Good morning, everybody. Welcome to the 2026 Morningstar Annual Shareholder Meeting. We're delighted you could join us. I think you'll find the meeting very informative and enjoyable. We've got a great meeting ready for you. Let's get started. We're also streaming this meeting live on the internet, so if you're watching via the live stream, welcome to you as well. Before we begin, take a moment and read the safe harbor statement as usual. 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 elect our directors, appoint our auditors for next year, the same thing though. That should go pretty quickly.
We'll go right into management presentations. Kunal Kapoor will lead us off, our CEO, followed by Mike Holt, our Chief Financial Officer. 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. We have Nizar Tarhuni, 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, it'll give you a sense of how serious we're taking AI and some of the things we're doing to embrace it. We're gonna take a short break. We'll come back. Always a highlight of the meeting is the Q&A session. We look forward to your questions. This is one opportunity you have each year to ask us questions in person. Beyond that, we encourage 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, file an 8-K. Again, this is the one opportunity you have to ask questions live and in person.
We really look forward to your comments, your candid thoughts on the business, and we'll have a good discussion. 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 for PitchBook, Morningstar Credit and Morningstar Direct. We hope you can spend time with those, as well as some of our analysts talking about some of their research. Something new. There'll be a light lunch that we'll serve in the cafe as well. 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 in 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 into 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.
First, I'll ask each of our directors to stand and face the audience. Anne Bramman, Robin Diamonte, Cheryl Francis, Stephen Joynt, Steve Kaplan, Kunal Kapoor, Gail Landis, Bill Lyons, Doniel Sutton, and Caroline Tsay. 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 appreciated. I also 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 director. 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 Anne Bramman to our Board. Anne is our newest Director. She's got deep financial expertise and a strong track record of value creation. Anne, we look forward to your future contributions. I'd also like to introduce our Executive Officers, Kunal Kapoor. In the white jacket, he's hard to miss. Mike Holt, our CFO. Representatives 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, Robyn Koyner, will act as secretary of the meeting. Greg Malatia, a representative of Broadridge Financial Solutions, 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 the of business properly brought at the meeting. 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 Bramman, Robin Diamonte, Cheryl Francis, Stephen Joynt, Steve Kaplan, Kunal Kapoor, Bill Lyons, Doniel Sutton, Caroline Tsay, 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 the ratification of the appointment of KPMG as our independent auditors for 2026. The board recommends a vote for, 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 you've, 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 the 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 8-K and more detailed voting results no later than May 13th. This concludes the formal business portion of the meeting. I told you it would be quick. 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. 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. As uncertainty around what AI means for data and software companies has increased. 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. Creates pain for management. Part of their compensation is based on total shareholder return. 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 a 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.
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 Kunal'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. These aren't just 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.
Then over the past 12 months, we've bought back a little more than 10% of our shares outstanding. Without doing anything, you've become a bigger, a 10% larger owner of Morningstar. As a long-term shareholder, I'm very happy about that. Again, these statements aren't just hollow, but we've backed them up with our actions. 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. Please join me in welcoming Kunal Kapoor.
Woo!
Thanks, Joe. Appreciate it.
Thank you.
Good morning, everybody, welcome. It's great to see all of you here as always. Gail, I wanna join Joe in thanking you for your partnership over the years. It's just been awesome. For those of you who know Gail, she's not gonna be slowing down. She's got a big plan already. Anne, welcome to our board. I think you're gonna enjoy it. It's a great company, and we're delighted to have you here as well. Also just wanna 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. 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. We fight a lot when it's appropriate. 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. Right after me, you're gonna hear from our CFO, Mike Holt. That, 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. We're gonna talk about how we think about the value of our business and make the case for why it's enduring. We're also gonna 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. 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 gonna impact our future and what we're gonna 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.
You may have used an LLM recently, and you may have gotten an answer, and you might have accepted that answer. You probably thought to yourself, "That's probably like a 75%-80% confidence answer I'm getting." I certainly have those moments and those experiences, and I move on. 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." In those moments, trust, independence, and judgment really do matter. We hear this repeatedly from our clients. 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. That's led to an enviable place with clients. We serve the smallest investor to the largest asset owners. 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 CRSP 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 are 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 wanna be in markets that are naturally growing. We wanna have that tailwind behind us. 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. 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.
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.
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. 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.
I'll grant 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 yourself. 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. Probably gonna put it in a separate account. It's gonna find its way into a managed portfolio or a CIT. Many different types of vehicles are proliferating around the world. I won't even get into the alphabet soup of things like LTIFs and ELTIFs in Europe. 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. 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.
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 of itself is very valuable, and we have many avenues to grow it and reignite what we're focused on. We're gonna 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, 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.
What I'll say to you is we certainly think that there will be ups and downs. It's natural in the credit cycle. When Mike, Detlef and I sit down and talk about this business, we talk about it from a long-term perspective and the long-term growth we wanna achieve despite some peaks and valleys that are inevitable with the type of business that it is. 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.
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've 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.
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 CRSP 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. We, 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 to lose about CRSP 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. When we talk about unique IP, one of the key attractions to owning CRSP 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 gonna try to grow it from there. Got a really good opportunity. Some of you may wanna wonder, like, does ESG have a future or not? 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. 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. You might be an investment manager investing directly into these, and our manager research analysts will be evaluating how you're doing. 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 wanna do things just like everybody else. We're gonna 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 gonna focus particularly on two, AI and owning the language of markets, particularly as it relates to public-private convergence. This is what we talk about internally.
When we had our town hall at the start of the year to talk about what's gonna 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. 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. What's wonderful about them is that they reinforce each other.
When Naz comes up here, when Laura follows, we're gonna 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 convergence comes along is so critical to that success. It makes sense. I referenced earlier the unique methodology of the CRSP benchmarks. 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.
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. 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, in the past five years, we've tripled the number of strategies we've covered, doubled the number of private companies in our database, more than doubled 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. 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. It's unique and feeds into that flywheel quite positively. One area that's growing massively in the market today is private debt.
Now, you may have different views on whether it's good or bad, we're at the bottom of the cycle or top of cycle. I understand that. That's what makes markets. I think you also probably have a clear view that data in private credit is not as good as it is in public credit. 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.
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. 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. 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 Baron 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? One of the things we're gonna show you afterwards is how some of the AI tools are changing within Morningstar Direct, for example. The commonality is they're 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 gonna share some ideas about how we're thinking about it.
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. 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, I think that is part of the value of what we bring to the table, is these different capabilities. They're 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, candidly, that's the approach we like. We like organic, even though we're not averse to inorganic, as we did with the CRSP acquisition. We have a really nice mix of businesses. We're focused on execution more than anything else. 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. 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. 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 interrupt it unnecessarily." I like that framing for how it helps share our thinking about long-term value creation. This year I wanna continue that theme of value creation, but focus you more on how we think about operationalizing that concept internally. The way we do that is by focusing on our North Star metric, adjusted operating income. I'll probably refer to it as AOI. We focus on AOI because we know if we grow adjusted operating income, we'll 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. 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've translated that into strong AOI growth. Let's bridge that gap between adjusted operating income and GAAP operating income. You see the purple and pink lines, bars here. The point here is that it's a very stable relationship over time. We focus on AOI because it neutralizes some non-cash, non-recurring items. It'd 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 wanna count that in the day-to-day noise. We're gonna 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 in the business. $182 million growing to over $600 million. 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 2026 in a little bit. Starting from left to right, you 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 gonna double-click into AOI here, and then I'll touch on free cash flow in a minute. Adjusted operating income expanded by $89 million. $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. What's happening here is we're growing our revenue faster than we're growing the operating expense in the business. 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. 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. You see over the last three years, it's been a steady progression. In 2024, 21%, 21.7% margin. The first quarter of 2026, 27.7% adjusted operating margin. Follow that pink line there. The two things you should take away from this slide are, one, it won't always be a smooth line. Right. Especially when you have big jumps in a given quarter, it can bounce around. 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. We're driving productivity. We're driving scale in the business. This only accelerates with the advances in technology. Now, you saw that free cash flow was flat year-over-year. 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 quarterly, a quarter-over-quarter view. 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 trends. On the left-hand side, 7.6% organic revenue growth. In the middle, 32% growth in adjusted operating income. Free cash flow, that's a little bit negative, but a noisy quarterly number as we discussed. That's at a company level. Let's discuss this progression and how the different segments contributed. A 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 of the progression over the last five years.
I'll draw your attention to that far right-hand side. We got purple, that's Direct Platform on top, working your way down. Pink is PitchBook. 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. 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 Sustainalytics. As we've been very disciplined on central costs, and we've improved the profitability on both index and Sustainalytics, that's been less of a drag on the overall profitability of the firm. Let's look at each of the segments.
There are five reportable segments, externally reported segments in the firm, and then there's two others. I'll touch on those in a minute, but let's start with the first five. Morningstar Direct Platform, our largest and most profitable segment. Mid-40s margin, 6% growth rate. Not bad. Most importantly, the data, the research, the analytics, the software, these are incredibly sticky solutions, and we're very embedded in those customer workflows. A lot of opportunity here, as Kunal Kapoor discussed. Let's turn our attention to PitchBook. The first thing I hope you notice, AOI has quadrupled over the last five years. It's a great growth story, has been since the acquisition. A big contributor to that AOI free cash flow intrinsic value story. 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? We're excited about the experimentation that Kunal referenced, but just the opportunity in front of us. With Morningstar Credit, it's been the 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. 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. We're building a meaningful and durable franchise here.
With Morningstar Wealth, you see a lot of helpful numbers on the slide here. What you see is we've made some big bets. They haven't all worked out. 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. 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, 401(k)s, tax advantage accounts for more than 2.3 million plan participants. We're excited about how well this is on mission, empowering investor success. We're also excited about 50% margins and 8% growth rates.
Tie that back to that free cash flow story. Great AOI, great free cash flow. Lets us invest elsewhere in the firm too. Now those are the five report 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. I'll start with Morningstar Indexes. Great growth story over the last five years. Kunal really set this up about the CRSP acquisition. As we look forward, the CRSP acquisition is very powerful. Gives us scale, gives us critical mass, gives us relevance and heft, so we can really get after the entrenched incumbents. That rebrand of the CRSP 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. Morningstar Sustainalytics, after the acquisition, had very high level of growth.
As you know, we've hit some headwinds here. We're getting refocused on a smaller set of core functions. 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. 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. When we do this well, we are 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.
That's what you see here. We're a 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's been deployed. $1.1 billion spent on M&A. $346 million delivered to shareholders directly through dividends. $1.3 billion spent on share repurchases. We've talked a lot about intrinsic value and how we focus on that. Market value is just as important. We get that. The market returns that you see here are not what we'd like to see, particularly on a one-year basis. 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. 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. 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. I mentioned $1.3 billion over the last five years. That is not spread equally over that time period. We're opportunistic here. When the share prices decline, 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. 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, bunch of taxes we've got to pay, also the CRSP acquisition, and of course, as we just discussed, $300 million spent on share repurchases.
That debt load's 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, 2x 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 flow in the firm. 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. 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. I'm gonna hand it over to Nizar, who will share how we are using AI to drive value creation.
Thank you. All right. Thanks, Mike. My name is Nizar Tarhuni, and I'm Executive Vice President of Research at PitchBook. 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 complexity. That's not new. What is new is the pace of which that complexity is building and then the corners of the market where that complexity is building. 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. In private markets specifically, that actually really doesn't exist today.
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. 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 signal through noise, but they expect us to do it with a lot more breadth, they expect us to do it with a lot more accuracy, and they certainly expect us to do it a lot faster for them.
We're very excited about the assets that we've put together to help them do just that, and we're gonna walk you through the next set of presentations. 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. 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, 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 wanna 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. 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 are pockets where our clients do need our data and our research or analytics in other locations.
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. 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." 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 ball game, and that takes deep domain and operational expertise. We've spent the better part of two decades building our proprietary methodologies to do just that. 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.
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. 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, and includes metrics like valuations and financials, fund terms, fund returns, and fund cash flows. You might wonder, why would they share that with us?
Over the years we've built a user base of over 100,000 users across every core private market segment. With that, we effectively become a pulse in the markets that we cover. That creates incentive for our clients who wanna make a lot of that data available to us. 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. One area I wanna 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. 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.
In a market with a lot of volatility, where the dispersion between quality and valuations continues to widening, sourcing and origination becomes the most important and critical workflow for many of our clients. Said another way, they gotta 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 U.S., if you look at U.S. middle market companies, only 5% of those companies are actually backed by institutions. The bulk are non-backed. I'm gonna 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. We're making concentrated investments in collecting bespoke private company financials that tie back to those companies. 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 gonna talk a little about non-backed companies here as well, I wanna address first, you know, some of the softness that we have in our business. I don't wanna 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. 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. 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. What we've 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. We've been investing from an origination perspective in growing the sourcing data sets that many of these clients need. We've grown our non-backed company data coverage from 5 million- 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. 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 wanna turn now to Well, I'll go back, actually. We talked about corporates. Let's talk about venture for a second.
In venture, it's interesting because we're here to talk about AI, 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. Non-AI companies in the most recent vintages, they're seeing their fund net asset values get compressed. Exit timelines are extending, the exit valuations are being compressed as well. That creates a much slower recyclization of capital back into the ecosystem for our VC clients. 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.
Our GP clients in the venture side are looking for datasets and information earlier in the investment life cycle, and we're spending a lot of time investing in our early-stage datasets, and that's at the pre-seed level and at the stealth level. 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 dataset 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.
Those conversations allow us to identify fund strategy shifts or emerging risks or changing sector dynamics, and we leverage those insights to be leading indicators before they become consensus. If you think about 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-powered 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. It's that human harnessing of AI when you then power that with our datasets, our direct industry outreach, and our own research frameworks that give us the ability to help our clients drive the alpha that all their end customers require.
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. 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 Bank's collapse or the pandemic or the recent software correction, that's when our analysts are most in demand. We're expanding our coverage as well. Most recently, we launched private company coverage. We're covering companies like SpaceX, Anthropic, and OpenAI.
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, we've launched a new BDC franchise. Allows us to cover valuations of the 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, our job as a research provider is to allow them to get to insights and get to decisions faster in those spaces. 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. 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. Every tool that we build starts with a question that our analysts get from our clients or from different market participants. 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 datasets.
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 VC 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. 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.
What's powerful about that is as we release those valuation estimates in the market, the data cycle and that flywheel starts to power where 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. We're providing forward-looking insights in markets where that typically hasn't been available. We also manage a suite of performance and benchmarking products. Our benchmarks and indexes, our manager scores, or 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 markets exposure. To build products like that, you need substantial universe coverage.
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. 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. I want you to try to think about our software the way we do, which is really in three different buckets. The first, we wanna 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, that underpins the foundation of the transition we talked about earlier, which is switching to an end-to-end workflow tool. We wanna reduce the context switching that they have to do from analyzing their own portfolio and figure out where that fits inside of the broad market context. Last, we wanna meet them in the AI ecosystems where they're increasingly working. 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. Now, that removes the typical friction you'd need to get from question to answer to action. You can do that now without the navigation that comes in between.
Our ambition here isn't just to be a 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. 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. 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. I wanna 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.
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. Lumonic 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. Because they solve for that hardest problem, we're watching our clients build their workflows around it.
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 ours. 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. That combination of Lumonic 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. 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. We're seeing usage grow tremendously. Usage of our connectors in these platforms has grown by over 35 times over the last four months. It's literally doubling every single month. That underscores the power of our data research and analytics, regardless of the surface of where it's consumed. We're very thoughtful about the distribution models there, ensuring that our clients can get maximum value should they need to use our data and our IP in other platforms. Make sure our economics are protected as well. Let me bring this together for you. We've built an interconnected system of capabilities. I wanna 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 incredibly deep and rich fund performance data set. If you're a private equity firm, you're likely going to be in 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 informing both sides of the table.
If you're a credit investor, you might find yourself in Lumonic 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. You're doing that with PitchBook's market context 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. At times, our clients are using the same set of data and analytics to power two distinct user groups doing two distinct workflows. 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.
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. With that, I'd like to hand it over to Laura, who will show you how some of these same convictions play out across other parts of Morningstar. I appreciate your time. Thank you for being here. There you go.
Thanks, Nizar. What is the difference between luck and skill? If you're choosing a fund or a stock, how are you gonna be confident over the long term that it's gonna outperform? Is it gonna 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. 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. 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. Morningstar tracks data on millions of investments, which allows our clients to compare investments across a country or around the world. Data volume is a Morningstar advantage. 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 develop the language of investing is through our fund categories. 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. 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. 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.
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. 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 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. 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. 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.
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. We're choosy. Major wealth managers and RIAs use this qualitative Morningstar 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. We offer both quantitative and qualitative Morningstar Medalist Ratings, but the distributors prefer and sometimes insist that they have a qualitative Morningstar Medalist Rating on all the strategies that they're distributing. Our analysts get requests to expand our Morningstar 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 analyst 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 gonna 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. These investments are not required to disclose their data to Morningstar or an AI agent. 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 PitchBook 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. 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 are 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. Then they need to estimate how much you're gonna 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. Here's the IP we've built so far. We created Morningstar categories using PitchBook's taxonomy.
We've developed a methodology to compare semi-liquid fund fees based on some income assumptions. Morningstar Indexes developed a benchmark that includes both public and private securities so we can measure performance. We've extended our analyst ratings to 19 semi-liquid funds. If you wanna meet some of the analysts responsible for those ratings, they'll be in the training room after the meeting. Here's how our clients are using that new IP. 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. 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.
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. Our software strategy is evolving to meet clients where they work because we want to bring insight to action. 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. 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. Just after a few weeks, several of our largest clients have onboarded this assistant and are putting it to work. 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. 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 Platform's 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. 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 wanna 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. Now I'm gonna hand it to Sean, who's gonna speak about our credit business and how it is benefiting from the AI trend. Sean?
Thank you. Thanks to Nizar 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. As it turns out, it's an incredible opportunity for us, and something that we're gonna 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. During today's presentation, I'm gonna 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. 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. 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. 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. 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 is important to understand that we ultimately are building a scalable platform here. 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. The results you are 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, structured finance ratings, infrastructure ratings, sovereigns, financial institutions. It is important for you to understand that we are 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. With that being said, I wanna thank everybody for the opportunity to share some insights, some key themes in the credit business. Now I'm gonna pass it back over to Kunal, who's gonna close it out. Thank you.
Okay. Hope you got a good flavor for what we're cooking up in the kitchen, so to speak. 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. Thanks.
There you go. I think you can hear me now. Welcome back. We're now going to kick off the Q&A part of the meeting, which I think is always particularly fun. We've got a couple of additions here to the stage. Rod Diefendorf, who leads PitchBook, who you've heard from at these meetings in the past. Next to Rod is Scott Brown, who's now running the Direct Platform. Scott's 60 days in almost. He's joining us from Experian, so we'll be directing all hard questions to Scott.
Of course. I'd expect nothing less.
As part of his initiation. Next to me is Julie Willoughby, who's our Chief Revenue Officer. Julie stepped into her role relatively recently as well, but is a longtime Morningstar leader, and I think you'll enjoy hearing from Julie. We've also got our management team, as I said earlier, in the second row, and I'll try to not to catch you mouthful, Marie, if there's a question for you. You know, depending on your questions, we'll certainly go in that direction as well. We're taking questions over our platforms as well, so feel free to send those questions in. My colleague, Steph Lerdall, you already have a question I can see, will, you know, read them out.
Let's start in the room, and my only request is when you do ask a question, if you could just introduce yourself as well, we'll kinda go from there. It's a little hard to see everybody from up here, but I see a question right there, and so we'll begin. Right. I think you sat in the same seat last year too.
I know, I know.
Impressive. We'll keep it reserved for you.
I know. Oh, it's working. Yes. I actually came in early and dropped my bag right here. No joke. It's Alex Kramm, UBS. On PitchBook, I think you addressed a little bit already, but the obviously the single or the simpler single use cases is where we see a lot of the attrition it sounds like. By the way, I would add that even on the, at least when we talk to clients, even on the corporate M&A side, you see some low-cost AI-driven providers, which I think are competing well. The question really is what, how does the commercial model of PitchBook maybe need to evolve, and what are you doing already to maybe have a different type of offering for those type of users without obviously cannibalizing your, you know, very moated and proprietary data?
Great. Great first question. Rod?
Yeah. It is a great question. You know, PitchBook's always been one product with a very rigid pricing structure for the last two decades. It's served a lot of different customers in a lot of different ways. You mentioned the single use or the non-investor, non-M&A type use cases. That is where we're having the most softness. That's where we're having the most attrition. We are evolving the pricing model, we're doing it in a way that AI really allows us to look at our product and develop our product more specifically for the various segments that we serve. Rather than just be Is this okay? Can you hear me?
No, I was saying.
Yeah
Suggesting we close the door.
Sorry. Rather than just be one product for everybody, we are now customizing it more to be in the workflow of each individual segment. When we do that, we can get better price, a better pricing structure that's not necessarily seat-based. About 10% of our customers today are not seat-based customers. They're basically firm-wide. Those happen to represent around 40% of our entire user base, so it skews to the larger customers. That's going to increase and evolve to more firm-wide customers, or firm-wide licenses where we don't necessarily care as much about how they get the data, whether it's through the platform, whether it's through direct data, whether it's through an LLM like Nas mentioned earlier.
It is evolving, and we're excited with the evolution of that because I do think we're gonna get some, we're gonna capture more fair value at the high end of the market, and we're gonna be able to price it more appropriately at the lower end of the market where we have more attrition.
Right there in the middle.
Hey, Ryan Griffin from BMO Capital Markets. I just had a question on the credit side of the business. Obviously, you've been outperforming many of the issuance metrics that we track and gaining shares not only in the private debt markets but in the fundamental ratings as well. Was just wondering what's driving those share gains aside from the level playing field dynamic that you talked about, and then how do you shake out from a pricing standpoint compared to the bigger competitors? Thank you.
Hey, Ryan. That's a great question. I'm happy to weigh in, but Detlef, you have a mic in your hand, so why don't you start?
Thank you very much for that question. It's actually the best possible explanation of the progress we have made on the corporate side. There is no interest to compete on price. The opportunity to grow into more of the corporate markets outside Canada, where we already are the leading player, are the additional use cases where you have either issuance in the private placement market, where you have issuance in the infrastructure or project finance area, or where you have investors who are operating in the private space and can decide themselves which rating agency to choose from. These are the incremental adjacencies that we have discussed before, and these are the areas where we are growing on an overproportionate basis.
Another question right there in the middle. Same row, I think. Yeah.
Ray Stoeckle with DF Dent. I wanted to ask a little bit more about the different ways that you can evolve PitchBook beyond 1 SKU. We noted the agreement with StepStone, which came out this week. That seems to be a little bit of a different SKU and maybe a more premium SKU. Can you talk about additional ways that you can separate that PitchBook SKU to that high end of the market and what sort of economic impact that might have over the next couple of years?
Yeah, great question. Rod, maybe you can start by explaining what the relationship with StepStone's?
Yeah
Going to be and then.
Yeah. Before I do, I want to say Joanna McGinley's around here somewhere. She deserves all the credit in the world for that agreement. It's a terrific agreement. We're very excited about it. StepStone has about 2,200 GP relationships, mostly on the advisor, from an advisor perspective, and access to over 130,000 deals. We're able to get deal-level data from StepStone that we're going to make available to PitchBook subscribers. Their platform we're going to make available to PitchBook subscribers for an additional fee. We're going to be essentially a reseller of the StepStone product to our customers. You mentioned that was announced, I think it was announced two days ago. We've already had an incredible amount of leads.
There's a lot of excitement around that. That will be a separately priced product, a different SKU, if you will. Yeah, I think you're going to see going forward PitchBook will have different SKUs. We essentially do today. We've got the PitchBook product. Direct Data is a different SKU essentially. We are working on a MCP-only product essentially, which is going to allow, to your earlier question, some firms that don't necessarily need the breadth of data we've got or the depth of data we've got but still want that trusted source of really accurate data, through partners like Claude or other LLMs, there's going to be a product available for them that we price separately. We're going to have a number of different, if you will, SKUs depending on the use cases that our users have.
We're really excited about that. I think it's gonna capture more of the higher end of the market and again, it's gonna capture and reduce some churn at the lower end.
You got one, Steph? Go for it.
I do. This question is from Mark Sellers at CastleMoore Partners. Rod has touched on this a bit, but he's asking about all the license-based businesses. He says, "My question centers on agentic AI. We know agentic AI is coming fast. It has the potential to have a very large effect on PitchBook and Morningstar Direct business. AI agents perform many more data queries than a human." He suggests maybe 25 to 100 times the number of data queries a human would. "Given the massive surge in agentic data queries that appear to be imminent, what is our plan to shift from seat-based pricing model to a usage-based pricing model? Seems like a big opportunity for even more revenue in an agentic AI world than a seat-based world.
Yeah, that's a great question, and Julie, I'm going to ask you to take the lead on it. What I wanted to start by saying in this context is also that we're trying to move at the pace some of our clients want us to move. I think one important realization is different clients are moving at a different pace, both in terms of adoption as well as how they view pricing and the conversations that they wanna have around pricing. Some do not want change, some want a lot of change. So it's worth kinda keeping that framework in mind because the timelines are a little bit different, and the opportunities with different clients can be a little different in that context. Julie, why don't you talk about exactly what we're thinking about?
Sure. Rod mentioned a little bit about the connections to the AI platforms. Those connections allow clients to access PitchBook data, but also the data that's available in Morningstar Direct. I'll remind you what Laura shared, which is for the Direct Platform Group, more than half of our revenue is not tied to seat-based pricing models at all. Not just our data business, but distribution of reports and content that clients create and generate on Direct Platform. Like PitchBook, we have across our licensing businesses more broadly, use these connections to the AI platform providers as both a way to offer the content where the clients are working. Often supplemented by licenses they have directly with us.
When they want to access just the data that's available for their own agents' use that they've built on their side, they can do that through MCPs that we've built that connect outside of the platforms and directly into their environments. From a business model perspective, we have for decades offered enterprise structures, which include, in some cases, access to software that has a seat-based element to it, but in many cases which is more focused on the value that they derive. With MCP connectors, we have the ability to actually have greater traceability of what's being accessed and then the volume of what's being accessed.
We have already offered clients the ability to use our software, use MCP connectors and platforms like Microsoft Copilot, and then also to leverage data and research in their own environment outside of those two, those two means of access. There is a consumption-based tier model to that. Our clients have not yet been ready to move to the model. If you think about the base that we work with, Nizar mentioned this, Laura mentioned this, and Sean mentioned this. The clients that we work with are using the information to make decisions that have compliance structures around them that require traceability and the requirement to prove that the data is the right data on which they base their decisions. Some of our clients are experimenting along with us and doing pilots. We have not seen the momentum to actually move to the models, but we've offered these models already.
Yeah.
I think that covers-
I would also just maybe add a little bit that we hear from clients that one of the roadblocks is that they want pricing certainty. The consumption-based approach does take away from that certainty. Those who are looking at it most seriously are trying to overcome that challenge in particular. Rod, you have a nice example of a recent client win.
Yeah
That maybe without naming names, just kinda maybe read out.
Yeah, this happened yesterday. We had a significant upgrade from a client because. Let me just back up on the seat-based for a second, then I'll get to that client. Seat-based pricing has always been a proxy for value and how much value a firm gets out of the data that we provide and the service they get from us. That proxy is evolving, and I think, and we need to be evolving with it, and we are evolving with it. The upgrade that we're talking about that happened yesterday was a significant upgrade that actually was seat-based, but it was because a lot of the folks in the firm wanted access to PitchBook via the LLM. We upgraded the entire deal, again, based on seats, but because the majority of that firm wanted access to us through the LLMs in addition to using the platform.
Scott, any additional thoughts on this topic?
No. I mean, I would just say that we've already got some early proof points beyond just focusing on usage. 'Cause I think what, you know, we stand to have a huge advantage on is just the demand for more data and the demand for more trusted data in particular. One anecdote that Julie and I were talking about the other day is that in the first quarter, there was a major global client that renewed, where the software revenue grew, but the data revenue doubled. The challenge is to figure out how we can replicate that across the rest of the client portfolio, but we have some early indicators that say, one, yes, there is an evolution that we all need to work through as an industry on the usage-based consumption.
To me, we have a whole new distribution channel that can now reach a lot more customers where there's growing demand for trusted data that Morningstar has.
Can I just add one more thing? When we launched direct data.
We're spending a lot of time on this topic.
Yeah, sorry. About eight years ago, when we really started focusing on direct data, we were worried about cannibalization of seat-based licenses. When we started saying, "Okay, if we're gonna provide data to firms directly, is that going to cannibalize some of our seat-based" The opposite happened. We provided more direct data, we got more seats. The early signals are the same with distribution through MCP servers and through these LLMs, that we're seeing seat-based licenses go up along with the additional license from this other distribution channel.
Question in the middle.
Yeah. Hunter Vinik with T. Rowe Price. I wanna ask about CRSP. You talked about some of the benefits with that acquisition around branding. Given that the rate that's being charged on the assets linked is relatively low, is there an opportunity to increase pricing?
Let's take the CRSP business, and I'd put it in two buckets, right? There's the indexing business, and then there's the data business. I think your question primarily is around the indexing business. Let me just say that certainly in the data part of the business, we see an opportunity to, you know, raise prices a little bit more. On the indexes side of the business, our strategy is going to be to grow. I'll give you a simple example. I was in Switzerland last year visiting a number of our consulting clients, and for a long time we've been trying to convince a number of them to recommend our indexes. The hesitation has always been you don't have enough assets tied to it. Today that argument has gone away.
In fact, the day we closed the deal, I emailed them saying that excuse has gone away. The other benefit is many of them have MBAs, and many of them use that data, so they actually know what's special and unique about it. Interestingly, I would just say that Vanguard has been a really good partner here. They too want to see the growth of indexes and usage of the indexes, and so we're very aligned on the fact that we need to create broader distribution in the market for those indexes. We are going to take a volume-based approach first and foremost on the indexes versus raising prices in any capacity. In fact, we feel like on beta indexes, the incumbents are charging too much. You know that too from your experience probably at T. Rowe Price. Come talk to us.
Yeah, question right here.
Jake Strole with Fiduciary Management. Kunal, you oversee a business that's grown substantially in the last 10 years, a portfolio of different but related businesses. I was just curious how you spend your time, perhaps outside of talent and culture, that I know is very important to you. How do you allocate your time? How do you get leverage on your time? How do you interface with the different businesses?
Yeah
How has that changed?
Yeah
Over the last decade?
No, I love that question. I think it's a great question to ask. I'll just start by saying, first of all, we have a great team, and we have a decentralized approach to running the business, the business heads have a fair amount of flexibility in terms of running their own P&Ls. I and Mike spend a lot of time on strategy and capital allocation. That is a big part of the job that we need to do, and I'm waking up and I'm thinking about that all the time. You might also be surprised, but I spend a lot of time thinking about composition of team, people in the organization, culture.
I don't allocate a ton of time to, like, I'm gonna go work on the culture, but the things I do are there to reinforce our culture, and it's very, very important, you know, in that context. Third, I'm a consumer of our products. I consume our products heavily, and I spend a lot of time thinking and acting like a user of our products and bothering our teams with feedback about our products. Some of them are always very thankful to get emails from me early in the morning with feedback about what may or may not be working. Having said that, I'm also, I think, very transparent about where I'm gonna spend my time and the kinds of things that are important to me.
I spend a lot of time on client relationships, so I'm in the field very heavily, working with the sales teams. Earlier this week I was in New York, hitting up some of our largest clients as well for that reason. Then two is I spend time on strategic priorities. When you hear AI and public-private convergence, I think I'm spending as much time on those and thinking about firm-wide impact as anybody at the firm. Right now, I'd say the majority of my time is spent on AI-related topics and that's learning as a user or meeting with Anthropic earlier this week, thinking about our products and kinda doing those kinds of things. Then there's always, like, the tactical kinda stuff that will come up.
We're gonna do the CRSP acquisition, suddenly that was a goal, and that takes a ton of time and effort. Scott is coming into the organization, I'm gonna spend time with him to help him get off the ground, there's things like that. It's a mix of those kinds of things, but certainly determined by our priorities and our capital allocation strategy. I don't spend time on investor calls, though.
There's one over here.
Sanjeev?
Hey. Sanjeev Math at Baron Capital. Just a question on PitchBook. You know, you obviously shared a little more information in Nizar's presentation, I think it's well understood, right? That's the data there is a combination of publicly sourced, you know, enriched or complemented through primary relationships that are obviously difficult to replicate. My question is if you could just drill down a little further in that. If I kind of Maybe this is not quite the right way to think about it, if I think about, you know, there's publicly available data that's easy to access. It's from the SEC, it's on the internet, it's TechCrunch, whatever. There's publicly available but difficult to access data that could be, like, FOIA requests, court filings, it takes legwork, you've got actual proprietary data, right?
That's truly inaccessible, whether on the internet or otherwise. Could you maybe just help us sort of map what data in PitchBook tends to come from where, and sort of maybe help us understand how the total map of PitchBook data comes from the different sources? Obviously more specifically on the proprietary data that's coming from your primary relationships, is that more sort of verification of data that's obtained publicly? Is that incremental data points? And so maybe help us understand how the total map of PitchBook data comes from a different source?
Yeah, sure. There's all the public sources you mentioned, and we do pull from them. We've got, you know, almost 20 years of history pulling from them. It's not just pulling it's organizing it's making sure that it's accurate. It's making sure that it's organized in a way that our customers can use it. That is certainly one way. We then not only human verify, but we reach out to the companies that own that data, that actually have that data. As Nas mentioned in his earlier presentation, they trust us to give us proprietary data because they wanna be part of this larger ecosystem. That's certainly one way. Our researchers, our analysts, as Naz mentioned, we produce 2,500 reports a year.
Most of that time is not spent writing those reports. It's talking to different people in the industry, talking to companies, learning more about their businesses and what's going on. All that information comes back and gets into the platform. There's a lot of different ways that we take what's a base level of public data, and then we enhance it, and we organize it in a way, and we add to it. Your question of are we adding to it, we're adding a ton to it. When you look at the profiles that are viewed within PitchBook, 90% of the profiles that are viewed.
Have proprietary data within them. When you think about what people are looking at, they're looking at data that is not just hard to find, but impossible to find outside of PitchBook. That's really where our value proposition from a data perspective comes from. You know, there's use cases we were talking about earlier where good enough might be good enough. For a lot of use cases where decisions are big and, you know, they're impactful, good enough's not good enough, and that's where I think our data really distinguishes itself.
Another question right next to Sanjeev, right there.
Ray Stoeckle with DF Dent. During the PitchBook portion of the presentation, it was noted that you all need to maintain your economics as you expand into additional partnerships, whether it's Anthropic, Perplexity. I think you've also announced things with Hebbia, et cetera. Can you help walk us through the nature of these agreements and what gives you the confidence that you are maintaining your economics to the point that you could say it in that presentation earlier?
Yeah. Do you want me to take it?
Yeah, go. Why don't you go first, and we can take this momentarily.
Yeah. When we think about these LLMs, there's a new revenue stream that's being opened up for us in really three different ways. The first is we talk about essential data. We're talking about what largely might be viewed as publicly available data. Those LLMs are coming to us and paying us for that data because of the quality of that data. Even though you might look at that and say it's not that proprietary, they're still willing to come to us and pay for that data to then provide to their users. That's one new revenue stream for us. The second one is that, as I mentioned earlier, we've got premium connectors through the LLMs where PitchBook subscribers, paying subscribers, can go to the LLMs and they can actually access PitchBook data through the LLMs.
You can only do that if you're a paying subscriber. Today, we do not charge more for that. We can. That will become part of the pricing strategy that we go through over the coming months. That's a second way. A third way is what we're labeling as essentially as MCP-only product. That'll be a for-paid product subscription that you can get access to PitchBook data through these LLMs and not necessarily have to go through the platform. We talk about pricing globally, again, going to moving away from seat-based and going to value-based by firm. Some firms are going to get value from seats and will continue to have seat-based licenses.
Others are going to see value in different ways, and they're going to get that value through the LLMs, and we're going to price appropriately that captures the value of the data that they're getting through the LLMs as well as the data they're getting through the platform.
Yeah, one of the things that I think we've done well over time is just acknowledging that our customers will come to us on some occasions, or they'll be on other platforms, and it's okay to meet them there. What I think we didn't do as well, if I go back to, you know, 20 years ago when we were starting up all those relationships, we didn't extract all the value that we could when we set up those kinds of relationships.
I think the experience we've had over time in setting up those relationships, having a large now, what we call internally a reseller type of, you know, business model, has allowed us to really understand how to capture that value, and we've brought that insight to how we're working with the LLMs and how we're taking an approach of pricing with them, including why we're requiring in many instances that you get a seat license, if you're gonna, you know, work with us in that capacity.
Kunal, before we go on, I just want to say, you know, when we're talking about data and access to data through LLMs and things like that, you do not get access to the PitchBook IP that Nas talked about earlier. We're talking about VC Exit Predictor, all this unique IP, time to exit, manager scoring, valuations, benchmarks, all that proprietary intellectual property is only available within the platform. There's going to be use cases for both access through the LLM and access through the platform. When we talk about data, I think it's important to think about data not just as information, but also the intellectual property that we create that we make available to our customers.
Steph's raising her hand.
Oh, go ahead, Stef.
I've got two questions, both about the direct platform and both from Vinay Prasad at MLP. The first one is, within the direct platform, we talked about mutual funds specifically. What data are we collecting directly from asset managers that aren't publicly available? I'll go ahead and say the second one, which is noting our Q1 commentary, we said that direct licenses have declined as some client workflows has shifted. What did we mean by that?
Sure. Julie or Scott
We can-
One of you wanna take the lead on that?
Yeah, I'll take it. I'll take it in pieces. First on the question around collecting data and on the mutual fund side, I'll remind you what Kunal said in his opening as he was discussing the flywheels, that increasingly Morningstar is at the forefront of collecting data from non-registered investment vehicles. When you look at that flywheel, that initial base, we called it curated data. That word curated is really, really important, so let me explain why. As Rod was mentioning, and this really plays out in spades in core Morningstar, first it's about access to the data at scale, and we're increasingly collecting more data across semi-liquid, across CITs, SMAs, et cetera. We're doing that in a way that is standardized.
The way that we collect that data and ingest it, as a newcomer coming in, it truly is world-class, the ingestion platform the team has built. We put it in a standard format. And that format makes it ultimately comparable. As Kunal mentioned, you know, we have an identifier that essentially links assets across different vehicles to give that total picture to the end customer. Anything you wanted to add there?
Similar to what Nizar mentioned about the reason that clients are willing to provide the information to Morningstar and PitchBook, we do even from asset managers who are contributing and disclosing information publicly, we do get the information faster. They have an incentive to be present in the footprint that we reach because those are their distribution channels. Even for publicly available information, we are actually doing all the things that Scott mentioned and that Rod and Nizar mentioned as far as curating it. We also have an advantage in getting it in a more complete fashion that we can transform more quickly, and we get it with greater breadth and in a more timely fashion.
Yeah. Then on the other question around Q1, a couple of things just to take it in perspective. One, yes, over the last couple of quarters, seats has been flat to relatively slightly down, but still growing. There's still price leverage in the market that we can continue to add value to. More specifically, we've seen revenue grow in the reporting side of our business, which is something that really I think sits on a lot of our core strengths, which is around putting reporting out into the industry that is highly compliant in line with regulatory requirements, and I think we'll, you know, continue to lean on going forward. I would, again, to my earlier point, zoom out and say we're really looking at seats as one metric of success going forward. It's really about the total customer relationship.
As we look particularly in Direct Platform, there's a lot we can do to grow the seat-based business, but there's also a lot of pull demand coming from new distribution channels like the LLMs and some of the tech platforms that we've integrated with.
Yeah. One thing I'll say on unregistered data, it is hard to get depending on where you are. Sometimes there are events that sort of trigger kinda better disclosure. I would say that on semi-liquid funds, for example, and some of you could argue that you might as well just call them illiquid, what's happened is a lot of firms who have bought those products to market have generally not sold into the wealth channel, they're not used to disclosing their data, they've been resistant to it. What happens when you have an event like what you just recently had in the last couple of months, where suddenly you have all these withdrawals, investors are pulling out largely because there's been a mismatch between their expectations and the reality of how those funds operate.
They start coming to us because they start to realize the importance of starting to disclose that data. There's a better narrative that they can tell so that there is consistency and comparability. Those kinds of opportunities do start to take hold. You also often have regulators looking at it, and they'll start to say, "If this part of the market is gonna grow so much, it probably makes sense to have some level of disclosure." We think that that'll start happening in these parts of the market as well. A lot of people forget, you know, when Joe founded Morningstar, you couldn't easily get mutual fund data either. It's only really in the last 20 years that even disclosure of portfolios globally has become as prominent as it is now.
Obviously now with ETFs, that has kinda reached a point where you're getting almost daily disclosure in some instances. We'll see this kinda transition. I think it's good for those who are trying to serve investors ultimately to be transparent. It just kinda helps.
Yes. Hi, Alex Kramm, UBS again. This is a two-parter on, I guess, on PitchBook. Do you I know it's early, but in terms of looking at these LLMs as a new distribution channel, can you just talk about, you know, what you've seen so far? It's probably hard to gauge a pipeline, but any sort of numbers that give us some confidence that, hey, there's actually a lot behind that and you're just literally getting to the beginning. Then I guess the other part of that, and it comes back to the earlier question I asked about the lower end of the use case. Do you have an assessment, in terms of how big your book of business in PitchBook is right now that you kinda worry about?
I know it's a tough question, but I don't know how much sleep you lose basically waking up saying like, "Hey, I still have 20% of my existing client base right now," and I'm just throwing out this number, right? "Where I need to worry that at some point they realize maybe there are other alternatives." I know it's a difficult question, but I was just wondering how much has maybe cycled through already in what we've seen over the last two years.
Yeah, it's a great question. Rod, you can take the lead. I'll just start by saying we lose sleep over any client we lose as a rule.
Yeah. Yeah. Let me start with the LLM question and just, it's really early. I will say the indications are really positive, and they're positive when we can provide value to a customer that allows them to save money, even if it means them paying us more money for access to more seats on the platform or more access to our data through LLMs. That's the indication we're seeing. It's very early. This example we gave earlier is a real-time example of a day or two ago that we got a significant upgrade from a customer for that exact reason. I'll just say it's early, but we're seeing really positive signals from customers on that.
In terms of who do I lose sleep the least about, to Kunal's point, I lose sleep about every customer, making sure that we're serving them as best we possibly can. That's exactly why we've taken a more segmented approach to the entire product. When we look at PitchBook for a long time, as I mentioned, has been one product for a lot of different use cases. We're now. Honing that into specific, essentially wrappers of PitchBook for PE, for VC, for allocators, for LPs, et cetera, for credit participants. When we do that, we can create workflows that are more tailored to them and exactly what they're trying to do in PitchBook. I think when we do that, we have pricing leverage to, again, price appropriately for the value we're providing to that customer. I, you know, there's a segment that I don't lose sleep over.
There's some segments that are, you know, certainly doing much better than others. You know, PE, for example, is doing very well and continuing to grow nicely. VC's under pressure. You know, as Nas mentioned, there's a lot of, you know, there's a lot of challenges with capital getting cycled back in, and there's fewer VC firms today than there was five years ago. There's fewer funds that are looking for data like ours. Yeah, VC's an area that worries me and that keeps me up, but, you know, there's others that are doing very well as well.
There's two observations I have too, Alex, when I look at our pipeline, and I think about how fast some of these deals might move. One is, I think legal and compliance departments are still getting comfortable with the language that goes in these contracts, and that is a bit of a hurdle at this stage as folks try to get a real sense of what they will and won't accept. We certainly have become more stringent with our language because we certainly don't want our data to be exposed on websites and the like without very careful and clear language about, you know, what's possible and not. That has changed, and that does sort of slow things down through the pipeline.
My other observation is on some of our largest such deals, they're taking time to get through because the business owners are very excited and ready to go, it kind of gets to the finance area, they're still trying to get their heads around what it means to pay for those kinds of deals. I think that's fair f rom your perspective too, that they've taken longer to kind of get through the pipeline once they get to a certain stage because of contracting and kind of final sign-off from finance.
Jake Strole with Fiduciary Management, Inc. Two questions. I wanted to follow up on the direct question from earlier. When customers churn, I'm sure you're asking them why. What does that survey data tell you today versus prior periods, and how does that inform your approach to the business? Second, as we think about the MCP channel and distributing through LLMs, how do you think about switching costs through that distribution mechanism versus the traditional model?
Yeah. Scott, why don't you take the lead?
Yeah. I'll start with the second question. I came here to play offense, and I fundamentally believe that one of the biggest opportunities for the business is growing the business through AI. One of the channels is MCP. I can tell you that just early few weeks, some discussions with some partners externally is they're really going to lean on companies like Morningstar because we have the curated data that I had mentioned before, as well as the framework in which the whole market operates on top of that. That will require a license from us. That will require, you know, relationships directly through Morningstar, and that is not going to change. We're able to deliver that capability in a very cost-effective manner.
I think that we're gonna be able to drive better financial results going forward, both from a top-line and an AOI perspective. On the software side, you know, as I had mentioned, we've shown some pretty good promise more recently in terms of being able to hold a fairly durable position. I think what's most interesting about where we're heading, and we're gonna demonstrate this after the break here, is around what AI allows us to do is to completely transform our entire software experience. When you look at our software, it's really built for a super user. There's a lot of capabilities in which there's value, I think, that Morningstar deserves to unlock that we'll be able to in the future when the tool is more, is more democratized.
Ultimately, I think the unit economics are actually in our favor to answer that question in terms of more of the pull demand coming through the MCP channels. At the same time, I think we're gonna be able to extract more value out of the software and produce the software with better unit economics going forward.
Yeah. In all candor, I think we've lost some people because our software modernization has been somewhat limited, partly because of some of the infrastructure short adapt of the way we build things. One of the things we're super excited about with AI is it disappears like this.
The churn analysis.
Yeah
Has informed the development that we've focused on. Absolutely.
Yeah. For example, if I look at the advisor segment within Morningstar Direct, we've got a great opportunity suddenly to go after RIAs, because of some of the agentic workflows that we're building. Candidly, Morningstar Advisor Workstation is a harder, like, direct fit for their workflows today.
When you look at some of the early results of rolling out some of the AI capabilities, both in Direct and on the Advisor side, the retention rates are actually higher from the customers who've actually been leveraging the AI capabilities that we've built so far, and we're just getting started with that. I've got a lot of bullishness in terms of how that's gonna improve the retention rate when the whole experience is AI native.
This is Ian Buchanan from Ashler Capital. Thank you for doing this. I appreciated your point on Direct and data. I appreciated your point during the presentation that even if a customer did decide to replicate their workflows in the product through other means, it would be more expensive and more cumbersome than just licensing the data from Morningstar. I'm curious, for a more casual user in Direct and data, how much do you think that would cost in terms of money, more t ime, and what are some of the most important features that even with a significant amount of money or significant amount of time, they could not replicate on their own?
Yeah. Julie, do you wanna take the lead?
We absolutely, on the portfolio analytics side, have an edge versus competitors who don't have the breadth of data that you need to actually do that accurately. Kunal showed the slides of the expanse of our databases and how they've grown over time. To tie back to what Laura mentioned, if you wanna analyze what's actually in a portfolio, you have to know what the underlying assets are, and you have to be able to view those through different lenses. I think portfolio analytics has been a strength from the beginning and continues to be a huge strength. Unless you wanna build a database of all the securities and the capabilities to match those and analyze them, you will need to work with Morningstar to be able to do that efficiently.
Back here in the middle.
Thank you. Ryan Griffin from BMO Capital Markets. I think when we sat here last year, you said you don't think your business is at peak profitability yet. I was just wondering how that view has evolved, and then where you see the biggest margin upside?
Yeah
Across the segments.
I think this might be the longest we've gone in an Investor Day without the CFO getting a question.
'Cause he did such a good job.
Mike, I was getting a little worried for you.
I was starting to wondering if I went to the wrong meeting. No, it's great. I think what we tried to convey this year in the presentation is the same spirit of that notion. We're very focused on profitable growth. That means driving revenue. We showed you that we've been able to unlock operating leverage in the business. We're not gonna give forward-looking guidance for our policy, but we think about this as we look at the Economic Moat that we have, the unique data, the trust that we have in our brand, how embedded we are in these customer workflows, and we see a business that can support a higher level of profitability. Not a straight line, it could bounce around, but over long periods of time, we feel like we can continue to unlock the margin of the business.
What we're really trying to do, and I just wanna point you back to AOI. Grow AOI. We're gonna do that by growing the overall business, and we think there's operating leverage we can unlock along the way. Grow AOI, we'll grow the free cash flow, we'll grow the intrinsic value of the firm.
Question, Steph, go ahead.
We had one submitted before the meeting from Edward Ryan at Mangian Partners. He was interested to hear the impact of AI and the strength of moats on each of our businesses, and he also asked if there are any business units Morningstar should exit. During the management presentations, we heard about moats and AI for Direct Platform, PitchBook, and Credit. Any thoughts for the rest of the business?
Yeah, I think it's a good question, and I'm gonna look to some of my colleagues here to maybe kinda chime in as well. Daniel's been waiting for his chance, I can tell. Daniel and Brock, I think you two run two of our more heavily regulated businesses, and so it might be a different lens to maybe talk about how you're thinking about it. Yeah.
From a competitive advantage perspective, we operate within the wealth segment. We have three main business lines. We have investment management offerings that serve financial advisors that are outsourcing the investment part of their practice. We have international wealth platform, which really provides a managed account technology for advisors that wanna outsource all of the investment workflow part of their practice. We have the individual business, which is a publishing website with Morningstar's data IP insights that operates an audience sales business as well as a subscription business. Probably the common aspect of those businesses is the strength of the trusted brand that we have with financial advisors and investors.
That brand allows us to effectively opening conversations with financial advisors to be able to win more effectively when we're competing for clients, whether on the website, we get people coming in from a traffic perspective. It allows us to generate revenue through advertising to financial professionals visiting our website. Morningstar's trusted brand and the IP that's based off the enriched data, the analytics, the insights, all combine together to give us an opportunity to deliver services. Within our investment management business, we leverage our research that we have within Morningstar to build portfolios that allow financial advisors to outsource the investments, knowing that it's backed by the data research and independence of Morningstar.
We think that the brand and the relationships we have with advisors and investors really does position us well to serve those clients going forward, which I don't think is very exposed to AI disruption, from that perspective.
Yeah, I mean, just kind of a quick response from a retirement perspective. I mean, we've, you know, financials, you've seen where our margins are right now. I think with AI coming in, it's only going to allow us to advance that even further. I guess we look at it from a retirement space, you know, kind of becoming more efficient, going faster, that's table stakes. I think when within our business and others, we're also looking to use AI to identify, you know, new revenue streams for us, things that we couldn't do a year ago or two years ago that we are going to be able to do now and do it more efficiently as we've kind of broadened our business beyond just the retirement plan record keepers.
Now we have a huge client base with RIA firms, broker-dealers, asset managers, all looking to do things with retirement. We believe AI is just going to basically allow us to not only be more efficient in what we're doing today and what we believe is working really well, but it's allowing us to do things that we couldn't do before and find even new, more revenue streams that don't exist.
It's definitely the case that we feel that our unregulated businesses are where we need to move fastest as it relates to AI. With the regulated businesses, we're not the only ones who have a say in terms of how fast we move. If I was to summarize what you just heard, we're really focused on ensuring that the experiences we deliver to investors are improved and made more agentic. On the other hand, the way we build those experiences should have more efficiencies built into them because of AI. We certainly are also only moving at the pace at which regulators will allow in those businesses.
To add what might be an obvious point is that, our ability to move what might have been manual work across all of the businesses into an AI-powered workflow that allows our human resources, our people talent, to spend more time generating more IP, that actually compounds the moat that we have as well. Internally, we can leverage AI, which allows us to spend more time broadening our moat across the business. By the way, Nas is Nizar. We've both referred to him as Nas. Mentioned the speed at which development has happened. Our ability to create those purpose-built workflows that are segment-specific for PitchBook or the experiences for advisors or due diligence teams in Direct is only possible at the speed that we're operating today because of those AI capabilities. It's absolutely compounds the advantage that we have across BUs.
Let's go to a question in the back, and then we'll come to you, Alex.
Hi. Aarsh Kak from Ashler Capital. Thank you all for great presentations. I believe you alluded to it earlier, but with the Perplexity deal, from what I can see, it seems that Perplexity paid subscribers have a preset number of PitchBook queries without necessarily having a direct commercial contract with you. I was wondering, just to double-check if that's correct, and what led to that decision, as opposed to going through a model where all clients, regardless if they're accessing it through an LLM, need a direct license with PitchBook or Morningstar?
Got it. Rod?
Yeah. I mean, I think all access to premium PitchBook data requires a PitchBook license. There's other data that we would call essential data, less proprietary data, that you can get without a PitchBook license. For that, they're actually paying us on behalf of their customers or their users.
A question here to Alex, and then we'll go back to the back again.
All right. Thank you. Alex Kramm again, UBS. Since we're now allowed to ask margin question, I'm gonna have one too. I think I asked three last year.
Four.
On PitchBook specifically, that had actually been on a pretty nice margin expansion trajectory over the last few years as I think the business just grew in scale. I think there was a comment in the last quarter that you're very focused on incremental investments right now, so maybe you can be a little bit more specific. Are you telling us maybe in the next year or two, don't look at the margins, maybe it takes a step back, maybe it's flat, or do you think that trajectory can continue?
Yeah. Mike, why don't you take it?
I don't think we're telling you anything specific about the next year. I like your angle there. What I will say, though, is we're trying to express a philosophy of how we're trying to invest. You can see, I think, from the presentations today and the comments that there's a lot of enthusiasm and excitement for how we can position the business in this era. Some of that's gonna take investment. What I'm doing when I'm speaking with Rod and Kunal is we're saying, "What's the return on the different investments we can make?" There's a lot of new investments we wanna get after, and that we're going to get after, and we're thinking about is that incremental or is that a repositioning of existing investment?
We go through that whole exercise, and then we try to make sure that we're supporting the business as best we can to make sure we don't leave any of that growth opportunity on the table, but that we're also responsible from a margin perspective.
Give you this one.
I would say Kunal and Mike are both very supportive of investments we're making in the business. There is a rigor to it that they enforce, which I think is really good, but there is no there's no lack of a support for the investment we're making.
Yeah, in the back.
Ian Buchanan, Ashler Capital. Given the philosophy of having the North Star being growing adjusted operating income, is it a fair interpretation of that philosophy that in a weaker revenue environment, margin expansion takes a higher sense of urgency, or vice versa, in a very strong revenue environment, a lower sense of urgency?
Understood. Go ahead.
I think what we're trying to convey there is that long-term growth of adjusted operating income is what we're trying to deliver, right? That's how we, that's how we track our progress on growing the free cash flow of the firm and ultimately the intrinsic value. I know you're hearing that theme a lot today. There will be ups and downs, especially if, for example, if the credit business is going through a cycle where there's less issuance in the market, that can definitely pull back on revenue. That might have a different impact on where we land with the margin because a lot of the incremental revenue that we're delivering from a credit perspective is very high margin. It's falling to the bottom line. In the short run, on a quarter-to-quarter basis, that number can bounce around.
What we're optimizing to, though, is long-term health of the and growth of that AOI number. If that means that in a shorter period of time, if there are some revenue headwinds but we feel like we need to deploy investment to make sure that we're growing the firm over the next 10 years. We will definitely take advantage of that.
I think our goal is to be disciplined regardless of environment, is what we're trying to convey to you, but not to walk away from opportunity. A simple example I'll give you, three years ago, the credit business was in a tough spot, and we didn't though shy away from going out and doing some lift outs of teams that were being pushed out of other shops. Today, when we look at some of the growth that has happened, it is because we made those investments three years ago. When Detlef comes and asks for those, we do the work, and then we hold him and his team accountable for delivering on it. That's how we try to think about it more than anything else. Ultimately, you know, we all like to think about growth.
Like, that's been a theme here. It's important to grow the top line, and we wanna do that. We're very focused too ultimately on how that arrives at growing the value of the firm, as Mike has been saying. There's different paths to that, but AOI is a really good way of kinda measuring us along the way. Question here again in the middle.
Ray Stoeckle with DF Dent. I'll throw in a few on Morningstar Credit, especially since it's, you know, relative to a couple of years ago, a much higher % of the implicit market cap. There's been a controversy over the last couple of years on the small credit rating agencies relative to the large ones. I think if you were to talk to some of the largest credit rating agencies, they would raise their hand and say, "Private credit now needs us," being, you know, Moody's and S&P, to make everyone calm and keep everybody happy in the private credit landscape. I think sometimes you all get thrown in with the Egan-Jones bucket that has been the small credit rating agencies. I know. Is this a situation where the incumbents are coming in they care a lo?
Just like, go for it. Let me just start by saying that we are all for competition, and we welcome competition. In fact, we would say that part of the reason the credit market has had issues in the past is because there have been areas where there's not been enough competition in the markets. I'd also say that like, you know, one of the things that surprises me, So I kinda grew up as a analyst myself here, and we're very used to looking at performance when it comes to judging anything. You look at credit ratings, and it's the one part of the investment landscape where there's less of a focus on actually looking like, did the ratings actually materially play out as they were given?
Sometimes you'll hear this narrative in the market, I think back to a few years ago, particularly with the European sovereigns. We came in for a fair amount of criticism because we were early in upgrading Greece and Italy, I think, and Portugal. Am I right on that? "So-so," says Detlef. We got a lot of criticism at that time, when everybody else moved their ratings up subsequently, nobody actually pointed out that we had been right. I think on private credit, we're bringing the same degree of discipline. Is it likely that you've seen a couple of blow-ups already in private credit. It's not that different than what you see in corporate credit. You do have occasional crashes.
What I say to the team, what Detlef says to the team, we're only gonna be as good as our research and how transparent we are about our methodology. We will take any competition on those two fronts any day with high degree of conviction, and we welcome others to kind of compete in the space, and we'd love to be able, you know, to compete more fully in other spaces as well. I don't know if you wanna add anything else.
No, I believe you said it all. Just on Portugal, when we initiated coverage many years ago, we were the lowest-.
Yeah
Among the-
That's right.
English rating agencies. We were the most stable through the financial crisis, and then we ended up actually being on the lower end again once the other rating agencies had adjusted their rating. Great research we published, you can read it up. Trust and track record equals performance. We are celebrating our 50th anniversary. Our performance and default studies are available. I would rather put the market not into legacy and newbies. I would rather focus on the market of quality players that effectively apply a service-minded, nimble approach to help the market assess credit risk. Here, as one of the four globally active rating agencies, we have our edge there. With the private market development, the private placements, the level playing field, as Sean mentioned earlier, is there for us to capitalize on. To me, this is the perfect environment, and we can show that performance matters.
Yeah. Follow-up question on credit? I thought you said you had two questions. Let's just do the follow-up question, then we'll go there, if that's okay.
Yeah, I was gonna ask a quick follow-up. When you do the math on your disclosure from the last quarter, you can kinda see how much private credit has contributed to your business. Even when you back that out, you have a pretty strong CAGR.
Yeah, it's growing nicely.
You have a pretty strong CAGR on everything that's not private credit. Non-public or unlisted. What areas are the strongest outside of that private credit piece? Last time you were here, we highlighted and talked a lot about private credit, but it seems like you have many other areas of the business growing. I'm trying to understand if that's a driver that continues from here.
Yeah, we certainly leaned into some of the areas that we were already having success in, but Detlef, maybe comment a little bit on where we're having new success as well?
Public credit is equally important and certainly the transaction-based markets where we are one of the leading rating agency, they have nicely developed. Keep in mind, we had an interest rate cycle. We had more predictable base rates. We had more predictable yields or return rates, and that was a very good and solid environment. We are one of the leading rating agencies when it comes to commercial real estate related assets, whether secured issuance or unsecured issuance. We have expanded our footprint in the corporate public markets as well. We have benefited from record issuance such as Canada, where we are the leading rating agency. We have expanded our footprint globally by opening an APAC office in Sydney, all these are relevant ingredients to all to service the public markets.
When we talk about 25% being related to unpublished ratings, there is 75% in the public markets. Very enjoyable, very nice business and definitely for us to grab, but also expanding into further use cases. Sean, I believe, mentioned the data side. I would highlight the energy transformation as well. The networks, the infrastructure, digital assets more broadly, not just in the private side. There are massive investments needed for infrastructure finance. In most countries you find, our taxpayers are not happy with the infrastructure. All these are areas that are either public or private or as we see hybrids. There's certainly many more evolving segments and that's why our strategy is based on capturing the next adjacency of where we already service existing customers, helping them to extend into the next sub-market that they cover as well.
Yeah. Yeah. Next question.
Yeah. Hunter Vinik at T. Rowe Price again. I just wanna take a step back and talk about Morningstar as a whole. Obviously, you guys have ton of different business lines. Maybe you could talk about the level of communication between the different business lines or, how you ensure collaboration as to no one segment is getting siloed.
I'll take the lead on that, and if you guys wanna weigh in, feel free to weigh in. I'll go back to an earlier point. I do run the business in a decentralized manner, it's certainly the case that the individual businesses do set a high level of priorities themselves. To some degree, we are emphasizing speed and effectiveness as much as anything in the way that we approach things. That being said, at a company-wide level, we do have obviously a strategy, you saw it there, and we have key tenets of that strategy. Elizabeth Collins, who runs our strategy team and works closely with Mike and I, a big part of her job is to kinda make sure that when we look across the business, we're hitting the right leverage points.
If you take, for example, something like AI, we're doing a lot of internal sharing of information, internal demo sharing. If one part of the business is learning something, others are leveraging it. When we think about the future, we're thinking more about issues like interoperability between our products. AI is starting to like lead to those conversations and we're having those conversations 'cause we think it actually makes sense for the businesses as opposed to just because it'd be nice 'cause we're under one roof. I always am very careful about that because I think in some organizations you can create a lot of busy work just 'cause you're under one roof and I think like we wanna make sure there's effectiveness when we are doing that.
Public-private convergence is another great example of something that is strategically important to the firm, we push it across the firm. Whether you look at the Indexes business, which we've been building out a series of benchmarks there that extend across both public and private markets. Whether you look now at the Retirement business where we have a methodology that includes private investments and we have some of our largest partners taking that to market and wanting to deploy that. In the Wealth business, there's active conversations around how we build portfolios for some of the large platforms that we work with. You heard earlier from Direct about semi- liquids and how we're gonna include that on the PitchBook side with all our public market information inside PitchBook.
On credit, you've kinda heard what's going on there. There's some obvious places where we're trying to lean in and lead and in those instances there is a bit more of a top-down directive in terms of what we need to do and how we're gonna get about doing it. There are certain people in the organization who kinda own that and drive that. Joanna McGinley was referenced earlier. Joanna's sort of been driving public-private convergence until recently. Dex McAuley on our team is kinda trying to drive AI across the firm and make sure the right points are hit. We try to come at it from that perspective and it manifests itself day to day in the KRs that the businesses build and how we talk about them and look at them.
Yeah, maybe I'll add some perspective as a newcomer.
Yeah
'Cause I think this is one of the most difficult balancing acts in a organization of this size or bigger. I think a couple things maybe just to underscore. One, it certainly helps when there's a company-wide rallying cry that Kunal mentioned around public-private convergence. We are not only working together because we love each other, but also because there is a strategic market opportunity that I think that Morningstar is very uniquely positioned to service across all parts of the business, and that leads to actual real work that needs to get done across the business. I'll give one example, and Rod, you could probably dovetail on this if you want, but, you know, our teams are working together on unifying our data collection platform. This is something that I talked about earlier.
It was in the presentations. The process in which we curate that data is so special. Replicating that across all of Morningstar and then distributing it to the different business units, is really, really important. I think the role that Direct Platform plays is really, you know, all the other business units are my customer as well. You know, just giving some perspective here, one, it really helps when there's a rallying cry, and two, that leads to real projects that we're working on together across the business.
Yeah. Yeah. Hunter, is that kinda covered? Okay. What else?
Also Kunal's doing a great job.
We have another question. We'll move to that. I know we had set the meeting time till noon, but we're happy to go a little bit longer as we have in the past. Maybe we'll go to 12:15 P.M. if you have questions, and then we'll move to the demos as well. Let's keep going, yeah.
Appreciate it. I know there've been some recent headlines about potentially moving to no longer mandating quarterly reporting. Kunal, I know you made some comments about that.
Yeah.
Just was wondering in a, in a situation where Morningstar didn't necessarily report earnings every quarter, what that would look like and kind of your receptivity to moving towards a different cadence?
Yeah. I was definitely speaking for myself in those conversations, and we're gonna have a, I think, internal conversation about it as well. I think the most important thing is we wanna make sure that whatever we provide is actionable and useful, and we're doing it in a way that lowers the burden on those who have to do the work to report it. So what I was pushing back against is, you know, some of the costs associated with doing that. I do think we have a real problem as to why companies are choosing not to go public. You know, Jack Bogle always used to say, "Own the whole market," right? The reality is, like, what he was referring to really was the whole market.
If you look at the world today, the universe of public companies has really shrunk. I think it's incumbent on all of us, including many of you who obviously do work in public markets, to think about why that is and what are the reasons we can do that. I also think of active management. I love active management. You know, I grew up as an analyst here, and I believe active management can add value. I mean, Hunter, I covered the Brian Berghuis of the world early in my career, and I'm big fans. You know, like, one of the questions I wonder too is, like, does going to a system like that start to provide more of an edge to active managers, right?
Who can really do the work and present something different versus what you kind of get, which is sort of the cycle of just kind of reporting and going along with what management is saying, which I don't think adds a whole lot of value. I think you have to look at it in the context of cost, ensure that you're providing useful information. That's why in the piece I wrote, I suggested that the Australian model was one that was particularly good for us to look at. Then I think we have to look at it in the context of, you know, how do we ensure that, you know, if you're an active manager, you're truly able to really talk about and think about how you deliver value in that context.
I think there's room for everybody in that market, but I think a really good outcome would be more public companies. Question. I think it's Hunter again. Yeah.
Yep. Hunter Vinik, T. Rowe Price again. I think you mentioned earlier in the presentation, by using AI internally, your software engineers are able to work, like, four times faster, but I might be getting that wrong. Given that, how should we think about product velocity at Morningstar going forward?
I love that question because we talk about speed and velocity very heavily, and you two are kind of at the center of that. Maybe I'll turn to you.
Yeah, I'll go first. First, I love the word velocity because what that is, it's the combination of speed and focus together. We're not just trying to go fast, we're trying to go in a focused direction to service the market in a more scalable way. I'll give one example, you can see it at the booth after if you didn't see it before. The Direct new native experience has been built just over the last several weeks. We built, you know, our capability off of some early traction that we got on adding some natural language capabilities into Direct a few months ago. Now we've, just in a few short weeks, as you'll see, have kind of made the experience very AI-native.
Before, I can't quantify how long it would've taken, but I can tell you that the pace is definitely quickening. I think what that gives us the ability to do, you know, as Mike has talked about, is create more operating leverage for the business. That allows us to then redeploy those resources to higher growth opportunities or drop to the bottom line, depending on what's going on in other parts of the business. I'm extremely encouraged by the velocity that we're seeing, and it's gonna be a proof point that you'll see just after the break.
Yeah, I agree with all that. I think there's two things that we're doing to that end. One is, we're not hiring a single developer that can't use Claude Code. We're big believers in Claude Code. All of our developers are using it, and we don't hire a single developer that can't use it. We have seen some efficiencies in other areas where automation can take the place of what humans were doing. Think of QA and things like that where we're reducing some of those roles. We're adding more roles that add a lot more value to building software much, much more quickly. The second thing that we're doing is we're trying to create smaller teams like we've had in the past.
Smaller teams that can act more nimbly, to build things more quickly with real feedback from not just the product team, but from customers directly. What we're gonna see on our side is faster development, more efficient product requirements being driven, and smaller teams doing that work.
Yeah. The small teams piece is really important in my view. It also comes with the reality that as we've been doing a lot of this experimentation, what I see is that that translation layer is disappearing. For example, Scott might have an idea, he can literally sit with a developer and get it done, or a designer can mock up something and immediately kind of move it to production. That is changing the velocity, vis-à-vis needing sort of that translation layer where someone will have to come up with a whole set of requirements and whatnot, and I see a lot less of that going forward. Sanjeev?
Thank you. If I think about the kind of the purple pyramid that you presented in the slides with, you know, I think this spans, you know, at least the direct business and PitchBook as well, and you kind of talk about getting the raw data and cleansing it and curating it and, you know, I know you've made the point consistently, I think, about human curation and enrichment over the top of that. Could you maybe just double-click into what the human curation process means? Because I suppose from my understanding, a lot of that is, it's kind of like a pattern recognition of knowing when a number is off and I wouldn't call it gut feel, but it's seeing the same thing over and over. It's pattern recognition and the like.
Isn't that something that would lend itself well to AI? You know, if so, you know, are you also using AI to kind of support that?
Yeah
Maybe just help us understand, like, as you've kind of embedded AI into your own processes, like, where does human curation still, you know, apply judgment in a way that adds value that models still cannot do today?
Yeah. Great question. Scott?
Yeah. I'll start with first, you know, as we talked about before the meeting too, it starts with, you know, just having access to the data to begin with. This is proprietary data that we're primarily talking about here, direct from the source that allows us to have a quicker turnaround time than public filings or anything like that. One is just having access to the data to begin with. A layer of human judgment is needed in order to bring disparate data sets together and put it into a common format for the markets to ingest and for the markets to make sense of.
In terms of how we can generate that, it is a significant area of opportunity for us to apply the logic layer, apply the judgment that Morningstar's had for decades, apply it to new vehicles, but apply it with AI tooling to enable us to do that faster and more efficiently than ever before. Ultimately, I think it's the combination of what Morningstar uniquely has, which is essentially the definition of how markets operate, and then getting to that definition much quicker by leveraging AI internally.
Yeah. I think your definition of human curation sounds like fact-checking, and that's not what we do. I mean, there is a very small amount of fact-checking that's done by humans. The human curation we're talking about is interacting with companies, sending them surveys, having conversations with them. You know, Nas mentioned that we launched valuations last year. One of the ideas behind launching valuations was to create some controversy. We know they're not gonna be perfect. That did exactly that. People called us and said, "Hey, your valuation on my company is wrong." We're like, "Great. Here's the model. Tell us. Give us more information, and we'll update the model." That's exactly what's happened. That's human curation, where we're actually interacting with companies to get better data from them.
It improves our models, whether it's valuations or whether other IP that we use. That's the kind of human curation we're talking about. Analysts and account managers on the data operation side that are actually engaging with companies.
Another question in the middle.
Ray Stoeckle with DF Dent again. I think this current quarter, you didn't call it out, but I think in the fourth quarter you had mentioned that there were some delays in customer decision-making because they were considering how to spend money on AI and/or perhaps AI was eating into the budget that might have been I wasn't totally clear, but perhaps AI was eating into the budget that might have been spent on Morningstar or PitchBook products. Could you address if that issue has gone away or if that is a big issue or a small issue? Then secondarily, are there any points, as we sit today? Your license revenue has decelerated for several quarters, if not years in a row now.
Are there any internal metrics that you look at, whether RPO billings or any external numbers that you look at, maybe as fund formation, something like that gives us hope that, I know you don't give guidance, but that that number can accelerate from here?
Yeah. Julie, why don't you take the first part, and then Mike, why don't you kinda bring it home?
Okay. The first part was the delay in decision-making for AI. We have seen a longer sales cycle with clients who are l aunching AI projects. At the same time, we've also had clients who have expanded, Scott gave an example, who have expanded their licenses to include more data so that they are prepared for that. The delays in the sales cycle related to AI decision-making on the client side is more about a nuance that Kunal you mentioned in getting the compliance teams in agreement and getting the legal teams in agreement between ours and the client side. We have actually solved a lot of that in the last two quarters, and we expect to when clients are ready to be able to pull the trigger more quickly.
We've also had clients who have It's a similar, dynamic to what, to the example that Rod gave, who had been looking at leveraging our direct platform, and then had ideas about off-platform with AI use cases. Those have actually, many of them have come through in Q1, and I won't make a comment on anything forward-looking. Is that fair?
Not much to add there. It's really commercial -
Yeah
- decision here. Once the sale is made, we're not seeing anything unusual in the data or the cycles. The cycle time to get to closing the sale, as Julie just spoke to, is where some of the friction can be.
Any other questions? Okay. Going once. Twice. All right. Well, I wanna thank you all for being here and for being Morningstar shareholders. We take our responsibility to you very, very seriously, and we're fellow owners and think and act like owners, and I hope you saw a great demonstration of that today on stage as well. I wanna thank as well just the team back there that helped put all this together, Sarah, Rob, and others, who've worked hard to kinda make sure this day goes well.
Steph, who's always heavy on feedback. Thank you, everybody. Thanks for being here, and I would really encourage you to please come and join the session after this one. If you have any other ideas for what we can do to improve this day for you know, do let us know. We're also eager just for any feedback on ongoing communications as well. Sarah's happy to, you know, talk to you about that. Thank you for being here. Travel safely, and we'll see you around.
Thank you.