All righty. Good afternoon, everybody. Thank you for being here. For those of you don't know me, my name is Manav Patnaik. I cover business and information services for Barclays. We're very pleased to kick off our post-lunch sessions here with MSCI, and we have Alvise Munari, who's the Chief Product Officer, also the Head of Client Segments, I believe. Alvise, maybe just to start, if you could just maybe give us a little bit of a background, you know, you're new to a lot of the people in the crowd and on the audience, so maybe just a quick bio of how long you've been at MSCI, what all you've done, and so forth. Sure. I joined MSCI close to 11 years ago after spending quite amount of time on the sell side working in derivatives. I initially joined as the head of EMEA. I got promoted to look after our clients globally. That was about, you know, six years ago. About, and then about two years ago, just under two years ago, I was asked to become the first Chief Product Officer in the history of MSCI. Actually, it's interesting that a company like MSCI had product heads for our different product areas but never had a single Chief Product Officer. About a year ago, I was asked to develop the segment effort, which is an important transformation in how we think of the opportunity set in the go-to-market at MSCI, in that we want to focus on understanding a lot more details what clients do in the day-to-day to essentially build more useful tools. We realized that a dimension that is sufficiently detailed but not too detailed to become clients, individual client-specific, is to look at basically industry segments. You know, active asset managers, passive asset managers, pension funds, endowment foundations, wealth managers, hedge funds, so on and so forth. Therefore, we do have now a fully fledged segment strategy. Got it. Just going back to your point on you're the first Chief Product Officer at MSCI two years ago, what was the catalyst for that? I guess, you know, there was obviously some deceleration in your business. Was there You know, what was the behind-the-scenes thoughts on that? As the chief of client, I had become, you know, very vocal with Henry about two challenges. One, that our solutions were not built on an integrated technology stack, so as a result, they were not easily interoperable. They, they were not easily interoperable for us to create further solutions also for our clients to operate within their stacks. That was challenge number one. Of course, it was clear to me that we were leaving value on the table because of that, we were not helping our clients as much as we could because of that. The second issue is that, I was convinced that we had lost focus on basic product creation discipline, and we did not have a clear function to connect the quantum of product needed to fulfill our growth model with a quantum of new revenues needed from new product quarter- by- quarter. That was not done in a deliberate, disciplined, systematic manner. For those, look, I guess two, three reasons, Henry said to me, "Okay, you've been complaining a lot." "Go fix it. Okay. Well, I mean, I guess credit to you then, maybe it's not a coincidence, but I know on the last call Henry said that MSCI has introduced more new products in 1Q 2026 than they did all of last year. You know, we've seen some of that momentum in the numbers as well. What are some of the key initiatives that have led to that? First of all, I wanna clarify one thing. I think on the earnings call, Henry might have either not been very specific or might have gotten slightly carried away. Let's say that either of the two, what is true is that in the first quarter of 2026, we introduced roughly as many new products as in the whole of 2024. Okay. In 2025, we launched roughly twice as many products as in 2024. If you do the simple arithmetic, it would follow that in 2026, we will launch twice as many product as in 2025, which will be four times as many product in 2024. The reality is that we hope that by the end of the year, our exit velocity in terms of new product launches will bring us to, roughly speaking, 5x where we were in 2024. That is our aspiration for exit velocity, new product creation. Got it. What we- Where are all these new products coming from? What are the key areas of focus categories? Very good question. We are innovating across the board. Because at the end of the day, you know, to grow, to generate new revenues, you need to basically bring to market solutions that basically add additional value, right? Everywhere we need to innovate. When I took over the challenge of being, you know, the first Chief Product Officer of MSCI, I said, "Okay." Where are we gonna innovate first and most aggressively? In those areas where there's a combination of two things. One, we have done enough work to our technology stack to be able to innovate faster. Two, we understand the usability of the new product better, and therefore the time to money is gonna be quickest. Therefore, we focused on our index business. Why? Because look, it's obviously the business that MSCI has in, has been in the longest, is the one which I would say we understand the best holistically, and we have done already a lot of work to the tech stack leading to last year, and therefore we could innovate faster. That's what we did. Therefore, I think you've seen in the last quarterly numbers our index revenue and run rate have shown an acceleration to a large extent because of that. It's the contribution from new products. Okay? We focused in areas where, you know, the market demand was stronger than usual, and we also had, you know, favorable competitive positioning. That was in the ecosystem centered and around private assets. A lot of innovation there as well. Of course, we bought Burgiss in October 2023. We then made further smaller but very targeted and specific acquisitions more recently, which helped propel our ability to bring to market new solutions a lot faster, new powerful solutions a lot faster. Also did leverage a franchise that MSCI already had in terms of target clients, and where we're quite reputationally established, so you know, asset owners. In particular, you know, total portfolio solutions. TPA, which we acquired through the Burgiss acquisition, and then we enhanced through some of the more recent acquisitions, you know, proved to be something we could accelerate the market impact of quite quickly, and we see that accelerating further. Look, it's a very simple number game. The endowment foundation in single family office space globally has been very underserved in terms of fit for purpose total portfolio tools. What endowment foundation and single family offices do is nothing other than total portfolio management. I mean, of course, they need to then understand some more specific aspects of what they do, asset class by asset class, et cetera. The most important aspect of what they do is the management of the total portfolio and the optimization of total portfolio. We, you know, we bring a fit for purpose solution, which actually has everything they need in the right place with the right data, because we had the private asset data thanks to the Burgiss acquisition. We paired up with some of the traditional MSCI strength, like the factor analytics, and all of a sudden you have literally a best-in-class winning solution. If you add elements of sustainability and climate, which the asset owner community still is very focused on, all of a sudden you have something that is really unique. Got it. To your earlier point on the technological limitations, I guess maybe two-part question. Was it AI that helped bridge that gap or some of these new technologies? Then what is the you hired a new CTO, so what is the new CTO bring to the table, I guess? Okay. We hired a new CDO. Okay. Sorry, yes. We are looking for a new CTO. Let me start with the first part of your question. There was, on the one hand, work that we were already doing in-house, which we continued and accelerated partly thanks to AI. We did some very successful targeted acquisitions. Foxberry was in that domain. Vantager also in that domain. More recently, Compass, which have literally given us bolt-on capabilities that have augmented the, you know, type and quantity of products that we can build and that we can service. Also we focused on restarting research engine. You know, those four things together are all necessary, and I think that's what you will see continuing. From the point of view of the tech stack and data stack work, I think there was a recognition that MSCI is a financial tool and services company, but these financial tools and services literally consist of data, models, and technology. The models is what we've traditionally been excellent at, you know, it's research. The data and the technology had not been as clear and as great of a focus as it had to be, Henry took the decision to say, "Okay, we really need to have somebody focusing on data object and data asset, data architecture, and data strategy. We need to have somebody really focusing on the, you know, product engineering platform and architecture side," and that's the person we're looking for. As soon as that happens, and I'm confident it will happen soon, you're gonna have an MSCI where, you know, the management team is equally focused-Own all these three ingredients, as well, of course, as our clients. Got it. Just touching on AI again, you know, Henry talked about how AI has been a godsend for MSCI. From your perspective, you know, can you just talk about some of the different avenues of how AI is improving MSCI? I know it sounds like new product innovation, but maybe some other client adoption examples, et cetera. Sure. AI is, of course, a rich set of tools, a rich set of powerful transformative tools that allow to do knowledge work in a substantially faster, more ambitious, and more impactful manner at relatively limited cost, as long as you manage it reasonably. You know, for a knowledge company like ours, this is revolutionary. That's why Henry calls it a godsend. First of all, the complexity and the scale of the research problems we can now tackle is literally orders of magnitude more ambitious than what it was three years ago. And that, I think, will You know, as we get better at mastering new technologies, and we also realize new and different ways that it can improve and transform what we're doing, I think this will accelerate. Okay, you know, as a researcher, you can now have 10,000 analysts working for you that can all process data extremely fast, can all reason extremely fast, all have access to a vast amount of information that they can absorb really quickly and keep the information current. That's unprecedented. Okay. That's My view is literally for an IP company, it's the most transformational event. Of course, there is the aspect of the efficiency, speed, scale, and cost with which you can build all these new objects and then run them industrially after you actually conceptualize them, right. Of course, you know, various type of AIs are of tremendous help there. You know, the quality, scale, and size of QA you can do using machine learning is unprecedented. This didn't need LLMs, but you can use LLM to run machine learning even more efficiently to do even better QA. You know, you combine the two, all of a sudden you can have incredible results. You got better research ideas, you can build them more efficiently, and then the third substantial transformation is that AI can help make it a lot easier for your clients to consume the tool, the content, and the services that you produce for them. That, I think, we are just at the beginning of discovering, right? It's not that this is just like, you know, a flick of a switch, everybody is ready to go. Both sides need to get ready. They need to make certain changes. They need to adopt certain technological protocols, and then it'll become a lot easier for this to happen. You know, in some of the more recent solutions that we have launched, of course, the AI empowerment is inbuilt in the solution that we built, and then the client just sees the transformation. You know, we launched this new service called MSCI Index AI Insights. At the first level, this is just a way to basically, you know, quickly and precisely retrieve information about MSCI vast index IP array, okay. Instead of having to, you know, call somebody, who then calls somebody, who then calls somebody and gets you information, you know, you access the tool, which is available as a standalone application to what we call MSCI ONE. It's also available to MCPs and APIs in both Claude and ChatGPT. You know, my assumption is that it will be available in many other ways as well, right? You get a lot of answers very quickly, right? Figuring out what's going on in index, figuring out why the value is this, not that, figuring out what happens with this stock or that stock, very easy. However, we've done more than that. You know, there is a very highly trained engine that basically we spent quite a while honing that you can use to do more sophisticated reasoning. You can start asking the engine to do a lot of things that beforehand you would have had literally a team of people helping you do. Them calling MSCI, them giving you back the answer, then iterate, and then maybe up to two weeks, get you what you needed. You can actually do it in a matter of minutes. Got it. Well, you know, one of the buzzwords being used is MCP, and a lot of companies are trying to provide stats on their partnerships, et cetera. How do you look at your current relationship with the LLMs? Like, what kind of revenue model, business model are you now, and where do you think it eventually evolves into? Yeah, look, I think we're at the very beginning of the journey, right? As it's always the case at the beginning of a transformation, you don't know exactly how the new world will be structured. You don't know exactly what, you know, what patterns, what protocols will prevail at scale versus which one will not. I think it's a fair bet to assume that at least for the largest users of our content and services. They will want a programmatic ways to interact with it. They already do. By the way, we've been using APIs, like, have many of our, you know, peers, competitors, and larger clients already for, you know, 10 plus years. Okay. The APIs were not architectured in the way you need to have it now to make everything, you know, easily AI fungible, but the concept is not new. APIs plus MCP is different because basically you're saying, "Okay, I'm gonna provide, like, a universal, you know, Lonely Planet guide that tells you exactly, you know, how to use and where to find my stuff," in a way that basically, means that you don't have to actually spend a whole lot of time figuring out how to go via the APIs. That's a transformation. Then if both sides develop agentic capabilities that are permeable through the, through the APIs, I think that's where the real transformation begins, right. I think that's what we are at the, at the early stages of. To be honest, right now, we're seeing more direct tangible client impact on the capabilities that we have agentified and make available to them via applications than via MCP and API. You know, for instance, MSCI Index AI Insights, we have many more users that come onto MSCI ONE than ones that go through the cloud MCPs. We do have a few that go through the cloud MCPs. You know, I mean a few means probably a few hundred, but you know, we do have many more through MSCI ONE. You know, same thing with many of our, you know, more complex tool services. For the time being, the client basically tell us, "Look, if you go through your own thing, we are more comfortable. We have a, you know, we are more sure that we're gonna get the right answer, we're gonna get the right reasoning. For the time being, we prefer to use that. Is that gonna change? Look, it depends what you're using the engine for. If it's just retrieval, and then you use your own logic, then I think you're gonna go to generic APIs and MCPs. If you're a very large scale players, all you're gonna focus on is the retrieval bit, and I think you're gonna go through that. If you're, you know, not such a scale player and so you want to benefit from the specialized purpose-built reasoning that MSCI provides, I think you're gonna go to our channels. Now, your point about pricing models and the evolution of the economic curve. Look, any way this is gonna shape up, it's clear that it will get people to call, you know, more data, more services, more functionality, more tests, more what-if scenarios, because there is a possibility to do it, and a lot of these things is useful. You know, they are useful, right? People didn't use to do it because it was very complicated, so you just really ask what you couldn't figure out yourself. As it become easier and easier, you're gonna say, "Sure, why do I want an answer which is directionally correct? I want the exact answer. I'm gonna ask the question, I'm gonna do it programmatically, and I can ask 1,000 questions, 100,000 questions, 1 million question, 100 million questions, right? The way I think this is gonna evolve is that we're gonna have a layered pricing models whereby you're gonna have access to a certain set of modules of data and functionalities depending on what you decided to pay for. There will be then a consumption layer, okay? Where there'll be a basic consumption pattern included in the basic fees. You know, you ask 10 question a day, we're not gonna charge you more money. You ask 1,000 question a day, maybe we start thinking about it. You ask 50,000 question a day, we're sure charge you more money, right? Because we, you know, we'd like to support all the traffic, right? Right. There'll be charges for that. There will be the question of whether you actually ask the engines to do, you know, bespoke analysis for you. You know, simulate a portfolio, do a stress test, try out a different score of this type or that type, build a custom index, build a custom model. Okay? We'll charge you by objects. You decide to build a new custom index, we'll charge you some money. You decide to basically run money off it, and there is a clear relationship on how you're gonna run the money, we'll charge you ABFs like we do today if you are somebody who's licensing an index to run an ETF. I think that's gonna be the evolutionary curve. Got it. You mentioned custom indices, so let's touch on that. You know, I think historically people interchange custom indices with self-indexing, and they thought that that would be the death of all the index providers. That never happened. You guys have grown your custom indices business in the low double digit rate for a long time, and last quarter it was into the 20s. Can you just tell us what's been driving the good growth? What drove the incremental growth in first quarter? There is, I think, a long-term trend, and there is specific events. The, you know, the significant growth in growth that you saw last quarter is driven by both. On the one hand, we do have a strong long-term trend, which I can talk about in a second. We also had, you know, a very substantial, you know, licensing event with a big investment house that basically decided to substantially increase the amount of IP they license from us in that space, and that helped sales. That did not happen in a vacuum. We see that as literally as the logical manifestation of the long-term trend. What is the long-term trend? The long-term trend is that the financial industry wants more and more portfolio recipes. Why does it want more and more portfolio recipes? Because, like, no two human beings, no two institutions have the same portfolio objectives. They have similar KPIs, potentially, they have similar, you know, challenges to tackle, but no two of them want exactly the same portfolio objective. Of course, because it really was not possible to build them truly customized and supportable individualized portfolio solution, we didn't do it. You know, we found all sorts of proxies. People used to have, you know, a few off-the-shelf model portfolio options to offer to their clients, a few versions of the same basic building blocks and, you know, various combination of this. Now, same with indices, okay? MSCI built its reputation building benchmarks, and the philosophy was a benchmark for all, okay? It's important. A benchmark for all. Basically, everybody who has essentially the same KPIs should be happy with the same benchmark, right? Because you're starting to say, okay, what's the right way of doing XYZ? Well, here it is. Here's the benchmark that tells you exactly what is the right way to do XYZ. If you wanna understand whether you're getting the right service, not the right service, whether you should go a bit more left, a bit more right, you look at the benchmark, and you see how much and why you're deviating from the benchmark, okay? You say, okay, great, so we have all figured out the ideal ways to do certain things, but let's face it, none of us is that ideal person. None of us need the stylized benchmark portfolio. We need different things. You say, okay, well, can index technology help me to literally formalize the expression of what I need specifically for my personalized portfolio. The answer is, of course it can. In fact, it's the best technology available in the world to do that, right. Because it can literally encode all the KPIs needed by the individual investor, you know, institutional or retail, into a programmatic set of steps that you can then feed to a computer and, of course, propagate forever, unless the KPIs need to change, in which case you can adapt them. Be sure that as long as the KPIs are still the one the clients need, the investment recipe will do the job the right way, okay. All of a sudden you say, "Wow, why shouldn't we do that?" That's exactly what has been happening. The technology has progressed first slowly, then much more rapidly to support this. People have understood first slowly and then more rapidly that this can be applied in all sorts of context. By the way, including in supporting active asset management, and therefore that's what you are seeing. You're seeing that in all context of investment and financial life, people are starting to look at this. Whether, you know, it's in the context of building model portfolio for individual wealth management clients, in the context of building multi-asset class benchmark for asset owners and insurance companies, in the context of building bespoke benchmark for, you know, investment institution that wanna make sure that their PMs are doing the job really as best as they could. Whether it is for the more traditional applications, ETF, whether it is to build structural products, whether it's to build customized, systematic investment recipes for pension funds, or hedge funds, you can do it by an index. Got it. Maybe just a quick follow-up, like in the indices business, but especially on the custom side, I think Foxberry you mentioned was an acquisition you made, recently acquired Compass as well. What do those two bring to the table? 'Cause the impression is you guys- Very good question. are an index powerhouse. Why acquire? As I said, MSCI built its reputation and its business on the benchmark for all era. Many components of the infrastructure that you need to support indexation services at large were not natively built during the benchmark for all era. You know, best in class corporate action engine, best in class way to calculate including factors that have to do with free float, market structure, and so on and so forth, and a whole host of other things similar to this. When it came to enabling very specialized custom index portfolio construction recipe, the engine had not been built for that. Foxberry had been built exactly for that. That's why we bought it. You know, we said, okay, we have the best in class central engine that does all these things that are needed at scale for a standardized massive index business. We then have the calculation engine that is very robust and reliable. We need to improve the portfolio construction engine, so we bought Foxberry, and we spliced it in, and that's how you actually get a massive acceleration in the custom equity index business. Now, again, MSCI was built to do benchmark for all, especially in equities. Especially in equities, right? We entered the fixed income business, you know, five or six years ago. We're now growing there. You know, it's a very attractive proposition, but let's face it, we did not have the same capabilities. We started working with Compass to satisfy needs from, you know, for instance, the structural product market, where people say, "Gee, guys, you're so good at equities, especially after you bought Foxberry, but, you know, we see demand for multi-asset class stuff." Okay. We see demand for non-linear payoffs that embed, you know, option component, futures component. You know, we were doing that, but we could do it only small scale. Started working with Compass. We saw that they were truly excellent at it. They had built a really impressive engine that could do this at scale with a high degree of flexibility. All asset classes, all type of payoffs, long, short, option-based, future-based, option and future-based. They also handle commodities and cryptocurrencies. We said, okay, maybe we should buy them. We did. Got it. Okay. That's super helpful too, appreciate that. Maybe let's just touch on the two other deals you did recently as well, Vantager and PM Insights. I believe they both fall into your private assets category, where Burgiss is obviously your main asset. What are those two assets? How do they fit into the plans there? Yeah. Vantager we bought because of the private asset business, but also because the capability they developed we think will be really helpful for us to have and then further develop across the board. What Vantager had developed is a highly capable, highly specialized AI-powered engine for, you know, data extraction to a model in the context of private assets documentation. You know, basically take a whole bunch of PDF, a cart load, and basically tell the engine to fit a specific data model you pre-created in a very dependable and competent way. You can actually turn millions of PDF pages into you know, highly conceptualized structural data that you can then use in the investment process. That's way more, right? This of course, immediately super helpful in our private asset business, but we think it's gonna be helpful across the board. Why? Well, because look, we think that the future of the investment industry on the one hand will keep on being about analyzing time series. On the other hand, it'll be about building very specific knowledge about individual assets. Some of it is done by, you know, the traditional financial analyst process, which does a fantastic job. A lot of it will need to be enriched by looking for all sorts of data sources in all sorts of unstructured forms where this type of capabilities will be critical to build the IP. Makes sense. okay. PM Insights. PMI, look, we got to know PMI because look, remember, we still are the benchmark guys, right? We build all the possible benchmark that one could ever think of in the equity space. We're now doing it in fixed income, although in fixed income, of course, you know, it's, there's already other people, we need to find ways to differentiate ourselves, which we're doing. We say, okay, we're gonna do it cross asset, cross public asset classes, thanks to the acquisition of Compass. We say, what about the private assets, right? In private assets, we've indeed started to build systematically fit for purpose benchmarks for all asset classes, sub asset classes, different type of cuts, different type of regions, and so on and so forth. However, those objects, they have the liquidity limitation that the underlying asset has. If you wanna start bridging the gap between public asset benchmarks and private asset benchmark, you need to try and find something in between. PMI had developed a very powerful technology based on having all sorts of agreements with the industry players in the secondary market to be able to essentially estimate evaluated prices. We built with them a tracking venture index. That worked out quite well. We said, okay, well, we'd like to do this more broadly. We said, okay, why don't we buy them? Right? Indeed, this will accelerate our ability to build really usable, in this case, not benchmarking really indices for the private asset industry. No, first on the equity side. On the credit side, we're doing it by the way also. We're doing it a different way. There'll be something hitting the news soon, hopefully there as well. As we heard, you know, I don't know how many of you heard the launch presentation from Ares, which I thought actually was really interesting. At the end of the day, what our investors are asking them is, how do you get your marks? Okay, how do you get? Look, in private credit, I believe that probably the answer that someone like Ares would give their investors may not be perfect, but would be reasonably solid. Probably we can do better because that's what we do for a living, but they are reasonably solid. In the private equity space, I think there's a lot of work to be done, right? we aim- to bring transparency to the world, make it easier to start seeing the, and comparing the, you know, the returns across all ways of holding equities. Therefore help the investor community be more effective in how they allocate capital. Got it. Okay, we got five minutes left. Let me focus on analytics. When it comes to indices, I think we consensus is it's a benchmark, probably not disruptable by AI. You know, private assets is evolving. On the analytics side, can you help us appreciate what the, what the moat there is versus, you know, all these AI solutions that could potentially threaten that? Look, the analytics business is a vast business. There certainly are aspects of what people do, including ourselves in analytics, that are substantially more prone to AI disruption. There are other parts of what people refer to typically as analytics that are substantially harder to disrupt. Okay. Let's talk about what is easier to disrupt. Anything that has to do with workflow. Even easier if the workflow is not particularly proprietary, not particularly unique, not particularly insightful into what a specific client or type of clients are doing. Okay. Somebody who's providing relatively standard, risk of performance attribution on, you know, liquid public assets, that's gonna get disrupted. Okay. At the other end of the spectrum is where you are building, you know, proprietary, you know, risk, and performance models based on, you know, 40 years plus of very granular data that has been cleansed in an extremely disciplined and pedantic, but fit for purpose manner across a multitude of markets that you can therefore use to understand the structure of your portfolio exposure across the board in a basically single coherent manner. That is not so easy to disrupt. If you then also say, Okay, let's think about the index benchmarking analogy. We built the benchmark. The benchmark essentially a standard. The standards are useful because not only they tell you what the, you know, the theoretical performance of a certain object should be, but they also help you crystallize norms that are going to be useful even if you do totally freestyle portfolio construction. Okay. Same with models. Enter new technologies, and we can now, you know, build new models and calibrate models in a way that it customize to clients in a matter of hours, you know, used to take us months. All of a sudden, we can innovate a lot more in the model space as well. Okay. My view is therefore there's going to be the opportunity to actually extend the moat thanks to AI, as opposed to seeing the moat get eroded because of AI. Okay, I think it's a question of where you are in this spectrum. The model capabilities are the hardest to replicate. The very generic, you know, risk and performance analysis are the easiest to disrupt. The total portfolio services may get easier to disrupt, but there is a lot of ingredients you need to understand how to put together in a coherent, systematic, and dependable manner. I think they're a lot closer to the far end of the model side than the other end. I guess MSCI's mix is more to the far end. Is that what you're saying? Well, what I'm saying is that, I think that there is way to do all of this in more intelligent and more value additive ways using AI if you understand the finance first and foremost, and you understand the client. In particular, within the model and total portfolio solution space, I think the advantage we have is so substantial that it'll be very difficult for, you know a very smart engine to actually replace us anytime soon. Okay. In the interest of time, one last question, 30-second answer. You know, everyone talks about the slow decline of the asset managers, that requires its own fireside chat probably. You've been growing a ton on the trading and hedge fund side. How early are we in that segment penetration process? Could that keep growing to help offset the asset? Listen, I always tell people, look, if you look at a large scale trading operations, and whether it's the trading operations of a broker-dealer, so it's officially called the trading operation, or whether it's the trading operation of a proprietary market maker, so it's called the market making operations, or it's a trading operation of a large platform hedge fund, so it's called the investment desk, or the pods. The point is they all do equities and fixed income. Within equities, they do idiosyncratic linear, systematic linear, you know, nonlinear, so typically or often involving one or more, you know, vol-based strategies, and they do much more complex relative value. Okay. Right now, we play a significant role in equity, you know, linear systematic. That's it. Okay. We got plenty of adjacent areas to capture. Now, what do I think is gonna happen? Okay. The world of investment, and therefore of trading, and therefore all of finance, is converging decisively to a portfolio-centric architecture. Okay. The unit of conceptual exchange will be not the individual idiosyncratic risk, it will be the portfolio. I mean, of course, some people need to slice and dice idiosyncratic. Of course, you do this for a living, right? The reality is the majority of people will understand portfolio. They won't even wanna touch the individual exposure, right? In fact, it's probably best for everybody that the vast majority of people never touch it, right? In that journey, you need somebody that basically offers understandable rules that everybody can agree around, and therefore communicate with. That's what MSCI aims to do across all asset classes, all form of portfolio trading, linear, nonlinear, you know, convex, non-convex, around the whole globe. Got it. All right, cool. We're out of time there. Thank you so much. Thanks Thank you, everyone. Thank you.