents, the proprietary knowledge and the skills that Synopsys has built up over decades, is all encoded into the into the platform. And this is how we are really kind of revolutionizing the way in which Agentic AI interconnects with engineering. Now one key point to touch upon is really what is happening underneath in terms of the ton calling. So let's take an example of a verification team in a hardware design group, and they've been handed a spec, and they have to write the test plan and the tests corresponding to that spec in parallel to the design team implementing the design. So in the typical setup, there's a lead who takes that spec breaks it up across their team of verification engineers, assigns each of them, a portion of the spec. And then they then go off essentially running our verification tools like VCS or debug tools like Verdi and essentially building those tests. But all that work that is being done is essentially gated by things like, hey, I've got meetings all day today or I don't work necessarily all weekends. I don't work late into the evening. To a certain extent, the total work that can be accomplished is gated by the amount of human cycles that are available. Contrast that now with an agent engineer, a long horizon agent engineer that is now and handed that same spec and essentially, with the right knowledge graphs from Synopsys embedded within the Synopsys Autopilot platform and all the task agents available, there's a rapid decomposition of that spec. And essentially, agents are fired off to work on different parts of the spec. And beyond just writing tests, these agents can also explore a much larger solution space to write high-quality tests. And so as a result, when you look at the two license/the tool profiles and the tool calling profiles. Essentially, we see -- and we have an expectation of a much higher tool calling profile than in the case where we don't have agent engineers because you're only gated by the compute on the right-hand side. The more compute you have, the more exploration, the more agents that can run in parallel, and as a result, finish this task with high-quality and much, much faster and essentially expand the capacity of this team. So that's really why we believe that the move to Agentic AI and agentic execution is going to significantly increase the tool usage. Let me finally conclude with the momentum that we have with our portfolio across the industry, right? Over 50 engagements with all the top customers, and many of them are now seeing the value of the agent engineers and the genic platform that we have delivered. For example, Intel is seeing a lot of value in the work we are doing with them on reducing their verification bottlenecks and improving the engineering efficiency of their design and verification teams. MediaTek, we're working with them very closely on something which is a very, very laborious design step, analog and mixed-signal design. And here, MediaTek is engaging with Synopsys to essentially take our Agentic analog design capabilities and essentially use agent engineers to achieve significant productivity benefit in the design verification as well as optimization of analog circuits. Samsung memory is also working with us very closely to use our agent portfolio to accelerate their engineering processes and design steps for high-bandwidth memory design and DRAM design and again, very close collaboration there. And then NVIDIA is both a partner as well as our customers. So of course, as a partner, we work together very closely on the Agentic platform and many of the components in our platform. We kind of codevelop it with NVIDIA. But then they are also a consumer of all the agent engineers and the agents that we are delivering to the autopilot platform, and they're also seeing tremendous benefit and value from all the innovations that we are driving. So with that, let me hand it back to Sasin to now talk about the AI monetization. Thank you.

All right. What you heard from Shankar and the snippet from Richard at OpenAI I hope I don't have to convince you that AI is absolutely in a TAM expansion for EDA. You need the essentialness of PDA to generate, to validate. And therefore, we expect our tool usage will only expand with the combination of a human and agent engineers. I have been over the last months describing that our customers will not apply AI in the same way across multiple customers. customers try to differentiate in the way they're going to implement and evolve their workflow. Therefore, Synopsys' strategy is to provide a solution that adapts to the various customer flavors of adopting AI. Let me walk through the various choices that we have and can offer customers. The Synopsys full stack. That's what Shankar just described, where the customer can come to Synopsis, they get the platform, the agents, the tools, and they provide objectives to AI and to achieve a certain outcome. This is a significant investment we've been making will continue on leading and put our resources energy effort in the Synopsys full stack. The second choice customers are making. And there are a lot of customers, by the way, in that second category, where they're saying, "I have my own special sauce. I want to build my own agent. I want to build my own platform. what I need from your synopsis is access to your tools and access to some of your agents. I don't want to reinvent a debug agent. I'll get the debug agent from you synopsis. But I want to own the rest of my platform for various very good reasons. The third model or choice is a frontier model. As you have seen and we have discussed it many times, when you have a model that comes out 1 day and say, "I was able to perform this specific task in semiconductor chip design. In many cases, those models were using open source because that's what they have available. And they saw good either productivity or proof of concept with the advancement in frontier intelligence and reasoning. I have no doubt that there will be a convergence with frontier models with EDA in order to achieve the best outcome as another customer choice. Across all choices, EDA and SNA engines are essential as the physics-based ground truth, you will not take out the chip if you're not going through the sign-off gates in order to ensure that whatever has been explored, proposed by AI is being validated. Now let me walk you through quickly how are we thinking in terms of monetization. We talked about subscription and consumption in the last couple of earnings calls. In the Synopsys full stack, the customer can subscribe through subscription license to the Synopsys platform and to the Synopsys agents as well as the Synopsys tools, now Shankar showed 5x to 10x more consumption, sometimes the agent can trigger. In a number of cases, the customers, they may choose to have a subscription for the tool and a consumption based. The consumption can be on-prem, cloud, we have it available, whatever customer choice they choose. So that's the monetization stack using the Synopsys stack. The second revenue stream is when the customers are saying, I may want to have a hybrid I want to license some of your agents and I need your tools to build my agents or your tools to run your agents because it's going to consume more licenses, we'll have the agent subscription that sits inside the customer platform and the tool subscription and consumption. The third, when you're training a model, you need tools to train it. So that's a tool subscription. When you're influencing the model you need tools to run it. So that's a subscription and consumption. And then there's a revenue share. A lot of the questions will come up how and based on what will you have revenue share is going to be based on outcome. If you're able to achieve a certain PPA with the best optimized RTL that you created. But the model, given it can explore much broader can provide a better PPA. Will that better PPA, be worse x dollar. And therefore, what's the revenue share model in this use case. Now here, we've been working for actually right now many months close to 2 quarters assessing who, how, what's the model to protect Synopsys IP if the model is trained on synopsis because when a model strain on open source is one way. When it's trained on synopsis, what is the role of Synopsys in training that model and influencing the model, who owns go-to-market, who owns the monetization process. So as we've gone through. Many of these iterations and talking and collaborating with many of the AI labs. I'm very pleased to announce today a partnership multiyear with Synopsys and open AI. And the cool thing about it, there will be a specialized GPT synopsis model. where it's post trained using Synopsys agents, tools, skills and workflows. So we bring in the knowledge with our tools, with our workflow and skills, OpenAI brings in their frontier intelligence and reasoning and the combination of both should provide the best outcome period in terms of as measured by an objective, as Shankar showed the demo, you can provide at a PPA objective, certain targets, et cetera. So this multiyear arrangement is available now to set of customers that are in early engagements to see what is the outcome of a marble converge with the best-in-class EDA versus an open source or other alternatives, EDA. Before I go into more details, actually, less here from Greg at OpenAI.

Hi, everyone. I'm Greg Brockman from OpenAI. We're really excited to be partnering with Synopsys to accelerate chip design for everyone. At OpenAI, our progress in AI depends fundamentally on the chips that we run on. And now we have an opportunity to use AI to help design those chips. Synopsys brings deep engineering expertise and the tools that chip designers already rely on. And together, building specialized AI model that is built specifically to master these tools. The goal is to bring together Frontier intelligence with the ability to use Synopsys EDA tools, and this will help engineers autonomously explore a much greater range of design choices to efficiently trade-off design targets and to get to a working trip faster to be able to shave off weeks, months from the design process and to bring more ships to the world. And what I find really exciting is that this is something that can build on itself. Better AI helps engineers design better chips, better chips, makes AI more capable, more efficient, more accessible to everyone, to empower people around the world. That is something that our work together will help accelerate. So thank you, Sassine. Thank you to the whole Synopsys team. We're excited to build this with you and to see what your customers will achieve.

All right. This is actually a very, very exciting options for customers as they're assessing how much do they invest in their own intelligence models workflows, the Synopsys full stack and the open AI Synopsys option. I know most likely, you have a lot of questions on your mind. And I did not give you much runway on this one like we did this morning with IP. So we'll take the questions later. But just to give you some color, the key things to emphasize, this is a specialized model. It's a GPT synopsis, meaning whatever learning that goes into the model, it stays Synopsis proprietary inside that model. So our IP does not leak or disappear into the bigger base model. The objective is to optimize verify deliver best outcome. So you give it objectives, you deliver outcome. And there are engagements underway. Let me add a little bit more color. How will customers access this? Think of it as a service. A customer will go to Synopsys, go to market or Synopsis go-to-market goes to the customer. And they can or an open AI directly with the customer. The customer will go to open AI, they get access to the model, GPT synopsis they get access to the compute. They get access to the EDA tools, agents. And through that engagement, it will be an outcome-based. And that's where we monetize back on the prior slide, I showed you the subscription of training the model, the subscription/consumption and the value. The revenue share component. I cannot be more thrilled for EDA that we'll be able to capture value based on the high impact we deliver to our customers as yet another option in this era where AI is making significant progress in intelligence and reasoning. If I were to summarize AI for EDA, is it a tailwind or a headwind. The key concepts that we all need to be very clear on what AI can do I can reason and explore. You need EDA when that intelligent model is providing a task or guiding, you need EDA to generate you need EDA to generate a GDS, a layout, a clock, et cetera, et cetera. and you need EDA to validate. So there is a I can explore can recommend, but you need synopsis to generate and validate Agent AI will reason, orchestrate execute. Synopsys ground truth engines are a must because they are foundry certified sign-off back toward the bridge for DTCO,were the bridge to foundry. They're deterministic. Their physics grounded and we've gone through the rigorous validation and manufacturing and yield assurances. That's what our industry and Synopsys has led and done over decades. So when you think of an intelligent model, Frontier model. That's a huge value, fantastic value to do the exploration, et cetera, with EDA to bring the high impact. And of course, the outcome will be a verified outcome. I'm not sure if you've noticed the centers, if I were to summarize what customers tell us all the time and you see it consistently can be summarized here. In AI, we believe, but in physics, we trust. Customers are not saying, "I'm not sure if I am going to adopt it or not. They are adopting AI. At the same time, they need physics to validate, they need sign-off to validate. From an EDA growth, we talked about the synergy of electronics and physics, more design start and AI-driven chip design, we are raising our long-term guide from double-digit to mid-teens growth. We talked about it will be '27 will be the year where we will see the revenue synergy from the ANSYS acquisition. We will see revenue from and we will see continued acceleration in EDA as well as HAV, the hardware-assisted verification. Let me next go to SMA, but let me introduce the S&A section actually with a short video. [Presentation]

It's always cooler to have SNA videos versus chip videos. Chip videos, they seem so boring. SMA, you can look at the car, the airplane, the data center but between electronics, et cetera. So that's a great video actually of co-design. Co-design of electronics, with the whole rack, the structure of the data center, the cooling, et cetera. So what are the complexity of intelligent system design. The failures that are caught late are so costly and it takes so much time. But the trade-off companies need to make, if I do a lot of modeling and simulation upfront, I need certain skills. I need to change my workflow. It's hard to do it. And companies, customers, they don't innovate unless the constraints are high. And when the constraints are high, can be driven that those systems are becoming more intelligent, those systems to serve need to serve various different applications that is necessary to bring in a different workflow for these systems. So today, with our SMA portfolio, we pretty much serve every one of these markets. The ANSYS acquisition expanded the Synopsys customer base by 10x, 10x expansion of our customer base. Now the beauty, many of those customers are looking for the next method of designing these intelligent systems. Now if they're building their chips and expanding to build their chips, that's fantastic. That's even a bigger opportunity for Synopsys. If they're sourcing the chip, but they're looking for a new way to improve from a traditional development model, which historically, until now, back to that of the $1.7 trillion is done through this traditional model where you may have -- you will have requirement, you design, you may or may not simulate, you build your test. Then if you find failure, you go back through the loop of testing than redesigning and building. The simulation usage is fairly limited by few inside these companies that called analysts that after the design is done, it goes through a different group to do some simulation as the product is being built and tested. Now the opportunity is how do we make simulation more accessible where the simulation is done during the design. And more, and more simulation is, again, accessible before the product goes into build and testing. Now that concept is not new to many companies that they are building the intelligence systems and they need and have to deliver these products on time. This is a set of customers out of many that have adopted early the simulation in the new way of building a product. And you can see what they see is a closer correlation to an actual physical testing without while reducing the need for as much physical testing. Now what Synopsys has with our S&A portfolio, is the multiphysics trust and sign off. And that's very important because you can simulate all day long. If you don't have fidelity in that simulation, you will not use it. So ANSYS and our SNA portfolio across 5 physics, fluid dynamics, structure, electromagnetics, optical and thermal. We have the leadership position. It is the ground truth of sign-off for the physics domains. AI is not only impacting EDA is the same thing for S&A. The democratization of simulation has been a long, multiyear effort to bring simulation from few analysts to a broader set of users of simulation. The next set or opportunity is AI-driven surrogate models. We have a technology called SIM AI and Optisling. What SIMAI does is you can upload the prior simulation data from the prior design and can predict and gives you through optic link options of design. If you were to change the curvature of a blade in a jet engine or in a car or in a robot, how does it change the performance of the product you're building? So you can get it done through surrogate models and fraction of the time, then you do the final simulation for sign-off. So it's an opportunity to do a different type of simulation faster through models and which is the surrogate model opportunity. Shankar talked about the agent engineer for physics. This is an investment immediately when we closed the acquisition, we brought the ANSYS R&D to operate into the same platform, rhythm, strategy around AI and the agent engineering. And as Sankar mentioned, we have the first wave of customer engagements and announcements there with agents. With Frontier models, the same as I just announced with open AI for semiconductor. I see it as a potential for systems, not only for silicon for systems as well. And the opportunity is very similar. Can you leverage frontier models with the ground truth physics to explore simulate, test physical AI products. Shankar already went through this. The platform, the Synopsys Autopilot platform with domain-specific agents, expand into physics. And that provides Synopsys the opportunity to have an AI-powered simulation across multiple domains and multiple industries. That's the simulation aspect of it. You recall about a year ago, we announced the partnership with NVIDIA where we will leverage the Omniverse from NVIDIA and synopsis simulation and analysis across various industries where Omniverse provide the customer the ability to envision, visualize their product, design their product and Synopsys engines are used to simulate because, again, they have the ground truth sign-off of physics. We have many customer engagements there. And actually, it was very cool earlier this week to see AMD buying world labs. It's the same approach. Why? Those intelligent systems, you need a digital twin of electronics. You need digital twins of the physics. You need a digital twin of the environment in which that system is operating. If it's a data center. Data center, it's a car, it's the world environment, et cetera. So the direction we took with NVIDIA Omniverse is a validation earlier this week that, that's a market that is expanding, and there will be similar type of collaboration that we expand as, of course, the acquisition will close, et cetera, et cetera. To summarize, for F&A, the long-term double-digit growth, that's organic, and that's higher than the traditional growth that ends as a stand-alone organic has been able to achieve and is driven by a number of tailwinds, more simulation are needed, more value capture through accelerated simulation. This is a GPU acceleration and other. The AI-driven inflection point we just talked about and the new opportunities through digital twin and other. Now to bring things to close and summarize, the long-term growth outlook is mid-teens CAGR through 2030. If you recall, that used to be double digits. And the reason is mid-teens is the growth we've talked about for EDA, mid-teens, IP, high teens and S&A the double digits. FY '20 outlook at 15% year-over-year growth. Some of you asked me earlier, how much did you prep for this meeting? Actually the meeting itself, it was that's the prep. The prep was the FY '27, we pulled by 2.5 months to provide you the FY '27 and not miss the opportunity to be here and share with you our enthusiasm, our excitement, what is driving it, and of course, some of the major collaboration and redefining some of the businesses that we have. Just to summarize, this is our opportunity at the silicon level, the expansion of application optimized silicon gave us the opportunity to create a new category of IP, which is AIP anchored with a license fee and a royalty, which is a significant opportunity for Synopsys. AI for EDA, along with everything that we do in terms of multiphysics fusion the entire platform, HAV, et cetera, the open AI option for customers. as well as the customer choice of building and mixing their agents or a full stack from Synopsys, that will all contribute to our growth in '27 and will only expand beyond '27. Systems, more SNAs needed for these intelligent systems and truly what differentiates our assets and company is the ground through highly trusted physics that we do in both silicon and systems. With that, big, big thank you. Now we'll take a break, and I look forward for the Q&As. Thank you.

We will now take a short break. Please return to your seats in 15 minutes. [Break]

Please welcome Shelagh Glaser, Chief Financial Officer.

Thank you. It's great to see everybody. Thank you so much for coming. I know there's a lot going on. So thanks for prioritizing time with us. I'm going to bring together everything you've heard today and show how it comes together in the financial model for the company. But first, Sassine talked about this being a major inflection point in the industry. And why is this moment different? The complexity and the pace that is happening in system and silicon design has never been faster. But there's a talent gap in a compute gap. So AI is going to help bridge the gap on that. And customers are increasingly designing integrated physical and digital systems, which require codesign. Shankar went into a lot of detail about that. and first-time right economics have never been more important to customers. Having to do another spin or another tape out is hundreds of millions of dollars of a new tape-out, but even more importantly, it's missing a market window. So that's missing revenue. When customers have to overdesign, that leads to loaded die sizes, which leads to yield problems, which leads to less units to be able to sell. And so customers need to come to us because we're uniquely positioned with the leadership portfolio in EDA, in S&A and IP so that they can be confident that they have the ground truth physics and sign-off. So the models they build of the products are going to be the same as what they're going to see in high-volume manufacture. We're the link that allows them to have that confidence. And we have traditionally been seen as we're solving R&D problems. But as Shankar and Sassine laid out we are driving even more value for customers. The problems we're solving are making sure that they're able to hit their revenue targets, their chips are on time. They're hitting the right PPA margin targets, they're achieving the yield and the cost that they want. We're making sure they have time to market and high confidence product schedules and high-confidence product success as they move from doing designs and models into actually taking those products into high-volume manufacture. So this is the early innings of increased value capture that Sassine laid out and the change in our business models that we are driving. And we are in the early innings, and this will build over time. Let me take a step back and talk about where we've been. Over the last 5 years, we've doubled the size of revenue of the company. Over that same period of time, we have driven significant margin expansion, 7-plus points and margin expansion. And we did that while integrating 1 of the largest acquisitions in the software industry and the largest acquisition our company has ever done. So we've proven that we can grow both scale and we can grow profitability. And in 2016, this has been a year of execution for us. It's our first full year, as Sistine said, bringing ANSYS in over the course of the year from our initial guide, we've raised outlook on all key metrics. We've raised outlook on revenue, non-GAAP operating margin, non-GAAP EPS and free cash flow. And importantly, this momentum has been broad-based across all of our businesses, and we've been translating more profitability into more cash versus disciplined execution. This helps set the stage for the next era of growth for us as a company. Before I go into the long-term model, I want to make it clear how we will present the company starting in fiscal 2027. We'll continue to have the same segments, design Imation and design IP. What we will change is the revenue disaggregation. Specifically, what we committed to this year was to provide full transparency on ANSYS our first year of this consequential acquisition, and we've delivered on that. As we move forward and we build out this multi fusion physics product lines, it will be harder to separate ANSYS and EDA products. So what we will be presenting in fiscal year 2027, we'll move the semiconductor business unit from ANSYS into that's about 10% through Q3 '26 of the revenue of ANSYS. We will also use that remainder advances and show that to you in simulation and analysis. Design IP will remain unchanged. These changes align to how the industry views these and it will allow us to give you full transparency of the performance on our EDA business and our simulation and analysis business. And throughout the course of the year, we'll provide apples-to-apples comparison because obviously, we'll have the comparison with '26 how we report it. Now let me get into the growth algorithm. And Sassine laid this out in his section, -- for this year, for '26, we anticipate revenue of $9.7 billion, and as we are driving to fiscal year 2030, we are driving to a model of mid-teens overall growth Underpinning that is mid-teens growth for EDA with a floor of 13%, double-digit S&A growth with a floor of 10% and design IP with a floor of 1%. This is all underpinned by what Sassine talked about in terms of increasing design starts, increasing complexity, increasing need to do co-optimization, and in the design IP, system laid out the new business model that we're driving with application optimized IP. Underpinning this is revenue synergies, which I'll talk about in a minute, and increased value capture as we evolve our monetization model and change the way that we work with customers on that. Let me go into synergies. So when we announced the ANSYS acquisition in January 24, we committed to both revenue and cost synergies, and I want to provide an update on both of those. So let's start with revenue synergies. On revenue synergies, the commitment is $400 million run rate and synergies by fiscal year 2029. What we have already done, which we talked about today, Sassine laid out again today, is we've already built a new joint road map, the multiphysics fusion products, we're seeing great customer enthusiasm on that, and those will start to revenue in 2027. We've also brought the sales teams together so the sales teams can have cross-selling really across the entire product line. As we exit fiscal year 2027, we will have greater than $100 million run rate in revenue synergies. So the synergies begin in earnest in 2027, and we have confidence in our ability to achieve the $400 million run rate synergies by 2029. Now let's talk about cost synergies. On cost synergies, we had committed to $400 million run rate by fiscal year 2028. As we've talked in each of the earnings calls throughout this year in fiscal year 2026, we've been accelerating those synergies, and I'm pleased to announce today that we will be complete with our $400 million run rate synergies in fiscal year 2027, which will be 1 year early. So we are executing against our synergies and feeling very confident in our ability to achieve these. What we done with cost in 2027 and we've got strong line of sight to 2029, given the strength of the first year of revenue synergies we'll have in 2027. Now let's talk about operating margin. So Sasin laid out the change we're driving in the business model. That change flows into operating margin. So I'm pleased to say that our objective in operating margin is approximately 50% by fiscal year 2030. That's up from our prior expectation of mid-40s and it's driven by the changes in the business model that Sassine [indiscernible]. And how we're doing that is on multiple levels. So we're scaling and bringing efficiency into everything in the business. In the scaling, we're working on higher value capture that as seen outlined today as we have evolved the business model and IP, and we infuse AI into our products. Portfolio optimization that's something you've seen us do year after year, making sure that we've got our investments in the highest return areas, and that's a constant evaluation that we do and then operating leverage in everything we do. Literally, how do we simplify every process and every approach in the company so we move friction, so we focus on high value add. While we're doing that, it isn't about cost cutting. It's about efficiency and leverage and investing in critical innovation, which fuels the strategy that Sassine laid up. Specifically, large areas of investment we're making is advanced node and multi-die design to be able to support our customers as they endeavor on these more and more complex designs. Making sure that we're building innovation and simulation and digital engineering to be able to make sure that we're supporting those customers and infusing AI for engineering and all that we do. So just as we're working with our customers tense AI, we're infusing it so that our team gets the benefit of that and funding the application optimized IP formerly called Factory II in IP. So building that out. We're not -- we're keeping both factories. Sassine talked about the Factory 1, which is our traditional IP. So we're keeping that, and we're adding on top of that investment to build out this new model. Then let's talk about the capital allocation framework. Our priorities are clear. Our priority, first and foremost, is to invest in growth in the business. and invest in R&D that fuels the innovation that drives the business. And on a regular basis to make sure that we've got the investment in the right areas to drive the growth of the company. we will drive selective and disciplined M&A. The second is to maintain a strong balance sheet and maintain investment-grade rating. You've seen us pay off the term loans early as an important part of our operating plan as we drove through this past year to get the term loans paid down. And the third is to regularly return capital to our shareholders. And here, we will return up to 50% of free cash flow to shareholders. And so this framework allows us to invest for growth, to build the innovation that fuels the strategy while creating shareholder value. Another thing that we've talked about, many of us have talked about is how we think about stock-based compensation. So we are targeting 8% SBC as a percent of revenue by fiscal year 2030. We will do this through revenue growth and scale and disciplined application of our equity program. You've already seen us make significant progress on this. We are down 3% from our peak in 2025 of approximately 13%. And like our investment dollars, we think in a very disciplined way about where we put our equity. And this allows us to ensure that we're investing in attracting and retaining top talent, which is absolutely necessary to ensure that we're building the innovation out and building the capabilities that will allow us to achieve our strategy. Now all of this will compound in both EPS and free cash flow. So with revenue growing in the mid-teens, operating margin scaling to approximately 15% and we will grow EPS and free cash in the mid-20s. This is a durable compounding growth plan that translates into profits and shows up in cash. So putting it all together, these are our long-term financial objectives. It's underpinned by three scaled market-leading businesses and the change that we're driving in the business model with AI and the application optimized IP and underpinned by our synergies realization. We're not dependent on any one single variable. We have multiple growth vectors that we're driving. And what we're driving is both growth and leverage, which shows up in EPS and free cash flow growing even faster. And we're doing all of this while we're investing to ensure that we can continue to fuel the growth and returning capital to shareholders. Now let's zoom into 2027. And as Sassine mentioned, we're giving our full 2027 guide today, which we would normally give in December. But since we're together, we thought it was very important that we talk about what does '27 look like. And so let's shift into that. So '27 is a very important validation year of this long-term model that we're laying out today. We expect to grow revenue 15% to $11.15 billion to expand operating margin 250 basis points to 44% and to grow EPS even faster at '27 to $19.08. All of these numbers are obviously at the midpoint. This represents the strength of our underlying business and it's a great start towards the long-term model that we've laid out today. And I want to talk a little bit on operating margin because this has been a question for quite some time. So I'm sure we're really clear on this. So what we've talked about in 26 is our expectation for full year '26 is 41.5%. So that's 420 basis points improvement from fiscal year 2025. On top of that, what I just guided for 27 is another 250 basis points improvement. And again, we'll have the full realization of synergies next year, and we're driving the greater scale in the business while we're continuing to invest in the business and continuing to invest in important strategic objectives to achieve the product lines that will help our customers scale. Putting it all together, our guidance for 2027 is extremely strong. We're very confident in this. This is why we feel comfortable giving this to you in September instead of waiting for December. You've seen us accelerate into the second half of 2026. You've heard the new deals announced today. And so we have strong progress towards these long-term goals, and '27 is an important year to be able to demonstrate that. On top of the metrics, I already talked about free cash flow will be approximately $3.1 billion, and that's up about $0.5 billion year-on-year. Just one more thing before we get into Q&A. Given the strength of the balance sheet and our business model, we are announcing our intent to repurchase $1 billion in Synopsys shares over the coming months. This is based on our confidence in the model and our cash generation, and it aligns exactly with the capital priorities I just outlined. Number one, to invest in our business, number two, to ensure we have a strong balance sheet; and number three, to make sure we're returning capital to shareholders. This allows us to offset dilution and over time, the repurchase program will allow us to reduce share count. So let me sum up. We are at an industry inflection point. Complexity is outpacing engineering capacity. First-time right is absolutely high stakes for our customers. People cannot -- customers cannot afford to miss critical market windows. We have the portfolio that allows them to have high confidence in being able to ensure that they're building products that will achieve their goals and will be right the first time. Our financial goals are mid-teens revenue growth with margin expansion and free cash flow expansion. We're confident in our execution, and we're confident in our value creation. And the first step towards this long-term model is our fiscal year 2027 guide. With that, Thanks. And we're going to move into Q&A.

Please welcome back Sassine Ghazi, President and CEO; and Shelagh Glaser, CFO.

So I'm going to -- we're going to have two mic runners, Chris and Christine, and they will come to you guys. And then I'll -- you can ask your question, please state your name and the firm. I ask you to limit yourself to one question, and then I can come back to you guys if you have more questions. So we'll begin with Siti.

Siti Panigrahi from Mizuho, First of all, congratulations as an amazing Investors Day. And thanks to [indiscernible] team has put together a really good Investors Day, and thanks for inviting us. Sassine, as you say guide, not only '27, even your long-term guidance, it's amazing much better than we're expecting. The question is, when you laid out a lot of growth opportunity, which -- how do you rank order and what gives you that confidence to hit that number? And specifically, on the AI opportunity, I would like to ask the difference scenarios areas you just talked about. Where do you -- who owns that value capture part, which scenario you have higher value capture versus other models?

Yes. Thank you for the question. We will not talk about 15% unless we have confidence we're going to meet the 15%, and it's really the layers that we described, starting with IP the -- even though the royalty will not show up in '27. But starting in '28 and then it starts ramping into 2019 and beyond, that becomes a fairly large percentage of our IP business that comes through royalty. And while Factory I will continue on executing and delivering to the double-digit expectation as well. So all in all, in IP, as Sheila mentioned, the floor is the 17% with an objective to grow in the high teens. And EDA is the same. I want to remind us that the floor is with the objective for mid-teens. The EDA between synergy of the multiphysics. AI definitely will start contributing in FY '27 and the -- I want to call it the classic EDA growth of delivering to just the best-in-class software and hardware in order to achieve these opportunities. In terms of AI for EDA, we talked about 3 revenue streams. Revenue stream one, the investment is essential because those agents, if the customer subscribing to them either an option 1 or 2, that investment and delivering to these agents is essential to have that capability differentiated capability. It's clear for our customers how to pay synopsis in stream 1 and stream 2 because they know they need to subscribe for the platform, the agent and the license or some consumption of the license. We already have a number of customer engagements. That's what gives us the confidence in '27, we will see revenue from Stream 1 and 2. Stream 3, the revenue share, we are currently in number of customer engagements, testing the GPT synopsis based on outcome. So you give it an objective if the outcome is better than what they're able to achieve, the monetization will happen based on what I call the service. So because that model runs on open AI cloud, so they provide the model the compute. So the customer gets a model, compute, EDA licenses, EDA agents and the harnesses, the contacts, the skills that are required. And then there's a revenue share split between us and open AI to -- based on these outcomes. So that's a whole new stream of revenue that we were not able to capture before. So that's a new revenue stream that we're looking at. That's why the mid-teens for EDA with the floor of 13%, starting in '27 is something that we're confident about delivering.

We'll go with Jim at Goldman.

Jim Schneider, Goldman Sachs. Congratulations on the targets. I was wondering if you can maybe talk a little bit about, given the accelerating growth rates you're expecting across the businesses, to what extent is pricing and pricing the value sort of driving or underpinning those targets? And can you maybe talk about any like-for-like pricing conversations you're having with customers to sort of drive that accelerating growth, or is that purely on the basis of either mix or top line benefits from elsewhere? And maybe just as a secondary point, can you maybe talk about to the extent the agenetic solutions gain traction in the market, what is the impact on the company's gross margins?

I'll take maybe the first one and Shelagh, you can address the second one. Pricing conversation with customers, they don't go far unless you're able to deliver more value to the customer. In EDA, typically, there's a renewal cycle when the renewal cycle comes in, customers assess their needs, and that's the opportunity to inject new technology. with almost every customer right now, the conversation is, I need more licenses because I need -- I'm building my own agents or I need to buy an agent from you. or I need the new 3D IC compiler fused with multiphysics because I need to achieve my next program that I need to plan for. So that's really where the opportunity comes in to lift the value that we are getting from the customer based on the value that we deliver. IP, I want to say, is a different story. With IP, if we're talking about the factory to, really, the reason we were able to successfully bring in number of customers in factory I is the scale, the trust, the quality that we have in our -- it does not mean it's more essential than EDA for chip design. They're equally essential. But the dependency for them to build a customized chip depends on Synopsys. When I go to the extreme and say there are no other options to deliver for a customized IP at the scale that we can deliver it opens up the conversation with the customer. We need to capture more value given the impact we're delivering to you. And this is where the royalty is coming in. The point I made as well royalty will be higher than the license that we capture. And that's the other opportunity to deliver value. Maybe Shelagh, if you want to take the...

Yes. So let me take a margin. I think about AI and sort of 2 buckets. Obviously, we're working with our customers, everything that Shankar and Sassine talked about. And our cost doesn't really change. So a lot of that is accretive to margin. Also think about our own internal consumption of AI and the way that we're looking at it is also outcome-based. How do we basically create more capacity for ourselves. We have areas where we are short on engineers, how do we create engineering capacity that allows us to get products out that otherwise we wouldn't. So that's also beneficial in margin because those are products I wouldn't have even had.

I don't want to be accused of prioritizing the front row, so we go to back Vivek in the second row.

Vivek Arya from Bank of America Securities. I had 2 questions. One, Sassine, for you on the revenue side and then Shelagh, for you on the operating margin side. So on the revenue side, Sassine, if you go back to the Analyst Day you had in fiscal '24, at that time, I think you had set expectations of somewhere in the low teens growth. So you're definitely raising that bar towards mid-teens. But the growth rate in the last few years, right, as an industry was lower than that. So I'm just saying, as an industry, right, what changes in the next few years to help you accelerate the right and have more confidence in that growth rate? And then also clarification there, how much is the royalties for next year and as part of your 2030 model? If you could help quantify that, that would be helpful. And then on the operating margin side, you want to grow faster and you want to expand operating margins much faster. Is it that the thing you answered to Jim, which is really just getting leverage? Is there something else? Like what if you were to limit your operating margins, Shelagh, to mid-40s, which is still pretty decent. Would you be able to grow even faster, right, than like what is the trade-off between sales growth and operating margins?

So for EDA, as Shelagh clarified, the 13% is the floor in order for the company to grow at mid-teens, you need EDA to grow close to where the company needs to grow. Otherwise, just the numbers don't add up. So with that, the confidence we have, given the solution, and I want to anchor on the multiphysics fusion monetization starting in '27 were ramped up to the $400 million in '29. AI starting in '27 and the expectation that the rest of the portfolio will grow with the market growth for hardware, EDA and as the core classic core EDA as you exclude AI and multiphysics. In terms of royalty, the $1 billion agreement with Amazon. The $1 billion is for license fee, and it's for multiple generations of the 3 products that I mentioned earlier. Royalty is not part of it. royalty will get captured as they go into production. So the moment they go into production, there's volume and we start capturing royalty. Some of these designs will start in the next few months. So then anticipate 14-ish months of design to tape out and production, and that's when you start the royalty seeing start seeing it ramping up. similarly to other agreements we closed with ASIC with a connectivity as part of that ecosystem is roughly in the similar time line.

And I'll make sure I answer. There's specifically no royalty in 2027 for what Sassine just outlined because that ramps over time. And...

Should I just do...

The total AOP is $1 billion. That includes licenses and royalties. But obviously, royalties has built up over those generations of chips. We didn't give a split. Like Sassine gave the -- the intentionality that we're driving to that royalties will be a lot higher than license. Back to your question on where are we kind of putting the balance between revenue growth and operating margin, our focus is on both. And what gives us confidence in doing that is everything Sassine just talked about on the business model, and you can think of the royalty as being 100% pure margin over time. So that also creates yet another lever in margin expansion that we didn't have before.

We will not trade off growth opportunities to just raise the operating margin from a 44 to 45 or 46. We are absolutely investing in the business, absolutely investing while making priority, leveraging technology to do exactly what Shelagh described.

Okay. What we'll do is let's get Jay, and then I'll do Josh, and then we'll come back to Jason.