Okay. We're gonna get back on track here. My name is Steve Tusa. I'm the Electrical Equipment and Multi-Industry Analyst here at JP Morgan. I recently picked up some of the industrial software names, including PTC, Autodesk, as well as who we have here today, which is Ansys. First, we're going to hear IR Director Kelsey DeBriyn talk about the forward-looking statements, and then I'm gonna hand it off to Nicole Anasenes and Ajei Gopal for a bit of Q&A. We'll first go to Kelsey, and then move right along. Today's session may contain forward-looking information. Actual results and future events could differ, possibly materially, from those anticipated in our statements and from historical performance due to a variety of risks and other factors. Information about such factors, as well as GAAP reconciliations and other information on non-GAAP financial measures we may discuss, is included in our SEC filings and investor materials. These statements speak only as of today, and Ansys undertakes no obligation to update them. Before I kick it over to Ajei for a bit of an intro, you can email an EventCenter, put in your questions into EventCenter. We're also gonna have Q&A at the end. We'll open it up for the last five, 10 minutes for Q&A. With that, Ajei, thanks for being here, and maybe a bit of intro on Ansys. Excellent. Since not everyone might be familiar with Ansys, I thought I'd take a couple minutes to introduce the company. We're the market leader in engineering simulation software. What that means is our customers use our technology to be able to design and validate their products using our software on the computer. To make it concrete, I'll give you an example. Let's assume you're a car manufacturer. Obviously as a consumer of cars, you expect your car to be safe, and you might imagine that the manufacturer builds a physical prototype of the car and puts crash dummies inside it and slams it against the wall in order to figure out if it's safe or not. That's certainly one way of doing it. Of course, the other way of doing it is to use our software to do that analysis. That allows you as a manufacturer to not just test the one scenario, but to be able to test multiple scenarios, front impact, side impact, different numbers of passengers, et cetera, and to be able to do so in a much more cost-effective way. That's just one example of how our technology is being used. We are being used by customers around the world. We have thousands of customers across multiple geographies. From an industry perspective, about a third of our business is driven from the high-tech and semiconductor markets. About 20% is the aerospace and defense market, about 18% thereabouts, automotive ground transportation. We have business in the industrial sector, energy, chemicals, and so on and so on. We're very split across multiple industries. We're very split across multiple geographies. We have customers ranging from small startup companies, all the way to the largest, and the best-known brands around the world. Great. Thanks for the intro. Simulation market is pretty dynamic. Some very nice growth. Lots going on in new product development across all the industrial companies I cover as well. Relative to 20 years ago, it's night and day. Maybe talk about some of the faster-growing areas, some of the white spaces that you see out there, that contribute to, you know, this well above average growth rate for the simulation market. Sure. Historically, simulation, as I said, has been used to validate product design. It's been used to obviate the need for physical prototyping. What's been driving in the recent past some of the incremental use of simulation has been in some of these emerging areas where customers have new challenges. Areas like electrification, like autonomy, next generation telecommunications, sustainability is obviously a driver. All of these areas create incremental pressure within organizations, and there's incremental pressure to bring product to market faster, and that's where simulation plays a role. Where, which end markets, maybe a couple of examples of what's driving growth and whether it's aerospace defense or the electronic side or auto, the main areas, or maybe one of those, smaller pieces of the pie that are growing fastest? Sure. I mean, since I started with the example of a car, if you think about the automotive industry, you know, for decades, for almost 100 years, it's been based on the concept of a human being driving an internal combustion engine vehicle. Both of those assumptions are being questioned at the same time. Certainly, we're moving to electrification and increasing levels of autonomy. That is a profound transformation for that particular industry. That's an example of the kind of drivers that you're seeing in industries. In high-tech and semiconductor, it's obviously different kinds of, you know, next generation semiconductor chips, 3D- ICs, stacked ICs, telecommunication, increased radio. There continues to be individual pressures for each one of these areas that these that our customers have to respond to. Certainly, with our technology, given the breadth and the depth of the simulation capabilities that we have, with the broadest range of simulation products, we're in a position to support our customers as they go through this transition. When you think about the simulation content, if you will, for the traditional ICE, a new ICE model versus an EV, maybe talk about what you see there and how much your customers are incrementally spending as they, you know, revolutionize their fleets? Sure. When you think about, when you think about a vehicle, there are a lot of moving parts, literally and figuratively in a vehicle. One of which is, of course, the engine, and the drivetrain, but there are other aspects of which need simulation. That kind of carries through between an internal combustion engine and an ICE car and an electric car. Electrification is still relatively new to the market. There is a lot of. You can't rely on the history of activity that's taken place in the past to be able to carry through from one model to the next. There is a lot of incremental simulation work that gets done for electric cars to be able to deliver them to market faster. Certainly from a manufacturer perspective, what you're seeing is some manufacturers have made the decision to move to sunset internal combustion engine and move to electric. Many manufacturers are maintaining both capabilities. They're maintaining internal combustion engine vehicles as well as hybrid vehicles, as well as electric vehicles. That obviously in the aggregate increases the amount of simulation that's being driven. We see this particular transition as certainly something that continues to drive the increased use of simulation in the industry. When you think about that transition from, you know, thousands and thousands of parts for an ICE to a, you know, more modular design for an EV, should we think about that as because there's thousands of parts that's actually more simulation intensive? Or is it, you know, now you're kind of dealing with a system, a brand-new system of things working together with more software and more electronics. I mean, maybe just talk about how you're, how you see that? Yes. Even though they may be more, fewer physical, if you compare a model internal combustion engine to a electric car, even though there may be fewer components in an electric car, there are some of the problems. You're dealing with a different system than you've dealt with in the past, and so that obviously introduces incremental levels of complexity. There are also other challenges that were not an issue, that in the internal combustion engine, for example, road noise, right? The engine tends to drown out road noise for an internal combustion engine car, for an ICE car. An electric car, any kind of noise is immediately visible or audible. Therefore, that puts incremental pressure on the design of the car to make sure that it's less noisy. Those are the tolerances that you're willing to accept in other areas then changes. It is a reengineering activity, and it requires, incremental, effort. It's not a, just because you have fewer parts, it's a simpler product. Got it. Switching to the A&D side. Paris Air Show coming up, in a few weeks. Maybe talk about the drivers there, because not only do you have, it's probably one of the best end markets out there, full stop, from a growth perspective for the next, you know, five years. There's also a lot going on with unmanned flight, electrification of the engines. What are some of the trends you're seeing there that are directly influencing how your clients are spending today? Yeah. I mean, you hit a couple of trends. You know, more broadly, there's a discussion about how do we make airplanes more greener, right? Whether it's a traditional engine, light weighting, and more energy efficiency in a traditional engine, or whether it's looking at different sources of energy, like, for example, there is work on fuel cells, there's work on electric aircraft as well. Certainly all of those are drivers. You mentioned autonomy for unmanned vehicles. That's certainly or air vehicles. That's certainly a driver as well. This broader discussion of how do you make commercial flight more sustainable is driving a lot of investment across the end-to-end supply chain in the aerospace industry. Another area which is driving investment is Space 2.0, and it's everything from low Earth orbit to, you know, getting people to Mars. There are people, there are companies at various levels of investments, but certainly we see this as being a driver of very innovative technologies and capabilities being brought to market, and of course, we're in a position to support our customers doing that. Then, of course, in aerospace and defense, given the increased geopolitical nature of what we're dealing with in the world today, there's increased defense activity, and that obviously, when you think about defense equipment, that is heavy industrial equipment, which obviously we're in a position to support with the design. That's also a long-term change, if you will, from a few years ago. Just lastly on the biggest piece of the pie on the high-tech on the electronic side. You recently announced a partnership with Synopsys. What are you doing there and kind of how far do you go there, when do you leave it to them as a partner? You know, what are the key drivers when it comes to the chips, the boards, the products and the systems? I think it's very clear from our, from our philosophy. I mean, our philosophy is very clear. We believe in an open ecosystem, we believe in open interfaces, and we believe in partnering with all the companies out there, all the vendors out there, to be able to create a better outcome for customers. Customers are not gonna pick a one-vendor solution. Customers are gonna look for best of breed, and they don't wanna be system integrators. They wanna make sure that the companies, the vendors are in a position to work together. That's our philosophy. That's what we do. We work with everyone who's involved in the chip manufacturing or in the EDA space as partners. We have an incremental relationship with Synopsys around the incorporation of some of our technologies into their products. In particular, we have a relationship around some of our power sign-off capability being moved into their, some capability being reflected earlier in the design cycle in some of their products. Philosophically, as you think about that part, that's the largest piece of our business. It includes both semiconductor as well as electromagnetics, as well as electronic design. There's a lot of scope starting from nanometers all the way up to, you know, millimeters to centimeters of design complexity. That means that these designs are increasingly complicated, and the kinds of technologies that we're in a position to bring to market, are able to help our customers navigate, at the right level to be able to produce product that's appropriate for their customers. Just one more on the end markets. Which smaller piece of the pie are you most excited about? Where do you see as the part that's emerging with the highest growth rate and could become a bigger piece over time? There's certainly activity across all of these markets that we're excited about. For me, personally, one area that I enjoy is healthcare. Even though it's a relatively small piece, there is opportunity in healthcare to use simulation as a way of validating devices. Rather than relying purely on in vivo or in vitro trials, it would be also using in silico trials. Certainly, I think governments, certification agencies are looking at whether simulation could be used as a way of accelerating that. Certainly, we've had customers talk about how the use of simulation has been able to accelerate the approval process to be able to bring, for example, medical devices to market faster. Obviously, you know, that has an impact. It has an impact on human lives in a very direct way. That's something that's viscerally exciting about healthcare. What in your view is the biggest barrier to entry, to, you know, more democratization or, you know, content customer growth? For simulation in general? Going forward, yeah. For simulation in general. If you'd asked me the question 15 years ago, I would have said, "Listen, the challenge with the simulation is it's relatively challenging technology to use, and our end users tend to be, you know, PhDs or engineers with a tremendous amount of experience." The second point I would have made was, "Look, it's really hard to get compute resources because these things are compute intensive, and some of our customers struggle to be able to get access compute nodes to be able to solve the problems at hand." The last, you know, 15 years, we've seen a lot of progress in that. We've, we're seeing, we've made our products easier to use, so the level of skill that's necessary is no longer what it used to be. More than that, we now see engineers graduating from undergraduate school with engineering group degrees who are proficient in the use of simulation because that's now part of the curriculum. We at Ansys have a relationship with over 3,000 universities around the world. We're taught as part of the curriculum. We're part of student teams. That means that the workforce that's entering or the students who are entering the workforce know how to use simulation, and that's very different from years past. That's one benefit. The other thing is that we've seen, we've driven integration and workflows of our products into other products to be able to better support the use of simulation in broader digital transformation concepts or digital transformation initiatives. That's also been a democratization, if you will, of lowering the barrier. Finally, I'd make the point that computation, which in the past was always a challenge, is much less of a challenge right now because obviously the cost of computation has come down. We've continued to ride that curve upwards and made our products take advantage of things like GPUs. Furthermore, with the availability of scientific compute nodes in the public cloud at a reasonable price, that now gives our customers the opportunity to use scale-out compute in the cloud, which we support. That means that, even very small companies or startup companies, are able to use massive amounts of compute to be able to solve problems, which was always a barrier in the past. Some of these barriers that we've historically had effectively been coming down, in part because of our investment and in part because of, the nature of the evolution of the industry. Do we have a question up front here? The question is, the five-year compounding annual growth rate of the addressable markets. The data that we gave in our last investor day, if you'd like to see the data, was that the core simulation market is about $8 billion. Our estimates are that over the next 10 years, that core market could roughly double. The emerging use cases that Ajei just referred to in terms of contextually embedding simulation for non-experts to add it, you know, our estimations are that if that continues to progress over the next decade, you know, doubling could nearly triple, right? It really depends on the rate and pace of the tipping points and the adoption around embedded simulation into other kind of contextual end uses. That kind of gives an overall feel of what we shared. Just to follow on, you mentioned the 15-year history on ease of use to this point from 15 years ago. Is that a higher growth rate for the next 10 on that? The question is, does that ease of use over the last 15 years, does that accelerate in the next 10, 15 years? Is it linear or exponential? As Nicole said, the point that she was making was, look, if you consider the use of simulation, historically, it's been used as a tool for product design. In the future, we see the opportunity, given the work that we've been doing, to componentize, integrate our technologies into other ways to make it available to other applications. We see an opportunity to be able to create, to be able to create a market where simulation is embedded within applications and providing any knowledge worker who has a need for this analysis, the ability to be able to use the analysis in their decision-making. Great example would be, we talked briefly about healthcare in our last investor update, investor day presentation, which was in August of last year. We talked about how there's a company that's doing providing software to ophthalmologists, to eye surgeons. Under the covers, unbeknownst to the physician, because the physician really has no interest in learning how to use simulation software, under the covers, the technology is taking advantage of simulation and presenting the results of simulation to the surgeon. The what-if analysis that the surgeon would normally go through based on their experience, they can actually use physics-based analysis to drive them towards the best surgical outcome. That's an example of a next-generation use case where simulation is embedded, that goes well beyond anything that we have that we have done in the past. Yes. I would say that core market, that 2x core market, there's still a lot of room to grow, right? If you think about global R&D spend, global R&D spend is about a little over $1 trillion, right? Within that, the vast majority of time spent in the R&D process is around avoiding failure. This is in our core market in traditional use cases, right? At the same time, products are becoming more and more complex. Ajei talked about those secular forces around next generation connectivity, sustainability, electrification, autonomy. Those things just make even simple products more complicated to build. When you combine product complexity with a large amount of spend, which is largely focused on avoiding failure or fixing things downstream in the process, that's where simulation sweet spot is. It is we have the ability to help customers avoid failure in the end by doing more upfront. The pressure is gonna continue to kind of do more with less on that kind of overall, relatively slower-growing R&D budget. That's just on the core market alone. When you add in this longer-term, like, exponential, as I'll use your expression, that's entirely new markets which are independent of even that really strong growth rate, which is why we're very optimistic about the growth potential of the business. Oh, right here. Just wait one second. Could you just explain at a high level just the mechanics of how you actually incrementally monetize some of these secular growth drivers? Around, you know, electrification and autonomy, is it, you know, a new product you're actually selling to a customer? Is it just ARP? Is it just a higher price because there's now potentially more functionality in product? How should we think about, like, how do you actually get more revenue from the customer, if they're, you know, sounds like doing a somewhat similar activity with your existing software? Just would love to understand that a little bit better. Yeah. Just to kind of level set the product, we talk about three vectors of growth in the business, right? We can grow by more users, more products, and more computation. How does that actually show up, and what's the difference between, say, a traditional use case and what we call these new use cases, which are driven by these secular forces? Think about a traditional use case is kind of a single physics modeling a component or a simpler problem, right? I wanna optimize the shape of this water bottle, and I want to, you know, do a structural analysis just on that shape, right? That's a single. These new use cases require customers to solve problems which are much more complex, right? Ajei's example of, you know, moving away from the old paradigm of a human being driving an internal combustion engine to a completely new electronic vehicle requires not only multiple physics, but it is a much bigger problem to solve because you're solving net new problems that have never solved before, right? That is more products. It's electronics, it's structures, it's fluids, and it's in a context of a different system that hasn't been done before. That's more computation. Now, if you extend all the way on to future use cases in just that same example of the changes in the automotive industry, you move on to autonomy, and you're talking about things like an example Ajei used earlier today in the discussion was, you know, moving convoys of trucks across, you know, of autonomous trucks. That's a mission-level problem, a system of system-level of problem. Every time you add a variable, every time you add an interaction, every time you abstract it up a layer to look at the interaction of those things is more computation. When you look at the market opportunity, out of the roughly $8 billion core market, these new use cases are a relatively smaller proportion, but they're growing much faster because they drive so much more computation and complexity. A question on the EventCenter. Simulation's important to metaverse and digital twins. Both have been submerged by ChatGPT, AI push. Is that good or bad for your markets? I think that was a question I was gonna ask, which is, AI and HPC, these seem like natural accelerators for your market as opposed to threats, but maybe, you know, you can clarify your stance on those? Yeah. When you think in my last earnings call transcript in the Q1 call, I talked about five areas where we're investing across the portfolio. The first area is numerics. Numerics is the core of the engineering and scientific methods that we incorporate into our products. Roughly speaking, physical phenomena are modeled by these very complex equations. These complex equations don't have exact solutions. These are typically higher order partial differential equations. They can only be solved numerically. Because of the complexity, you have to pick exactly which equations to solve depending on the end-use case. These are very complicated choices, and that's what we include. That's the core of what we've done forever. That's the nature of numerics. The second aspect where we invest is high-performance computing. High-performance computing is scale-out compute along the lines of what Nicole and I were talking about earlier. Because a single simulation could run across thousands of cores. You wanna make that as efficient as possible. And it's not just using traditional compute cores, it's also using things like GPUs to take advantage of what GPUs can do, and in the future, taking advantage of quantum computing. That is the second area, HPC. The third area that we identified was AI/ML. AI/ML, in particular, the use of neural networks, deep learning, physics-informed neural networks. This technology gives us the ability. We've been working with AI now for a number of years, but this technology gives us the ability to be able to improve the effectiveness of not just a single execution of our, of one of our simulations, but we've incorporated this technology in many parts of our products, including in driving optimization. The natural area of using AI is helping with multivariate optimizations, which are intrinsic in product design. Again, there are many examples of that. The fourth area is cloud and platform, which is really about taking and making our technologies available to support scale-out compute on the public cloud, but also support SaaS delivery of the product capabilities, and that's the fourth area. The final area is something we call digital engineering. Digital engineering is a recognition that the traditional way of doing product design, which is a traditional waterfall methodology, is better served for our customers by moving to a much more agile iterative methodology, where simulation is being used often and frequently upfront and early, as opposed to waiting till the end of a design process, wherein a lot of costs are baked in and changes take a significant effort to propagate backwards. Changing that methodology requires changing the way customers work, that's digital engineering. These are five areas across our portfolio where we make investments. AI/ML is one of those areas where we were excited about, and we've continued to make a lot of advances over there. So obviously there's, you know, hype and reality around this, but, is this, you know, does this accelerate that? Sure. You know, relative to even a year ago, what's coming out now? Sure. I mean, should we be this excited about this stuff? We should be excited about this. I mean, AI has been 70 years in the making, and now you're seeing the advances that have been invested in over the number of years. As I said, we've continued to invest in neural networks. What I think people are excited about today perhaps is, you know, LLMs, large language models, things like ChatGPT. And that's perhaps the most obvious right now because you see the rapid advances between GPT-3 and GPT-4, for example, in terms of what it can do for human-like responses. That gives us actually an incremental opportunity to think about how we can better support our customers. When you think about our own customer log or our own interactions with customers, we've got 50+ years we've been in business of information of how we've interacted with customers. Our support information is not about, it's not about, you know, customers don't call up to set up an Active Directory or some very simple situation. Customers call us, and they ask, you know, profoundly complicated questions like, "You know, I have a model, it isn't converging. Can you help me get it to converge?" These are complex models of how the technology can benefit them. Those are areas where we can, incorporate all of our learnings into better customer support, and that's an incremental area of acceleration. Certainly, the use of AI/ML today within our products continues to support the accelerated deployment of our products and makes them easier to use. Do you think these can have a meaningful impact in the next, you know, a year out, 18 months, 24 months out? As I said, I mean, we are selling products with AI capabilities in them today. That is part of our portfolio of capabilities today. Can you talk about the strategy, around acquisitions? What, you know, how you kinda look at bolt-ons and what areas, you know, you've looked at in the past and perhaps, how those have performed versus initial expectations? Sure. So we have our approach on M&A is really comes down to a very simple philosophy. M&A is not a strategy. You have a strategy that you execute through organic and inorganic means, right? That is the core philosophy we have. The second tenet of our M&A strategy is that it is based on a very strong foundation of partnerships, not just the strategic partnerships that you see in press releases, but a deep pipeline of technology partners and partners within the space that are constantly building these capabilities that are breakthrough thinking in adjacencies to what we do. Ajay talked about numerics. Numerics is the core physics of what we do. Now to solve certain problems, you know, someone may be working on a problem for a decade to go solve a very specific area. We have a very big network and pipeline of partners that we work with on a regular basis. The basis of our tuck-in strategy is really based on these longer term relationships, seeing these long-term trends, seeing where problems are emerging, and then how big those problems get, so that we can directly address those opportunities, either through the partnership strategy or through our tuck-in M&A strategy. That's how we build the pipeline. The M&A strategy that we have, and the reason why it's been so successful is because we have a deep understanding of the value we can create. We tend to, not always, but often have an existing relationship and an understanding. The sales team understands the value drivers that they can communicate effectively to customers, and our product teams understand how we work together. That is the strategy we've been on. We intend to continue to execute against it. It's worked very well for us in kind of filling out and reducing the execution risk. You asked the question about performance. We perform very well on our acquisition business cases, and it's because we have a lot of operational conviction behind what we're doing because we have existing existence theorems to work from as we build those business cases and integration plans. What's probably the standout that you know, something you may have acquired five, six years ago that is now, you know, to you, your, you know, your best example of how successful you guys have been on this front? I mean, I think if you look across the portfolio and look at the evolution of Ansys, right? Ansys started off as a structures company, and for many years was essentially growing organically. If you look at our expansion into the second major physics area, which was fluids, it was driven by two acquisitions. One probably in 2002 and one maybe in 2004. I forget exactly the dates. I could have them wrong. Maybe it's 2005. Those two acquisitions together, and then subsequently, we've done, I think, over a dozen acquisitions that have been in the fluids space. It's been a combination of both organic investment in the fluids business as well as inorganic activity. That's a great example where we've got really good capability. Electronics as well. What I would point to recently is optics. A few years ago, we didn't really have an optics presence, and we identified autonomy as an area where there was going to be growth, and we didn't have support for camera and lidar. So we looked around for an appropriate partner, and then eventually decided to acquire a company called OPTIS. This was in 2018, I believe, or 2019. I forget exactly. We acquired OPTIS. That obviously went well. From there, we used that as an anchor to build out a couple of other optics capabilities. We acquired a company called Lumerical, which gave us photonics capabilities, and we acquired a company called Zemax, which gave us lens design capabilities. More than just simply the acquisitions, we've organically invested in them and integrated them together to have an integrated optics offering. That's typically what we do. It's not just simply a matter of doing an acquisition and letting it live separately from the rest of the business. It's doing acquisitions, integrating them together, because ultimately, customers are not looking for point solutions, they're looking for an integrated offering across multiple physics, and that's what we try to do with our offerings. Speaking of an integrated offering, at PTC LiveWorx, last week, I mean, I think they brought you up as much as they brought themselves up on stage. I mean, they were very complimentary of how they're integrating, simulation into, you know, the newest version of Creo and Creo+, and you were up there on stage, and you guys looked, you know, pretty comfortable together. Dassault obviously has a different approach. You know, one of your main competitors. Does this industry ultimately, consolidate and you have kind of the customers have one throat to choke across, you know, all these technology applications, whether it's PLM, CAD, and then, you know, simulation? I mean, or d o you guys, you know, you're more valued as a, as an open architecture standalone? I think our philosophy and I think what customers are looking for is really taking advantage of the best technology that's available. Customers don't want to be general system you know, general system contractors integrating things together. What customers want is best-of-breed products that work together. Our businesses in simulation, we believe we have the best offerings in simulation. We believe in open interfaces, and we believe in working with the other vendors in the industry. You know, we have a great partnership with PTC, where PTC has incorporated some of our technology into their offerings. They're taking them to market. We have a relationship around materials. There's a number of areas where we have, participation with PTC. Of course in a customer, a customer can choose to buy from whichever vendor they want to, and our commitment is to always make sure that our interfaces are open and that we work well with other players in the space. I believe that's the right approach for the industry. Yeah, it certainly worked, so far. Just one last one here on the bottom line. With a question on EventCenter, just discuss why the pre-tax margins were so weak in the last several years. I know you're talking about getting a little bit more leverage here going forward, but maybe explain that dynamic. Yeah, sure. About five, I guess it's six years now since Ajei joined, there was a deliberate strategy to accelerate growth. We were kind of in the low to mid-single digit growing category with very, very high margins. The intention was to make deliberate investments to accelerate growth. If you look back five years, up until about 2020-ish, 2021, you would have seen that margin investment in the business. Since over the past couple of years, margins have been relatively stable, and there is an overall, if you look at our outlook on a go-forward basis, the guidance that we gave in our investor day was that we see significant operating leverage in the business through our cumulative unlevered operating cash flow guidance of $3 billion from 2022 to 2025. That compares to approximately $2 billion over a similar comparable period prior. Underwriting that is the 12% CAGR of ACV growth that we guided to over that time period, and operating expenses growing at a relatively slower pace than that ACV. Margin expansion. Yeah. More or less. Great. I think that's all the time we have. Thanks a lot, guys. Really appreciate it. Best of luck. Thank you. Thank you.