Good afternoon, everyone, and welcome to today's call. We're excited here at Moody's to have Andrew Steinerman, Managing Director and Equity Research Analyst at JPMorgan, moderating this session with Cristina Pieretti, who is the General Manager and Head of Generative AI Solutions at Moody's Analytics. The questions have been pre-submitted, Andrew will be moderating. Andrew, thank you so much for doing this, and over to you. A pleasure, Shivani. Thank you. Thank you, Cristina. We enjoy this research dialogue with you. Cristina, why don't you just start out with how should people think about the AI strategy at Moody's? Of course. Thank you, Andrew, and thank you, Shivani. A pleasure for anyone that's listening to be here speaking about this topic that is highly relevant and which we're extremely passionate about. When I think about GenAI strategy and Moody's GenAI strategy, the first thing I think we have to keep in mind is everything sits inside Moody's agentic solutions, right? I would encourage that everyone that's looking at this thinks about two layers and a third pillar that is about how those layers reach customers, right? If I think and if we can show in the slides, we're going to show what those three pillars are, right? The first pillar is connected intelligence. This is highly relevant. This is a foundation. The sequencing actually matters here, right? Because you cannot build decision-grade agents on poor data. What makes Moody's unique, it's just not the volume of data, which, of course, is 600 million entities, 2 billion ownership links, our research, our ratings, but it's the depth of the domain expertise embedded in this data over decades, right? In credit risk, as an example, we have Moody's Ratings, which are originally proprietary. For KYC and compliance, we have our Orbis database, which provides beneficial ownership mapping and entity resolutions across 170 data sources, right? What we do is we collect that data, we connect it, we curate it. That's the foundation of everything. We go to Pillar 2. Pillar 2 is about agentic workflows, right? It's how we package that connected intelligence into purpose-built end-to-end workflows. There are a couple of things that are very important. First, we focus on workflows where making bad decisions is going to cost you a lot of money. What we mean by that is you don't want to make a bad credit risk decision because you're going to lose a lot of money. You don't want to underwrite the right insurance policy and not look at the risk. Again, there's big financial consequences. You don't want to lend to and engage into a relationship that is a result of making a wrong KYC check because, again, it's going to cost you fines, a lot of money, a lot of reputational risk, right? It's about developing workflow solutions in those high-stake areas that are leveraging all the connected intelligence of pillar one. In the case of pillar three, it's how do we then reach and distribute both the connected intelligence and the agentic solutions, right? The idea behind this pillar three is we want to meet customers wherever they are building and working with AI. When we think about Anthropic, about AWS, about Microsoft, OpenAI, Databricks, Salesforce, we want to make sure that we are meeting our customers where they're doing their work. We don't really see them as competitors. We see them as partners that amplify our reach. I think there's a couple of very important things in terms of that. First, it allows us to reach new buyer personas. Second, in each of these cases that I named, we are maintaining the customer relationship, and we retain the IP, right? If you think, again, to recap what I said about the strategy, we are addressing three things. We are addressing the customer need for trusted, defensible intelligence in high-stake workflows. We are addressing also customers' Gen AI maturity from those building their own models with our data to those that prefer to consume decision-ready workflow outputs. We're also reaching them where they need us to meet them. That's basically the strategy. Okay. Great, Cristina. Moody's has announced partnerships with four of the big AI players, AWS, Anthropic, Microsoft, and OpenAI. Yeah. Could you just give more color about the nature of those partnerships? Absolutely. I'm going to start by Anthropic, probably because Anthropic is every day on the news with a new announcement, right? When I think about Anthropic, it's probably our most architecturally distinctive partnership. We have basically built two things with them. Back in November 24 last year, we announced the launch of MCPs. Right? MCPs that allow our common clients to access our data through Claude. The other thing that we most recently announced, and we believe it's the first of its kind, as far as we are aware, is the launch of an MCP app, which is an interactive agentic interface that lets users access Moody's agents, generate outputs, and trace the sources without leaving the Claude environment. Right? It's not a data feeder or API. It is Moody's intelligence that is rendered in the first case as data that can access through chat, and in the second case, through actually a workflow, right? An example of that workflow would be running an ownership check or running a portfolio monitoring workflow or running a credit memo write-off. That's Anthropic. In the case of AWS, think more about two things. One is access of our agents and our data through the cloud marketplace, through the AWS Marketplace. Then most recently, we've also integrated into Amazon Q, which is AWS native generative AI chat interface. Which means that customers can query Moody's intelligence conversational within the AWS environment. You don't have to leave again. If you're using AWS, you can buy our agents, that gives us, again, increased customer reach through the marketplace, then you can also converse with our data and our agents through Amazon Q. I'm going to move now to the third partnership, which is Microsoft. We think Microsoft as our productivity layer play. We all know the reach that Microsoft have. We are all users of Microsoft. We are basically embedding decision-grade intelligence directly into Microsoft 365 Copilot, Researcher, and Excel through a dedicated Moody's agent and MCP integration. I want to be clear when we talk about a dedicated agent is this is the way that the Microsoft environment works. What it means is if you are using Microsoft 365 Copilot, if you're using Excel, if you're using Teams, you can interact with the Moody's data directly through any of those environments, of course, provided that you're already a Moody's customer. Then the fourth partnership, which I'm going to describe today, is OpenAI. There are MCPs live in ChatGPT Enterprise. Basically, again, similar model to the other ones. You have to be a customer of Moody's, and you can then you are using ChatGPT Enterprise, and you can access the Moody's data. If you think about the four partnerships I described, there's a common thread here in every case. Moody's retains the customer relationship, Moody's controls the pricing and fulfillment, and our data is not used to train third-party models. The partners are the distribution surface, the intelligence, the IP, the customer relationship remains ours. Right. Cristina, you would imagine those premises that you just said will continue going forward as well. I mean, obviously, we're at the early days with these partnerships. Absolutely. You actually see how we have now dedicated teams inside Moody's. When I talk about dedicated teams, I would describe them as squads that are working with these partners. We've been very deliberate in the partnerships we form, but this is not a one-off. You're going to see continuous announcements from Moody's as these partnerships launch more features, more skills, more tools, etc. Okay. We'll talk a little bit more about the partnerships. What I wonder is about LLM token economics. When I hear the words agentic and MCP applications for Moody's customers, I wonder who bears the cost of the tokens. Obviously, tokens could be inflationary. Is the client bearing the token cost, or are there also times when Moody's in these agentic workflows or MCP applications are bearing token costs? Yeah. This is a question we get. We get a lot of questions around this topic. I think it's worth answering it very careful because the model of token economics is going to depend on how the customer is accessing Moody's intelligence. There are basically two paths. In the first path, the customer is consuming Moody's workflows directly through Moody's own environment. Let's leave those partnerships that I described aside for a second. We are basically contracting with the customer directly because the customer is buying from us an MCP or it's buying from us an agentic workflow. In that case, Moody's carries the underlying token cost and builds them into our pricing. If you think about the risk of the token, it's on us to understand what is the cost per token. Of course, we do negotiate the volume with the customers, at the end, that token cost is there by Moody's, and it's including in the price. The customer gets kind of a clean, predictable relationship. They pay for Moody's intelligence and outputs without managing the variable token consumption separately. They do have to manage kind of the volume, right? If they contracted for a certain number of outputs, of course, if they go above that output, they would pay more. That's when they contract directly with Moody's. In the second path is when they're contracting, they're using our MCP on our agents through the partners I described in your previous question, Andrew. Basically, the customer is interacting with Moody's MCP through a third-party AI environment like Claude Enterprise, like ChatGPT, like Microsoft Copilot. In this case, the token cost sits with the customer because they are already operating within and paying for the platform they're running. They already have a relationship with Claude. They already have a relationship with Microsoft Copilot. Moody's is not in the billing path for those tokens. The customer has a relationship with that provider, and then they have a separate relationship with Moody's for the intelligence layer on top of it. Right? You can see. Yeah, I'm sorry. Go ahead. Yeah. I totally understand the second point. Yeah. Just make sure we get it on the first part where you're like, hey w hen we're contracting directly, we build token prices into the contract. My question to you is, as token costs go up or the volume of consumption goes up, you're saying the pricing to the client adjusts, and so this isn't a possible mismatch for Moody's, right? Yes. It could be, but we are, needless to say, very careful about it, right? First, we are monitoring every single thing that the customer consumes, and we're also monitoring very closely our token costs, right? It's not only about the token costs, but what is the model. We have the ability to select the model we're using for everything that we're providing to the customer, right? We are very careful to use the model that makes more sense, not only from an economic standpoint of course, but also from a performance standpoint, from a reasoning standpoint, from a follow direction standpoint. That gives us freedom to say, well, we're not maybe used for this task, the most pricey model, because it's not worth, it's not really going to make a difference, right? We have several levers here to control. First, we are looking at token costs very closely. Second, we're looking at the volume and the consumption. What is the cost for us of clients consuming? Then we have the lever of controlling the model. As of now, we feel pretty comfortable, and we have the necessary buffers built in. That's how we're approaching it right now. Okay. That sounds good. I think this sort of just led towards this term that we hear a lot about consumption pricing. Maybe you're going to say, I just defined it for you, but because there's so much discussion about consumption pricing in an AI context, how does that work for Moody's? Yes. I think this is something we've been extremely careful about, and I think we want to continue to be careful about. Right? Yes, I think consumption pricing can be something very powerful because as customers consume more data, run more agentic workflows, this is something that can be beneficial for us. I see it as a potential uplift, right? Now, we've talked before with you and many other of our analysts and investors about there's a downside to it as well, right? Which is the volatility here. When we're thinking about consumption price, we're basically thinking about a base price, and we always price our arrangement so far as a base price that guarantees a minimum consumption. Then if you go above that consumption, then there's consumption-driven pricing, right? Yes, as this gets more ingrained in the customer and the customer consumes more, we kind of benefit from the uplift on that. Of course, with a pre-agreed pricing arrangement with our customers, but we also want to make sure that we minimize the variability or the volatility of our revenues. It sounds like the volatility can really only be to the upside, right? Yes. You have your base amount of pricing, and then you're paying for overage if you go above that. Yes. That's basically the idea, right? I also want to be mindful with our clients here. Right? When you think about the data, in the data, there's a potential to be more overaged because as the data gets more democratized, and we're seeing when we talk to our clients and we engage our clients, there's more appetite for enterprise licenses, right? One of the things that has happened with GenAI, which we actually see as a tailwind, is it has democratized the access, right? It makes it simpler to use the data. It allows for more data to be used in more parts of the organizations. You could see more increase there. I would say when you're talking about agentic workflows, you kind of know what's your business volume, right? It's more difficult to go above that. Again, what you stated, Andrew, is yes, it's going to be generally upside. It's difficult to be downside because we're protecting that through a minimum, that guaranteed subscription, basically. Okay, great. I'd love to get into specific use cases or solutions. When you look at agentic AI at Moody's Analytics, could you go through maybe two or three solutions that you're prioritizing with Moody's clients today? And why did you choose these use cases to kind of be the priority first? Yeah. I would say, I think there's two things that I would highlight here, right? When we think about prioritization, and most importantly, we think about our right to win, we're going to think about two things. One is where do we have data that is proprietary, that is connected, that is that connected intelligence that we refer to. We all know that, again, you cannot build decision-grade agentic workflows on poor data. It doesn't matter who's building those agents. If it's Moody's agents, if it's third-party agents, we want to make sure that we have the right data, the right context data, that it's AI-ready first to make sure that we can focus on those agentic workflows. The other thing that we've been very deliberate about is that concept of prioritizing those places where the stakes are highest, where a wrong answer has legal, regulatory, or financial consequences, and where Moody's has domain expertise. I'm just going to repeat that, I'm going to give you a couple of examples. Places we have proprietary data that is connected, that has a proper context layer, it's basically AI-ready. Second, those cases where stakes are higher the wrong answer has a lot of consequences. Third, places where Moody's has domain expertise. You put these three things together. We basically, as of now, have come into three areas. One is credit risk, that's where we started at the beginning. It's what are the type of data and/or workflows that you need to leverage GenAI for credit risk assessment and for lending. Examples of that is how with automated credit memo, how we're doing automated early warning. It's all the MCP that we have rolled out, either independently or through the partnerships in terms of ratings, research, probability of default models, firmographics, financials, etc. Second use case, it's know your customer. That's our second priority. Those are things such as entity profiling, ownership mapping, adverse media, sanctions screening. Basically a lot of analytics coming from Moody's Orbis database. The third one, which we're just starting on, it's basically the insurance underwriting path. Moody's risk models, climate analytics, ESG data that can create a differentiated foundation for underwriting workflows. I wanted to get a sense of if a client, I mean a current client that's already accessing Moody's Analytics data, probably through an API. If they choose a Smart API or more likely a MCP server, are they paying more to access the data in an additional way, or is that part of the existing contract? In other words, when they go from API to MCP, even if it's the same dataset, same customer, is that like an upgrade where they're paying more? If they are paying more, why would they switch? Yes, absolutely. This is a great question. Yes, even if you're an API customer, we are charging a premium for that. The reason for that and how we justify to our clients is the following. When you're thinking about an API, an API is going to deliver raw data, which means that on the customer side, a group of data scientists, developers, quant analysts, have to take the data and build something on top of it, a model, a workflow, a dashboard. Of course, the data is valuable, but it requires a lot of investment, expertise, ongoing maintenance. Basically, you can think about when it's an API, you're buying kind of an ingredient. When you think more about what they're buying with an MCP, and we are already packaging the data in a way that makes the agent and again, we're not talking necessarily about our agents. We're talking about large language models. We're talking about customer agents or any third-party agents. It makes the job for that agent much easier. The agent has an easier time understanding that it has to use this data and how it has to use the data because it basically has instructions for the agent on how to use the data. You might say, well, Cristina, that's great, but isn't that a nice-to-have? And why would a client pay more for that? The answer has several reasons behind it. First, it's speed. You can basically by giving these clear instructions and that clear context layer, it means that the agent can go leverage, connect with the MCP, and get you an answer extremely fast. Second, there is the cost element. If you don't find the answer, if you are working with an agent or an LLM and it doesn't find the answer, it's going to keep looking everywhere it can to not only find the answer, but also if, for example, here, a connected intelligence comes into play. If it needs an answer that requires several things, that looking for an answer might take more and more time as it constructs the answer. While if we are packaging everything in one MCP and we're giving clear instructions, that means that your token use is going to go down. The third is kind of the auditability, the knowing that the answer you're going in GenAI, it's backed by Moody's. I would really emphasize the first two. One, it's speed, and the second time it's cost on the client side. Okay, that makes sense. You're using a lot of phrases, and I just want to make sure the audience catches what you mean by each of these phrases. I'm just going to mention three phrases. Context layer, you say that a lot. Yes. Decision-grade data. I forgot if you said this one today, but I definitely hear Moody's talk about knowledge graph. Yes. If you can go through, in the context of AI and Moody's, what each of these mean for the Moody's universe. Yes. I'm going to go through the three of them, and actually go through the three of them in the way we construct them. The first one, of course, is we have our raw data, and we like to talk about it as decision-grade data because we never expose to our customers or to our internal application just the raw data. What we end up exposing is what we call decision-grade data, which is basically the standard we hold our data to. What does it mean? It's sourced, it's curated, it's explainable, it's auditable. Which if you think about where we are focusing our efforts, it's extremely important. Because it's then feed for decisions that carry legal, regulatory, or financial consequences. If you think, for example, about data you scrape from the web, that's not going to be decision-grade. If you think about data that has been collected, sourced, QA-connected, that then is what we called decision-grade. Decision matters a lot because in regulated financial services, the provenance and the auditability of the data is as important as the data itself. That's what we call decision-grade data. Data that we can stand behind, that our clients can say, I can trust the data. It's coming from Moody's, and that it can say that to the regulator. The second term you asked me about, and I don't think I have mentioned it in the call yet, but we're talking about it a lot, and we believe it delivers a lot of value to our clients. It's constructed on all the years of data and different acquisitions that we've made, is a knowledge graph. This is basically the architecture that makes that decision-grade data interconnected rather than siloed. It connects those 600 million entities that we have in the Orbis database with 2 billion ownership links. Those 2 billion ownership links are across jurisdictions. It connects then those ownership links with ranks, I'm sorry, with ratings, with other credit scores, with catastrophe models, with tenants if we think about commercial real estate in one single intelligent fabric. Of course, the other thing we're doing with the knowledge graph is we're doing knowledge graphs that are specific then to use cases. You have a knowledge graph for a sales and marketing use case. You have a knowledge graph for a compliance use case. You have a knowledge graph for a credit risk use case. Because the type of data that is relevant for you and that you want to be connected is going to differ by the use case. That's the knowledge graph piece. The third piece is once we've connected all that data. Think about the process. First I described decision-grade data. You're cleaning, standardizing, collecting, making sure you can stand by that data. We're connecting that decision-grade data so you can get all the relevant insights when you're analyzing something and you're not, again, I'm going to go back to the previous question, you're not relying on an agent or your token cost to build all those links. Then once we have that connected data, then we're going to build this context layer. The context layer is what sits between the knowledge graph and the AI reasoning engine. Think of it as the instruction layer for AI, a structured, governed representation of what that data means, how it relates, when it should be applied, what caveats apply. That basically what it translates is into increased accuracy and increased efficiency. Right? It's fair to say that without a context layer, an LLM can access data but cannot reason about it in any way that it's defensible in a regulated environment. I'll stop here, see if you have further questions on this. No, not on those three terms. Maybe we'll move on to the data moat. Obviously, MA breaks up its business into three sub-segments, Data and Information, Research and Insights, Decision Solutions. My question is, what is the strength of your data moat in each of those three sub-sectors? Then also a lot of terminology goes around this word proprietary. Yes. Maybe you should just, as you talk about the strength of your data moat, just define what you guys mean when you say proprietary data. Yes. The first, I probably will say that if I think about the proprietary data moat, I think it's not necessarily in each of these places. It's kind of the foundation of each of these. Right? The three segments is how do we organize and deliver value, right? We deliver value by providing Data and Information. We deliver value by providing you research analytics, then our Decision Solutions, which are KYC, lending, insurance, etc. Actually, when we think about the data moat, it's what makes everything defensible. It's basically the foundation, right? Let me tell you why we think this is a moat. There's three important components on it. The first one is access, right? A large portion of the data we have, it's not publicly available, right? We have, over the years, have created and have developed a lot of commercial agreements, licensing arrangements, royalty relationships with over 170 sources that again, are either exclusive or semi-exclusive. That provides a barrier to entry. You would have to basically reconstruct a global network of supplier relationships from scratch, right? It's not data that you're going to go and access in one place. It's basically built over a network of relationships in different jurisdictions, different countries. The second one is kind of proprietary creation, and those are assets that Moody's originated and that exists nowhere else, right? Of course, the prime example would be the Moody's ratings. Right? No LLM can generate a Moody's rating, no competitor can replicate the regulatory acceptance and the institutional credibility behind it, right? The third angle of this is the construction and curation. It's what I've been talking again about connected intelligence. It's the work of linking, resolving, standardizing, and continuously maintaining data across jurisdictions. I think the part of maintaining it's incredibly relevant, right? Because you can do this once, but this data changes constantly, right? All the time we're continuously refining those links, refining that entity resolution, working on the standardization, and making sure that everything is data, it's decision-grade data, right? If you put all these things together, you put the fact that you have all those relationships with providers. More than 170 sources of data that is not publicly available. You have the assets that you are creating, right? The ratings being the prime example. You put the construction and the curation and the linking of all of this, you have a pretty robust moat, right? Going back to your original question then, I would say Data and Information, it's basically more of kind of the pure data, right? Again, you have which I described, right? It's all the linkages that I described, it's all the curation, it's all the standardization. That's number one. When you move to Research and Insights, it's transforming that decision-grade data into analytical output, right? Into credit opinions, into sector research, into those probabilities of default that we build out of our historical default database. When you think about Decision Solutions, it's when that intelligence becomes workflow-ready tools, right? An example of that being CreditLens or some of our Catylist solutions, etc. Right? The point that I would like to leave is it's a combination of all of this that makes you powerful, right? It's a combination of having that connected intelligence as a foundation. It's how we build that through, as I described first, the access layer with data. Second, the proprietary creation, third, the curation, the analytics we develop on top of those, the subject matter expertise and the relationship we have with our clients to be able to automate those workflows. I don't know if I answered your question about the data or Okay, good. Yeah. Here is a question. Within your MCP protocols, what data protections do you have to prevent the LLM, a third party LLM, from memorizing your datasets, training on your datasets, and particularly in your partnership agreements with companies like Anthropic, is it specifically in your agreements that they are not allowed to train on your data? Yes. We are extremely deliberate and focused both with those partnerships and with our customers, that there is no training allowed in our data, right? Number one is from a contractual position, there is a firm contractual position across our partner agreements, right? We have a dedicated privacy program, information security program. All are publicly documented that govern how the data is handled across all products and integrations, right? The same standards are going to apply with, again, as I said, customers or with the partners, right? That is number one. Number two, the MCP, we are being very focused on MCP architecture as a way that we want to distribute our data for GenAI purposes. Because of what it means, what are the implications of an MCP, right? It basically allows our data to be accessed through a controlled interface, so it is not transferred. When a customer runs a workflow inside Claude or another partner environment, they are querying Moody's data through the MCP. They are not receiving a copy of the underlying dataset. The data remains within Moody's governed infrastructure. The outputs are generated on demand. They are sourced, they are attributed, and the underlying data is not really exposed in raw terms, right? That gives us a lot of. I would say the third angle is we do monitor, right? We monitor the volume of calls that is done through an MCP or through a Smart API, etc. I would say, between the contractual agreements, the fact that you are not receiving a full copy of our database, the fact that we are monitoring all of this, there is a robust framework there to protect our MCP protocols and prevent the training by LLMs. Maybe I'll add one more thing, Andrew, which is, because of the nature of the MCP, even let's say that you pull a lot of volume at one point, it's going to be a point in time kind of data dump. Right? When you think about the nature of our data, it's very important that you have real-time data. Even if a snapshot was theoretically possible, it would not solve the customer's problem because our data is continuously updated, curated, and enriched. The value is not in the static data set. It is in the living, governed, current intelligence that reflects what are today's entity structures, what are today's ratings, what are today's news. Yeah, that makes a lot of sense. Cristina, a term that you used just a moment ago that I caught is that we can monitor the volume. Yes. If one of our clients are trying to download an unusual amount of data, unusual relative to them. Yes. My question to you isn't just could you monitor the volume, but do you have audit rights? Obviously, you have these contracts with partners and clients, and do you retain the right to ensure, to audit that the data is being used in the scope of the contract and not, let's say, goes outside the contract? Yes. We usually have audit rights within the contract, and that's something that we have even before GenAI. Again, I'm going to say yes to your specific question, but I would say again, it's two things, right? In all our agreements, we're defining very clear what the data can be used for, in what context, and by which users, right? Then, yes, then we have the monitoring in place in terms of not only the volume, but what type of data they're using. Of course, it's not only because we want to monitor, it's because we want to make sure that we are investing in the right places. Then the third thing is, yes, we do have auditability clauses in our contracts. Okay. Usually when you find that you audit the data and there's, let's say, more users at a client, the client usually just pays for that, right? I'm sorry? Yes. When there's increased usage by a client, yes, the client will pay for it. Yes. Okay. Got it. How about let's talk a little bit about cross-selling and upselling. What within AI capabilities across Moody's, the Moody's platform, will drive more cross-sell and upsell? Yes. I would say, I'm going to point to three things, Andrew. One is the metrics that we see. We, in general, when we look at our customers that are using GenAI solutions by Moody's, we observe two things. We observe higher retention in that cohort, and then we observe that they tend to consume more content. That's a clear indicator that when we have AI adoption, it deepens the commercial relationship rather than substituting for it. We actually see higher retention and we see higher consumption, which of course is a leading indicator for us to be able to increase our revenue or our commercial relationship with that customer. The second thing, I touched on it earlier, is when I think about the possibilities with GenAI, if I think about the data, we are seeing, especially for Tier 1 institutions, more of a drive to enterprise licenses. We want to use our data, kind of, throughout the organization as opposed, versus in silos. Of course, that drives more consumption of the data. When we think about the agentic solutions, then there's the possibility of automation, which also allows us to tap into a different part of the wallet of our customers. The third part, when I think about cross-selling, and I'm extremely excited about this, is the partnerships. In that, it's not only that we're meeting the customers where they're working, but it also allows us to tap into new buyer personas. That means customers that were not necessarily previously direct Moody's customers, but that now can access our data through this new platform. I would say there's a deepening of the relationship we have with our existing customers through more retention. There's increased consumptions for those organizations because of that. Use of more data for GenAI solutions, the need for reputable data in GenAI solutions. When we work with workflow solutions, we're tapping that into the automation budget. Lastly, there's the ability to tap into new buyer personas through our partner ecosystem. Okay. Obviously that all sounds credible and good. Research analysts are supposed to have some healthy skepticism as well. Yes, of course. My question is, that's what I'm going to ask you about. When your team looks at Moody's business, what are the credible risks from AI? In other words, when the Moody's leadership team realizes that there's benefits and risks, what's one area of risk where you're like, we have to get this part right? Can I give you two? Yeah, I'll take two. Okay, good. Great. The first one I would say, and that's one that gets me on my toes every day is speed. I think we have to make sure that we are really focused on the speed of embedding our data. I think the risk here is not that our data becomes less valuable, it's that the customers establish agentic workflows with other intelligence providers because we were not there. That's why you've seen from very early on, you saw us launching in 2023. I'm sorry, I can't believe I launched this product and EDF-X platform the same, Moody's Research Assistant. Then you saw us coming with agentic workflows. Then you saw us coming with MCP very early on. We were the first ones to launch an MCP app in the market. You'll continue seeing this from us. Sometimes they ask us, are the clients there? I would say some of the clients are, the most sophisticated are there, some others are not. We want to make sure that when the clients are there, we are ready with all our data, all our analytics, all our agentic workflows ready for them to implement. I would say it's about the speed of embedding and making sure we keep that momentum. I think in this market you cannot say, you asked me at the beginning, Andrew, you said, well, you're going to continue all this work with Claude, AWS, Microsoft. Absolutely. You cannot skip a beat here because then you have the risk of not being in the play when a customer is going to finally start their GenAI journey. I think the second we talked about it. The second is we need to make sure that we're protecting our IP. That's why we're so laser focused in the type of engagements that we sign with our customers and with the hyperscalers because we want to make sure that, yes, we are there, we are embedding again our connected intelligence, but we're also very mindful of retaining our IP and retaining the customer relationships. We want to do it extremely fast, but we want to do it safe. I would say that is the approach. That's where we are very focused on making sure we make this a win. That sounds right. Okay. Last question is really, Cristina, it's a summary question. Feel free to bring together things that we've already spoken about. I'm sure you realize investors are sensitive to the AI risk in Moody's business. Yes. Why should investors see AI more in total as a tailwind than a risk to Moody's business going forward? I think this is probably not only the summary, but it's probably one of the most important questions here. I think there's two scenarios and we hear it every day. I'm going to start by the not good scenario, what I would call the bear scenario. The bear scenario goes a little bit like this. AI will commoditize the data, the LLMs will synthesize everything from public sources. The customers will no longer license proprietary datasets. All these hyperscalers are going to be able to automate all the workflows that we sell through Decision Solutions. There's no data to sell because everything has been synthesized by LLMs, everything has been commoditized, and then there's no workflows. Why I think this bear case does not stand is because basically this bear case is misunderstanding what Moody's sell. We do not sell data. I'm going to go back to we sell decision-grade intelligence, data that is structured, that is governed, that is continuously updated, that is explainable, that is auditable, again, for decisions that carry legal, regulatory, and financial consequences. Yes, you can go and scrape all the data of the world, but if you're JP Morgan, as I'm mentioning JP Morgan because it's your firm, Andrew, and you have to stand in front of a regulator and the regulator asks you, well, how did you make this Gateway Two decisions? How do you make these credit risk decisions? How do you make all the decisions and all the reports that you have to do in front of a regulator? Your answer is not going to want to be, well, I scraped this data from here and I don't know if I have the necessary risk. Yes, there was an issue in linking this data. You want to be able to stand and say that this came from a reputable source. I would say that takes me then into the good scenario. The bullish scenario, which is with GenAI, we have an amplifier for that data. The importance of good data, it's more important than ever, right? The data becomes more importable because you want to make sure you want to avoid the risk of hallucination. You want to have data that is sourced and auditable. Once you start embedding that data in agents, switching that data becomes extremely painful, right? The data is going to become more secure. Not only there's an increased demand for data, as you embed those data in your agentic workflows and as you embed your data in those automation workflows, it becomes more embedded. There's basically, as more agentic workflows are adopted, Moody's becomes more deeply embedded in the decisions our customers make every day. There's finally the partner ecosystem through which we are reaching buyer personas we have never reached before, right? I would say those are incremental relationships with incremental revenue, not substitutions, right? That's, I think, the picture, right? First, we are not playing in places where you're going to be comfortable with straight data. We play where high-stakes decisions are made. Our data, as it's more used, it becomes more embedded, more secure, more intelligent. The third part, we don't see the hyperscalers as substitutions. We see them as amplifiers of our reach, by that, we see that as mechanisms to deliver incremental revenue. I think. Well said, Cristina. No, go ahead, Shivani. Thank you. I was going to say, I think that's a great kind of note to end the call on, and I just wanted to thank you both for making the time to help us kind of educate our external stakeholders on Moody's GenAI strategy and the topics that have been top of mind for many investors and analysts out there. Absolutely. Thank you very much. Okay, thank you very much. Thank you. Bye. Bye. Bye-bye. Bye.