Moody's Corporation ($MCO)

Good afternoon, everyone, and thanks for being at this far to the last the day. Very, very pleased to have our next slide side chat Moody's Corporation. Pleased to welcome back again once again, Moody's President and CEO, Rob Fauber. Rob, thank you very much for coming back to the conference. And thank you today. I know you've been in meetings all day, so I appreciate you making it all the way here.

Christian, first of all, thanks. I've been in a windowless room in the basement all day. So it's great to be above ground. But I did get the 4:00 slot. So I know we've got to be exciting here. But I just want to say thanks. This is a really high-quality conference and some great investor discussions. So thanks for inviting us.

Good stuff. No better place to start than AI strategy. How did I know that was going to be the first question. I would say is from my observation, your AI offering is involved. It's gone from stand-alone assistant tool. Now you're doing more MCP based API models, more integrating into developer workflows like Microsoft 365. Maybe talk us through what you see as the evolution of your thinking around AI, what did you learn from what you've done so far? And then sort of what's the next step from what ties in Moody's Woods Intelligence?

Yes. So Christian, over the last, let's call it, 8 years or so, we've assembled a massive content to state around -- really around risk. And we've been pulling a lot of that together, and it's interesting when AI kind of first came on the scene in 2023, he said, look, this can be a threat or this can be an opportunity or both. But I think we really, really believe there's going to be an opportunity for someone who has an intelligence estate like we do. And for -- I would say we've had a collection of point solutions and we offer data and models, and we offer it through, in some cases, workflow software and it's web-based delivery and all sorts of things. And you look at this and this has to be a positive for a company that has as much valuable content as we do. And increasingly, I mean you mentioned Microsoft increasingly we're just thinking about how do we make sure we get that intelligence into the hands of our customers whenever and wherever they need it, when they're making decisions you don't have to come through our software. If you want to do it through teams, you want to do it through Claude, you want to call it into your own AI environment. Really, we're fine with that because at the end of the day, what I think we're offering to customers is access to our what I call connected intelligence. And so I think AI is a huge unlock for us.

Okay. Let's dig into Microsoft. Clearly, it seems like compelling distribution opportunity for for your work. Just talk through, I don't know, the commercial structure, economics and how ultimately you're able to protect the value of your data as you embed yourself in third-party interfaces.

Yes. What's interesting is in 2023, we announced kind of a partnership with Microsoft. We deployed Copilot to all of our employees. But we didn't quite crack the code together on being able to bring the power of Moody's content into the Microsoft ecosystem. That was the idea, but it took a little bit of time. And I think we're at a really exciting point with this announcement and so the way to think about this is that very shortly, you will be able to actually access Moody's content on the team's tool bar and be able to call Moody's content into your Copilot answers. So if you want to develop a credit memo and strengthen weaknesses and do pure analysis and do it with Moody's content and your own content, of course, you can do that right there in Copilot. Now Microsoft is -- I think, this is appealing to Microsoft because it creates greater utility for Copilot, right? And it provides a trusted intelligent source that the financial community is -- uses and trust, and you can use it right in Copilot. And for us, it exposes us to a much broader set of users. And the way it works, you asked about the commercial. For now, the way we're approaching this is a bring-your-own license model. And so already, we've got -- we've had a number of engagements. And I get a lot of questions from investors about, hey, there's all these announcements, but when are we going to see the revenue. And the cadence of this really is we make the announcement and make the capability available we start to then engage with the customers. We've gotten really good engagement. We announced this several weeks ago, we have in the teens number of engagements with major financial institutions, okay? And then from there, we have a handful of situations where we're already now in very active discussions about a pilot. And that's just in the span of several weeks. From there, we go to signing and that then is about -- it's a commercial opportunity at that bank where we say we're going to make a core parts of our content and intelligence system available now through our -- through AI surfaces, whether that's teams or whether that may be Claude as well, we announced something with anthropic or whether it's your own internal AI workflow, right? So we're now going to make that available to you, the bank will have a new agreement There'll be a new pricing opportunity. There'll be new IP protections and agreements when it's in your AI environment. Then we're going to see usage and then we're going to start to see actual revenue. And it's not -- we're not doing consumption-based pricing at this point. What we really want to do is drive embeddedness and usage of these financial institutions and have them get tremendous value out of our content. So we're -- it's early days, but already some exciting, I think, momentum. And I think what we owe in the investor community is some visibility now as we move forward into the engagements and the POCs and the signed contracts resulting from these various announcements.

Okay. Maybe for just people at newer to the story. The biggest concern is around defensibility of the data mode. And you've talked about your proprietary data being the context layer, if you like, for financial AI. Maybe describe exactly what makes the data set difficult to replicate just to give a sense of the.

Yes. So let's just -- I'm going to cover the broad components that are -- you're talking about analytics. I'm going to cover the broad components because sometimes when people take data, they're only focused on the company data, which we call Orbis. But let's start with -- we're the only place that you can get Moody's research, right? So that has -- I think there's a lot of resilience to that. In the banking franchise, we have a proprietary contributory default database we've curated for over 3 decades. And that default database allows us to calibrate our credit models, and we have public and private credit models. And those then are used in our lending suite. They're used by bank credit departments. They're really the gold standard in credit risk assessment at banks. And that is calibrated from a proprietary default database. And it's credentialized because when your regulator comes in and looks -- to the loan file, and they know that you're using the Moody's scoring models, they know that, that is being calibrated against actual default history. We moved to insurance. Our catastrophe models are built using the contributed claims data from the insurance industry. So the insurance industry says, "Hey, we want to get better at understanding wildfire risk or flood risk or hail risk, we're going to give you access to our claims data and you will build the model and then provide the model back to the industry. In some cases, we actually form industry working groups and the customer community, the insurance community actually invests in the tooling. So that -- so there's a real -- between the catastrophe models and the actuarial models, these are very proprietary, hard to replicate. The last part I want to get to is the massive company database. And that is curated through a collection of hundreds of information providers that we have commercial relationships with, think of these as credit bureaus and companies houses around the world. And we have commercial contracts and IP rights and ability to create derivative derivative works off of that, and we pay back royalties to these information providers. Some of that information, Christian, I will acknowledge the basic address and company information. And that can be aggregated by web scraping companies today and already is. That's not where the value is. We also get information from those companies houses and IP providers that is private that you have to have a contractual relationship with. You could have a contractual relationship with them, but it's going to be hard for your agent to do that. it's likely going to take a human to go around to these hundreds of providers around the world. And those providers have become more conscious of who's consuming the data about companies in their country. And the last part of that data is derived and transformed data where we create ownership hierarchies that's where the value is. And the primary use case for that is financial crime compliance. We're understanding the connectedness that we have created is particularly valuable.

Cool. Let's dig into your businesses, we'll start with Moody's Analytics. And just a near-term term here, as you think about sort of ARR growth in that business, it's kind of held in around 80-ish percent as investors think about the second half of the year, are there any catalysts or which sort of specific product launches or catalysts gives you confidence in your ability to sustain that growth or potentially accelerate it?

Yes. So it's a broad portfolio. I'll probably just touch very quickly on 5 things. One, when you think of our flagship product is really the credit research right? And we are in the process now of pulling together our credit research, our economic content, our structured finance content that's offered through multiple platforms, and we've pulled that together into 1 offering with an agent layer over top of that has -- creates a lot of utility and the ability to see things from our content to state that we haven't -- our customers haven't been able to access. So in the second half of the year, we're going to be moving a lot of customers from simply the credit research platform to this, what we call kind of OneView platform. That's 1 agent ready data, AI-ready data. We formed a sales SWAT team at almost every major financial institution or bank, we're having dialogue about how can the bank consume more of our intelligence, our credit models, our credit research and our company knowledge graph and consume that into their own AI platform and third-party AI platforms, whether it's, Claude, Teams, would open an eye. And so that gives us a commercial opportunity and also an opportunity to embed our content much more deeply across the institutions. So that's true, and there's a lot of interest in that. We have a very nice pipeline. Three, in insurance, we have a a set of product enhancements, high-definition models, continuing to migrate customers from on-prem to our cloud platform. That's a great pricing opportunity for us and cross-sell opportunity. We're also leaning into casualty. That's an area that's behind the property space, and we're bringing real science and analytics to the casualty space. And then last, I would say, banking. So I know the narrative is that software is dead or dying our fastest-growing product at the moment is our loan origination software that we sell to kind of Tier 2 and Tier 3 banks. We had close to 20% growth in the first quarter in that. And we have a kind of an agent layer that sits on top of that. And so we're experiencing really nice growth there. And we're also taking the the agent capabilities that are in that lending suite, and we're also providing those on an a la carte basis to banks wherever they want it. So if you want to consume simply our automated credit memo agent, you can consume that into your own AI workflow. So there's a number of things that are kind of contributing to growth across the portfolio.

Okay. So you recently brought in a new head for the MA business, which was kind of an interesting choice similar that didn't come from a traditional financial background. I'm curious what is what is the signal for how you want that business to evolve and are there 1 or 2 things that you think are very important for hard to accomplish for the past couple of years.

If you didn't see the announcement, we have hired Cristina she has employed something like 200 at Salesforce. She was 1 of the founding members of their customer success organization, which was a pioneer in the industry, 15 years at Salesforce culminating and running what they call customers for life, which was all of the renewal and up-sell, which is extremely relevant for us because -- we have very broad penetration across the banking segment. The real issue is reducing buying frictions for banks to be able to consume more of our content. She then went to Slack and was part of the team that rolled out enterprise go-to-market Chief Customer Officer, when they went from $90 million to $1 billion and then was at Logic monitor for 5 years of Vista backed company. So what I was really looking for, Christian was I know the Capital in SaaS is a dirty word right now. But the as-a-service that model, that business model is extremely relevant to our industry. And at at Moody's at the analytics business, I think we have a fairly complex product array, and we have had predominantly field sales and we found that there's a gravity to that selling model when you're at about $4 billion in revenues, right? And you've seen a little bit of a deceleration in revenue growth because it's hard to sell a complex product array and without a well-developed partner channel. And so Cristina is coming in to run a different playbook to help simplify the product, the pricing and packaging to help us think about how to engage differently with our partner ecosystem and to be able to really, I think, help reposition us and to capture this opportunity that's in front of us. I wanted somebody that was different that had a different skill set. And I'm having dinner with her right after I'm done with you, and we're going to be talking all about this, and I'm very excited about it.

You sounded for sure. Good. A question on just MA around regulation. Historically, that's been a catalyst for incremental demand of some MA products. As we're in a environment, how do you think about that as a maybe headwind for that business?

And you're right, Christian, there has been -- we have benefited from regulation. Interestingly, we just sold our regulatory reporting solutions business in banking. We didn't have a lot of cross-sell -- some of that was still on-prem and it's in a good home. I would say that 1 of the probably not well understood enough value props of what we offer across MA, and I think I touched on this, but there have been a lot of meetings today. So I'm losing track. But our models and our data are heavily credentialized with regulators. -- not necessarily endorsed by the regulator. But I mentioned earlier that when the lender comes in and looks at the loan tapes and they know that you're using the Moody's credit models or the the Moody's stress testing solutions and Mark Zandi's economic forecast to do their own CCAR. Most major banks use our solution. There is a power in that credentialization that from across the franchise definitely in the credit franchise, right? There's real strength and safety in using Moody's for credit and the same is true in insurance, as I just talked about. And I think the same is true generally in KYC. I mean how many times do you think regulators come in and done an investigation and an examination of a decision that a bank made and realized that they were using Moody's data, right? And they want to see the data. They want to see the source files. They want to see the right. And we are able to provide the traceability and the auditability of all of that. And so I think that's not to be underestimated how powerful that credentialization is across the franchise.

Okay. Let's talk about sort of margins in -- you've done some decent amount of margin expansion in.

Slightly typical equity analysts Decent margin the I'm going to get.

The context though is over the last 5 years, the business has nearly doubled in revenues you've gone from 80% subscriptions to 95%. It is a business that should have structurally much higher margin. So what is holding back sort of get into maybe like a 40% type margin number in that business?

Yes. So you can see we're well on our way, right? We're making very steady progress. You see our guide for the year here and you see our medium-term targets. So we're getting there. and we have increasing confidence about our ability to get there because I I get asked sometimes are we making enough investments? I think so for sure because we're also creating a lot of investment capacity as well, right, by getting more efficient with our product development life cycle and leveraging agentic coating and things like that. And we're able to harvest some of that to make investments and then give some of that margin back to investors. I'm going to come back, Christian, to a little bit to the complexity of the model, right? What we have been working on has not -- I don't want to sound defensive, and I'm not looking for kudos. But we don't have 5 different divisions. I have ratings and then I have everything else. And we have been working on pulling all of that together and bringing together different tech stacks and going to 1 sales force and creating a platform layer under our applications and that has taken a lot of work. There's a lot of cost in that complexity. And so we've been going after that. And as we've -- we've been making progress on that, that has been also contributing to our ability to start to get some margin I think Cristina as she comes in, is going to be able to continue that.

Okay. Let's move to your ratings business believe or not, that is your actual biggest. How they get to ratings.

That 2 biggest business that you have -- maybe just talk about 2026, -- your revenue guidance is notably much more constructive than your main payer.

Maybe just walk through how you're thinking about 2026 in terms of the building blocks to get there. There's clearly a lot of tailwinds. So curious also balance between tailwinds and risks.

Yes. And we didn't change our guidance in the first quarter. And obviously, we had a war break out, and we had a SaaS pokes and all sorts of stuff. But like last year, right, we had Liberation Day and tariffs, and we kind of lost April -- we did change our guidance, and I wish we had enough because ultimately, we came in right where we thought we were going to come in at the end of the year. And it was interesting, we had a stat that something like 80% of U.S. investment-grade issuance in March came in 6 days. And that's an extraordinary stat because what that tells you is there's a lot of financing demand but we had these risk off windows, right? We had all these headlines about the war. And so you had all this issuance supply waiting to hit the market. And when there was a risk on day, boom, it hit the market. So I think our view is it was too early to make an adjustment. And the market is pretty constructive right now. Spreads have come back in since the start of the war. I think we've been surprised at how resilient I think the economy and the markets have been. We've seen really strong hyperscaler issuance in the first quarter. I don't think we're done with that. And what we haven't -- and we've seen M&A pick up, right? And we had called that last year, and we were mostly right. It just -- we lost a quarter -- and we saw the M&A pick up in the back half of the year. That's continued into this year. What really hasn't picked up full steam yet, and you asked about some upside. And I always say to people, it's this private equity exit and M&A cycle hasn't really kicked into high gear, right? And when it does, it is a very virtuous commercial cycle for us. because oftentimes, we'll get multiple commercial opportunities from this M&A and leverage finance activity and loans go into CLOs, and we rate the CLOs and all of that. So that, to me, is still an upside. The biggest risk, I'm not going to give you any great insight here is just it's hard to predict what's going to happen and what the headlines are going to be and whether we go into 1 of these risk-off periods. Our guidance doesn't really take into account a risk-off month, right? So I think that's something for us to watch. But right now, the markets are quite constructive. And so I continue to feel good about it.

Okay. So about one of the tailwinds, just AI-related issuance. Maybe just talk through how to think about the economics of this in terms of the business. how ultimately it's monetized between frequent issuers, nonfrequent issuers. And if issuance of sort of like hyperscaler debt has an impact on ratings margins of economics over time.

Yes. So there's a number of different ways that all of this AI infrastructure build-out is being captured in ratings. And of course, that's with pure hyperscaler issuance. It's with data center issuance. So that could be project finance or CMBS or structured credit. We're also seeing it with our utility and power issuers. And so there's a variety of there's a lot of issuance that's going on that's related to this. There's a lot of focus on the hyperscalers in particular. And we mentioned in the first quarter that we had already seen almost as much of our full year expectation for issuance from hyperscalers in the first quarter. And we don't think that they're done. So I would say a couple of things, Christian, just as we think about the economics of that and how that rolls into the business. In general, investment -- frequent investment-grade issuers are on a little bit different pricing construct than infrequent issuers of debt. And that's not surprising. That's the same kind of model you see in many industries where you have high volume, right, and you ultimately start to achieve discounts when you have high volumes. Same in our business. And so the hyperscalers who have been very cash risk companies have issued a lot of debt and over time, have taken on the profile of what looks more like frequent issuers. So -- when we have a lot of investment-grade issuance from frequent issuers, including banks, we call that revenue mix unfriendly. It means that the issuance issuance growth would be higher than revenue growth when that happens. When there's a lot of spec grade issuance or issuance in things like CMBS and CLOs, complex asset classes that's revenue mix friendly, where you would expect transaction revenue growth to be faster. We're getting both of that from AI. With the hyperscalers, we're getting a frequent issuer -- and with some of the data center build-out and some of the -- it's flowing in other places of ratings, where it's revenue mix friendly. But in general, it's one of -- but this is not a one-trick pony. It's 1 of the medium-term funding drivers that we feel very good about. I'm happy to talk about others, but it's not like if this AI CapEx bubble burst -- there are a number of other major drivers of funding around the world that are supporting our business.

Okay. Perfect. Let's talk about another tailwind, which is private credit as pet all the news. It was a big tailwind for you in the first quarter, I think, grew 80%. If I read the correctly. Can you remind us again how you make money from private credits? And then given all the noise you're hearing in that ecosystem, how does that inform your outlook for that business?

So I've made some progress because Christian just described private credit as a tailwind and 3 years ago, when I would do these investor meetings, this was the #1 topic, and there was lots of investor concern that we were going to be disintermediated. The public markets were being disintermediated and in turn, Moody's was going to be disintermediated. And in fairness, we were a little slow on the draw, right? Because we -- I don't think we had a full suite of methodologies and all of the engagement with the private credit community. And so we were slow in the draw. But we understood that, that market was going to need independent credit assessment even though a lot of times I heard that was not the case, and I think there's a much broader understanding now of the benefit of third-party credit assessment in some form of transparency. It will look different in the private markets than public markets. But there are needs for investors to have a better understanding of the credit profile of what they're investing in. And we have a very extensive relationship with the big private credit players. And when we talk about we created the language of credit risk and the benchmarks and the data and the scorecards that helped investors to be able to compare and understand credit risk, public credit risk across asset classes and geographies, and we can play the same role in private. That is our job to help investors understand credit risk, whether it's public or private. And shame on us if we were slowing the draw on private. And so what did we do? We built out methodologies and teams and go to market. And we really see private credit rolling through the rating agency and structured finance, so this is asset-backed finance and fund finance. Fund Finance is a booming $1 trillion ecosystem, lots of demand for credit assessment there. we don't play nearly as actively in the direct lending market. Now what we have seen is loans get originated into the direct lending market and then come back into the public markets because the public markets are typically cheaper. And then -- we've also seen a lot more investor demand for our credit scoring and assessment capabilities. And remember, I was talking about we have these incredible credentialized credit models turns out those are very valuable for understanding middle market credit risk and to be able to help investors understand that. And so we've seen more and more demand from investors who say, "Hey, I'd like to -- it may not be a rating, but I'd like for Moody's to be able to give me a probability to fall, maybe mapped to a credit rating to help me understand, give me a third-party view of credit risk. So I kind of say it's a great time to have the world's best commercial credit franchise because there's a whole new segment of the market that's originating and investing in credit.

Right. How do you think about competition in ratings, particularly around products and middle market credit rating with some of the smaller agencies will be public about just attacking that space. So maybe over the next couple of years at that particular areas where you're monitoring share dynamics.

Yes. So after the financial crisis, the competitive landscape in structured finance ratings changed, and it -- I think it changed permanently. It was a 2.5 agency market, something like that. and it is now kind of a 6 agency market. And particularly, there's more rating agencies in the more transactional parts of the market. This is plain vanilla asset-backed finance, where the transactions tend to be the same. And you'll see rating agency rotation going on -- you don't see that typically in the fundamental space. That has had very little change since the financial crisis because it's a much, much more relationship-driven part of the business, where we've rated these companies for decades, literally decades. So we do see a more active competitive environment. That's been true in private credit as well. And I guess the 1 other thing I would say, Christian, is the coverage levels, you would think of it as market share, we call it coverage. They ebb and flow. -- much more so than they do in the fundamental space. There are times where we or 1 of our competitors will make a methodological change, and that will be informed by for us will be informed by historical default experience and other things where we'll say, it's time for us to update our methodology. And there are times where we may provide an update to the methodology and the market may move away from us. And that's where you have to have the conviction in your beliefs. And I say that there's a cost sometimes to having an opinion. And I think we came through that financial crisis and realized the #1 asset we have is trust, and we never want to violate the investor trust. And so there are times where we take a different view than others in the market and the issuance may move away, and that's the cost of having an opinion.

Let's go back to the here and just think through margin and investment appetite. Clearly, you've done fairly well, margins are -- have improved but as you invest in AI, I imagine platform reorganization, you bought in the new MA CEO. So she might have her investment priorities -- how do you think about balancing continued margin expansion versus just investing for growth?

So 53% margin. rating agency in the high 60s. I do get asked, can it go higher, right? But we've done a pretty good job of driving operating leverage into this business. And Christian, I -- again, I continue to think about you want to make sure that you invest in this moment. But at the same time, there are so many opportunities across our company, and I'm sure many other companies to be able to drive efficiency. And we -- and AI is part of that. It's not the only part, right? They're good old-fashioned ways of becoming more efficient, but AI is definitely an accelerator and customer service was, one, we don't have a huge customer service organization, but that was an early easy one. Our product development life cycle is a much bigger one. This is how we develop product between product and engineering teams. And we're obviously not an AI-native company. So we have to transform the way that we develop products. right, from people writing code, that's how we have done it to agents writing code and humans checking code and that kind of thing. So we're well down the path of overhauling our product development life cycle across our engineering teams are smaller in ratings, but we've done that and then in MA. They're much bigger there's a much bigger efficiency opportunity. And some of that efficiency, we're going to harvest and invest where we need to invest, and some of that efficiency opportunity is going to go into the margin and go to investors. That's 1 place, and we feel very confident about it because you can very clearly see the efficiency metrics and know that you can get savings, not only savings but we can get increased cycle time. This same is true in ratings. There's less headcount in ratings, but I just sat down with our ratings operations team the other day, and we were going through how many checks we have to have before we put out a rating, and we have a team that does 4 eyes. There's 2 different human teams that do the checks because we can't always get the first team to get all that right. And so we went through, we automated something like 1/4 of those checks with agents and we were immediately able to see some very significant savings in terms of time and improvement in QA and the team already said, "Hey, we're going to be able to pull out x number of people out of this process. Some we may be able to use elsewhere right? And in some places not. So there's a lot of opportunity across the enterprise, I think.

Good tough. Let's talk about acquisitions. I would say Moody's generally seen as good acquirers, even Dean of still RMS, bringing a firm, you are embedded into the company, grow it much faster just curious in this AI wall, the need for proprietary data, your own balance sheet capacity. How are you thinking about M&A here?

Yes. Those were 2 really important acquisitions for us in terms of the capabilities that it brought to us. And the ability to monetize those content sets across the broader customer base. So I'd say that's one thing is if you think about this massive content estate, this intelligence system, we want to be bringing content in to that intelligence system that is going to enhance the value of the system overall and be able to be consumed by multiple customer segments and serving multiple workflows, right? I want to be able to sell it many, many times. So that's one. And two, when looking at anything that looks like workflow or software, we're going to look very, very hard whether there is actually a proprietary data asset embedded into that software. In some cases, there is and hasn't been monetized. And so those kinds of things will continue to be very attractive to us, where we might buy something not because of the software, but because of the embedded data asset inside of it that we think is uniquely valuable that we can That we can monetize.

Okay. All right. Let's bring it all together and just think through the stock, clearly, stock Australia is a pretty healthy premium of peers for a very long time. So some of that compress over the last year or so. What's your compelling case to investors as to sort of why gain its premium valuation?

All right. So I'm going to start with -- we are anchored by one of the world's great businesses. And if you don't know ratings, I encourage you, I'm happy to spend more time with you and get to know it. It is an incredible business. That's why Berkshire Hathaway is our largest shareholder and has been for a long time. And it benefits from tremendous network effects and has fantastic medium-term drivers as we talked about. I mean, think about what the world has got to get done over the next 5 to 10 years. BlackRock said $68 trillion of infrastructure investment by 2040. And that's not just AI. That's bridges and roads and there's energy grids, energy transition there's military buildups. And there are enormous drivers for funding and fiscal -- there is very little fiscal space in sovereign balance sheets, right? So the public and private markets have got to get this done. And there's a real understanding of this. I was just in Europe last week at a forum on European capital markets. And this was what I was talking about. They said, "What do you think can happen with European -- can markets I said, I assume they're going to have to grow substantially. You're going to have to figure out how to support capital markets growth because there's an enormous funding agenda in -- across Europe. And we are the way to play that. And we have a tremendous franchise and market position -- so that's one. That is a fantastic business. We started MA by monetizing the exhaust from the rating agency, the research and the ratings data. And as I said, we're now in a moment where it's like a renaissance in terms of a desire to understand credit risk. There's a whole new segment of the financial market that is originating and investing in credit. That is super exciting when you own a rating agency and the world's best credit modeling and data franchise. So that's the second piece. But Christian, now we're going to get to the AI piece. And in 2023, again, I said this is either going to be a threat or opportunity or maybe elements of both. But we're going to make this an opportunity. We're going to capitalize on this because it must be an opportunity when you have a content proprietary credentialized content a state like we do. And so this is a fascinating time because it forces you to think about the real source of competitive advantage. My competitive advantage is not from building the best software. Our competitive advantage when it comes to the analytics side of the business is, I have the world's largest company knowledge graph. And I have this credentialized model and data estate and we're in the process of connecting as much of that as we can. It was interesting because at GTC, Jensen Wong, recently said that structured data is the ground truth of AI. And his point was that over the last few years, we've all focused on the models, the frontier models, who have the best model this version, that version. We're now in a moment where and I wrote an op ed about this, I said, AI has a trust problem. I think people understand that, right, which really means that if you want to drive enterprise adoption, the AI has got to connect to trusted content and data. We call that decision grade intelligence. This is what financial institutions have have trusted and relied on for years and decades, right? Our models, our data. And now we're pulling all of that together and I think we're in a moment where the world is realizing it's not just about the models. The models have got to connect to the data. The first-party data sitting inside of institutions and intelligent systems like Moody's. And the other thing I'd say to this is, Christian, we're in a world where institutions want to understand the intersection of risk, right? It's not just I want to understand credit risk. That team understands the credit risk. Over here, that team will understand the operational resilience of this company. And and it's a siloed view of risk across institutions. That is changing. Everywhere I go, people are talking about wanting to create a more 360-degree view of who they're doing business with, who they're making a loan to, who they're insuring, right? And that means that you have to make these connections. And we're doing that. We are creating what I think of, ultimately, the core asset is a connected intelligence system where every model, every rating assessment, forecast, benchmark, insight is resolved to any given company and I can understand the relationship between that entity and that person and this building and that entity, right, and resolve it down to 1 company. That, I believe, is a uniquely powerful asset in an AI world. We are assembling a connected intelligence system that I believe will be an essential component of a broader AI ecosystem, right? It's the contextual intelligence layer that is going to be a required component of any AI ecosystem. And I think we're in the process of building that. The world is in the process of understanding what is needed in this AI ecosystem, right? And I believe that you put those things together, and I hope I'm making a compelling case for a premium valuation.

Good stuff. I'll let the audience to say that. So thank you very much for the time.

Thank you.