Synopsys Inc (SNPS) September 30, 2026 Capital Markets Day Transcript

Please welcome Tushar Jain, Vice President, Synopsis Investor Relations.

Thank you. Thank you, everyone. Good afternoon, and welcome to Synopsis' 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 Synopsis, 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. Sasin 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.

This is the moment. AI is moving beyond the digital world and deeper into the physical world. Pervasive intelligence, enabling innovators to build more efficiently, transform mobility, reach further, save more lives, plus extraordinary advancements that will soon make the leap from imagination to reality. As the demands of AI increase, so does design complexity. But conventional engineering design workflows can't meet the challenges of what comes next. So, the world turns to engineers. And engineers, they turn to Synopsis. Because we are re-engineering engineering. Advancing the design of physical AI. Intelligent systems so complex, you can't build them without co-design and simulation. Welcome to the new agentic workflows for designing the AI future, where hardware and software, electronics and physics, silicon and systems are co-optimized. Where digital twins transform simulation, revealing how every part of a system will behave even before development has started. And where Silicon IP simplifies your design, moving you to market faster. From advanced silicon to intelligent systems, Synopsys removes the barriers between concept and deployment, enabling fearless innovation, unlocking bolder AI, and helping humanity build things it has never built before. At Synopsys, our mission is to empower innovators to drive human advancement.

Come myself. 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 the 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 with staying on the leading edge of innovation and delivering the technology that our customer must have in order to deliver the best product that they're designing. The other important milestone for us this year is the year one of the new synopsis of the fully integrated Ensys and Synopsis. 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 customer 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 one level of the stack. It has to happen at the entire stack, from silicon to systems. Over the last five years, we've been on a mission to transform the company. Five years ago, Art entrusted me as the president, then CEO, to lead the next chapter of Synopsis. 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, it's very rare for companies to have three parts of their portfolio, and each part holds the number one leadership position in its market segment. We are the number one in EDA, in silicon IP, and simulation and analysis, or as we call it, SNA. 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 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, the need to co-design becomes a must. What does co-design 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 re-engineering engineering 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 co-design and digital twinning and modeling of the end product, it's 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 synopsis 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 five 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 to compute for this intelligence. These chips are increasingly becoming application-optimized silicon, custom silicon. And it's 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 This 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-aware co-design crosses silicon and system. On the chip level, the need to have physics 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 sub-agents 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 a 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 IP, 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 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 IP. We have the leadership position in 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 foundries support in our IP business, about 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 a CPU. You need a memory. You need a 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 IP. So a big part of the system is coming from synopsis 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 standards 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 UCIE, 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 the standard, you validate, you provide it to your customer. The AI leaders, they're not waiting for a standard, yet they want the interoperability of a 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 This is a nightmare of how to manage to deliver on a standard and customize way before the standard is defined. Now, the custom silicon opportunity, I'm sure you have your own numbers, absolutely increasing. And these are the forecasts 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, you 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 I've been communicating in that language, Factory 1, Factory 2, Fact 3.1, think of it as our standard-based IP, where you wait for the 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 one, build one, 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 2, where we build an application-optimized IP, AOIP. An 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 1, build an IP in Factory 2, from an engineering point of view. The business model, the first one, factory one, is your license once for a program, and if there is any NRE, we'll charge based on an NRE. In factory two, there's the license per program, there's customization fee, and you see here it's 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 2. Factory 2, 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 one and a factory two. What's the difference from a customer engagement? And again, we'll maintain both. A factory one will start with the standard being defined, the IP gets developed, the silicon gets validated with test chips, And we provide it to the customer, and we license it broadly. In a factory tool model, we work with the customer very early in understanding their workloads. We become part of their system definition. Based on the workload, we co-design 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 synopsis. This is IP, EDA, and SNA opportunity for synopsis. 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 ourself inside our customer workflow and deliver to this opportunity. So that's the factory, too. I remember as well, when we talked about it, many doubters. How will you ever change a business model that's been established for three 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 is not a one-time, 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're working with you, and your IP is interoperable with what they're 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 the 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 about this? How about this? How about that? I cannot share many of the terms of this agreement, and I hope you respect that, because terms of an agreement between us and a customer are confidential terms. But I can tell you the following. This is a multi-year agreement. It's a multi-generation agreement. The billion dollar 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 billion dollar 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 synopsis and leading into the AOIP 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 3DIC. 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 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 18A? And 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 Synopsys, 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 wow, 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, we are giving customers a trusted path to advanced node adoptions, with silicon-proven IP, certified EDA flows, and solutions based on our PDKs. For Intel 14A, we are going deeper, co-optimizing silicon, IP, AI-driven design flows, and advanced packaging for the next generation of authentic and physical AI systems. AI is also changing how chips are designed. Synopsys is helping engineers shorten design cycles and spend more time innovating. And with ANSYS, Synopsys brings design and multi-physics sign-up closer together, which is important for advanced packaging and multi-die systems. Together, Intel and Synopsys are helping customers design better products and bring them to market faster.

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 DTCO, 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 Manan, president of Samsung Foundry. Investor days are usually about numbers, roadmaps, and the future. But before getting into any of that, I would simply like to say congratulations to our good friends at Synopsys on this exciting occasion. As AI continues to expand across industries, from data centers and automotive to physical AI, Samsung Foundry has evolved beyond just being a way for suppliers 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. More recently, to provide optimized solutions for AI applications, we have been deepening our collaboration, beyond standard IP, to develop customized IP. By combining Synopsys' proven design IP and EDA tools with Samsung's 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 Synopsys have built together and even more excited about the many layers of innovation still ahead of us. Once again, congratulations to the entire Synopsys 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, AOIP expansion, this factory is firing up and accepting and ramping up on customers. The multi-foundry enablement, the projected growth for AOIP by 2030 will be a billion dollar 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 AOIP factory delivering to a billion dollar by 2030 based on the current customer engagements that we have. EDA. Now I set up the stage for IP and the need for customization, application-specific chips, etc. 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 AOIP, 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 designs 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 hyper-convergent flow for the best PPA. Okay, 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, It's 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 this system with a multi-die advanced package? The Fusion of Physics, our leadership position, number one position with Fusion Compiler, 3DIC Compiler, Primetime for timing sign-off, VCS Verdi for functional simulation and debug, PrimeSim, which is a transistor-level simulator, Redhawk, the industry standard sign-off for thermal. About maybe a month and a half ago, when Jalapeno 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 Sim or VCS, and EDA is doomed because a 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's the head of hardware, where he emphasized the essentialness of the synopsis 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. So, multiphysics fusion. The whole thesis 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 3D 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 fusing 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 Ensys started. We took their 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 multi-physics technology of ANSYS now is fused inside the digital implementation platform. There is no one 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, it's the multi-physics, 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, nine 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 five 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 it, 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 400 million synergy by 2029. Sheila will talk in more details what is it that we'll see in 27, and how does it build our confidence toward the 400 million by 2029. Given the first wave of customer engagement and early deployment in production in 26, 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 this startup that came out of stealth mode a few months ago, and I got many questions from investors, like, hey, there's this company, Edge, do you guys work with them? We worked with them at day one, 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. In here, what's exciting was they were big users of our Zibu 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 a process it would have took about six, seven 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 spans from the emulation to prototyping to what we call EP, which is a hybrid emulation prototyping system. That's 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 synopsis. 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 increases 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 27, the code name is Artemis. It's our next Zebu 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, Sasin, and thank you all for joining us here today. I wanted to start by going back to a theme that Sasin spoke about earlier. We are in this incredible era of intelligence 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 design up all the way through a system-level design like a rack design is really opening up tremendous opportunities for Synopsys because of the co-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 intelligent 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 is accelerating because market windows are shrinking. We talked about building these chips and systems in three-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 nine 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 two 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, it 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 two years, in terms of the ability to launch and orchestrate sub-agents, 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 much higher level and coarser 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 tool invocations, task-level agent invocations, using all the proprietary knowledge assets and knowledge graphs that we have built around our tools, proprietary APIs and data we have built around our tools, 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 and validation of the designs and silicon and systems are basically done by the portfolio that we have across the silicon to systems. 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 agentic portfolio across engineering What we are doing in every domain is delivering, per domain, a set of long-horizon agents that are able to take very coarse 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 benches 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 to simulation and analysis. So a CFD engineer can essentially provide a geometry, 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 CFD 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 agentic 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. You know, 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 1,000 words, but I believe a demo conveys 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 RedHawk 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. So if you could please...

This demo shows the Synopsys implementation agent engineer for complex 3D multi-die design. It leverages the Synopsys 3DIC compiler platform with multiphysics fusion technology and sign-off solutions. The Synopsys implementation agent engineer can generate a 3D system from an architectural diagram and system-level specifications in CSV or PDF formats, including handwritten notes. The agent extracts the design intent to create a design plan. Then the Agent Engineer constructs the 3D-stack design including the substrate, interposer, and dyes, leveraging multiple task agents. After design construction, the Agent Engineer implements high-speed routing for HBM channels and power network while ensuring performance specifications. Now, the Agent Engineer shifts to system optimization using Synopsys Multiphysics Fusion technology and sign-off solutions. It optimizes for thermal and mechanical stress. The Agent Engineer runs power integrity analysis and uses the results to optimize power density. Then, the Agent Engineer runs signal integrity analysis and optimization for high-speed channels. The Synopsys Agent Engineer accelerates multi-die design and sign-off convergence across all domains from weeks to days.

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 increase 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 an EM analysis of a design, find specific signals where they think there might be problems, do some sweeps, simulation sweeps, to determine whether or not the simulation results are meeting some industry standard requirements, which is encoded in something called a CISPR 22. 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 has 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, you know, 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 level, two 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 CyWave results and then compare it to CISPR-22, which is a standard for EM interference. And again, here, it's basically opening up the CISPR-22 PDF, understanding what the requirements are, firing a plot, taking these results, and essentially generating the comparison to the CISPR-22 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 agentic AI, and how, in this case, the red line is the CISPR-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 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 tool 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. So to a certain extent, the total work that All 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 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 tool 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. Over 50 engagements with all the top customers, and many of them are now seeing the value of the agent engineers and the agentic 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 are 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 agentic 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 a customer. 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 co-develop it with NVIDIA. But then, they are also a consumer of all the agent engineers and the agents that we are delivering through the autopilot platform, and they are 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.

Thank you, Shankar. 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 a TAM expansion for EDA. You need the essentialness of EDA 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 five, six 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 Synopsys, 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. We'll 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. Yes, I want to build my own agent. I want to build my own platform. What I need from you, Synopsys, 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, Synopsys, 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 one 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 tape out a 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 earnings calls. In the Synopsys full stack, the customer can subscribe through a subscription license to the Synopsys platform and to the Synopsys agents, As well as the synopsis tools. Now, Shankar showed 5 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 inferencing 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? It's 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. Okay, will that better PPA be worth 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 two quarters, assessing who, how, what's the model to protect Synopsys IP if a model is trained on Synopsys? Because when a model is trained on open source, it's one way. When it's trained on synopsis, what is the role of synopsis in training that model, in inferencing 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 multi-year with Synopsys and OpenAI. And the cool thing about it, there will be a specialized GPT synopsis model where it's post-trained using synopsis 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 in the demo, you can provide it a PPA objective, certain targets, etc. So, this multi-year arrangement is available now to a set of customers that are in early engagements to see what is the outcome of a model converged with the best-in-class EDA versus an open-source or other alternatives EDA. Before I go into more details, actually, let's hear 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, we're building a 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 chip faster, to be able to shave off weeks, months from the design process, and to bring more chips 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, Sasin. 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 very, very exciting options for customers as they're assessing how much do they invest in their own intelligence models, workflows, the synopsis full stack, and the open AI synopsis 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 synopsis go-to-market, or synopsis go-to-market goes to the customer, and they can order 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-slash-consumption, and the value, the revenue share component. I cannot be more thrilled for EDA that will 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, AI 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 AI can explore, can recommend, but you need synopsis to generate and validate. Agentic AI will reason, orchestrate, execute. Synopsis ground truth engines are a must because they are foundry certified sign-off. Back to we're the bridge for DTCO, we're the bridge to foundry. They're deterministic. They're physics-grounded. And we've gone through the rigorous validation and manufacturing and yield assurances. That's what our industry and synopsis 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 sentence in quote. If I were to summarize what customers tells 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 AI, I'm 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 AI, and we will see continued acceleration in EDA as well as HAV, the hardware-assisted verification. Let me next go to S&A, but let me introduce the S&A section actually with a short video. It's always cooler to have S&A videos versus chip videos. Chip videos, they seem so boring. S&A, you can look at the car, the airplane, the data center between electronics, etc. 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, etc. So, what are the complexities 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 up front, I need certain skills. I need to change my workflow. It's hard to do it. And you know, companies, customers, they don't innovate unless the constraints are high. And when the constraints are high, it can be driven that those systems are becoming more intelligent. Those systems need to serve various different applications that is necessary to bring in a different workflow for these systems. So today, with our S&A 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 90% of the 1.7 trillion, is done through this traditional model, where you may have requirement, you design, you may or may not simulate, you build, you test. Then if you find failure, you go back through the loop of testing, then redesigning, and building. The simulation usage is fairly limited by few inside these companies that are 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 while reducing the need for as much physical testing. Now, what Synopsys has with our S&A portfolio is the multi-physics 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 five 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. It's the same thing for SNA. The democratization of simulation has been a long, multi-year 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 technology called SimAI and OptiSlang. What SimAI does is you can upload a prior simulation data from the prior design and can predict and gives you, through OptiSlang, 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 in a 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 Shankar mentioned, we have the first wave of customer engagements and announcements there with agents. With frontier models, the same as I just announced with OpenAI 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 provides the customer the ability to envision, visualize their product, design their product, And Synopsys' engines are used to simulate because, again, they are 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 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, it's a data center, it's a car, it's the world environment, etc. 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 S&A, the long-term double-digit growth, that's organic, and that's higher than the traditional growth that Ansys as a standalone organic has been able to achieve. And it's driven by 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 it's mid-teens is the growth we've talked about for EDA, mid-teens IP, high-teens, and SNA, the double digits. FY27 outlook at 15% year-over-year growth. Some of you asked me earlier, how much did you prep for this meeting? I'm like, actually, the meeting itself, it was less the prep. The prep was the FY27, we pulled by two and a half months to provide you the FY27 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 AOIP 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 multi-physics fusion, the entire platform, HAV, etc., 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 truth, 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'll look forward for the Q&As. Thank you.

We will now take a short break. Please return to your seats in 15 minutes. Please welcome Sheila Glazer, 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 and 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 co-design. Schenker 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 over-design, 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 in 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 Shanker 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's a scene 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 five years, we've doubled the size of revenue of the company. Over that same period of time, we've driven significant margin expansion, seven plus points, and margin expansion. And we did that while integrating one 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 26, this has been a year of execution for us. It's our first full year, as Asisine 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 automation 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 will move this semiconductor business unit from ANSYS into EDA. That's about 10% through Q3 26 of the revenue of ANSYS. We will also use that remainder of ANSYS 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 17%. 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, Sucine 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 of 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 in synergies by fiscal year 2029. What we have already done, which we talked about today, Sissene laid out again today, is we've already built a new joint roadmap, the multi-physics 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, excuse me, fiscal year 2027, we will have greater than 100 million run rate and 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 one year early. So, we are executing against our synergies and feeling very confident in our ability to achieve these. We'll be 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 Sassine 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 outlined. 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's a scene 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 remove 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 Cicene laid out. 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 to infuse AI, we're infusing it so that our team gets the benefit of that and funding the application-optimized IP, formerly called Factory 2, in IP, so building that out. We're not. We're keeping both factories. Sassine talked about the factory one, 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. It was 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're 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%, 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 in to 2027. And as Sissine 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 2027 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 half a billion dollars 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 synopsis 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 We're going to move into Q&A.

Please welcome back Sasin Ghazi, President and CEO, and Sheila Glazer, CFO.

Questions? So I'm going to, we're going to have two micrunners, Chris and Christine, and they'll 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 Panigrai from Missouri. First of all, congratulations. It's an amazing investors day, and thanks to Sushar and its team. It's put together a really good investors day, and thanks for inviting us. Sushina, it's a guide, you know, not only 27, even your long-term guidance. It's amazing, much better than we're expecting. The questions come, when you laid out a lot of growth opportunity, 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 different scenarios you just talked about. Who owns that value capture part? Which scenario you have higher value capture versus, you know, other models?

Yeah, thank you for the question. And 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 describe, starting with IP. Even though the royalty will not show up in 27, but starting in 28, and then it starts ramping into 29 and beyond, that becomes a fairly large percentage of our IP business that comes through royalty. And while FACT31 will continue on executing and delivering to the double digits 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. On EDA, it's the same. I want to remind us that the floor is the 13% with the objective for mid-teens. The EDA between synergy of the multiphysics, AI, definitely will start contributing in FY27, and 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 three revenue streams. Revenue stream one, the investment is essential because those agents, if the customer is subscribing to them either in option one or two, that investment and delivering to these agents is essential to have that 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 three, the revenue share, we are currently in a 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 OpenAI cloud, so they'll provide the model to 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 OpenAI 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. Thanks for doing the presentation, and congratulations on the targets. I was wondering if you could maybe talk a little bit about, you know, given the accelerating growth rates you're expecting across the businesses, to what extent is pricing and pricing to 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 authentic solutions gain traction in the market, what is the impact on the company's gross margins?

I'll take maybe the first one, and Sheila, you can address the second one. You know, 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'm building my own agents, or I need to buy an agent from you, or I need the new 3DIC compiler fused with multi-physics 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 a Factory 2, really the reason we were able to successfully bring in a number of customers in Factory 2 is the scale, the trust, the quality that we have in our IP. 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 synopsis. 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 opened 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 Sheila, So let me take a margin.

I think about AI in sort of two buckets. Obviously, we're working with our customers, everything that Shanker 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're 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'll go to Vivek in the second row.

Thank you. Vivek Arya from Bank of America Securities. Thank you so much for an informative analyst day. I had two questions. One, Shasin, for you on the revenue side, and then Shilak for you on the operating margin side. So on the revenue side, Shasin, if we go back to the analyst day you had in fiscal 24, At that time, I think you had set expectations of, you know, 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, right, and have more confidence in that growth rate. And then also clarification there. How much is 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 know, 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, you know, leverage? Is there something else? Like, what if, you know, you were to limit your operating margin, Sheila, to mid-40s, which is still pretty decent, would you be able to grow even faster, right? Then, like, what is that trade-off between sales growth and operating margins? Thank you.

So, for EDA, as Sheila 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 multi-physics fusion monetization starting in 27, we'll ramp 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. Thanks. 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 three products that I mentioned earlier. Royalty is not part of it. Royalty will get captured as they go into production. So the 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 the connectivity as part of that ecosystem is roughly in the similar timeline.

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 the total

AIOP is a billion. That includes licenses and royalties, but obviously royalties has built up over those generations of chips. We didn't give a split. Nothing in 2017. Sassine gave the intentionality that we're driving to that royalties will be higher than licensed. 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's 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 Sheila described. Thank you.

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

Thank you, Chief. Please, Howard Griffin. One of your slides earlier showed core EDA as foundational to growth, and that's undoubtedly true given the size of, let's call it Synopsys Classic core EDA, which is still your single largest piece of business. However, we talked about this, you and I, just a few months ago. That business has shown low to maybe mid-single-digit growth. There's even been some sequential decline in a couple of quarters. So to get from that percentage growth to mid-teens, you would have to add anywhere from $350 million to $400 million a year for that classic business and compound upon that, all else being equal. So what drives that Synopsys Classic core EDA business, that improvement over what you've seen the last year? And then secondly, with regard to the investments you've been highlighting, can you speak a little bit more in detail about what you're doing, particularly on AE expansion and go-to-market? Sure.

I'm assuming when you talk about the EDA classic, you're talking about the core EDA of software and hardware. Just software. Okay. On the software side, there are two tailwinds that it's becoming very obvious that we're seeing them, and we are in active conversations with customers as they're looking at the next renewal. So we have a number of renewals that we're in discussion with customers, that they're looking for more capacity for AI. They're looking for the advanced technology that we have, the multi-physics fusion. As we modeled FY27, and we communicated this a couple months early, it was modeled based on a bottom-up roll-up of our contribution that comes from, Multi-physics, AI, as well as the growth in the business due to more consumption and more need for that software. As we look for the long term, the double digits is based on further acceleration on all these vectors. So our confidence in delivering to it is fairly high. Otherwise, we will not put it as our long-term guide or specifically for FY27. In terms of investments, with AI, the workflow, the engagement with customer is changing, is absolutely changing. As our customers are deploying our full stack or their hybrid approach, and further when you go into the open AI synopsis model, the agent is becoming the expert of how to use the tool. And that's very different than the past. Our AE investment needs to evolve, and our field investment on how to sell in the world where an agent is becoming the expert user of a task, of a domain, where you have a model that is able to orchestrate reason across. And this is not only an opportunity for synopsis. As you know, the entire software industry is trying to evolve, too. How does it open up new use cases when you have a model that's able to explore far more than a human can? How do you support the model in that use case? The support is going to come through fidelity checkpoints, because whatever the model proposed, recommend, the tool is generating, you need to have a sign-off to check it. And that's the uniqueness and differentiation we have in our portfolio. This is where the Synopsys Ansys portfolio brings in the richness of that sign-off as we integrate more, and these agents are able to generate and validate.

Yeah, and I know you know this, Jay, because you are a deep study of it, but when we gave the EDA growth, that does include hardware, and as Sassine gave us a preview for an announce, we'll be introducing a new hardware platform, and the need for hardware across our customers is critical because they're building bigger and bigger and bigger, more complex designs, and they absolutely have to have that insight so they have confidence when they go to Take out a product.

Joshua, can we make sure that mic is on, please?

Do I need the mic? Oh, there we go. Joshua Tilton, Wolf Research. Thanks, guys, for doing this. I thought it was an awesome use of time. And I apologize. Maybe I'm going to sneak two in here. The first one is just, can you help us understand what the pipeline for these Foundry 2 deals look like? You know, Amazon's awesome, but we're always looking for what's next. So help us understand, like, what some of these deals look like coming down the pipe. And then maybe my second one is, is there anything in the OpenAI partnership that you announced with this GPT Synopsis that will keep this type of relationship unique to Synopsis, or do you expect some of your competitors to come out with something similar down the road?

Okay, so I chose my word carefully when I say the billion dollar by 2030 is based on the current agreements that we have signed up. Factory 2 is not limited to few customers. Factory 2 will expand because that application-optimized IP is needed for any COT. Today, you cannot invest and deliver a competitive COT, and as you know, every hyperscaler is building COT without having an application-optimized IP. The strategy we took, and it's been almost a year in the making, is how to use our scale and the current investment in Factory 1, Continue on delivering without missing a beat. Open up a new factory and have a completely different engagement model with the customer as I outlined. We're going to be embedded with the customer from a system requirement to a system validation. The best way to learn how to do it is to run with the leader and the company who has been most successful in building their own silicon. Second, the conversations are happening with others, and today is a very important day that we can right now be more open in the conversations with others to say, and here's the model, here's what we've done, here's how we're doing it. That does not limit our opportunity to IP. While we talk about IP Factory 2, when you're embedded at a system level to a system validation, that brings in the multiphysics, that brings in the packaging, that brings in the whole portfolio. So I cannot be more excited to have a validation point with the lead COT and their ecosystem And start opening it up as we engage further with customers. So that's on IP. On open AI, we're not biased. We don't pick, we want to work with this versus this versus that. The strategy we took, I want to say close to now seven, eight months ago, AI labs were approaching Synopsys and saying, I'm experimenting with my model using open source, and I'm seeing something very cool, some good outcome, good results, but I know for a fact I'll get far better outcome if I collaborate with you. It's a great conversation, but then how do we engage while making sure that we protect The Synopsys IP and the skills and knowledge that we're bringing. The requirement we have is our knowledge and IP cannot get sucked into a model that becomes the base model and without our control is available to the world. That's a non-starter for synopsis. So the GPT synopsis was a big investment from OpenAI. OpenAI will have to invest hundreds of millions of dollars to post-train GPT to make a GPT synopsis. That does not come for free. So that's a big investment they have to make. The investment we are making is bringing skills, assets, R&D to work with them to fine-tune that model and make it achieve the best outcome between the intelligence and reasoning with our tools. As others are willing, and there's a market traction behind that willingness, we'll assess. As far as what do they do with the rest of the market, et cetera, that's not for me to answer. It's really for the others to answer. But we're very pleased, actually, with the leadership position we took to architect it, define it for the market.

Hi. Jason Salino from KeyBank Capital Markets. Maybe to build off of Josh's question with the open-air relationship, presumably, outcome-based pricing has been difficult to prove in software, right? Right. So can you maybe just tease out what that would look like with this OpenAI partnership? Because customers, you know, they may be using other models, right? They may be using other competitor tools. So what if, you know, there's an outcome where you do improve the PPA, but an alternative method improved it more? Would that customer pay in that situation?

Yeah, that's why we have three choices for the customer. If the customer in lane two, that they're using our agent, their agent, their model, we're very neutral to that. That's fantastic. We will sell our agents if they're using any of our agents. If they're deciding to just build everything themselves, they need our tools. And the tools, as Shankar showed in one of his slides, it's anywhere between 5 to 10x for the verification, the VCS Verdi use case that Shankar showed, that they need more capacity. We have customers coming to us. They're needing more capacity for our software because they're using that hybrid lane number two that I described. And, of course, the same thing applies for the Synopsys stack. By the way, if you're using a full synopsis stack, the one thing we've been able to demonstrate to customers, you can be more token efficient. Why? We have access to the guts of the tool. We have deep API that they're not available in lane two or three. So, again, it's customer choice. One, two, three. The third one, the outcome base, if you heard Greg, the comment he made regarding RSI, where a model can recursively determine the architecture of the silicon in order for the silicon to determine the next architecture of the model. And this is not an open AI-only thesis. AI has demonstrated over the last year, year and a half, that each generation of models, the capability is truly exponential. With that frontier reasoning and the frontier intelligence, with our tools being post-trained, our skills being attached to it, and the knowledge of the chip design, I have no doubt there will be many use cases that the outcome will be able to achieve much better PPA in a much faster time. In few of the customer engagements that is happening, to be clear, the few customer engagements that are happening today, they're open AI-driven customer engagements, meaning open AI, buy chips from many. They decide the architecture. They decide what type of spec they need to provide their chip suppliers. And in the various handoffs, the model is able to prove that use this RTL because it will provide me better power or performance once you take it into implementation. In these early use cases, that's a wonderful opportunity for Synopsys, because that's a revenue stream we would not have captured before, because we were not there. We did not play. Right now, we're part of that service offering that OpenAI has. Now, again, for FY27, we will see revenue from AI, and as the technology and the partnership evolve, it will only accelerate into the future. There's a question right there. Yeah, so we'll go with Lee. Don't want to get into the middle of who gets the question. I'll be quiet. We decide.

Thanks. Lee Simpson, Morgan Stanley. Thanks for today. It was very informative, actually. Absolutely. Just maybe going back to AOIP, I'm just trying to understand, where are you hoping to impact here? Because obviously, interface IP has been something of a go-to for you guys, particularly CERDs and PCI, etc. Is most of this work going to be done around the IO block? Is that how it's going to work? And where does it stop becoming a chiplet? And then alongside that, it looks at how a lot of this is going for the hyperscaler market. Will this include China as well? Will we see regrowth in China? Thanks. Yeah, excellent question.

There are really about five IP titles, interfaces, that customers are needing desperately in optimization of that IP because it consumes quite a bit of area. The optimization of that IP will provide a higher bandwidth, lower latency, and these are the one that you could imagine what they are. The PCIe, the 224 gig type of an Ethernet service, HBM is all customized. So custom HBM, it's to optimize the logic to memory interface. It's UCIE, even though it's called a standard, there is nothing close to a standard the way UCIE is implemented by many customers. Now, the way we have engaged with AOIP with the customers, we are not picking and choosing which IP needs more customization, and I'll pay you only for this. They will come to Synopsys for their IP needs for a system. And the reason is a customization fee, not an NRE. Think of it as a priority fee to put our scarce resources to work on that engagement, and based on that, we will capture the royalty. So, will other customers ask for it beside the hyperscalers? Yes. I want to say over time, meaning if you are looking at an IP optimization for robotics, for automotive, it's really a trade-off. How much do you get by customizing the chip? And do you, the customer, want to make that investment to customize that chip? Now, the decision synopsis needs to make, is there much upside if we do that effort? Because, again, it's a royalty base and scarcity of resources to deliver to it. As far as China, the China opportunity as it relates to IP has slowed down, In particular, for the most advanced IP, because China is unable to design, in China, the most advanced IP. They don't have access to gate all around. They don't have access to 3DIC. So it's not us, our desire to do it. Our Chinese customers are looking for ways to continue on investing and designing, given the restrictions and constraints. We will absolutely engage with them in a similar model as we have for the rest of AOIP, but it all depends on the when, the how to drive it.

Okay, so we'll go to Andrew, then we'll get Charles, and then Ashish.

Thanks, Andrew DeGasper from BAP Paribas. Just in terms of the three lanes you discussed earlier, I was just curious to know, I'm right here.

I'm looking for Andrew, I just hear the voice.

Just curious to know, which one do you think delivers the best economics to Synopsys, if you were to, like, fast forward in four years, and which one do you think would be the most popular with your customers?

I missed the first part. For AI? Yes, AI minus. The three revenue steps.

Yes, full stack, customer-owned platform, and I think frontier models.

Today, I want to say most of the explorations is in lane number two, naturally, because customers, they're all racing to figure out, I'm seeing some good outcome with AI. How do I integrate it into my workflow? How do I change the way I'm doing design, leveraging AI? Why? Almost every customer in lane number two have came to Synopsys and said, you know what? For the debug agent, for the linting agent, for the whatever other agent, you already have it. I benchmarked it. I don't need to do it myself. I'll buy it from you. That's why it's not where does Synopsys make the investment. We will absolutely continue on investing on having the full stack because that investment in the full stack are needed for two and three. Because the open AI, I go back to what is the relationship with open AI? Synopsys is bringing not only the tools to train the model. We're bringing our agents. We're bringing our workflow. So, today, most of our customer engagements are in two, then one, then three. Do I envision there will be more post-trained models, frontier models, similarly to what we've done with OpenAI, with other companies, that they see the same opportunity that OpenAI is seeing and they're willing to invest in it? I believe yes. It will absolutely expand there. Because investing in a frontier model and frontier intelligence, you're not going to make money by just having the intelligence. You're going to make money by having the whole stack, including the compute, the service that comes with it for different markets. And engineering, it's such a sweet market because it's complex. Can you bring in that added value? So I do believe lane three will expand beyond one frontier relationship that we have right now with open AI. I believe lane number two will mature and use more of Synopsys agents, because the customer does not need to reinvent and put their resources if the agent is doing a better job that they can get from Synopsys. And that's how the EDA matured over the years, customers or IP. They build their own. Then they say, it's not worth the investment. I can get good support and a competitive offering from Synopsys. So that's how it is today, and that's how I see it going into the future. Charles.

Sasinghe, Sheila, thanks for taking my question. This is Charles Shea from Needham. I have a question on lane three, lane number three. I think one of the things people were worried about throughout this year has been can AI design chips completely bypassing the EDA tools, and there has been a thought that