All right. Good morning, everybody. I'll just keep this on time. Thank you for being here. For those of you who don't know me, my name is Manav Patnaik. I cover business information professional services for Barclays. I'm pleased to have back with us again this year Martina Cheung, who's the CEO of S&P. Martina, thank you for being here.
Thank you.
Martina, maybe the first place to start, it's been about a year and a half since you took over as CEO. You've been at the company obviously much longer, but maybe this reflecting on this last 18+ months, what has surprised you overall since you took over? A lot has happened in the market, of course, but maybe just from your position.
Yeah, well, I think the last 18 months has been phenomenal in the sense that we've been able to see, I think, more and more the importance of what we do in the market and, you know, whether that is massive geopolitical disruption and the importance of our information, our benchmarks, and our expertise to our clients. You saw that, for example, with the record numbers that turned out at CERAWeek this year to be able to participate in dialogue around the future of the energy markets and the commodity markets at large, all the way to the importance of our unique and proprietary content in the conversations that we're having with our customers around how they get the best use out of some of the AI tools they're actually implementing themselves.
For me, it is general excitement, I would say, about the growing importance of the role that we play. Of course, the plan that we put out in November at our Investor Day is all the ways in which we will look at leveraging the set of capabilities that we have to grow our business sustainably over time.
Got it. You know, one of the topics obviously that I'm sure you get a lot is around AI. You know, you guys were early with the concept when you acquired Kensho in 2018. People didn't really pay attention. Last year there was an aspect of AI is gonna be good for everybody, and then suddenly it went to it's a disruptor. What is your overall take when you, when you focus on the portfolio you have at S&P? You know, what are the pros and cons of AI that you would talk about on a high level?
Yeah. Well, I think obviously we framed it from the perspective of how we actually integrate AI with our products and how we create a great experience for our customers so that they're getting the benefit out of AI as well. Obviously, we also have our own internal opportunities to integrate AI to generate productivity, to increase speed to market, and to be able to create things that we don't create today. I think on the top line for us, the advantages there are really persisting with the model that we've had for many years now around flexible distribution. If our clients wanna receive through Snowflake, through Databricks, through any number of third-party channels, and now through these LLM providers, we're enabling that, and the technology is giving us ways to enable that so that the customer experience is a lot better.
You know, our ability to, for example, deliver our LLM-ready content through now the S&P Global plugins in Anthropic, Claude for Financial Services, that's generating some very interesting conversations with some of our more sophisticated clients. Actually, we just did in Q1, we had a competitive displacement on that where the conversation for our datasets available through our plugins in Claude for Financial Services, this conversation was led by the Kensho team who demonstrated really the value of the combination of our plugins with our LLM-ready data, and that actually enabled us to displace an incumbent for the dataset sales. We think there's a lot of additional value here and it gets back to how we actually generate that value for customers.
I think, you know, if we go with pros and cons, there's been a lot of narrative around, well, you know, these LLM players and some of these other sort of native AI startups could displace some S&P products, and how do you think about that? I start first with the customer. We always look at the customers and how those customers are gonna evolve, and we have a very diverse array of customers, as you know. The adoption within that customer base is going to be lumpy and uneven.
We will have customers who are at the bleeding edge, and that is a very small number today of customers that are looking at how we would actually interact with them and build with them and within their internal environments they're creating, all the way to customers who we have to actually help find the AI capabilities who don't use them today, and many in between who will rely on the AI capabilities that we're actually building into our products, like Document Intelligence, for example, in CapIQ Pro, or like how we are integrating ChartIQ into our Platts Connect product, for example.
You know, I think for us, we're certainly looking at this as a very strong net positive to the customer experience, whether that's by giving access into new channels or improving the experience on existing products. All of those experiences, those new use cases, growth in usage, et cetera, all of that features into our price negotiation for enterprise licensing come renewal. There, that's where we see real tangible value for our customers. I would say internally, think of it right now as we'll call it sort of like basic integration of AI across various different functions. For example, within our enterprise data organization, we've seen really accelerated adoption of the capabilities. That's been happening for some period of time.
They've been using some of the Kensho capabilities as well over a period of time within the enterprise data organization. Now we see opportunities to be more transformational in other areas of the business as well. I think there's some really great opportunities there to generate productivity. You know, we'll always be balanced. Some of that can go to additional product development or additional reinvestment, and a lot of it will go to improving margins as we go forward. I'm excited. This is, you know, this is a Many people will say, of course, this is, you know, a massive step change or paradigm shift in our end markets and in how S&P operates, and I see that as an enormous opportunity for us going forward.
Got it. I wanna come back to the revenue side. You talked about, you know, margins being a big benefit. Is there any way to think about how long this takes? Like, are you running duplicate systems and, you know, when do you shut off the old systems before you see the margins?
Yeah
Think about that kind of productivity showing up in the numbers?
It would be more about how we actually transform the roles across the organization. Our people expenses are our largest expenses. I can tell you the first example, and we tried to be to lean into this, I would say, over the last two quarters. Obviously, we've said that in the Enterprise Data Organization, we'd expect to see about $100 million of run rate benefit in 2027 from the integration of AI. That's a combination of creating the Enterprise Data Organization and reducing redundancy, you know, and more classic productivity, as well as what we would see around the integration of the capabilities and actually automation of stuff that may have been done manually before. You know, that's a good tangible example.
That's on a base of about $450 million in terms of run rate. Lots more to be done overall, I would say. I think that the thing that's very important right now and, you know, you may see this in your own organizations as well, but, you know, if you think about an area that's a classic opportunity for AI productivity, that would be in engineering, right? Much of what we do, we're not selling toilet paper. Much of what we do is actually running critical workflows for our clients, and we are essentially in many cases trying to change the wheels while the car is actually running, right? It's really important that we do this in a very systematic, stepwise way. This isn't just the integration of the tooling, it's the actual complete change of jobs and roles, right?
We think of it as agentic software development life cycle and how a classic software development life cycle would work with, you know, various different, very predictable roles, whether it's quality assurance, engineering, user story generation and all that sort of stuff. All that's gonna change in that life cycle. There will be new roles. People will need to transition into those new roles. The agents will then need to pick up the stuff that's been left by the people, and that requires time. We have a lot of activity happening there. We have one agentic SDLC project happening across the organization right now, with three divisions actively engaged, and then the fourth will come on board.
As we run these very thoughtful and careful POCs, then we'll learn a lot and be able to accelerate into it. You know, this bigger transformation, and I would characterize that as really transformational, those opportunities were not factored into the guidance that we gave at our Investor Day in terms of our medium-term plan, and we would expect to see that, you know, generate some incremental margin over a period of time, I would say a longer period of time.
Got it. Maybe just one follow-up to that. you know, I think people underappreciate, the value of Kensho, you know, at your organization. You guys talk about it a lot, but maybe a few recent tangible examples of how having them has been an advantage.
Yeah
Period of rapid change with all the technologies out there.
Yeah, absolutely. Kensho Labs, which is essentially just the term that we're giving for Kensho, is almost like a forward deployed engineering team helping our clients. This team is really getting down to, you know, helping our clients to actually extract the value from the tools that we're offering. You know, they're talking, as we said during our earnings call last week, they're working with about 25% of our CCO accounts right now. We have several examples that Couple of the ones that we thought were interesting signaling. I say this, it's important because it's still very early days for many of our clients on this, and they're testing things out.
I say that even the most sophisticated of our clients are testing a lot of this stuff out and trying to figure out how to get the value from it. We have one large, extremely sophisticated global bank that has leaned into AI very heavily, and the Kensho team worked with the market intelligence team on a recent very large renewal. What they were able to do was to demonstrate the value of our data in this particular client's internal AI tool that they've created themselves. As a result of that work, and part of the renewal, the client basically standardized on our data for all of the content that they needed for those datasets essentially within their own internal AI tool. They also expanded the CapIQ contract.
It's an interesting example of what we're seeing, which is even with the most sophisticated of our clients, they're gonna find areas in the organization where tools like a CapIQ with our own embedded AI content will persist and grow even while they are using and standardizing to our AI-ready data for other use cases in their own tools. I found that to be very, very interesting because they're so sophisticated and a good and interesting signal for us going forward on this. You know, Kensho's really key to some of these conversations.
They're key to unlocking another opportunity, which is that clients just have an increased appetite for other datasets, and the Kensho team is very quick to be able to show the clients how some of these additional datasets can actually generate value for them as well. Exciting times. They're very, very busy right now, I would say.
Okay, got it. That's helpful. Thank you. Talking about CapIQ, let's focus on the Market Intelligence business. That's your largest segment. It's also where most of the debate hits on the stock in terms of the AI topic. Before we get into that, maybe just to help the audience appreciate MI has a lot of different businesses or subcomponents in there. Just how you would break out that mix between, you know, the rating, resale, desktop, et cetera.
Yeah, absolutely. Well, I will start with acknowledging your point around MI having generated so much of the dialogue, I do want investors to remember that our benchmark divisions generate over two-thirds of our revenue and about three-quarters of our profit. You know, it's easy to forget that with all of the furor around, you know, AI and software and all that sort of thing. Within MI specifically, what we were at pains to do over the last couple of quarters is to really help all of our stakeholders understand the value of what we have there. There's one way to look at it, of course, which is that we have the CapIQ solutions, including the desktop and datasets. We have the ratings and some of the related credit products.
We have the third group within there, which is enterprise solutions, which is comprised of many workflow tools and critical platforms used not just by our clients, but by entire industry groups and networks. The other thing that we wanted to be able to do for all of you as we went into our earnings call last week, was to help understand how we think about the intellectual property, the uniqueness, et cetera, of what we offer within Market Intelligence. You'll see from the additional disclosures that we made that about 12% of Market Intelligence revenues, what we would think about as perhaps undifferentiated. Undifferentiated is important too in the sense that oftentimes it's expected as part of a package.
For example, we have a third-party redistribution news content piece that we would include in undifferentiated. We've assigned other pieces of data to that as well. I would argue maybe a little bit conservatively. The rest of what we offer is really broken down. We have benchmark, which is essentially we are the distributor of S&P Global Ratings content, both the research reports as well as the data. That's roughly 25%-- I think it's like 23% of what we do. Then just over a quarter of, or rather, over a third of what we have in there is also workflows. Those workflows are comprised of many of these industry standard tools like Wall Street Office, iLEVEL, serving private markets.
A lot of those tools and critical workflows are not just critical for an individual client, but actually function as a standard for an industry group. ClearPar, WSO, these are good examples of these tools. Many of them will work with our own proprietary content as well. For example, our pricing and reference data for loans is pumped through WSO. In iLEVEL, we offer increasingly unique data, including the first tranche of data offerings from our partnership with Cambridge Associates and Mercer. You know, that's the benchmarks piece, the workflow piece. We have the advisory and events piece and that piece of it is very unique to us as well.
Here we would see, for example, some of the With Intelligence events, with what we acquired there, through the With Intelligence acquisition at the end of last year. Those events are incredibly important for us because they actually generate our datasets as well. We have intentions and preferences datasets that are created from the actual feedback that we get from participants during the With Intelligence events that really are unique in the private market space. Then last but not least, let's get to data. We have three different sorts of data that we wanted to highlight last week. The first is curated, the second is contributed, and the third is reference data.
The third one is very easy to explain in the sense that it's what I think of as the glue. You know, it's our unique classifiers that, you know, when we use one of them, everything else links to it, and it becomes very easy for clients and entire industry networks to use that, whether it's GICS or LoanX IDs and other reference data. The first two have their own unique attributes. On the curated front, this is data that we've been collecting for decades. Oftentimes, because we've been collecting it for so long, it is actually not available publicly in a digitized format. It's not the sort of thing where, you know, a startup, for example, could go out and scrape and collect, you know, et cetera.
Good examples there would be Compustat financials or the SNL financials, Panjiva data. The second, the other piece of it there is contributed data. Visible Alpha is probably, I think, a really good example of that. We have contributed data With Intelligence as well. So those would be highly unique. The data that we are launching with the Cambridge Associates and Mercer partnership is also contributed data. You know, you look at all of this is all the data that forms, you know, the basis and the foundation for the unique IP that we have in Market Intelligence.
We wanted to really provide as much information and transparency into that as we could last week because we think it's important that shareholders understand that, you know, just over 12% or just over 10% rather of our MI revenues are what we would consider to be undifferentiated.
Got it. Thank you for that. Yeah, I think that disclosure last week was helpful. I think just to double-click on the workflow piece of that business, I think one of the other things you did last week was also sell your software workflow.
Yeah
Of the energy upstream and focus just on the data. One of the questions we obviously got was like, is that something you would consider on the MI side? Maybe just help us differentiate why the MI workflows are different than the energy workflows you sold.
Yeah. The energy software portfolio is a group of software products that are highly specialized with very, very specific use cases. I, I would contrast that to what we have in Market Intelligence, where a lot of the platforms that we have actually serve entire industries that have standardized around the actual platforms themselves. You know, we run what I would characterize as industry standard workflows with ClearPar, Notice Manager, Corporate Actions, you know, Debtdomain, and then industry networked platforms. In other words, it works because an entire industry group is pumping information and volume through there like WSO, iLEVEL, and others.
You know, these are large scale products that are performing critical actions for our clients that are also embedded into compliance systems for our clients, and in many cases are supported by managed services that we provide through the enterprise solutions business as well. That I think is where I would kind of lay the differentiation there.
Got it. On the, on the data side, I think, one would be interested in your perspective, like a lot of people are thinking about the data desktop side, unbundling, kind of like the media world had done. There's another case to be made that there's a power of the bundle, to your point, if in some of that undifferentiated data has value to it. I, it sounded like from your Investor Day, this was a customer by customer decision, but I don't know if there's any views you would have on that today.
I would say we're leading very much with enterprise pricing on this. You know, we I think I might have shared at Investor Day or even in the last quarter, we've actually only had one customer ask us about unbundling and doing something different like consumption-based or, you know, and things like that. I think it comes back to really where customers are in their own journeys on this. Over a period of time, could we see maybe, you know, some tiering or something like that? Possibly. What we're not gonna move away from the primary mechanism for pricing, which is the enterprise value that we generate for our customers. Because of that, we are letting our customers, you know, access our data through MCP.
We are distributing through third parties like, you know, like Anthropic and OpenAI and others. This is all essentially contributing to the conversations that we have come renewal that you'll see come up over a period of time as we actually renew with customers. You know, we're starting to get those really good indicators now and signals of value, and we began to give you some of those in last week in the earnings call where we saw, for example, API calls double just from February to March of this year. Those are the types of things that we're gonna be tracking very closely over time so that we can actually have that value-based conversation with customers.
Got it. Then just on the MCP partnerships you talked about earlier, you know, yourself and a lot of the competitors have obviously started throwing out how many partnerships and MCP feeds, et cetera.
that they have. Maybe you can remind us of the stats, the question is more around, you know, what exactly is the revenue model there, or how does that partnership work?
Yeah
some confusion around what's being shared, what the model is, et cetera.
Yeah, sure. I'd separate it between, the frontier models like an OpenAI and Claude for Financial Services or Claude otherwise, that and we'll call sort of like the AI native startups, which are sort of like a different subset of partner. With the first category, which is, the larger one, Claude for Financial Services, you know, and ChatGPT, there, the principle and this is true actually for all of our distribution through these third parties, is, A, the model player does not get to train the model on our IP, so it sits on our servers, and B, the relationship from a commercial perspective is with us, it is not with the third-party player.
So that's going to be true for everything that we do here. I think that maybe the one difference that I would call out with the smaller AI native players is that we have had a stream of inbounds from them because they have the, you know, some tooling, but they've no data, and so they have been asking us for data. Because we partner with hundreds of redistribution partners, we've also been partnering with them, and there we charge subscription fees for them to carry the data as a, you know, kind of a base cost, but our customers who want to access the data still have to contract with us. So that's been interesting.
I think for that, those startups, honestly, my expectation would be that we'll see some consolidation there just because of the capabilities that we've seen with the larger players.
Got it.
Yeah.
Just one more on MI in terms of the competitive dynamic. You know, a lot of your traditional competitors are seeing similar things as you are. I think everyone's growing in that 67% range. At the same time, a lot of the startups are getting, you know, raising money at crazy valuations. What's really going on in that marketplace? What are you seeing? Who's winning? Who's losing? What's going on there?
This would be for.
For the MI, for the desktop data.
Yeah. Yeah.
Yeah.
Look, I mean, candidly, on some of these smaller providers, again, I come back to the sophistication in Cloud CoWork and, you know, Opus and other models that we're seeing. I think it's just gonna be really hard if you don't have IP and differentiated data. We have seen we've gotten inbounds from some players who are interested in us taking stakes in them, you know.
Okay
That says a lot.
Okay.
Yeah.
Fair enough. All right, let's move on to the ratings business.
Maybe let's just start off with just current trends from what you saw last quarter and beyond. What is the current pipeline and mix of, you know, issuance categories look like?
Yeah. For us, I think the guidance that we've given for this year essentially calls for, you know, several maybe, you know, underlying assumptions. The first, obviously, we always start with the refinancing wall. That was 12% up year-over-year as of the end of Q4. We've seen a little bit of pull forward, but, you know, we're not assuming any massive pull forward from out years, for example, as you go throughout the rest of this year. When we look at the non-refi piece of the transaction revenue line, there's a couple of sort of interesting assumptions here. The first was, of the $650+ billion in announced CapEx from the hyperscalers this year, we had a couple of key assumptions.
The first was not all of it would be debt financed, and the second was that not all of it would materialize this year. Relative to our full- year expectation on that, we basically saw some pull forward of hyperscaler issuance into Q1, again, relative to our expectation. If we were to assume a more even spread of hyperscale issuance such as it was last year, which we're not assuming right now. It's possible we could see a little bit more on the transaction line for the full- year. We're sticking with our views. We think it's just too early in the year right now to adjust otherwise.
Particularly with some of the uncertainty around the rest of the market with the continued geopolitical strife in the Middle East and, you know, when that could end. We're assuming in Q2. You know, every day it's like you see-
Yeah
something that says it could and something that says it might not. You know, those are some of the pieces. Generally, stable mix of IG high yields. We'd expect to continue to see IG play a key function throughout the rest of the year as well. Those are some of the puts and takes there.
Maybe just one more on M&A. What are your assumptions there?
Oh, yeah.
What have you seen?
Yeah, I Look, we've been thoughtful on M&A. I mean, interestingly, M&A was very healthy because of some very chunky large deals in Q1. Even without the hyperscale issuance, because of M&A, IG would have grown in Q1. Pretty healthy quarter for that. I think we've taken what I characterize as a prudent outlook for M&A for the year.
Got it. Private credit, obviously a big topic out there as well. Maybe first high level, are you from what you guys read, from what you guys see, is private credit a systemic issue? Is it just, you know, narrow pockets? Like, what are you seeing there?
I'd say, the first thing that's important as a sort of a frame of reference for my answer, is that when I talk about private credit, it's the private credit that we rate at S&P-
Yeah
We don't rate everything. There's good reason for that, because we have methodologies that may, you know, that some issuers may not find suitable.
Yeah
for their objectives. From our perspective as our analytical teams look at the overall landscape, they're seeing some elevated risk, but nothing that we would consider as systemic. There have been some, you know, two high-profile things over the last six months or so, and the view from the team is that these are, you know, idiosyncratic examples of fraud, etc. You know, clearly there would be some sectors from time to time that would show different stresses, but that's gonna be specific to that sector, basically. By sector, I mean it could be something like retail or, you know, that kind of thing.
I would say as a general statement, the team doesn't necessarily see, you know, what perhaps in some media, would be characterized as, you know, as catastrophic.
Yeah
portfolio that we're looking at, right?
Okay.
Generally the, you know, private markets continue to be a really good opportunity for us. I mean, obviously we exited last year well north of $600 million, and in ratings in Q1, we saw about a 25% growth in the private credit revenues. It continues to be a very healthy contributor.
Got it. Maybe talking about margins and adding index to the question here, but I think people would agree ratings and index benchmarks probably not disruptable. To your point in using AI for a lot of internal efficiencies, productivity, like the already impressive margins in these segments, are there more opportunities with the use of AI?
I think we would definitely see the potential for what I would characterize as more differentiation in what we do, and that's how our ratings team, for example, is thinking about this. They were very early adopters of the AI tools. I think, in fact, they were the first group to get the Spark, internal Spark product that we launched. They've been using that, I think, to great effect. We've been using it actually in parallel with a kind of a modernization of the underlying workflow within the ratings analytical workspace.
I think what we're seeing here is an opportunity to augment what we can do with our analysts, whether it's more high quality and really differentiated research, putting even more content in front of them as a result of being able to sort of like synthesize and get that into their hands more quickly as well. You know, there we'd expect, you know, to see this as an augmentation in their capabilities and augmentation in timeliness and in relevance and quality and things like that. That's really the objective we're looking for. We have taken advantage already of the early AI capabilities within ratings in the non-analytical functions. It's the analysts really, there we look to preserve that and improve and get as much as possible out of that capacity.
Got it. If we just try and touch a little bit on the indices business.
Yeah.
What are some of the incremental opportunities you see being possible thanks to the use of these technologies?
Our new CEO of S&P Dow Jones, Catherine Clay, is excited about the potential there for AI, you know, across, again, across, I would say, sort of the internal functional areas, whether it's, you know, product areas and otherwise. I'd expect to be able to perhaps move more quickly on product innovation, for example. Particularly when it comes to ways in which we can be thoughtful about the types of things we can do with data. From the subscription line, you know, I think there's some good opportunities there from a product development perspective. We're also being really thoughtful in ratings and index on blockchain and how we think about both the external opportunity and the internal opportunity to deploy that.
I think it's sort of like a tale of two cities there with, you know, with blockchain as well as with AI for both of those divisions.
Got it. Maybe we can just take a few minutes, touch on energy. We talked about obviously, you selling the upstream software businesses.
Maybe with what you're left with, I know you lowered the growth this year because of obviously what's going on in the Middle East, but long- term, how do you see that as a strategic part of your portfolio?
Look, there's absolutely no question that the energy portfolio is of critical importance to the world, really. The reason why I say that is because of the, you know, 270+ benchmarks and price assessments that we conduct, 15,000 daily price assessments that we do, and you pair that with massive growth in energy demand that we see over the next several decades. That energy demand in and of itself creates demand for additional refined products, it creates demand for commodities, it creates demand for any number of downstream products that we look at as well. You know, we are perfectly positioned with the stable of assessments that we have and the unique data that we have to really unlock additional value for our customers.
CERA Titan, which we launched at CERAWeek for our upstream content, is one great example of that, where we're seeing massive uptick right now in the use of our chat functionality and Platts Connect. When the war started, everybody turned to Platts Connect, let me see what else I can find, et cetera. The best way to find that is to use the AI chat feature, which has really seen a massive volume increase. You know, our goal here is to continue to play this central role that we play thanks to this incredible franchise. You look at what we were able to cover at CERAWeek, we had 2,300+ customers from over 90 countries, 11,000+ participants.
We had policymakers, we had hyperscalers come in and announce, you know, where they're providing their own power, and, you know, as part of that dialogue. You know, as the convener of this important conversation and the provider of the independent insights to inform the conversation, I think we are just ideally positioned for this market as it grows going forward.
Got it. Maybe in the last five minutes, if we can touch on capital allocation and maybe use mobility as a lead into it. Maybe just a quick update on the spin there and how that would change the capital allocation priorities once that's completed, if at all.
The spin will not change our capital allocation priorities. We did announce that we'd expect this year to return over 100% of adjusted free cash flow. Then we expect it to return to our normal 85% framework going forward. The spin itself is going very well. I'm sure many of you, if not all of you, are aware that that team's Investor Day is coming up on May 12th, and I wouldn't wanna get in front of the management team on what their message is gonna be. I encourage you to listen in there as well next week.
Got it. Maybe just to focus in on the M&A aspect of your capital allocation, you know, obviously you guys have shown us with the buybacks, the dividend, how are you thinking about what are the areas of M&A that would, you know, attract you to doing some deals? It sounds like you have a lot of what you need, but just.
Yeah
Help us with, you know, what the white spaces or scale areas might be.
Yeah. Look, our priority is always gonna be on organic growth, and we laid out areas during Investor Day like private markets, energy addition, DeFi, supply chain. These areas, in many ways are areas where we can grow with little incremental investment. We have an incredible stable of supply chain capabilities and assets and, you know, the ability to sort of bring them together more effectively from a go-to-market perspective is a real opportunity for us to connect those dots. The CCO is helping us think through that go-to-market, for example.
We also, from an energy perspective, in Q1 we launched a massive swath of energy content on the CapIQ Pro desktop, and so these are ways to get the energy insights that we have to our financial end users, where we see a lot of opportunity there as well. We're very focused on the organic opportunities. Obviously, the integration of With Intelligence is top of mind, and I'm pretty excited by the pace at which the team has been able to move there.
I would say going forward, look, the lens and the criteria through which we might look at opportunities, you know, tuck-in, you know, et cetera, is I'd say the bar is raised, because, you know, the premium, I shouldn't use the word premium, but the value that we would ascribe to the uniqueness of IP is, that's basically it, right? It's gotta be something that is unique, hard to get, not anywhere else, for it to really qualify, I think, you know, as we go forward. That's, you know, that's where we're putting a very, very fine-tuned lens on everything that we might look at. I would reiterate, like I always do, no transformational M&A. Absolutely not.
We're very confident in the business as it is.
Got it. Perfect. All right. We're just about out of time, so we'll leave it there. Thank you so much, Martina. Thank you, everybody.
Okay. Thank you.
Appreciate it. Thank you.