Morningstar, Inc. (MORN) May 7, 2026 Annual General Meeting Transcript

Good morning, everybody. Welcome to the 2026 Morningstar Annual Shareholder. We're delighted you could join us. I think you'll find the meeting very informative, enjoyable. We've got a great meeting ready for you. And so let's get started. We're also streaming this meeting live on the Internet. So if you're watching me, the live stream, welcome to you as well. Before we begin, take a moment, read the safe harbor statement as usual. So the format to today's meeting is the same in years past. There's three parts to it. There's the formal business part of the meeting that I'll run. It won't take that long. Just three items to do. We've got to collect our directors, point out auditors for next year, to stay on payback. So that should go pretty quickly. Then we'll go right into management presentations. Pranau Kapoor will lead us off, our CEO, followed by Mike Holt, our chief financial officer. And then this year, we're doing something a little different. Usually, we take a deep dive on one or two of our business units. This year, we're taking a more thematic approach, given the importance of AI. We're looking at how AI affects certain key parts of our business. So we have Nazar Tahuni, who is the head of research at PitchBook, talking about how AI is affecting PitchBook. He'll be followed by Laura Lutton, head of manager research, talking about how her area is embracing AI, followed by Sean O'Connor, talking about credit. Sean is the sales leader for our credit business, and he'll discuss how the credit business is responding to AI. This will give you a flavor of how overall we're embracing AI. A couple of our key units, it's not everything we're doing, but it'll give you a sense of how serious we're taking AI and some of the things we're doing to embrace it. Then we're going to take a short break. We'll come back. And always a highlight of the meeting is the Q&A session. So we look forward to your questions. This is one opportunity you have each year to ask us questions in person. And beyond that, we have heard your questions. but we ask that you send them to us we gather them all up we answer all the questions post them on our website ViolinAK and again this is the one opportunity you have to ask questions live in person and so we really look forward to your comments your candid thoughts on your business and we'll have a good discussion and then following the Q&A session something new this year we have down in the training rooms you may have seen them on the way in We have some product demos going on, the pitch book, credit, and direct, so we'll spend time with those, as well as some of our analysts talking about some of their research. So something new, and there'll be a light lunch that we'll serve in the cafe. So this meeting is being simulcast on Zoom and the Broadridge platform, so all participants will be able to hear and watch the meeting in its entirety. If you have questions you'd like us to address during the Q&A session, you'll have an opportunity to ask them via the questions text box on the Broadridge platform, or if you're joining through the Zoom platform via the chat button. Anyone here in the room can simply raise their hand when we reach that portion of the meeting and we'll bring a microphone to you. We look forward to your questions. I will note that if you are not here in person and would like to vote during the official part of the meeting, you must be logged in to the Broadridge platform using the control number provided with your proxy materials. Now before we get started, I'd like to introduce our board who's in attendance today. Here's a picture of us, taken a few months ago. But first, I'll ask each of our directors to stand and face the audience. Anne Brayman, Robin Diamante, Cheryl Francis, Steve Joint, Steve Kaplan, Kunal Kapoor, Gail Landis, Bill Lyons, Danielle Sutton, and Carolyn Say. I'd like to thank all of you for all of your contributions and guidance during the year. You're a terrific Independence Board, and it's much, much appreciated. Also, I want to recognize Gail Landis, who's with us today as she steps down from our board. Gail joined our board in 2013, so she's been on the board 13 years, and has been an outstanding She has an investor-first mindset, deep expertise in the asset management business, and definitely an independent thinker. She represents the very best of Morningstar, and we will miss her insights and guidance. So, Gail, thank you for all that you've done for Morningstar. At the same time, I also want to welcome Ann Brayman to our board. Ann is our newest director. She's got deep financial expertise and a strong track record of value creation. So, Ann, we look forward to your future contributions. I'd also like to introduce our executive officers so Kanal Kapoor and the white jacket he's hard to miss and then Mike Holt our CFO and then representatives are of our independent auditors for 2026 KPMG are in attendance and available for any questions at the end of the meeting I'd now like to get started with the official business of this meeting our corporate secretary Robin Koiner will act as secretary of the meeting. Greg Malatea, a representative of Broadridge Financial Services, is here today to act as the inspector of elections. Our corporate secretary has informed me that we're holding the meeting pursuant to a notice mailed on March 27th to each shareholder of record on March 9th. A certified copy of the list of shareholders of record has been available in our office for the last 10 days. We've established a quorum for the conduct of business properly brought at the meeting. So voting will begin after all the proposals have been presented. The first item of business today is the election of our directors. We will elect 10 directors at today's meeting. The directors elected today will hold office until the 2027 annual shareholders meeting or until their earlier resignation or removal. The nominees for directors are Anne Brayman, Robin Diamante, Cheryl Francis, Steve Joint, Steve Kaplan, Kunal Kapoor, Bill Lyons, Danielle Sutton, Carolyn Say, and myself, Joe Mansueto. Our second item of business is the annual Say on Pay vote. Each year we ask our shareholders to vote on an advisory basis to approve the compensation of our named executive officers as described in our proxy statement. Our executive compensation program is designed to attract, motivate, retain our top talent, and align pay with Morningstar's performance and the experience of our shareholders. The third item of business is a ratification of the appointment of KPMG as our independent auditors for 2026. The board recommends a vote in favor of the proposals one, two, and three. If anyone has a question related to the proposals, please raise your hand and wait to be recognized. As I mentioned, there'll also be a general Q&A session later in the meeting, so at this time we'll only take questions that are directly related to the three proposals. Any questions on the proposals? Hearing none, there's no further business scheduled to come before the meeting. I now declare the polls are open. If you are a shareholder joining on the Broadridge platform, you may use the vote here button on the screen to vote your shares at this time. If you're here in person, please raise your hand now, and the Inspector of Elections will bring you a paper ballot. Please remember that if you've already voted, your proxy card or voted online, your shares have already been voted and counted. You do not need to vote today unless you are voting for the first time, or you wish to change your vote. If you've not already done so, please provide your proxy or ballot to the Inspector of Elections. okay i now declare that polls are closed the inspector of elections has advised me very quickly that more than a majority of shares represented in person or by proxy and entitled to vote at this meeting have been voted in favor of each of our director nominees listed in our proxy statement the compensation for our named executive officers and to ratify the appointment of KPMG for 2026. We will file an 8K and more detailed voting results no later than May 13th. So this concludes the formal business portion of the meeting. I told you it would be quick. But before we adjourn the formal meeting, I just have a few final comments. One, it's been an unusual year for Morningstar. On one hand, we feel it's been a year of very good progress. Our organic revenue growth has been mid- to upper single digits, adjusted operating profits, low double-digit growth. We've seen growth across our core platforms. So in the context of our 42-year history, it's been a pretty normal year for Morningstar. On the other hand, as I'm sure you're acutely aware, our stock price has declined pretty significantly over the past year. Its uncertainty around what AI means for data and software companies has increased. And we're not happy about that. It's not something we like to see. It creates pain for you, our shareholders, especially those of you who bought in at higher prices. It creates pain for management. Part of their compensation is based on total shareholder return. But despite all of that, I really want to express our optimism, continued enthusiasm for our business. Morningstar is a great business. It's great for our clients, investors. It's great for our shareholders. We believe we're very well positioned, not just to navigate in an AI world, but to thrive in it. The fundamental problem that we address is not going away. Investors still need help. They want an independent, trusted partner to help them find the best investments, do the diligence on them, assemble them into portfolios to meet their goals, and to do the ongoing monitoring. We believe we have some unique capabilities to help them do those tasks. Our advantage is rooted in very large databases of data, proprietary data and analytics, independent research and ratings, transparent and accepted methodologies, and exceptional trust in our brand. It's all deeply embedded in investor workflows and in the fabric of the investment industry. We have large, loyal audiences of individuals, advisors, and institutions around the world who love working with us. They're not looking to displace us. They love working with us. But our advantage goes far beyond data and analytics and sits atop it. It's our independence, our unwavering investor-first mindset, our voice, our judgment, that supercharges the loyalty and trust that clients have with us. I really encourage you to read Kuhnow's letter in this year's annual report. He lays all of this out in detail, what our advantages are. It's a really good letter, so if you haven't read it, I encourage you to do that, about why we're uniquely positioned to win in an AI-driven world. And these aren't just hollow worlds, hollow words, but we've backed them up with our actions. You may have noted that last fall, we announced a $1 billion share buyback because we believe that there's a big gap between our intrinsic value and the market price. And over the past 12 months, we've bought back a little more than 10% of our shares outstanding. So without doing anything, you've become a bigger, a 10% larger owner of Morningstar. And as a long-term shareholder, I'm very happy about that. So again, these statements aren't just hollow, but we've backed them up with our actions. But let's dive in. Let's get going. We'll discuss all of this and more. And let me call up our first speaker, Kunal Kapoor, our CEO. Kunal is in his 10th year as our CEO and just doing an outstanding job. So please join me in welcoming Kunal Kapoor.

Good morning, everybody, and welcome. It's great to see all of you here, as always. Gail, I want to join Joe in thanking you for your partnership over the years. It's just been awesome. And for those of you who know Gail, she's not going to be slowing down. She's got a big plan already. And Anne, welcome to our board. I think you're going to enjoy it. It's a great company, and we're delighted to have you here as well. Also, just want to welcome my colleagues here in the second row, the executive team, some new faces that you'll see up here on stage a little bit. We've got a great team as well. You'll obviously hear from a number of us today, but I think you should feel really confident in the makeup of our team, the people who are leading various parts of our business. We're a good group. We get along, but we fight a lot when it's appropriate. And, you know, right now we're all digging in and really thinking about the future of the company as we'll talk to you about today. So right after me, you're going to hear from our CFO, Mike Holt. And as Joe said, that'll be followed by a presentation that digs a little bit deeper into our three largest segments and how the impact of AI is maybe running through them. Joe already mentioned the stock price. It's one of the elephants in the room, and we're going to talk about how we think about the value of our business and make the case for why it's enduring. And we're also going to talk today about AI and why we think it increases the value of the business over time. There's certainly some uncertainty associated with it. There's certainly a lot of experimentation associated with it. But I want you to leave here feeling confident that we're embracing it, that we're diving into it, and that we have a plan for how it's going to impact our future and what we're going to do to use it to create value. The good news is, as Joe said, we're starting from a very strong base. And that base is based first and foremost on our mission of empowering investor success. Now, you may have used an LM recently, and you may have gotten an answer, and you might have accepted that answer. But you probably thought to yourself, that's probably like a 75% to 80% confidence answer I'm getting. I certainly have those moments and those experiences, and I move on. But many times, you also ask yourself, I'm doing this for work. I'm doing this for a client. I'm doing this for something that really does matter. And in those moments, trust, independence, and judgment really do matter. And we hear this repeatedly from our clients. And so when you hear announcements like the one Anthropic made a couple of days ago, there's a reason that Morningstar is plugged right into what they're rolling out. In an industry that has lacked trust, Morningstar has it in droves, and this matters. We've removed friction from markets, we've made it easier for participants, And we challenge what's hidden, especially as investors have more options than ever before. And that's led to an enviable place with clients. We serve the smallest investor to the largest asset owners. And so whether you're a super fund in Australia that's suddenly thinking about how to serve the Wealth Channel, or whether you're one of our institutional clients that used to use the CRISP benchmarks that's suddenly thinking about how to expand usage of those benchmarks. You're talking to us partly because you know we speak the language of many investors and we've created this common framework for them to use. I love our position as well because we're focused on secular growth. It's one of the things I spend a lot of time thinking about when I think of the future of our business and of investors and where they're going. We want to be in markets that are naturally growing. We want to have that tailwind behind us. And partly, we continue to benefit just from the reality that capital markets, being in capital markets, is a good business. Over time, they tend to grow, they tend to provide opportunities. But even within capital markets, there are different areas that matter. And as you can see, we generally have been in areas that year over year tend to increase their size and provide more opportunities for us. Most recently, that has involved model portfolios, private credit, benchmarking in the private equity space, reporting in that space, helping people make sense of things that have not been easy to do up to this point. And so it's led to a robust financial picture, and it's allowed us to enter this year in a position of strength and with momentum in our business, as Joe pointed out. We're delivering broad-based growth and continuing to grow our operating income in a very positive manner. If I take a step back, and if many of you who were here three years ago think back to the conversation we had at this meeting, we talked about putting a focus on margins while we kept growing the business profitably. If you go back and watch that meeting, we surpassed what we said we'd be doing in the upcoming three years. And we continue to feel like that momentum remains. Over time, the markets are efficient. I firmly believe that. And so, as Joe said, it's sort of painful in the near term to see what's happening. But it's also provided us an opportunity to put money back into a business that we have high conviction in and that we know so well. One of the most steadfast parts of our business is the Morningstar direct platform business. We've got new leadership in this business, and Scott will be on stage a little later on. But I continue to point to how steadfast and valuable this business is to those who use it. Most often, the question I get around whether this business can hold its own is the question around the future of mutual funds. Now, I'll grant you, you can model scenarios where mutual funds stay flat or even start to decline a little bit. But what that scenario doesn't take into account is that this business is not just focused on mutual funds anymore. The world has changed. Many of you are asset managers yourselves. When you manufacture a strategy today, you don't care as much about the vehicle. You might put it in a mutual fund, but you're just as likely to put it in an ETF. You're probably going to put it in a separate account. It's going to find its way into a managed portfolio or a CIT. So many different types of vehicles are proliferating around the world. I won't even get into the alphabet soup of things like ELTIFs and ELTIFs in Europe. And what does that mean for this business? It means that the demand for data continues to grow meaningfully. The demand for research on top of that data to make sense of all of it continues to grow. And we've got the ability to meet investors where they are as they navigate the increasing amount of choice that they have. And you'll hear some really good anecdotes around this from Laura in a couple of minutes. PitchBook is a fantastic, valuable business. I always love to talk about the high NPS in this part of our business and how much customers just love using PitchBook. Now, I'll grant you organic growth has slowed. And part of what we want you to take away from the conversation today is we're not standing still, but I also want you to realize that the business in and off itself is very valuable and we have many avenues to grow it and reignite what we're focused on. And so we're going to talk about that and this is really a part of the business where we're doing some of our, you know, fastest experimentation when it comes to business model, interacting with the LLMs, and so we're happy to get into that a little bit later on. Morningstar credit continues to grow, thanks to many of you as well who have started to use and accept our ratings. It seems to me that the market doesn't always believe that this revenue is sustainable and what I'll say to you is we certainly think that there will be ups and downs it's natural in the credit cycle but when Mike, Datalif and I sit down and talk about this business we talk about it from a long-term perspective and the long-term growth we want to achieve despite some peaks and valleys that are inevitable with the type of business that it is and we think the opportunity is so significant that over time this business will compound in a very meaningful way and that we will be able to sustain a level of profitability that you know is not that far off from where our competitors sit today. We've also got a really nice set of capabilities in the asset management space, and that starts with Morningstar Wealth. But if you put Morningstar Wealth, Morningstar Retirement, and Morningstar Indexes together, our AUM linked businesses add up to contribute a fairly significant portion to our overall health and opportunity set. With Morningstar Wealth, much of the focus in the last couple of years has been to ensure that we're focused primarily on a couple of capabilities. The business is now focused, as we've been writing to you, It has a base on which we're very confident that we can grow on. It's also far more capital-light than it used to be, and a lot of the choices we made to exit certain areas were based on the fact that we wanted to exit capital-intensive parts of the business. Morningstar Retirement now serves more than 2.3 million investors retirees. Patrick, pretty good number relative to when you were running that business. So we've continued to really grow and compound who we serve. Brock Johnson, who leads the business, is also very much focused on growing fiduciary services as well as bringing more advisors into the fold. Advisors, as some of you know, have largely not really interacted with the retirement part of investors' portfolios, and we see more and more of them beginning to start to do that, and we have the solution that really allows them to dig into that. On indexes, really good news story here with the crisp acquisition. Hopefully you all saw that we'll be renaming them over to the Morningstar indexes, and Vanguard will be renaming its offerings to include Vanguard Morningstar Total Market Stock Index, for example, going forward. So we like that branding. We love the fact that we are side-by-side with a firm that we have a great deal of admiration for. What I don't want you to lose about CRISP and the value it's bringing is also just the fact that the methodology here, the way indexes are built, the way they're calculated and maintained is really unique. And when we talk about unique IP, one of the key attractions to owning CRISP has been and was the methodology that the firm built up. And it is really something worth digging into if you have not up to this point. With Sustainalytics, our strategy has been a little bit different. We've been shrinking to grow, and we've exited parts of the business where we felt like we didn't have pricing power, as well as parts of the business that were not data or research-oriented. We're now in a position where the business is largely focused on the matters and the items that we want it to be focused on, and we're going to try to grow it from there. We've got a really good opportunity. Some of you may want to wonder, like, does ESG have a future or not? And what I'd encourage you to do is not just think about it in that way. We think about this as a data business that is non-financial, fundamental information, and so on. It's really valuable, and in a world where the demand for data is only growing, the use cases are quite substantive. In fact, if you take a step back, if you're interested in the theme of AI, imagine the capabilities across Morningstar. You might be a firm building a large data center, and you'd come to us and you'd say, hey, do you have energy transition and water usage data? Turns out we do. You might be the firm trying to finance some of those data centers. You might come to us and say, give us a rating. Can you do that? Turns out we can do that. You might be a firm looking to raise money as well, and you'd go into PitchBook and look at what your competitors are doing. Or you might be an investment manager investing directly into these, and our manager research analyst will be evaluating how you're doing. So you start to think about that flywheel and our capabilities that exist, and it's really core to our strategy, which is to be absolutely essential to the investor workflow. That word essential is something we focus on because it drives our differentiation and the fact that we don't want to do things just like everybody else. We're going to share with you today what it really means in our minds to be essential and how in a time of technology change, that word in particular is a driving force for how we think about driving value. It starts with these company goals. They're slightly modified in wording from perhaps what you saw last year, but the spirit of what you saw last year is very much what's driving them. And I'm going to focus particularly on two, AI and owning the language of markets, particularly as it relates to public-private convergence. And this is what we talk about internally. When we had our town hall at the start of the year to talk about what's going to define success for Morningstar in 2026, it was these two. When we get together as a management team and we look at our key results, those are the two things that we're talking about. And there's real unity and focus within the firm right now on winning on those two points. And it starts by a focus on understanding where our conviction about the future comes from. And it's the strength, as I said, of our starting position. We're entering this next phase with a rare combination of assets that you can see here. Curated data, trusted research, differentiated IP, and software that's embedded in investor workflows. And what's wonderful about them is that they reinforce each other. And so when Naz comes up here, when Laura follows, we're going to talk to you specifically about how that plays out in the Morningstar Direct and PitchBook ecosystems. What we are focused on is accelerating and extending that system to meet investor needs today. And that's why AI and owning the language of markets as public-private conversions comes along is so critical to that success. And it makes sense. I referenced earlier the unique methodology of the CRISP benchmarks. But if you step back and you look at our ratings, you look at what we've done with the Moat franchise, you look at what we're doing with private market valuations, these are all unique things to Morningstar. And what they do is they bring clarity, consistency, and standards that many of you can use and that investors can understand and relate to when you talk to them and when they think about their own portfolios. It's a system of trust that comes with Morningstar, and we think it becomes the truth layer in a lot of the AI systems and what they end up anchoring on. And we're trying to extend this framework from beyond public markets to private markets as well. Key to that is our data coverage. If you've been coming to this meeting, I show this slide every year because we care so deeply about how that data grows. Not that I'm keeping score, but in the past five years, we've tripled the number of strategies we've covered, double the number of private companies in our database, more than double the number of deals that we have, doubled our annuities database, doubled the number of private market participants, we've added to the ETF portfolios, separate accounts, closed-end funds, you name it. And our analysts are key to this process because they add value through proprietary enrichment, primary surveys, and consistent methodologies. Naz is going to talk to you a little bit later about the fact that sometimes the data we have, you just can't get publicly. And it's our analysts who survey and find ways to get that data into our databases. And so it's unique and feeds into that flywheel quite positively. One area that's growing massively in the markets today is private debt. Now, you may have different views on whether it's good or bad, or at the bottom of a cycle or top of a cycle. I understand that. That's what makes markets. But I think you also probably have a clear view that data in private credit is not as good as it is in public credit. And so one of the areas that we've really been focusing on is going deeper in collecting this data and making it one of our newer, stronger databases. It started with our acquisition of LCD. And since then, we've been adding more organically. We've added Lumonic. From our credit side, we've been adding in capabilities here as well. And so you're going to hear this thread a little bit later, but we view this as a long-dated opportunity for Morningstar, partly built on our ability first to gather the data and then extend the kind of analytics that I was talking to you about earlier. And to be successful with all this data, we have definitely turned explicitly to AI. what I want you to know though is we firmly believe that AI is not changing what investors need to do but it's changing how fast and how confidently they expect to do it so for us that's meant redesigning workflows and really trying to get people from questions to insights faster Joe referenced earlier that right after the meeting today we'll have a demo session in the back this was actually Sanjeev's idea, Sanjeev from Barron last year, that since all of you are here, why don't we show you some of our software working and some of the things that we're excited about. And one of the things we're going to show you afterwards is how some of the AI tools are changing within Morningstar Direct, for example. The commonalities are grounded in our data, research, and IP. But we're really trying to rethink what that experience needs to look like. Now, I know there's a lot of other questions you're likely to have when it comes to AI, about business models, for example, pricing. And we're happy to take those questions when we're up here on stage. We've got some thoughts. We're going to share some ideas about how we're thinking about it. But for the purposes of the presentation, we first wanted to ground you on how we're approaching things so you have a clear sense of what we're doing. Now, before I transition over to Mike, I wanted to wrap up by saying we've got a great business as Joe said. Sometimes different parts of the business do better than others, but I think that is part of the value of what we bring to the table is these different capabilities. They're really relatively unique. We're also very much long-term investors in the way that we think about investing in these capabilities. It's probably not surprising to you, for example, that as you saw some private market databases get snapped up at really rich prices in the last two years, we did not participate. We didn't think we could create value for you by paying those types of prices. Instead, with PitchBook, we invested even more in our core capability. And candidly, that's the approach we like. We like organic, even though we're not averse to inorganic, as we did with the crisp acquisition. And so we have a really nice mix of businesses. We're focused on execution more than anything else. And it's true, the stock price, it's painful to look at when you feel like you're performing well, but ultimately we think what we can control is what we deliver. And so the management team and I are just focused entirely on continuing to execute in the way that we've been executing. Mike, since he's stepped into the CFO role, has done a great job of keeping everyone focused on profitable growth and driving our AOI. He's the AOI man around Morningstar, probably could have a t-shirt. He's a great partner and friend to all of us, and I think you should feel very confident that the financial health of this company has a fantastic steward with Mike at the helm. So Mike, welcome to the stage.

Good morning. It's the same reaction I get at home, but that's okay. We'll keep going here. It's my second time at the annual shareholders meeting as the CFO, and last year I started the meeting with a quote from Charlie Munger. Charlie said, the first rule of compounding is never interrupted unnecessarily, and I like that framing for how it helps share our thinking about long-term value creation, but this year I want to continue that theme of value creation but focus you more on how we think about operationalizing that concept internally, and the way we do that is by focusing on our North Star metric, adjusted operating income. I'll probably refer to it as AOI, but we focus on AOI because we know if we grow adjusted operating income will grow the free cash flow of the firm which will grow the intrinsic value of the firm. Growing the intrinsic value of the firm is our goal. AOI is how we measure progress. So with that in mind let's look at that progression of value creation. Every great story about profitable growth begins with revenue growth and that's exactly what we've delivered over the last 10 years. You see us growing from 800 million to over 2.5 billion and we translated that into strong AOI growth. So let's bridge that gap between adjusted operating income and gap operating income. You see the purple and pink lines bars here. The point here is that it's a very stable relationship over time. So we focus on AOI because it neutralizes some non-cash, non-recurring items. It might be adding back amortization or stripping out the gains we have when we have the sale of customer assets to AssetMark, for example. We don't want to count that in the day-to-day noise. So we're going to show both, but AOI is the best representation of the business's core earnings power and that's why it's our North Star internally. Now let's zoom into these purple bars just so you can see that progression more clearly. Over the last 10 years we have tripled the adjusted operating income into business. 182 million, growing to over 600 million. So we feel good about that. And when we talk about our focus being on profitable growth, this is how we're operationalizing that concept internally. With that framework in mind, let's turn to the 2025 financial performance. I'll also touch on the first quarter of 26 in a little bit. Starting from left to right, see growth, profitability, conversion into free cash flow. 8% organic revenue growth, 18% growth in adjusted operating income, and free cash flow that's relatively flat. We're going to double click into AOI here, and then I'll touch on free cash flow in a minute. So adjusted operating income expanded by $89 million. So $494 on the left, growing to $583 on the right, we added $89 million. You see the little cheat sheet there, the bracket on the right-hand side. We did that by growing. We added $170 million in revenue. We invested some back in the business, but more than half of that incremental revenue fell to the bottom line as profit. So what's happening here is we're growing our revenue faster than we're growing the operating expense in the business. And when you do that, you deliver margin expansion. Now the goal is grow AOI, grow free cash flow, grow the intrinsic value of the firm. But we're able to do that by growing revenue and delivering the operating leverage in the business or unlocking additional operating leverage in the business. So you see over the last three years, it's been a steady progression. In 2024, 21.7% margin. The first quarter of 26, 27.7% adjusted operating margin. Follow that pink line there. So the two things you should take away from this slide are, One, it won't always be a smooth line, especially when you have big jumps in a given quarter. It can bounce around. But two, we believe that we can continue to grow the firm and deliver that operating margin expansion over long periods of time. One of the big ways we do that is by leveraging our talent. You can see we've delivered this higher level of operating result without adding headcount to the business. So we're driving productivity. We're driving scale in the business. and this only accelerates with the advances in technology now you saw that free cash flow was flat year over year so what you see here purple bars represent the growth and the very positive momentum we have in growing AOI the white line represents free cash flow it bounces around it's got a bit more noise in it sometimes it's above the AOI in the quarter sometimes it's below over time we expect these to grow together this relationship is very highly correlated over longer periods of time. It's just a little bit noisy in a quarter over quarter view. So that's 2025. This is the same chart, but focused on the first quarter of 2026. We reported earnings last week. What do you see here? A continuation of those strong tens. On the left-hand side, 7.6% organic revenue growth. In the middle, 32% growth in adjusted operating income, and free cash flow that's a little bit negative, but a noisy quarterly number as we discussed. So that's at a company level. Let's discuss this progression and how the different segments contributed. So similar look here at the growth of AOI, 494 on the left growing to 583 on the far right, adding 89 million. The point here is every segment contributed to the growth of AOI in 2025. To put some more context to that, let's look at the time series is the progression over the last five years. I'll draw your attention to that far right-hand side. We've got purple, that's direct platform on top, working your way down. Pink is pitch book. The core earnings power of the business has been in these two groups over the last few years, and it continues to grow healthily. Second, I'll look at the yellow bar. That's Morningstar Credit. Now, you know that that's been growing really well. This gives you a sense of how much it's starting to contribute to the overall profitability of the firm. And third, I'll draw your attention to that bottom gray bar. There's three things in there. There's central costs that are unallocated. There's index and sustain-o-lytics. So as we've been very disciplined on central costs and we've improved the profitability on both index and sustain-o-lytics, that's been less of a drag on the overall profitability of the firm. Let's look at each of the segments. Now, there are five reportable segments, externally reported segments in the firm, and then there's two others. So I'll touch on those in a minute, but start with the first five. Morningstar Direct Platform. Our largest and most profitable segment. Mid-40s margin, 6% growth rate, not bad. But most importantly, the data, the research, the analytics, the software. These are incredibly sticky solutions, and we're very embedded in those customer workflows. So a lot of opportunity here, as can all discuss. Let's turn our attention to PitchBook. The first thing I hope you notice, AOI has quadrupled over the last five years. So it's a great growth story. It has been since the acquisition. A big contributor to that AOI free cash flow intrinsic value story. Now it's an evolving landscape. You've seen some deceleration. One of the things we're going to talk about here is how do we leverage AI to continue to create value? And we're excited about the experimentation that can all reference, but just the opportunity in front of us. With Morningstar credit, it's been a star of the show the last couple of years, but you can see in these numbers it's also a business that can have some volatility to it. So you might think about this as cyclical nature of the business, but it's not just the cyclicality. There's a meaningful tailwind. There's a secular trend here where we're expanding in different geographies and asset classes. We're building beyond that traditional strength that we have in Canada and moving into adjacent markets. This includes investment grade private placements and infrastructure finance. So we're building a meaningful and durable franchise here. With Morningstar Wealth, you see a lot of helpful numbers on the slide here, but what you see is we've made some big bets. They haven't all worked out. But we've had the discipline to recognize when the returns don't appear that we need to pivot. We've pivoted and gotten focused on three core areas that we're very excited about. Investment management, the international wealth platform, and the individual investor segment. So you see that focus is already driving profitability, and now we're turning our attention to make sure we're driving that growth. Finally, with Morningstar Retirement, we manage retirement accounts, 401ks, tax advantage accounts for more than 2.3 million plan participants. So we're excited about how well this is on mission, empowering investor success. But we're also excited about 50% margins and 8% growth rates. Tie that back to that free cash flow story. Great AI, great free cash flow. Let's us invest elsewhere in the firm, too. Now, those are the five reporting segments. The other two are part of this corporate and all other reporting segment, so I'll just touch on them from a revenue perspective. And I'll start with Morningstar indexes. Great growth story over the last five years. We can all really set this up about the CRISP acquisition. As we look forward, the CRISP acquisition is very powerful. It gives us scale, gives us critical mass, gives us relevance and heft, so we can really get after the entrenched incumbents. And that rebrand of the crisp indexes to Morningstar and how that flows through to the vanguard, that's great marketing for us. We're excited about the path forward here. And Morningstar Sustainalytics, after the acquisition, had very high level of growth. But as you know, we've hit some headwinds here. We're getting refocused on a smaller set of core functions. So as we exit certain things, you see a lot of noise in the top line. The team's getting very focused on where we think we can add that value. And we're pleased with the progression here. So that's company level and the segment level. Really, I think about that as how are we generating cash, how are we generating capital. Let's turn our attention to how we deploy that capital. There are three priorities here. Reinvest, acquire, and return to shareholders. In that order. and when we do this well we're growing the profitability we're making sure that we have that discipline on the balance sheet side to maintain a spread between our return on invested capital and the weighted average cost capital and that's what you see here so we're pretty capital light business but the acquisitions can move the invested capital base around this is how you know that we're demanding and executing and delivering the expected return looking back in the last five years let's put some numbers to how capital has been deployed $1.1 billion spent on M&A. $346 million delivered to shareholders directly through dividends. And $1.3 billion spent on share repurchases. We've talked a lot about intrinsic value and how we focus on that. Market values, just as important, we get that. And the market returns that you see here are not what we'd like to see, particularly on a one-year basis. But we're continuing to focus on what we can control. We're continuing to drive operational excellence. We're continuing to drive those operating results that we just talked about. And over time, we'd expect there to be a convergence between the intrinsic value of the firm and the market value of the firm. In the meantime, when you do see a decline in the share price, it creates an opportunity. An opportunity to repurchase shares at a price that's lower than what we believe the fair value of the firm is. And that's exactly what you see on this slide. That white line, that's the share price. The purple bars are the concentration of share repurchases. So I mentioned 1.3 billion over the last five years. That has not spread equally over that time period. We're opportunistic here. When the share price has declined, we've been more excited about the gap that's been presented, and we've deployed more capital to take advantage of that. We do this because we believe it creates value for investors. Sometimes the best acquisition you can do is of yourself. We know that target extremely well. So this pulls all the capital deployment together over the last five years, but I'll just draw your attention to that row that's second from the bottom, total debt. At the end of 2025, we had $1.1 billion in total debt. At the end of the first quarter, we had $1.7 billion in total debt. There's many good reasons for this. Some normal things such as the bonus payment, a bunch of taxes we've got to pay, also the CRISP acquisition, and of course, as we just discussed, $300 million spent on share repurchases. So that debt level has gone up to $1.7 billion, but that's still a very comfortable level of debt for the amount of profitability we have. When you think about the leverage ratio, two times debt to EBITDA, we've been here before, we know how to operate at this level, and we just keep focusing on generating more profitability and more free cash from the firm. But we still prioritize having a pristine balance sheet. We still believe we're in that position today where if an opportunity jumped in front of us, we'd be able to take advantage of it. To wrap things up, let me return to where we started. A focus on that North Star metric, adjusted operating income. Over the last decade, we have tripled this number from $182 million to more than $626 million on a trailing 12-month basis. The story gets even better on a per-share basis. We've reduced the number of outstanding shares, so the AOI per share has more than doubled over the last three years. The great thing about this story is it's broad-based. Every reportable segment has contributed to the growth of AOI in 2025. So looking ahead, our priorities remain clear. Grow AOI, convert it into free cash flow, and increase the intrinsic value of the firm. Thank you for your time. I look forward to your questions. Now I'm going to hand it over to Nizar, who will share how we're using AI to drive value creation.

All right. Thanks, Mike. My name is Nizar Tarhouni, and I'm Executive Vice President of research at PitchBook. Now, historically speaking, capital markets have always grown more complex. That's not really new. You see the chart on the left here. As markets evolve, new asset classes are created, and that creates more complexities. That's not new. But what is new is the pace of which that complexity is building, and then the corners of the market where that complexity is building. And that creates a fairly straightforward problem for many of our clients, which is they also now need an evolved and consistent analytical infrastructure across both the public assets that they own and the private assets that they own. And in private markets specifically, that actually really doesn't exist today. Now, from an AI perspective, AI is already rapidly changing the way that all of our clients work. Their diligence periods are compressing, and the way that they look to screen for targets or monitor their portfolios is being automated every single day. And we're excited to help them along that journey. But most importantly, and most relevant for all of you in this audience, it's also forcing them to hold all of their data and research partners to a much higher bar. They don't only expect us to help them find and decipher a signal through noise, but they expect us to do it with a lot more breath. They expect us to do it with a lot more accuracy. And they certainly expect us to do it a lot faster for them. And we're very excited about the assets that we put together to help them do just that. We're going to walk you through the next set of presentations. But I want you to take away a few things, and three specifically from this presentation on PitchBook. The first, the fundamentals of our products across our data, our research, and our analytics are stronger than they've ever been. And we're quite excited about the continued investment we're putting into those fundamentals. And we're really excited about doing that in the pockets where we feel we have a differentiated and a proprietary edge to source data that others can't and to build the analytics on top of it that they can't. The second, and this is a big one, we are working to transition our business from a reference data and analytics platform but into an end-to-end workflow solution where our clients can do their work. We don't just want to be a place where our clients can come to and pull data and research out just to go do that work elsewhere. That's clunky. What we'd like to do is create a seamless experience for them to do that in our platform. And the third, in an era where the surface areas where all of our clients are working continues to widen with various AI tools out there, there are pockets where our clients do need our data and our research analytics in other locations. And we're investing in the right relationships with our clients and with different AI tools to ensure that they can get value from our IP both on our platform and off of our platform. And we can do that in a way that also protects the economics of all of our IP. Now, the foundation of all of that is our data. Let's start there. In public markets, you've had decades of standardized reporting, central repositories, and a common taxonomy. In private markets, none of that exists. In fact, the vast majority of enterprise-generated private markets data is actually both unstructured and unactionable. Now, you might push back on that and say, well, I think actually AI is precisely built to take the unstructured and to turn it into the structured. But when we're talking about sourcing data from literally millions of different endpoints every single day, the process of just sourcing that, collecting it, validating it, enriching it, and then orchestrating it in a way that our clients can actually use is a ballgame. And that takes deep domain and operational expertise. And we've spent the better part of two decades building our proprietary methodologies to do just that. And what our clients get from that is a data set that they can trust. They can trace it. They can verify it. They can defend it to their stakeholders. They can feel comfortable using it in their risk models, in their benchmarks, or in their investment committee memos. So in an era where AI-generated content is literally everywhere, the valuable component for us is that our data becomes a valuable input into AI-powered workflows, not a source that they compete with. Now, additionally, we feel that over the years, we've built something that's even harder to replicate from a data perspective, which is a network of data sourced directly from our client networks. That network today generates over 500,000 data points for us. It includes metrics like valuations and financials, fund terms, fund returns, and fund cash flows. And you might wonder, why would they share that with us? Well, over the years, we've built a user base of over 100,000 users across every core private market segment. And with that, we effectively become a pulse in the markets that we cover. And that creates incentive for our clients to want to make a lot of that data available to us. And so then our job is to take that data, actually our responsibility is to take that data and create the IP and the product sets that power the workflows of all of our clients. Now one area I want to call your attention to right now is credit. Because I think that's a dimension of our leadership that might go a bit unnoticed when you're comparing us to other data and index providers. Across all of Morningstar, we've assembled a credit franchise that I think is one of the most comprehensive ones in the market, with capabilities that span ratings, indexes, benchmarks, data, analytics, and portfolio monitoring. And our data sets are more granular than most that you'll find out there, tracking information from the credit agreement level all the way into the individual facility level. And we're doing that across the syndicated loan market, the bond market, both investment grade and high yield. We're tracking BDCs and CLOs, and increasingly in private credit. In fact, over the last four years, we've grown our private credit coverage by over 21 times. We're tracking nearly 100,000 private credit transactions, including both primary issuances and secondary market transactions. Now, outside of credit, we continue to see demand grow for decision-grade data from our asset management clients like our private equity clients. And in a market with a lot of volatility, where the dispersion between quality and valuations continues to widen, sourcing and origination becomes the most important and critical workflow for many of our clients said another way they got to make their money on the buy now our clients source companies in a couple different spots you've got this chart here on your left hand side it's basically an example here where you can see in the united states if you look at u.s middle market companies it's only five percent of those companies are actually backed by institutions the bulk are non-backed so i'm going to start with the value proposition of when our clients are sourcing transactions from sponsor-backed companies. Over the last decade, we've grown our sponsor-backed coverage by over 400%. We're tracking over 700,000 institutionally-backed companies across 1.5 million transactions. And we're making concentrated investments in collecting bespoke private company financials that tie back to those companies. And we're using AI every single day to enrich scarce company profiles so we can get client-ready data in their hands a lot faster. I'm going to talk a little bit about non-backed companies here as well, but I want to address first, can all address some of the softness that we have in our business. I don't want to hide from that. The softness in our business really stems in two main segments. It's in our corporate book and it's in our venture book today. And in our corporate segment, the concentration of that softness is in a pocket of that corporate book that uses our data sets for non-M&A related use cases. You can think about things like business development, identifying strategic partnerships, or researching contacts and people. Now, over the years, we've always been very focused on our product and our data investments, addressing our core investable use cases and investing clients. But what we found over that time is that many of these same corporate clients get value out of those data sets as well, albeit in a more cyclical manner. And so we've been investing from an origination perspective in growing the sourcing data sets that many of these clients lead. We've grown our non-backed company data coverage from 5 million to 10 million companies over the last two years alone, and we will double that by the end of this year to 20 million. We're adding millions of people records to our database literally as we speak to connect the contacts to these non-backed companies that our clients need. We're incredibly excited about the opportunity that that creates for our asset management and our advisory clients. But at the same time, we also think these investments will support our corporate clients as well, which helps us reduce churn and add more value to them. Now, I want to turn now to our, well, I'll go back actually. We talked about corporates. Let's talk about venture for a second. Now in venture, it's interesting because we're here to talk about AI, but it's a bit of a double-edged sword because while the bulk of capital that's flowing into the venture ecosystem, into AI companies coming from early stage VCs, it's a double-edged sword because it's coming at the expense of their recent vintages. So non-AI companies in the most recent vintages, they're seeing their fund net asset values get compressed. Exit timelines are extending and the exit valuations are being compressed as well and that creates a much slower recyclization of capital back into the ecosystem for our VC clients. Now at the same time what we're also seeing is that in today's world early stage companies can generate value a lot faster with AI and so our GP clients in the venture side are looking for data sets and information earlier in the investment lifecycle. We're spending a lot of time investing in our early stage data sets and that's at the pre-seed level at the stealth level. And that's just the tip of the iceberg. We'll talk a little bit later about the analytics that we're building on top of that to get our clients to data-driven decision support a lot sooner. I'll transition now. We'll talk about research. Why does human research even matter in an AI era today? Well, for us, it starts with an edge where our research is built on top of the largest private markets data set in the industry. We then layer in thousands and thousands of conversations our analysts are having with GPs and allocators and companies every year, every month, and every day. And those conversations allow us to identify fun strategy shifts or emerging risks or changing sector dynamics. And we leverage those insights to be leading indicators before they become consensus. And if you think about it, it's not that dissimilar than much of the work that many of you in this room do every single day. Where it makes sense, we build our own AI power tools to make our analysts a lot more efficient, to help them synthesize and structure data and get presentation-ready materials ready a lot faster so they can spend their time doing what's proprietary and developing the research insights our clients need. And so it's that human harnessing of AI when you then power that with our data sets, our direct industry outreach, and our own research frameworks that give us the ability to help our clients drive the alpha that all of their end customers require. And we're also able to do that at an immense scale, a scale that we think is unmatched. Our analysts today are publishing over 2,500 reports in private markets alone, spread across 140 analysts and journalists. Our public newsletters are opened over 200 million times a year, literally 4 million times a week, reflecting a voice and trust that's been built in the market over many years. And the value of our research spikes precisely when our clients need trusted perspectives the most, typically in times of market turmoil. When you think about periods like Silicon Valley banks has collapsed, or the pandemic, or the recent software correction, that's when our analysts are most in demand. And we're expanding our coverage as well. Most recently, we launched private company coverage. We're covering companies like SpaceX, Anthropic, and OpenAI. And when those companies transition to the public markets, our Morningstar counterparts will pick up coverage. On the credit side, we've added private credit franchises, both in the U.S. and in Europe. And we've launched a new BDC franchise, allows us to cover valuations to credit markets, distress monitoring, or credit risk, especially across the largest publicly traded BDCs, which is the main barometer really used to measure the active direct lending space. We have more sector coverage, covering quantum computing and AI data infrastructure, climate and energy tech. These are the themes that our clients are allocating around, and our job as a research provider is to allow them to get to insights and get to decisions faster in those spaces. Now we'll transition now and talk a bit about our analytics. It's a bit of a newer capability for us, but we've been investing quite a bit over the last few years here Now our ambition here is pretty straightforward. Our ambition is to be the shared analytical language for private markets Our analytics embed directly in our clients workflows allowing them to make faster and better risk-adjusted decisions much quicker And every tool that we build starts with a question that our analysts get from our clients or from different market participants And we build our analytics across four primary categories First is opportunity identification Later this year, we'll launch our private equity deal sourcing tool, which allows our clients to surface non-obvious acquisition targets, leveraging our proprietary deal and company data. That's an expertise that wouldn't be possible without the breadth of our data sets. We also build signal and transparency tools, allowing our clients to translate latent patterns in our data into actionable insights that they can use to position ahead of the market. Examples of this include our exit predictor, where our venture clients can use that to get a sense of which companies in their portfolio might actually go through a successful exit. And we're seeing that usage continue to grow. Today, over half of our venture clients are using that product every month. We're enhancing that later this year with our exit timing predictor, allowing those same clients to get a sense of when those companies might actually exit. And that's powerful in their liquidity planning. We build forecasting tools. In January of this year, we launched our valuation estimates. Leveraging two decades of our data, we built our own proprietary model to place valuation estimates on 15,000 private businesses and what's powerful about that as we release those valuation estimates in the market the data cycle and that flywheel starts to power our clients are reaching back out to provide data to help power our models. We also recently launched our loan default predictor on the credit side providing a forward looking aggregate set of default rates in the syndicated loan market so we're providing forward looking insights in markets where that typically hasn't been available and we also manage a suite of performance and benchmarking products. Our benchmarks and indexes, our manager scores, our portfolio simulations, allow our clients to evaluate fund performance, to help them select managers, and also allow them to model different allocation scenarios across their entire private market's exposure. And to build products like that, you need substantial universe coverage. And once these things are adopted as standards in the market, they only get more powerful with every year of vintage data and every vintage year of data that we layer in. Now, we talked about our data, our research, our analytics, and everything we described reaches our clients through our software. And that software layer is where our competitive advantage really comes to bear because it's the most visible and where all of these capabilities converge in the places where our clients work. And I want you to try to think about our software the way we do, which is really in three different buckets. The first, we want to make our clients radically more efficient in how they reach the insights that they need, how they reach the fundamentals of our data research and analytics. The second, we want to serve as a system of record for their own data. And that underpins the foundation of the transition we talked about earlier, which is switching to an end-to-end workflow tool. We want to reduce the context switching that they have to do from analyzing their own portfolio and figure out where that sits inside of the broad market context. And last, we want to meet them in the AI ecosystems where they're increasingly working. And believe it or not, that actually takes some technical capabilities as well in order to allow for that to happen. Our core investment in this space, in our software, is what we call Navigator, which is our conversational AI search tool. And that removes the typical friction you'd need to get from question to answer to action, and you can do that now without the navigation that comes in between. And our ambition here isn't just to be an AI search bot that sits on top of our product, but our ambition is to create purpose-built agentic workstations tied to the specific jobs that our clients do, from sourcing to diligence to investment committee preparation to portfolio monitoring. And we take a segment-first approach there. Where it makes sense, we'll package our data, research, and analytics into shared experiences that can be used across many different client segments. But where the workflows are distinct, and they often very much are, we'll build purpose-built solutions. The needs of our credit clients looking to monitor covenant compliance is very different than the needs of our venture clients looking to evaluate pre- and post-money valuation step-ups. And I want to also make one thing clear, that this is a current, present-day investment that we're investing heavily in, and it's an ongoing one. And we're able to do that a lot faster because our engineering teams today are committing code at a 4x greater pace year-over-year with AI-assisted development, which means all of the product sets that we're talking about reach our clients materially faster. In 2025, we acquired Lumonic, an AI-first and AI-native portfolio monitoring company, first focused on private credit, but we've recently expanded it into private equity and venture as well. Now, Lomonic solves for the hardest problem in portfolio monitoring, which is ingesting and normalizing the messy and inconsistent data that lives between borrowers, lenders, and their counterparties like their private equity sponsors. And because they solve for that hardest problem, we're watching our clients build their workflows around it. Now, this is strategically important for PitchBook because it positions us as a central data and analytics pillar with our clients leveraging the data that sits in their own portfolio, their own data, not just consuming hours. And so it gives them a single source of truth where they can understand what's in the ground, layered right next to the market context that we provide to them. It's also valuable for us because it expands our market. It moves us from providing front office workflows and moves us into the middle office. And so that combination of Lumanic and the agentic workstations I just talked about, they create more value for our clients. and that's what enables our capabilities to extend from being a reference and analytics platform but into a workflow solution and these are just the beginning of those investments. PitchBook's data is also now embedded in the AI tools where our customers are increasingly working. We're working with collaborators like Anthropic or OpenAI or Perplexity and many others and we're seeing usage grow tremendously. Usage of our connectors in these platforms has grown by over 35 times over the last four months and it's literally doubling every single month and that underscores the power of our data research and analytics regardless of the surface of where it's consumed and we're very thoughtful thoughtful about the distribution models there ensuring that our clients can get maximum value should they need to use our data and our IP and other platforms but make sure our economics are protected as well so let me bring this together for you we've built an interconnected system of capabilities and I want to show you what that looks like in practice if you're an allocator building a private markets program you might be using our benchmarks and our manager scores to evaluate GPs. You're using our portfolio simulations to help stress test allocations. And that's all grounded in an incredibly deep and rich fund performance data set. If you're a private equity firm, you're likely going to be a navigator and using our deal sourcing tools and our proprietary company data to help you find opportunities that the market likely hasn't priced yet. If you're a banker, you're using our valuations data, our estimates, our transaction comps to anchor a sell-side process, and you're using the same deal and company intelligence in forming both sides of the table. And if you're a credit investor, you might find yourself in Lumenic, monitoring covenant compliance or credit risks or trying to get a sense of what your actual budget versus actuals look like across all the portfolio companies and credits that you own. And you're doing that with PitchBook's market contacts layered right next to it. Every workflow powers the next. At times, there's a sequential flow of data feeding the analytics that our clients need. And at times, our clients are using the same set of data and analytics to power two distinct user groups doing two distinct workflows. But there's an integrated system of data, research, analytics, and software that we have meticulously and carefully built over the better part of two decades to allow for those workflows to happen. Now, we'll continue to invest in the core fundamentals of our business across our data, our research and analytics, but we will also continue to embrace and drive change and evolve the way that we create and deliver the software and products that we create every day to our clients. So with that, I'd like to hand it over to Laura. We'll show you how some of these same convictions play out across other parts of Morningstar. I appreciate your time. Thank you for being here.

Thanks, Nazar. What is the difference between luck and skill? If you're choosing a fund or a stock, how are you going to be confident over the long term that it's going to outperform? Is it going to be driven by luck or by management skill? This is the question that every analyst asks, including those of you in the room who are here to assess Morningstar. I lead Morningstar's manager research team, and there are 130 of us that are separating luck from skill among 3,000 investment strategies per year. How do we do that? How do we separate between luck and skill? We tap into Morningstar's data and analytics, we use it to create new insights, and then we share it with our clients through our software. In the AI era, this process is getting faster and our reach is getting wider, and that is the flywheel in action. We believe that AI will have a greater long-term impact on investment research than any prior technology cycle, and that's because AI gets to the heart of our business. It gets to decision-making, differentiation, and judgment. It touches the very attributes of our business, and AI will change investor behavior as we move from a world of scarce information to one of abundant intelligence. In that environment, speed alone is not a strategy. Trust, transparency, and accountability become the new scarcity. As Kunal mentioned, Morningstar's competitive advantage starts with its data. We aggregate data across 10,000 sources, leveraging 40 years of relationships with asset managers, exchanges, and regulators. And Morningstar tracks data on millions of investments, which allows our clients to compare investments across the country or around the world. Data volume is a Morningstar advantage, but what's even more valuable is our data structure. As we've built this sea of data, we have set the standards, the definitions, and the methodologies that put the data in context and define success. One way that Morningstar has developed the language of investing is through our fund categories. So we assign strategies to a category not based on what they tell us they're going to do, but we look for evidence in their holdings over time. And once we assign a fund to a category, then we study its performance, and we measure it relative to category peers over time on a risk-adjusted basis. And you know this story. It's the story of the Morningstar rating. We assign five stars to strategies that have outperformed and one star to the laggards. The star rating is one of the most widely licensed pieces of Morningstar's IP because it delivers insights in a simple, intuitive measure. And in the AI era, investors will trust the stars because we're very clear about how we got there. Morningstar's category framework is the language of fund investing. You ask any asset manager to describe an offering in their lineup, and they start with the category. Oh, they'll say, it's a large growth strategy, or no, no, that's the intermediate term bond strategy. Asset managers are using Morningstar's category framework for competitive analysis. They go into our software, and they are studying holdings, fees, fund flows. They are drilling into what is driving a strategy's edge relative to other options in the market. And this category framework and the millions of other data points that we've created, they belong to Morningstar. Structured global investment data defines our moat, and our clients use it to find skill. AI is only increasing the demand for structure. Practically speaking, the cost of calling and collecting all this data in a time series to drive insights is far more expensive than licensing that data directly from Morningstar, and that is why our licensed data business is accelerating. We're bringing our data to where our clients are working, whether that's through our software, a Morningstar data feed, or a cloud-native data environment like Snowflake. Many of our institutional clients use Snowflake's secure data cloud to support easier integration of Morningstar IP into environments that they've already built. Longer term, we believe investors get a premium for data that's structured, auditable, and anchored in transparent methodologies rather than probability-based outputs. No matter what the environment, Morningstar's category framework, its ratings infrastructure, and 40 years of analytics and standards remain the truth layer that AI must anchor to. The authority that establishes the language of investing is a structural advantage that's difficult to displace. Now, if Morningstar's data is a superpower, so are our people. Across Morningstar's businesses, we use a combination of data, human-driven insights, and entrepreneurial drive to advance our analytics. Each Morningstar team is using data to drive its ratings, but we have humans in the lead on our most important research. When the success of a client's investments or their business is on the line, they look to analysts that have demonstrated their expertise over decades and own their conclusions. Now, one example of this critical human-in-the-lead research process is the Morningstar medalist rating. My manager research colleagues conduct forward-looking assessments of investment strategies. This is an auditable stream of recommendations that separate the strong from the disadvantaged. And you'll see on our screen that the analysts are showing their conviction in a strategy's future performance with the ratings gold, silver, bronze, neutral, and negative. On the right of the slide is the distribution of those ratings. Fewer than 10% of the strategies that we're rating get gold ratings were choosy. Major wealth managers and RIAs use this qualitative medalist rating to determine which strategies they make available to their advisors on their platforms. These are the strategies that are making it into client portfolios. And we offer both quantitative and qualitative medalist ratings, but the distributors prefer and sometimes insist that they have a qualitative medalist rating on all the strategies that they're distributing. And our analysts get requests to expand our medalist ratings coverage every week because asset managers know that those ratings influence advisors' buy and sell decisions. We are validating investment decisions among more than 425,000 financial advisors every day. We are expanding our analysts' capacity for more research and ratings through AI. We're using AI to speed up our analysis by accurately assessing long-term data sets in the matter of seconds. That gives analysts more time to dig into differentiators and spend less time sorting data in spreadsheets. Now the skeptics in the audience may be thinking, Laura, we don't need you and your analysts and your ratings. We're just going to buy a low-cost index fund and call it a day. Low-cost index strategies are often a great choice, but you'll see this data and ratings on the screen. our analysts are differentiating between outcomes of index strategies that all track the same index. I'll also note that most industry assets are not indexed, and it's more likely today that assets are flowing into a non-registered, customizable investment, a separately managed account, a collective investment trust, or a model portfolio. And these investments are not required to disclose their data to Morningstar or an AI agent. But Morningstar creates transparency by leveraging our relationships in the industry to collect data on non-registered investments and drive insights. As Nizar mentioned, Morningstar and HBQ are bringing much-needed transparency to funds that hold both public and private securities. Semi-liquid funds are one type of fund that owns public and private securities, and they offer limited periodic redemptions. So if a GP in PitchBook's database and a fund in Morningstar's database had a baby, it would be a semi-liquid fund. Semi-liquid funds are a challenge to analyze because they have a privacy shield. They're disclosing data on a lag, and they operate under layers of non-disclosure agreements. To understand semi-liquid fund fees, our analysts are digging through several regulatory filings. They're making some income assumptions, and then they need to estimate how much you're going to pay for a strategy over time. That's not as transparent as we would like it to be. Now, remember how the language of investing provides Morningstar with an advantage? The language of semi-liquid funds is relatively undefined, and we view that as an opportunity. Morningstar and PitchBook are collaborating to bring our skill advantage to create analytical frameworks for public-private markets and funds. We're leveraging Morningstar and PitchBook relationships to collect data through our established electronic channels. And here's the IP we've built so far. So we created Morningstar categories using PitchBook's taxonomy. We've developed a methodology to compare semi-liquid fund fees based on some income assumptions. And Morningstar indexes developed a benchmark that includes both public and private securities, so we can measure performance. And we've extended our analyst ratings to 19 semi-liquid funds. And if you want to meet some of the analysts responsible for those ratings, they'll be in the training room after the meeting. And here's how our clients are using that new IP. So it's all getting channeled into our software and data feeds where clients can compare this to something fully liquid like an ETF. Advisors can add semi-liquid funds to a portfolio and monitor that asset allocation holistically. And private asset managers are using our software to study the competition or to create a fact sheet that they can use with an advisor who might be considering one of these funds for a client portfolio. So I've described for you how our clients get access to our data and research. To be clear, every new data point, rating, research paper, that all ends up in Morningstar software. And our software strategy is evolving to meet clients where they work because we want to bring insight to action. In the AI strategy, in the AI era, our strategy is moving from an information advantage to a judgment and governance advantage. And our delivery extends beyond data. We are delivering interactive intelligence in context in real time. And the intelligence is conditioned by our domain expertise. Clients can engage, interrogate, and interact with that intelligence through our software platforms, or an AI assistant, or an AI native workflow. I'll give you an example. In March, we launched an AI assistant inside Morningstar Direct that summarizes methodologies, it streamlines investment list creation, and you can add data as you're working on the fly. And just after a few weeks, several of our largest clients have onboarded this assistant and are putting it to work. And here's what's next. We're developing an AI-native persona-based user experience, and this workflow will enable even faster access to our data research and analytics. Every experience, whether you're an asset manager designing a new ETF or you're a wealth manager optimizing a portfolio. AI will help shorten the path between truth to truth and skill. We're speeding up the flywheel. And if you'd like a sneak peek of this tool, my colleague Tom Nations is running some demos in the training room after the meeting, and we'll also post a video of that to our investor relations website. The most important insight into our software strategy is this. We are using AI tools to provide the intelligence layer inside our solutions. Our software is just one way that we deliver our data and insights. Roughly half of direct platforms 2025 revenue was driven by Morningstar data. Our data and research are being embedded not only inside our own platforms, but also directly into client workflows through integrations with firms like Microsoft, OpenAI, and Google, all the big AI providers. These major platforms are featuring Morningstar model context protocol workflows on their menus. Our objective is to ensure that AI models reason through Morningstar's frameworks, methodologies, and standards consistently, without any drift. We are the intelligence infrastructure, the layer that shapes the investment decision itself. And we're not waiting for clients to find us. We're already at the table. Every major technology transition looks like an efficiency story at first and becomes an identity story later. We know who we are. We know where we are and where we want to be. and we know how AI can augment and enhance the unrivaled value that we deliver to our customers. We're steadily evolving because we know Morningstar's future moat will come from being the institution that the machine must rely on to be trusted. We see the edge that comes from the human capacity of discernment while the rest of the market increasingly sounds the same. That's how we continue to separate luck from skill. And that's how we empower investor success. And now I'm going to hand it to Sean, who's going to speak about our credit business and how it is benefiting from the AI trend.

Thank you. Thanks, Dazar and Laura. Really solid insights in your presentation today. We've been talking a lot today about how Morningstar sets the standard. We've also been discussing how we position ourselves in an AI world. Many of you, when it comes to credit, the last thing you think about is AI. But as it turns out, it's an incredible opportunity for us and something that we're going to dig into today. By way of introduction, my name is Sean O'Connor, and I'm responsible for the business development group at Morningstar Credit. And during today's presentation, I'm going to spend a few moments digging into a couple of themes that I believe are important to provide you with insight into some of the growth drivers of the organization. Some of the areas that I believe that are important to talk about today are opportunities in AI, private markets, and how we'll win. Let's first dive into opportunities in AI. When you think about AI, there's a very key ingredient that's critical for you to understand the deployment of capital that is occurring in this market right now. And that key ingredient is capital expenditures. It's not happening at a small scale. It's massive. It's an enormous opportunity. Let's take a look at two areas in particular that are relevant for this discussion. Digital infrastructure and energy transition. Look at the size of that. So capital expenditures are fueling part of the AI boom. In order to achieve that, you're ultimately going to need numerous financing solutions. Those financing solutions are going to take the form of numerous debt structures. Those debt structures are going to require credit opinions. Amazing opportunity for us. Think about that. The AI build-out ultimately is going to need credit opinions in order to distribute that debt. That debt could be distributed into the public, broadly syndicated markets, and increasingly into the private markets. That is an equalizer opportunity for us. This is a disruptive event. Again, you're seeing this occur with the expanse of our ratings and our ability to increase the market participants with which we operate. So let's go into a little bit some of the underlying areas that make up some of this energy transition and digital infrastructure. Some of the things where we provide our credit opinions are in data centers, LNG terminals, battery operators and storage, cell towers. It's incredible. If you think about where this goes, it creates an opportunity for us to work with market participants, including banks, asset managers, insurance companies, and sponsors at a global scale that had not been available to us previously, ultimately driving growth, ultimately putting us in a spot where we can be part of the disruption. Let's dive into this slide for a second and talk about booming private credit. Now you heard me previously say, that the debt that is being utilized in order to fuel the AI boom, it could be in the public, broadly syndicated markets, but it also could be in the private markets. Look at the growth of the private markets. When our credit opinion is shared in this debt distribution lane, we have parity with our larger competitors. In the public markets, there are barriers to entry, there's index inclusion, there are bylaws with investors. In this lane, we have none of that. We can invest in the business and be at parity with our larger competitors. That's an incredible opportunity. It's also important for this audience to understand that 25% of our credit ratings revenue comes from the private markets. This is allowing us an opportunity to share our credit opinions, to be nimble, and ultimately to create a platform that allows us to grow into new geographies that previously had not been available to us. Now, you heard me speak a little bit about the AI boom opportunity leading to the growth in credit ratings. I gave you some insights into the private markets, creating a level playing field for us, and allowing us to share our credit opinions and credit risk opinions in a growing geography. Let's dive into what makes up some of the credit rating business, though, for a second, because it's important to understand that we ultimately are building a scalable platform here. And when we make investments in the credit business, you heard this earlier from both Kunal and Mike, there are investments being made in both good markets and more challenging markets. And the results you're seeing today are a reflection of having that long-term investment view. When you think about all the areas that we have right now, where our senior analysts can provide quality credit opinions in a growing landscape with increased investor acceptance, we have the ability to work in areas such as corporate ratings, structure finance ratings, infrastructure ratings, sovereigns, financial institutions. It's important for you to understand that we're creating diversity here. We believe we can scale this business. We believe the opportunities based upon the disruption are going to allow us to compete in a larger and larger platform. Let's conclude really quickly here about the level playing field. I gave you some insights into the credit business. I gave you some insights into how we can grow. I provided you with some of the themes that are going to allow that to happen. I want everybody in this room to understand we firmly believe we can scale our business we firmly believe we are making the correct investments here and we firmly believe we can continue to grow and build this platform and with that being said i want to thank everybody for the opportunity to share some insights some key themes in the credit business and now i'm going to pass it back over to Kunal, who's going to close it out. Thank you.

Okay, hope you got a good flavor for what we're cooking up in the kitchen, so to speak. And now when we transition over to the Q&A, we'll welcome your comments and questions about how we take that to market, some of the issues we're thinking about, and of course anything that is on your mind. It's always one of my favorite parts of the year. It's 10.37 on my watch right now, so let's say we're back at 10.50 and we'll kick off the Q&A right at 10.50.