Thank you so much for joining us. My name is Hamza Fodderwala. I'm a U.S. software analyst at Morgan Stanley, and, is it good morning or is it good afternoon? Excuse me. And this afternoon. It's been a long week. This afternoon, I have the pleasure of hosting Ajei Gopal, CEO of Ansys. Before we begin, a brief programming note on my end. For important disclosures, please see the Morgan Stanley Research Disclosure website at www.morganstanley.com/researchdisclosures. And I think, Kelsey, you have a programming note of your own. I do as well. Yes. Thank you. So today's presentation contains forward-looking information. Important factors that may affect our future results are discussed in our public filings. Forward-looking statements are based upon our view of the business as of today, and Ansys undertakes no obligations to update any such information. Thank you. Okay. Thank you, Kelsey. Ajei, thank you so much for joining us- My pleasure. This afternoon. So maybe just to start off, I think, you know, there's some investors in the audience who may not be fully familiar with the Ansys story, but maybe just give us a brief overview of Ansys, your products, the kind of customers you serve, and the markets that you operate in, to really lay the foundation for the rest of this conversation. Sure. So, Ansys is an engineering simulation software company. And what that means is, we build software that allows our customers to be able to analyze and evaluate the behavior of a physical product completely in the virtual or the digital domain. So using our technology, our customers can visualize the behavior, the failure potentials, the response of any product that they're trying to design completely in the computer, without the need for a physical prototype, without the need for experimentation. The way we do this is our software really encompasses the basics and the fundamentals of mathematics, of physics, of computer science. We leverage the knowledge in those areas, and we bring that together to create very highly technical, very highly technically capable software that can solve, you know, these really complicated problems. And we pride ourselves, of course, on the accuracy, the effectiveness, the fidelity of our solutions, because our solutions are intended to mimic the behavior of real-world phenomena or to anticipate or predict the behavior of real-world phenomena. So as such, they have to be very accurate. We serve customers both large and small. Some of the largest companies in the world are customers of ours, and of course, we have startup companies as well, who are using our technology around the world. From an industry perspective, we have customers... The largest sector that we service is the high-tech and semiconductor vertical. That's about a third of our business. A little north of about 20% of our business is in the aerospace and defense vertical, and about 20%, slightly south of that, is in the automotive and ground transportation. We have industrial equipment, chemicals, materials, energy, a number of other areas as well that we serve. We're very well diversified from an industry perspective across a number of different industries. Yeah, that's a good segue into the next question. I mean, I think relative to some of your other vertical or design software peers, you're quite diversified from an end market perspective, largest industries being high tech, aerospace and defense- Right. And automotive, like you mentioned. Can you discuss some of the trends within each of those verticals that's really fueling growth in these industries and ultimately for Ansys? Sure. I mean, the... I'll talk specifically about the industries in a second, but just to give you some, a general perspective from a customer point of view, the reason that you would use our technology as a customer is we will help you. By using our technology, you'll be able to design your products faster, and more efficiently, and that translates into faster time to market. And using our technology, we show our customers, and we can demonstrate chapter and verse from use cases of how they can save money, because they can reduce the amount of physical prototyping or they can reduce their warranty costs. So the benefit to our customers is really they can accelerate the top line as well as achieve bottom-line savings. So we're very excited about that. From a vertical perspective, from an industry perspective, when you look at each of these industries, they're all going through transformations where previous design parameters and ideas don't necessarily translate into the new world. So for example, if you consider the semiconductor industry, one of the transitions is moving from two-dimensional, so traditional systems- on- chip to 3D IC structures. You could think of that as a bungalow versus an apartment building. So when you're considering a traditional system-on-chip analysis or traditional IC analysis, you use your more traditional tools. But the moment you're now considering a 3D IC structure, you have to worry about the integration or the interaction between the different layers. So think of that in the metaphor that I used about the apartment building, think of that as noise from the neighbor upstairs. All right? So you're thinking about electromagnetic interference and signal integrity or other kinds of analysis, and that requires physics-based analysis, which is where we come in. So our customers are going through this transformation in that particular space. We have technology to be able to help them to skate to where the puck is. That's one example. If you look at the automotive industry, for example, it's going through a transformation, from the internal combustion engine, now to electric vehicles, and autonomy, and that transformation is very profound. And so the old ways of doing business, the way in which you designed, ICE cars in the past, that's not relevant anymore. Of course, there are many more global competitors, so the rate and pace of innovation has advanced. So the nature of the problems has changed from what it was before to these new generation of problems. And these are what we call multiphysics problems. So these are, these require multiple kinds of analyses to be performed at the same time. The problems are more challenging than before. They're more. The speeds are faster, in terms of the design cycle times are faster. And so this is creating more of an imperative to take advantage of simulation. If you look at aerospace, for example, the trends there are towards sustainability, so moving away from traditional fuel to SAFs, moving to electrification, moving to hydrogen, thinking about VTOL aircraft, for example. All of those things have profound implications, and of course, if you factor in sustainability across these, in the aerospace industry, they're making design decisions now that are gonna last them for the next 25 years. And so all of these industries are going through a process of transformation, and they're all about fundamental analysis of where the products will be and how they'll go. And they require to facilitate this transformation, it requires multiphysics analysis, and that's where we come in. So our technology is able to support this multiphysics analysis that's necessary to help these companies in these industries go through their transformation that's being driven by their business imperatives. Got it. Sounds like solving some very complex problems for a lot of your customers. You know, from a macro demand perspective, I've noticed the demand has been quite resilient for Ansys across, you know, geographies and the industries that you serve. I think it sounds like it's 'cause you're helping your customers solve a lot of these transformational problems. But anything else from a business model or market dynamic perspective that you would say has contributed to this resilience? Well, certainly, you know, certainly we've been going through a business model transition. We've been moving towards more of a lease-oriented business, and that's obviously been helpful for us and helpful for our customers. When you think about the core business and the demand, the demand across our different sectors has been strong, as I indicated. And if you think about the broad Ansys business, we're geographically, you know, quite distributed. About slightly shy of half our business comes from North America, and a little over a quarter of our business comes from Asia-Pacific, and a little over a quarter comes from Europe. So that diversification is also, you know, supporting our business. So there are lots of, there are lots of drivers across the, across the spectrum that are, that are moving our business forward. Of course, most, most importantly, the demand that customers have for next-generation innovation and products. Got it. So, you know, the breadth and depth in the core simulation offering, you know, really stands out when it comes to Ansys. Can you maybe talk a little bit about what sets Ansys apart from some of its other, you know, simulation companies? Yeah. So one of the... There, there are a couple of dimensions. One is, one is we really care about accuracy, and, and, and that's sort of been the hallmark of Ansys, I would say, from for many, many years. It's the accuracy of the simulation that I think is really important, because at the end of the day, you're using simulation as a way of, of evaluating a physical phenomenon. And you have two two possibilities. You could rely on a physical experimentation. You could take a prototype, you could build that prototype, and then physically evaluate it in a lab. So, for example, if you were testing the safety of a car, you could build a model of the car, and you could slam it against the wall at 30 miles an hour, and that will give you some perspective, and instrument it accordingly, and you would get some perspective, or you could do that in software. The price differential is significant. Building a physical prototype in advance of the production model of the car can be quite expensive, and then slamming it into the wall requires a significant amount of instrumentation. That's a very expensive, maybe $1 million to do a single crash test, and that gives you one scenario, a head-on collision at 30 miles an hour, for example. But if you could do that in software, you can do that with significantly less cost, and you can run multiple experimentations, and you can do so really early in the design process before you've locked down any of the decisions. So there's real advantage in doing it in software, but it only works if the software is accurate, right? If the software is accurate, then what's the not accurate, then what's the point? So the most important thing is accuracy, and we certainly pride ourselves at being very accurate across our simulation capabilities and our product capabilities. That's point number one. The other thing that I'm really proud about in the portfolio is the completeness of the portfolio. As I mentioned before, the problems that our customers are dealing with are not tied to a single individual physics. These are not uniquely structural problems, or fluid dynamics problems or electromagnetics problems. This is really the integration of all of these, and that's what we refer to as multi-physics problems. So if you take the example of the crash that I gave earlier, you might imagine that that's a structural analysis. You're trying to analyze the physical integrity of the, of the car or the body of the car around the driver to see if the driver would be safe. But the reality is it's a multi-physics analysis because you're not only analyzing the, the structural integrity of the, of the car, but simultaneously, you're analyzing the airbag and the deployment of the airbag, and that's a fluid dynamics problem. So these are two different kinds of physics that need to be integrated together in order to get the right answer as to whether the driver is protected or not. So the ability to support this multi-physics is important, and we've got that across. We've got representation across structures and fluids and electromagnetics in the semiconductor space, optics, safety analysis, embedded software, and I could go on. I mean, we've got a representation across a number of areas, and that's also something that's very important. And we've also invested in making our portfolio. We've moved our portfolio from being sort of a tool-oriented view towards more of a platform-oriented view. While we have a lot of simulation capabilities as individual tools, and customers can use them, we've also integrated this together as part of a platform to deliver simulation capabilities to our customers. That evolution, I think, has put us in a very strong position to deal with these emerging use cases that customers have. Very interesting stuff. You know, AI has been obviously very topical these days among the investment community, but I think in your space, this has been a topic for a while, particularly generative AI. Can you talk a little bit about Ansys's strategy there, some of the products that you've launched or hope to launch that leverage the power of generative AI? Yeah. So AI is, we've obviously been using AI, you know, as part of our portfolio for a number of years, you know, we are investing in a few areas, and I can talk about them in a moment. But AI is particularly important. Obviously, there's a lot of excitement based on recent advances in AI. The way we think about AI is it's another tool that we can use to ensure that the customer gets the result that they're wanting. It's one more tool to help us deliver to the customer a better simulation result. And we've been using AI, as I said, for a number of years, and we've integrated AI across our products in several areas. One is in customer support. We have a technology called AnsysGPT, which we recently released in beta mode to customers, and that is really about. That is a GPT technology that's been trained with Ansys information. And when it responds to customers, it gives them information as well as access to knowledge-based articles and how-to guides and so forth. So it really is a, like a very smart tutor to help them understand how to use simulation, which is very helpful because sometimes simulation can be difficult for people to use. So that's one area. We've also gone through individual products, and we have announced a sequence of products called the AI+ products, which include AI capability to enhance the solving capacity of individual products, which we're very proud of, and some of those are being released and will be released over time. And then we have a new offering called SimAI, which is really focused on a very common design metaphor that engineers have, which is optimization. So much of what engineers do is to optimize, and when you're optimizing, you vary multiple parameters, and or you have multiple variables that you vary to create a design space. And so this, this exploration of the design space, this multivariate optimization, can be quite challenging, and we've got some product capabilities in there, a new product in that space, as I mentioned, SimAI, which leverages traditional simulation capabilities and AI simulation capabilities to significantly accelerate this multivariate optimization problem. So, so we are excited about the use of AI. We've used AI for a number of years, and we continue to integrate AI into our, into our product lines to make it, our products even more efficient for customers and even more accurate and even more timely for customers. Just on that topic, are there ways that you're leveraging AI internally to perhaps make, you know- Sure, yeah ... operations more efficient? Yeah. Yeah. I mean, and of course, like many companies, we're looking at how AI can improve the effectiveness of our own internal operations, and that's something, you know, that's obviously an area of effectiveness, and that's something that we continue to incorporate. But that's like many other companies, it's not fundamentally differentiated, but in the use of AI within our portfolio, I think we're very excited about the value that we can bring to our customers. Got it. Thank you. Another significant topic in software has been cloud adoption, right? Yeah. Can you talk a little bit about the cloud strategy and how it maybe deviates from your traditional enterprise software company? Yeah. So in a traditional company, in a traditional enterprise business, you know, like you have a CRM system or, you know, an ERP system or something like that, a typical interaction with the software is typically in the form of a database lookup or a database update. So it's relatively small amounts of computation on a per interaction basis, and you may have dozens of people interacting with the system during the day. And so that's typically how an enterprise system works, and it's relatively straightforward. When you think about the computational needs that are that go with that, it's relatively straightforward to understand that you have so many users, and that's the amount of computation that's needed. In our case, with simulation, a single engineer could kick off a job that could run for across thousands of cores, and that could run for days or weeks at a time. Because you're running you're solving these incredibly large problems, and a single engineer could kick off a number of different jobs. So the amount of computation that's being driven by our technologies can be quite significant, and it could be a massive workload. So many of our customers have invested in data center technology to try to build out the compute capability. In fact, this is a dimension of growth for us, which separates us from traditional enterprise companies, because most enterprise companies, when they sell, they sell software. They can grow by selling to more users, or they can sell more products. We can sell more users and more products, but we can also sell more computation. We don't monetize the hardware, but we sell a license to be able to use more and more hardware, software across more hardware. So that's a really interesting avenue of growth, and it lends, and it comes because of the nature of the technology. It's computationally intensive. So with our cloud strategy, we're trying to support our customers, even if they have data centers and they have on-premises hardware that they've invested in, we absolutely support them in the use of that hardware on-premise, on-premises. But if they have, if they want to take advantage of the cloud, we also support them to be able to use our technology in the cloud for scale-out compute, right? And that's a really a very important use case. And so that's the basis of our gateway products. We work with Microsoft Azure, we work with Amazon's AWS to allow our customers to leverage, to work directly with those third-party cloud providers, to negotiate their contracts and then leverage our capability to use simulation at scale across their cloud environments. And then we also have an Ansys a cloud-native effort, which provides a multi-tenant cloud solution for our customers who directly wanna take advantage of computation that's built into the Ansys cloud solution. And that's another area that we've invested in. So we have several different cloud products, depending on the nature of the customer and what they're looking for. Maybe I'll sneak in one more and then open up the audience for questions. You know, another important part of the strategy has been, for Ansys, has been pervasive simulation, really trying to expand the power of simulation to non-traditional users. Can you touch on that opportunity and the strategy there? Yeah. So, let me try to make it... I'll try to give an example that may make it a little bit more real, or more tangible. So historically, simulation has been used as a tool, and as I said before, historically, when customers would do a single physics analysis, so an engineer would sit down to do, say, a structural analysis. Will this metal bend in the right way? And if it does, what's the impact gonna look like? That's the historical way it's been, engineers work with it. And then over time, as I said, we're moving towards a world or customers are moving, have moved to a world where they wanna do multiphysics analysis. So they, it's not just a single physics, it's multiphysics working together, but it's still a very tool-oriented analysis. What we've also recognized is there's a significant opportunity to take the insights of simulation and make them available, not just in the traditional usage of validation of a design, but to be used throughout the design cycle. It can be used really early by the designers when they're trying to come up with the ideation phase. It could be used in manufacturing, it could be used in operations, digital twin technologies, for example. So there's the simulation can be used in multiple areas, but in order to be able to do that, you have to be able to package the simulation capability and make it available in context for when it needs to be used. So a great example of broadening to a different user domain now and moving beyond engineering to a different domain and I'll use an example here is. Let's take a hypothetical example, and this is hypothetical, hypothetical example of a surgeon. So we at Ansys have developed a physics model of the human heart, right? So this includes the structures, the fluid flow, the electrochemistry of the heart. So that 3D physics model of the human heart can be personalized based on the imaging to a particular patient. So we can evaluate a particular patient's heart to understand how it's gonna behave. And you can imagine the benefit of that, for things like heart valve replacement, to try to figure out what that might look like or to assist in surgical planning. Now, this is the future-facing part. This doesn't. Now, what I'm describing doesn't exist. But imagine if before performing surgery, a surgeon could evaluate and interact with this real-time physics human heart model to plan out the surgery. What happens if I do this? What is the outcome gonna look like? How is the heart gonna behave? And that's an example where, underneath the covers, you would have full-fledged multiphysics physics running, right? Integrated multiphysics running to be able to actually do the analysis, but the result of that is simplified and presented to the physician in a format that the physician can understand. Mm. and that can help them plan out the surgery. That's an example where the simulation technology is encapsulated through a layer, through an API layer, and exposed programmatically to another layer. Mm. And so what we've built is that programmatic layer in Python that allows for other people to build applications on top of our simulation capability, build these value-added applications, whether it's targeting a non-engineer, or whether it's targeting manufacturing, or whether it's targeting different parts of the engineering process. So that the insights of simulation can be made available to people who don't necessarily even understand how to run the technology, because of the encapsulation via APIs. So we think that's a very powerful statement, and we think that opens up the aperture and the opportunity for us in the future, and allows us to sort of expose the benefit of our technology to customers in different domains than just our traditional domains. Fascinating. I'll open up to the audience for any questions. Just raise your hand. Oh, we have one here. Hi, not directly relevant, but I just wondered if you had a view on quantum computing and whether or not you were involved with it in any way, or what you consider to be its ramifications for the future since you're doing physics simulation? So, the question is on quantum computing. So we have several areas that we invest in in our product portfolio, and HPC is one of those areas. High-performance computing is one of those areas. And these, I'll mention a few areas that are applicable across our portfolio. The first area is what we call numerics, which is the fundamentals of the algorithms that go into... That's the physics understanding, the mathematical algorithms that go into a product. That's the basics of what we do. The second area that I talked about briefly was AI, and we do a lot of work on AI that goes across our portfolio. Another area is high-performance computing, and with high-performance computing, we're looking at how to take advantage of the latest advances in computing to accelerate the results of simulation. So one area is scale-out computing with traditional computers. Another area is the use of GPUs, and we're evaluating the use of quantum computing. And that's very much in the research phase, so quantum computers are not really necessarily available at scale at this point, so it's in the research phase. But we're trying to figure out whether we can use that technology as another tool to be able to accelerate the performance of our products, as we have across, you know, over the years, across a number of different areas. Of course, for people who are building quantum computers, some of them are our customers because they're using our technology to be able to build the quantum computers, of course. So that's certainly, there's a nice symmetry there. But with respect to the use of quantum computing to accelerate our algorithms, that's very much of a research topic, and we collaborate with universities to do that. Any other? We have one over here. Just getting back to the competitive question, you touched on the breadth and depth of your portfolio. Cadence were obviously here yesterday, chatting about their own aspirations in your arena. Could you just highlight the competitive differences between the two, or is the market big enough for you both to grow? Thank you. Well, when you think about, when you think about our market, you know, we talk about the simulation market as being about an $8 billion market in the aggregate today. But you have to consider that in the context of the broader opportunity. So there's about $1 trillion of, over $1 trillion of, money that's spent on R&D. That's like the global R&D budget. That's a big number. And about three-quarters of that, or about, you know, $750 billion of that, of that, is spent on what's called failure avoidance. And failure avoidance includes testing, it includes validation, but includes a number of other things. And so there's a real gap between $8 billion, which is our addressable market, and $750 billion. I'm not suggesting our addressable market is $750 billion, but what I am suggesting is that when you consider that gap and how much money is being spent on failure avoidance, it's very clear that the use of simulation is, relatively speaking, underpenetrated into the market. So what's pretty obvious is that when you consider the engineering market, where there is opportunity is in simulation, right? This is an extremely attractive market. There's upside potential. And from our perspective, we believe we have the best products in the industry. We've got the most complete multiphysics portfolio, which is what customers are looking for. And so we feel that we're very well positioned to take advantage of the opportunity as the market continues to expand. But because of the attractiveness of the market, I don't doubt that others will want to enter the market. And we've seen the situation in the past where companies have tried to enter the market. In some cases, they've exited the market. In some cases, they've stayed in the market through acquisition. And, you know, it's a very, very big market, but we certainly feel like we're extremely well positioned, and we feel like we're the market leaders, and we feel like we're well positioned to maintain that leadership. Maybe I'll sneak in one last financial question. You recently reaffirmed your outlook from 2022- 2025, I believe 12% constant currency- Correct. ACV growth, and $3 billion of cumulative operating cash flow through that timeframe. Just what gives you confidence in that outlook? Just speak to sort of the strength of the business model to get there. Yeah. So firstly, you know, as I said, we went through a business model transition towards more of a subscription business, or to a lease business. We think that obviously the business model transition helps us as well. The market continues to be strong. We're excited about the market, and we're excited about our opportunity in the market. We've also talked about how, as we look forward, obviously, we see ACV growing, but you should expect to see cash growing ahead of ACV. And so that's certainly demonstrates the leverage that's available in the business. All of that, all of those, factored into our view of the long-term guidance. Okay. Ajei, thank you so much for your time. Thank you, Kelsey. All right. Thank you very much.