MSCI at RBC Conference: Embracing AI for Future Growth

On Tuesday, 04 March 2025, MSCI Inc. (NYSE: MSCI) presented its strategic vision at the RBC Capital Markets Global Financial Institutions Conference. The discussion centered on MSCI’s technology transformation and its integration of generative AI. While the company highlighted significant advancements in efficiency and product development, it also acknowledged the challenges of managing costs and integrating new technologies.

Key Takeaways

• MSCI’s technology transformation includes partnerships with Microsoft and Google, focusing on cloud adoption and data distribution.
• Generative AI initiatives at MSCI are categorized into workforce transformation, data scaling, and client experience enhancement.
• The company has doubled data production throughput at a 25% lower cost, thanks to AI.
• MSCI aims to achieve mass personalization and real-time processing through agentic AI.
• Integration of acquisitions is accelerated using AI platforms.

Financial Results

• Data Production: MSCI has successfully doubled its data production throughput while reducing costs by 25% through the implementation of generative AI.
• Investment Strategy: Return on investment is measured by cost avoidance, reduced data collection costs, and new revenue from AI-driven products.
• Cloud Spend: All investments, including cloud and AI modernization, are assessed based on their return on investment.
• Data Collection: AI has enabled faster scaling of data collection efforts using a platform built on Google’s data stack.

Operational Updates

• Workforce Transformation: Generative AI is improving efficiencies across various functions, including development, legal, and marketing, through custom GPTs and no-code tools.
• Data Collection: A multi-agent debate architecture using AI has enhanced the classification and assessment of controversial data.
• Product Development: Product development has accelerated, exemplified by the rapid creation of the index product center using generative AI.
• Partnerships: MSCI is evolving its partnerships with Google and Microsoft to focus on top-line growth and solving complex problems like performance attribution in fixed income.

Future Outlook

• Product Roadmap: MSCI is focusing on mass personalization, real-time processing, and agentic AI to drive future growth.
• Scaling Laws: The company aims for significant growth in data and capabilities, leveraging its technology platform.
• New Spaces: MSCI plans to enter new markets and product lines, driven by generative AI and technology investments.
• Integration: The company is integrating acquisitions like Burgess, Foxbury, and Fabric using AI platforms to enhance capabilities and efficiencies.

Q&A Highlights

• LLM Usage: MSCI is experimenting with various AI platforms, including OpenAI, Google’s Gemini, Hugging Face, and Anthropic.
• AI Agents: AI agents are seen as beneficial for simplifying processes and enhancing user experience.
• Data Collection Efficiency: The company is improving data collection efficiency and reallocating data analysts to develop new products.
• M&A Integration: MSCI is leveraging its generative AI platform to drive efficiencies in mergers and acquisitions, such as with Burgess.

Readers are encouraged to refer to the full transcript for more detailed insights.

Full transcript - RBC Capital Markets Global Financial Institutions Conference:

Ashish, Host, RBC: cover information services companies here at RBC. I’m excited to host Jigar, CTO of MSCI. Jigar, thanks for giving us this opportunity.

Jigar, CTO, MSCI: Thank you, Ashish. It’s great to be here again.

Ashish, Host, RBC: Thank you. Jigar, so you joined MSCI, I believe, in July 2018, and we’ve seen significant technology transformation at MSCI under your leadership. So I was just wondering if you could provide a brief overview of what we what changes from a technology perspective have you seen at MSCI and what’s your vision for the future?

Jigar, CTO, MSCI: Yeah. Thanks, Ashish. Yeah. It’s amazing how time flies. It’s been six and a half years.

And we went through a pretty multilayer transformation of the company from a technology perspective. First was to lay down the foundation of shared engineering and shared services and DevOps and and CICD and so on and so forth. And top of that, we’ve built the cloud partnership with Microsoft in, 2020 to start the the projects we already built on the cloud to accelerate the pace of innovation and client needs and so on and so forth. And then we launched the idea of, end with the platform of investment solutions as a service. And investment solutions as a service, we looked at our headage from 1969 when the first indexes were launched.

And from that point on, we kind of democratized all the code and the data through thousand APIs, data explorer, developer community, index builder, and many, many products we launched after that over the next several years. And that was a more platform level, new change. And then we launched MSCI one, which was an integrated platform for all product lines across MSCI. And that has grown quite a lot since then. And then so that was another layer on top of the the platform, and then we started doing data distribution through Snowflake and under the partnership with Google for building the data acquisition platform, which we did a lot of innovations and how fast we can collect data.

We’ll talk about that when we talk about AI. And since then, you know, in the last two years, all the focus has been moving further up the stack with the power of Gen AI. No. That’s a great segue into

Ashish, Host, RBC: my next question, Gen AI. Right? That’s the question of the day, question of the year. What are you doing on the Gen AI front? Can you just talk about what are the three, what are the key initiatives and the key categories of initiatives that, for GenAI at MSCI?

Jigar, CTO, MSCI: Yeah. So in in a there are hundreds of projects going on across the 6,000 people of MSCI with GenAI. But if you wanna simplify them, you can categorize them in three buckets. One is the complete transformation of how we work. Right?

Driving massive efficiencies and operational transformation of the company. So whether in that category, let me finish with three categories and I’ll get each of them in detail. The second category is the whole company is about data and data production and models and so on and so forth. So how do we use generative AI for creating a lot more data, scaling much faster, much much, cheaper, much, have much larger coverage, and much better accuracy than we ever done in the past. So that’s another huge area of investment for AI.

And the last, but probably the most exciting one for me personally is the client experience. Right? It’s not just AI, it’s not about simply driving efficiencies, even though that’s not a small enough it’s a massive contribution to the company, but also client experience. Everything we are doing been doing in the investment industry for all these decades, around performance and risk and portfolio construction and indexes, sustainability, private assets, public assets, how does generative AI transform their client experience and make them become a lot more efficient, make make better decisions at a much faster pace. If we were able to do that, and I have several examples, I’ll tell you about where this is where it’s gonna be pretty game changing for

Ashish, Host, RBC: the industry. Okay. Why don’t we go into examples then? I that may be a good segue. Just drilling down further on these examples.

Jigar, CTO, MSCI: Sure. So the first category was workforce transformation. Right? So let’s start with developers. There is so much excitement amongst developers.

We are building products, like, index product center was something we built in a matter of few weeks on our website. So all our indexes are you can go view it and it’s a a new new modern website is coming soon as well. Now in that example, the entire code, architecture, the test cases, we heavily use generative AI for doing all of these things. And that entire product, and there are three other such examples in the company over the last twelve months, but things were moving at a much faster pace than ever we have seen before. The other example I would give is massive amounts of code, which is written in, let’s say, Ruby and our developers are more of Java experts.

Now they are looking at generative AI to help them, understand this code and manage it better. Huge amount of code we want to upgrade for, for example, vulnerabilities in cybersecurity threats. All of that work is going so much faster. Even this framework in security or MITRE framework, which is more advanced techniques of, protection against cybersecurity threats, we’re able to use generative AI to predict these threats and also look analyze our code base and show us where the improvements need to make. And I’ve, in fact, even do it for us.

Right? So that’s just an area just in developers where we are seeing massive transformation. But with, things like, custom GPDs, every function, you look at our, legal team. Look at our marketing team. They are they are building their own custom GPDs.

We have had so many hackathons across the company, three fifty AI champions in every single function. They are teaching everybody what to do, how we can use this thing. And there are a lot of these things are no code tools. And so the awareness and adoption of these tools is increasing dramatically. I look at the statistics of the custom GPs created in marketing or sales or servicing amongst engineering itself, obviously, in finance operations.

It’s everywhere. Everybody’s excited, and it is taking away the grunt work from what everybody’s doing. So on the one hand, you are able to not deal with the grunt work, and you save a lot of time and it it provides you a lot of efficiencies that you can invest back in the company. So those are some of the exam customer service is another huge one. Right?

I mean, the amount of answers we get through AskMSCI done, so it cuts down the time where clients can get their own answers themselves without having to contact us sustained in especially in the case of sustainability and climate as an example. In the second bucket, which is data production, simply put in the last couple of years, we have doubled the throughput and at the 25% lower cost or slightly even lower than that. And the quality is getting higher. So we are able to scale at a much faster pace in data collection. We built the Google partnership and, the entire data collection process, data acquisition platform was built from scratch on the entire data acquisition platform was built from a scratch on their entire data stack with, which is very heavily infused.

So we’re able to use the best models in that area and, able to produce data at a much faster pace. One exciting example I’ll give you is our controversies product. We have a very rich product to share controversies data for the issuers and the companies we we cover. And normally three years ago, there were you had these extremely complicated documents which explain our methodology on how to determine what kind of controversy is it. Is it related to sustainability or is it a governance issue or is it a, a scandal of an executive?

What how how severe is it? Is it does it have a material impact on the stock price or is just something on the side? There are these documents you would have to read. What we have done is a multi agent debate of architecture we have created where we we we trained three or four different agents in different aspects of these controversies and they debate amongst each other and they come to a conclusion of what is the classification of this controversy, what is the severity of this controversy. Finally, when it produces the result, then a human gets involved.

And even the prompt generation is getting done for these kinds of things using AI itself. So we are at a level which is unseen two years ago before chat g p t and all of these things came out. So data collection is a completely different world right now. If you look at our data analysts and you look at the demos they’re doing, it’s all about AI at this point. And the last and probably the most exciting to talk about is what is the client impact.

Right? I’ll share three examples of that. We last year, we launched, the Geospatial Explorer. We launched AI insights for portfolios. And, we are working on, things like Ask MSCI, which you can ask a lot of questions about, private companies or or sustainability or even indexes going forward.

And we’re doing quite a bit of work on private assets data collection as well. So in case of, I’ll give one example more little bit more detail. Right? The over the years, we’ve had a large amount of institutional portfolios with extremely complex instruments, thousands of them with billions of dollars of assets in them. And in six years ago, we would give you data files with all the risk and performance and go do something with it, figure out what’s going on.

Then we build the insights product to say it’s a Snowflake data warehouse. On top of that, there is Power BI dashboards which tell you all the insights about your portfolio. And then we added the generative AI layer. Now this is not something you’ll get an answer from chat GPT or anthropic or or whatever your favorite, AI tool is because you have the portfolio context. You have the decades of model of risk and performance and investment models that our research team has created.

And then you combine all of that with the latest news reports on on on the markets, the economy, the industry, and the the company specific events. And then you can simply, in very simple words, you can chart with your portfolio. Ask really complicated questions. Show me a chart of, the biggest tracking error from the top five companies. Where is the where are the biggest, downgrades in ESG ratings for the assets alone?

Those kinds of things. In the Geospatial Explorer product, for example, you can ask questions about various different, assets like a data center or or a physical offices for the companies inside your portfolio and ask questions about where are the risks. And it shows you on a map with the in a very simple manner. Imagine the amount of processing and the data analyst time that is cut down from our clients. Clients.

And it is not something a hotshot tech company can do without the context of all the investment models we have, all the portfolios that we have, and the highest quality content we have in the investment industry.

Ashish, Host, RBC: That’s very helpful color, and there are a lot of questions that I want to follow-up on. But before we go there, you mentioned obviously OpenAI, Anthropic. I was just wondering, if you could share, you obviously talked about Microsoft and Google partnership as well. I was just wondering if you could share which LLMs do you use? How do you expect this LLM landscape to evolve?

And then also, if you can talk about some of the disruption in the LLM industry like DeepSeq and your view on on that front.

Jigar, CTO, MSCI: Yeah. The whole industry is driven by the AI scaling laws. Right? The more basically, it’s very simple. The more compute you throw at it, the more data you throw at it, and the the bigger the model is, number of parameters.

The bigger the number in all three areas, the better, smarter engine you model you’ll create. And that is the war everybody all the AI players are on. They’re running very fast on it. Now the general convention study was the more hardware in the GPUs from Nvidia you throw hundreds of thousands that, you know, you saw with Grok and, what Elon Musk’s team did in a very short amount of time. The more money you spend, the better it gets.

And then to your point about DeepSeek, they came up with some infrastructure innovations, do it at a lot cheaper price. There’s also a little bit advantage they have for being late to the party in some ways. So there is I’m not here to, you know, comment on exactly, whether can you in one time training, they just spend 6,000,000 on that, for example. Right? And those kinds of things.

But from our perspective, all of this, we don’t pick winners here. We try out everything. We have an AI platform inside MSCI, which wraps APIs from OpenAI directly with Microsoft partnership as well. Gemini from Google. We have models from Hugging Face, which are open source models.

We are working with Anthropic because they have some unique value propositions there. For internal tools, we talk to companies like Glean. We talk to companies like, Perplexity. We’re looking at that. Many different tools, and and enterprise level GPT, which we can use, which we are partnering directly with OpenAI on.

We are discussing things with Databricks and, especially Snowflake. We are distributing our data through Snowflake. How can we enable more AI in directly on top of our data on Snowflake? So there are so many elements of where the elements are going. Now the interesting question for me is, yes, there are these AI scaling laws and everybody’s talking about it in the industry.

How does it apply to MSCI? What are the scaling laws at MSCI? Which if we think about the next ten years, how are we going to multiply our revenue and sales and number of products by a certain amount of number based on the power of generative AI? For that, our three parameters are what is the context we have? How do we get more portfolios and more custom indexes built on our platform, what are the richest models we can create more and more of.

We’re using AI in our research very heavily, so creating better and better models using AI. And lastly, combining all of this with how smartly we apply the latest technology at the great price point and and and performance and, and and put it all together at a fast pace. So that’s how we will scale. So if we have to watch out for that and, Ashish, to your question about these partnerships, see these partnerships have evolved, with especially with Google and Microsoft. Initially, it was about buying capability, you know, cloud and storage and all of that.

And, and now it’s going more towards higher level conversations that how can you help us add top line growth? How can you help us solve the real problems we have? For example, with Google, we are discussing performance attribution on fixed income. How can we do it much at a much faster pace? Not 20 or 30% faster, but five x faster.

Can we prototype using their GPUs that they use for AI to run these things? And the cloud players are also interested in helping add value to us at the top line as opposed to simply providing cost optimization. So I think these partnerships are maturing. We’re also creating some cool projects like Geo Special Explorer that Google invited us to the Google conference last year, and we did a present our engineers did a presentation of what we’re doing. So they also want to highlight that it’s it’s not about AI for the sake of it.

How does a company like MSCI, a premium provider of investment tools, use their AI, and what do we do with it? That’s unique value we’re adding for our clients, and it’s also unique and exciting for the cloud players to show off, like, what we’re up to. That’s that’s very helpful color.

Ashish, Host, RBC: And I think the way you explained it, it definitely feels like MSCI is ahead of the team compared to most of the companies that we speak with. Maybe a question on agent AI agent TKI. Right? That’s another one which is a lot more in focus lately. I was just wondering if you had any views or thoughts on AI agent or agent TKI.

Jigar, CTO, MSCI: Yeah. I think it look. I think, it’s in multiple levels. So if you look at the workforce, right, you’ll have agent TKIs who can do some of the work that a financial director does, for example, or what a data analyst does or a developer does even. Right?

So what what happens? Do you think AI is gonna replace the knowledge workers and the cognitive work they do? It’s it’s a it’s a it’s a changing landscape because what knowledge workers like us do is also shifting with the power of AI. We’ll get out of the grunt work we are doing so we can do more higher value tasks. So it’s a moving target to some extent.

So I’m not too worried about these conversations about will it replace humans or not. At the moment, we’re in a growth phase. So if they can replace half our humans, we’ll use those humans to do a lot more innovation, a lot more products, enter new markets, and new segments, and so on and so forth. Right? So we’re not we’re focusing more on what more we can do with existing people we have.

Regarding agent API, look, there is, there is, an advantage there. We have built MSCR one with a great new interface and integrating all the product lines into one place. But we still have a lot of rich heritage applications like risk manager, BADA one, BADA portfolio manager, and so on and so forth, hedge platform, and EGS and so on and so forth. How do you rewrite all these things? Billions of lines of code, heavily used, products, and old code base.

Well, with agent tech AI, you may not need to. Right? So if you can ask intelligent questions about your portfolio for performance attribution, for example, or risk in a particular portfolio, you’ll need to rewrite these complicated pieces of UI. If you look at if you look at the the entire SaaS industry, I I was the first developer on dynamic CRM many, many moons ago. Spent five years building a CRM system for Microsoft.

All these SaaS applications, like, what is their future? There is a database, there is business logic, and there is UI on top of it. This UI is extremely old and extremely complicated. No salesperson likes the UI in Salesforce nor in Dynamics. So what happens if a salesperson or a servicing person gets a quick answer for what they need to do?

What if the what happens if they can just converse with the platform, agentic AI, which says do this for me and done. Right? So if you look at deep research that is coming out as an example from, OpenAI and Google, and, in these areas, we the way we think about is we have a roadmap for application platform strategy. We obviously do a lot of distribution to Snowflake, but we also need these tools to be having a longer term strategy of convergence. And agent tech AI is a big boon to us because a lot of these complicated processes running on top of extremely intelligent engines like the risk server or the index platform, common index factory we’re building with Foxbury and f rine.

We can provide a very simple experience. An agent take care. You ask questions, you get the answers, and you get rid of all this complicated UI. So for us, it’s actually, really, we see it as a boon to our industry, to our company, because there are too much there is too much complication and lots of acquisitions we do and lot of and even if you look at our client landscape, they have their own platforms. Nobody wants to have 15 different platforms.

Everybody is trying to sell and install them all and everything has to be now people have to figure out how to use these things. With an agent tech AI approach, we can show up with our content and our insight wherever you are. You are sitting on Teams. You’re sitting on Bloomberg. Wherever you are, we can give you the answer you need from us.

So we we are investing very heavily in this area.

Ashish, Host, RBC: That’s great. And the way I understood it, you’re willing to disrupt yourself in order to make sure you’re ahead of the game. There are three buckets that you talked about. I just wanted to go back to the first bucket, improving developer efficiency. You mentioned a lot of you provided a lot of good examples there.

I was wondering if you could talk anecdotally how you think about improving efficiency or improving time to market and maybe elaborate further on how you’ve been able to leverage that to drive faster product developments.

Jigar, CTO, MSCI: Yeah. I think that’s a very good question. I think everything new we are doing, it’s much easier to drive faster pace of innovation. And, the recent projects we saw, one in private assets, one in index, we we are we are looking at we look we have historical data. We have Jira tickets, like how long does it take for a storyboard to get completed?

Number of features written by a developer, how many check ins do you make, how many, commits you made. All of the data exists for last several years. Okay? So you can easily compare when a new project gets started. There’s a detailed accounting from finance and from product perspective, how many engineers are working on something.

We are able to show that we are actually moving at a much faster pace than before. Now, you have to remember, a lot of people are working on massively old code bases and very complicated, millions of lines of code as well. Now, in those areas, you may not immediately see the pace of innovation getting faster, but you will see immediately is the pace of maintenance, things like security updates, quality checks, automation, modernization of the code base, fact refactoring of the code base. Those if you have 20,000 lines of code in a file and you need to and you’re new to this language even, it could be intimidating. But with the part of generative AI and GitHub Copilot and Cursor and other applications like that, we’re able to tackle those things with a lot more confidence.

So there are a lot of ways we are able to measure this. The other example I’ll give you is that in areas like data collection, we are now we we had you know, every time we became more efficient with data collection, we will reinvest that those humans to be applied to new products. Right? And it was easy difficult to see what was going on. So now we became very deliberate.

We tell the product team and we tell the finance team that every quarter we’ll show you how many data analysts, for example, are now freed up. It’s efficiency. We have multiple options. Right? You could say, okay, we don’t need them.

Very likely that is not the answer. Obviously, no. We deliberately put them on the new product. And the reason to do it very explicitly is then you can have a contribute to show that truly we became x percent more efficient in data collection. So we are now also going to each function and saying how marketing, sales, finance, HR, all of them will become more efficient through the power of generative AI.

We’re looking at the most high value task they are doing and helping them find a way to automate those things. And we are starting to track these things. So this is there are financial discussions going on about how do we track these things to make sure we are accountable to ourselves. That’s one reason. Second is to justify the cost of AI tools.

Right? When we partner with these vendors and we spend billions on these kinds of tools, we negotiate with them if we don’t see the right kind of efficiencies or we use the right tool based on where we really are seeing the ROIs there.

Ashish, Host, RBC: Yeah. Actually, that was gonna be my next question, and I’m glad you brought that up. Like, can you discuss what your investment strategy is for investing in cloud AI? And how do you measure those ROIs? Like, what are the key metrics that you track?

Because Gen AI obviously can be quite expensive. So Yeah.

Jigar, CTO, MSCI: So I think, Ashish, if you look at our cloud spend, our modernization spend with AI or or anything, we there’s no differentiation between that and let’s say you wanna spend on some building something in the wealth market or something for fixed income. We simply look at what is ROI. Okay? Now in some areas, you show cost avoidance as an ROI. Yeah.

If I don’t use this piece of AI, which let’s say cost me a hundred thousand dollars, then I’ll have to spend these many people to do this task and that will be more expensive. So cost avoidance, but it has to be done very rigorously and shown very clearly. The other example is to say you compare the cost of data collection for a million data points year after year after year, and you show how is that dropping. And then for many areas of investment in AI, it’s very simple. If you are building products like your special explorer, ask MSCI or AI insights, then these these products are either generating new revenue of their own in case of, let’s say, AI insights, or they are utilized as a tool to increase the prices when we are really versioning a contract, when a contract is up for renewal.

And that happens at a particular period. And these are very large enterprise deals. So, you know, different deals have different ways of, getting compensated for the value we are adding.

Ashish, Host, RBC: That’s

Jigar, CTO, MSCI: right. So when you take that into account and you say, okay, how much new top line growth came out of these things? And you compare that with the cost we had for the human capital involved, the cost of the models and the compute, and then we track all of that. And we do we do talk to ourselves, and sometimes we look at some part of the product in MSCI one where we have very world class investments in, insights and AI. And if the usage is not there for various different reasons, we discussed we should shut it down.

Right? So we we are very active in looking at these things. It’s nothing is like constant. We we we valid what should we shut down, what should we keep going, what should we double down on based on very detailed financial tracking. If you don’t do that, this will be pretty expensive.

Ashish, Host, RBC: That’s that’s very helpful, Cutter. And you talked about obviously, lot of efficiency on the data generation process. You have ability to scale up your data collection process. One of the adjacent topic there is synthetic data. I was just curious if you use it generate or use synthetic data, how do you think about evolution of synthetic data if there is any, role in in the investment industry?

Jigar, CTO, MSCI: Yeah. There there is a role for that for sure. For for QA, for example, it helps a lot to create synthetic data. For in things about, like, prompt generation, creating synthetic data to test out a few things is very helpful. But it it is limited to those areas in my mind.

We are not using it for any of the real core models creation, for example, or index production or anything or or EHT sustainability or climate data. In those areas, we are not heavily leaning on synthetic data. Yeah. But when you want to do some anomaly detection and testing and QA and we want large amounts of data, there is pretty heavy usage for that.

Ashish, Host, RBC: That’s helpful. You mentioned obviously, the M and A. The company, MSCI has done a lot tuck in acquisition, some big acquisitions, transformative acquisition like Burgess with the private capital data. How is the technology team involved and then some of the tech transformation that you’ve done helps accelerate the integration of those products or those offerings?

Jigar, CTO, MSCI: Yeah. That’s a great question, Ashish. So if you think about Burges, Foxbury, Fabric as three examples, generally, they overlap with the last two years of generative AI investments and the big boom in the industry. And, what everything we had invested in building something like AskMSCI, you can apply to real estate or private asset side. The the investments in AI we have done can be utilized towards when when Foxbury gets fully integrated into our custom index platform, and we are very, very close to that, then you can ask, questions and create use AI to create a custom index.

They don’t have to build that capability from scratch. For MSCI wealth platform, which is what used to be fabric, and same example, if you wanna create world class search with auto suggestions and AI elements infused with it, all you have to do is connect the data sources that is powering that application with and blend that with our AI platform. And you can turn these things on at a much faster pace than if you had to do it from scratch. Burges, in particular, what you asked. The the GenAI platform of a data collection we built on GCP was almost perfectly timed with the acquisition.

So the LPGP data collection we are developing and some of our clients want us to provide them as a service to them. It is it is going at a phenomenal pace. We would have otherwise looked at some acquisition three years ago to buy somebody for quite a lot more money, then we we wouldn’t even think about doing that now because we have all the AI models and AI tools we need from data collection in this particular case from Google. And we’re able to apply that to Burgess and drive tremendous efficiencies in the way they used to collect data. Because remember, a lot of this was built years ago, long before generative AI was a big thing.

Ashish, Host, RBC: That’s great color. You highlighted several products which are AI driven or, or tech driven, like, for example, the AI insights. Can you just also talk about your product roadmap over the next, let’s say, three to five years? How do you think the products will evolve?

Jigar, CTO, MSCI: So I think it it it it is going to evolve around mass personalization and customization. It’s going to evolve around more going towards real time as opposed to batch processing. It’s going to evolve towards more agentic AI that you were talking about earlier. And it is going to evolve to a much faster scaling, across either its markets or coverage of the securities or the amount of data you need to in some areas to go from billions of data points to billions of data points. Once we create this capability horizontally across all product clients, it will power a lot of these vertical scenarios.

What do we do in wealth? What do we do in private assets? What do we do before total portfolio footprinting? Right? What do we do with climate?

All of these questions, the business strategy could be different and the, go to market plans could be unique, but the capabilities needed for all of these things are tied with the entire conversation we had over the last few minutes.

Ashish, Host, RBC: Yeah. No. That’s great. And maybe the last question would be just what are you most excited about? You know, the, the thing that is most exciting is if I think about

Jigar, CTO, MSCI: the next ten years of MSCI, I keep reflecting on the scaling laws of AI. But if you think about what are the scaling laws of MSCI, we are writing a document to explain what are the different vectors in which we need to go 10 x in data here, thousand x in here, 300 x in in this area. How can we use the entire platform we have created on technology? Six and a half years ago, I came in. Bulk of our content was distributed on FTP folders and SFTP’s folders.

Today, we have AI driven insights running on top of Snowflake and MSCR one. We are we have a we have come such a long way. Now it is time to reap the benefits of all the investments we have made. So the next ten years as we enter new spaces and new product lines, I’m very excited about the profound impact of generative AI and all the investments we’ve made in technology and data over the last six plus years. It’s exciting.

It’s a fun, it’s good to start now.

Ashish, Host, RBC: That’s great. Thank you. Thanks, Jigar. Thank you, everyone. Thank you.

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