S&P Global Inc. ($SPGI)

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. So Martina, thank you for being here.

Thank you.

Martina, maybe the first place to start, I mean, it's been about 1.5 years since you took over as CEO. You've been at the company obviously much longer. But maybe just reflecting over this last 18-plus 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.

Yes. 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 whether that is massive geopolitical disruption and the importance of our information, our benchmarks and our expertise to our clients. And 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. And so for me, it's -- it is general excitement, I would say, about the growing importance of the role that we play. And 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. One of the topics, obviously, that I'm sure you get a lot is around AI. And you guys were early with the concept when you acquired Kensho in 2018, people didn't really pay attention. Then last year, there was an aspect of AI is going to be good for everybody and then suddenly, it went to it's a disruptor. So what is your overall take, when you focus on the portfolio you have at S&P, what are the pros and cons of AI that you would talk about on a high level?

Yes. 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. And then 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. And so 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. So if our clients want to 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. And so our ability to, for example, deliver our LLM-ready content through now the S&P Global plug-ins in Anthropic Claude for Financial Services, that's generating some very interesting conversations with some of our more sophisticated clients. And actually, we just did in Q1, we had a competitive displacement on that where the conversation for our data sets available through our plug-ins in Claude for Financial Services. This conversation was led by the Kensho team who demonstrated really the value of the combination of our plug-ins with our LLM-ready data, and that actually enabled us to displace an incumbent for the data set sales. And so we think there's a lot of additional value here, and it gets back to how we actually generate that value for customers. Now I think if we go with pros and cons, there's been a lot of narrative around, well, these LLM players and some of these other sort of native AI start-ups could displace some S&P products and how do you think about that? And I start first with the customer. We always look at the customers and how those customers are going to 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 that 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 Cap IQ Pro or like how we are integrating ChatIQ into our Platts Connect product, for example. And so I think for us, the -- 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. And 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. And so there -- that's where we see real tangible value for our customers. And then 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. And now we see opportunities to be more transformational in other areas of the business as well. And so I think there's some really great opportunities there to generate productivity will 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. So I'm excited. This is a -- many people will say, of course, this is 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 want to come back to the revenue side, but you talked about margins being a big benefit. Is there any way to think about how long this takes? Like are you running duplicate systems? And when you shut off the old systems before you see the margins? How should we think about that kind of productivity showing up in the numbers?

Yes. So the -- it would be more about how we actually transform the roles across the organization. So our people expenses are our largest expenses. I can tell you the first example, we tried to be -- to lean into this, I would say, over the last 2 quarters. So we've -- 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 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. And so 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 the thing that's very important right now, and you may see this in your own organizations as well. But if you think about an area that's a classic opportunity for AI productivity, that would be in engineering, right? Now 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? So 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? So we think of it as agentic software development life cycle and how a classic software development life cycle would work with various different, very predictable roles, whether it's quality assurance, engineering, the user story generation and all that sort of stuff. All that's going to 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 so that requires time. And we have a lot of activity happening there. We have one agentic SDLC project happening across the organization right now with 3 divisions actively engaged and then the fourth will come on board. And as we run these very thoughtful and careful POCs, then we'll learn a lot and be able to accelerate into it. And so 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 generate some incremental margin over a period of time, I would say, a longer period of time.

Got it. And maybe just one follow-up to that. I think people underappreciate the value of Kensho at your organization. You guys talk about it a lot, but maybe a few recent tangible examples on how having them has been an advantage in today's period of rapid change with all the technologies out there.

Yes, absolutely. So 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 -- it's getting down to helping our clients actually extract the value from the tools that we're offering. And so 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, a couple of the ones that we thought were interesting signaling. And 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. And 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. And so 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. And 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 data sets essentially within their own internal AI tool, but they also expanded the Cap IQ contract. And so it's an interesting example of what we're seeing, which is even with the most sophisticated of our clients, you're going to find areas in the organization where tools like a Cap IQ 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. And so 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. So Kensho is 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 data sets and the Kensho team is very quick to be able to show the clients how some of these additional data sets can actually generate value for them as well. And so exciting times. They're very, very busy right now, I would say.

Okay. Got it. That's helpful. Talking about Cap IQ, let's focus on the Market Intelligence business. That's your largest segment. It's also where most of the debate is on the stock in terms of the AI topic. But before we get into that, maybe just to help the audience appreciate MI has a lot of different businesses or subcomponents in there. So just how you would break out that mix between the Ratings resale, desktop, et cetera?

Yes, absolutely. Well, I will start with -- and acknowledging your point around MI having generated so much of the dialogue, I do want investors to remember that our benchmark divisions generate over 2/3 of our revenue and about 3/4 of our profit. And so it's easy to forget that with all of the furor around 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. So there's one way to look at it, of course, which is that we have the Cap IQ solutions, including the desktop and data sets. We have the Ratings and some of the related credit products. And then 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. And so 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. And so 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. And undifferentiated is important, too, in the sense that oftentimes it's expected as part of the package. And so for example, we have a third-party redistribution news content piece that we would include in undifferentiated, and 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. So 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 -- I think it's 23% of what we do. And then just over 1/4 of -- or rather over 1/3 of what we have in there is also workflows. And 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. So ClearPar, WSO, these are good examples of these tools. And many of them will work with our own proprietary content as well. So 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. So that's the benchmarks piece, the workflow piece. And then we have 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. And those events are incredibly important for us because they actually generate our data sets as well. So we have intentions and preferences data sets 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. And then last but not least, let's get to data. And we have 3 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. And the third one is very easy to explain in the sense that it is the -- it's what I think of as the glue. So it's our unique classifiers that 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 2 have their own unique attributes. So 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. And it's not the sort of thing where a startup, for example, could go out and scrape and collect, et cetera. And so good examples there would be Compustat financials or the SNL financials, Panjiva data. And then the second -- the other piece of it there is contributed data. And so Visible Alpha is probably, I think, a really good example of that. We have contributed data in With Intelligence as well. And so those would be highly unique. The data that we are launching with the Cambridge Associates and Mercer partnership is also contributed data. And so you look at all of this, this is all the way -- this is all the data that forms the basis and the foundation for the unique IP that we have in Market Intelligence. And 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 just over 12% -- or just over 10% rather of our MI revenues are what we would consider to be undifferentiated.

Got it. Yes, 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 of the energy upstream and focus just on the data. So one of the questions we obviously got was like, is that something you would consider on the MI side? Or maybe just help us differentiate why the MI workflows are different than the energy workflows you sold?

Yes. So the energy software portfolio is a group of software products that are highly specialized with very, very specific use cases. And 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. And so we run what I would characterize as industry standard workflows with ClearPar, Notice Manager, corporate actions, 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. And so 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. And so that, I think, is where I would kind of lay the differentiation there.

Got it. And then 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. But there's another case to be made that there's a power of the bundle, to your point, even some of that undifferentiated data has value to it. So 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. And 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 things like that. And 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 some tiering or something like that? Possibly. But we're not going to move away from the primary mechanism for pricing, which is the enterprise value that we generate for our customers. And because of that, we are letting our customers access our data through MCP. We are distributing through third parties like Anthropic and OpenAI and others. And 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. And 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. And those are the types of things that we're going to be tracking very closely over time so that we can actually have that value-based conversation with customers.

Got it. And then just on the MCP partnerships you talked about earlier, 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, but the question is more around what exactly is the revenue model there? Or how does that partnership work? Like I think there's a confusion around what's being shared, what the model is, et cetera.

Yes, sure. So 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 start-ups, which are sort of like a different subset of partner. With the first category, which is the larger one, Claude for Financial Services and ChatGPT, there the principal -- 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. And 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 some tooling, but they have no data. And so they have been asking us for data. And 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 kind of a base cost, but our customers who want to access the data still have to contract with us. And so that's been interesting. I think for that, those start-ups, 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. And then just one more on MI on the -- in terms of the competitive dynamic, a lot of your traditional competitors are seeing similar things as you are. I think everyone is growing in that 6% to 7% range. At the same time, a lot of the start-ups are getting -- raising money at crazy valuations. So what's really going on in that marketplace? What are you seeing? Who's winning? Who's losing? What's going on there?

And this would be for the...

For the MI, for the desktop data.

Yes, yes. Look, I mean, candidly, on some of these smaller providers, again, I come back to the sophistication in Claude Cowork and Opus and other models that we're seeing. I think it's just going to be really hard if you don't have IP and differentiated data. And we have seen -- we've gotten inbounds from some players who are interested in us taking stakes in them. So I think that says a lot.

Okay. 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 issuance categories look like?

Yes. So for us, I think the guidance that we've given for this year essentially calls for several maybe 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 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. So the first was of the $650 billion plus 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 will materialize this year. And so 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. But 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 when that could end, we're assuming in Q2, every day, it's like you see something that says it could and something that says it might not. And so those are some of the pieces. But generally, a stable mix of IG, high yield. We'd expect to continue to see IG play a key function throughout the rest of the year as well. So that's -- those are some of the puts and takes there.

And maybe just one more on M&A. What are your assumptions there? What have you seen?

Yes. 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. And even without the hyperscale issuance because of M&A, IG would have grown in Q1. So pretty healthy quarter for that. I think we've taken what I'd 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 rate, from what you guys see, is Private Credit a systemic issue? Is it just narrow pockets? Like what are you seeing there?

I'd say the first thing that's important to, 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, and we don't rate everything. And there's good reason for that because we have methodologies that some issuers may not find suitable for their objectives. And so 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. And there have been some couple of high-profile things over the last 6 months or so. And the view from the team is that these are idiosyncratic examples of fraud, et cetera. Now clearly, there would be some sectors from time to time that would show different stresses, but that's going to be specific to that sector basically. So by sector, I mean, it could be something like retail or that kind of thing. So I would say, as a general statement, the team doesn't necessarily see what perhaps in some media would be characterized as catastrophic. Again, that's our portfolio that we're looking at, right? Generally, the 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 revenue. So it continues to be a very healthy contributor.

Got it. And maybe talking about margins and adding index to the question here. But I think people would agree Ratings and index benchmark is probably not disruptable. But 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. So 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. And they've been using that, I think, to great effect. We've been using it actually in parallel with kind of a modernization of the underlying workflow within the Ratings analytical workspace. And 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. And so there, we'd expect to see this as an augmentation in their capabilities and augmentation in timeliness and relevance and quality and things like that. And 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. But 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. And if we just try and touch a little bit on the Indices business. What are some of the incremental opportunities you see being possible, thanks to the use of these technologies?

Yes. So our new CEO of S&P Dow Jones, Cathy Clay, is excited about the potential there for AI across -- again, across, I would say, sort of the internal functional areas, whether it's the product areas and otherwise. And so 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. And from the subscription line, 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. So I think it's sort of like a tale of 2 cities there with blockchain as well as with AI for both of those divisions.

Got it. Maybe we can just take a few minutes to touch on energy. We talked about, obviously, you're selling the upstream software businesses. So 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?

Yes. Look, there is absolutely no question that the energy portfolio is of critical importance to the world, really. And the reason why I say that is because of the 270-plus 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. And so 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. But we're seeing massive uptick right now in the use of our chat functionality and Platts Connect. So when the war started, everybody turned to Platts Connect. Let me see what else I can find, et cetera. And the best way to find that is to use the AI chat feature, which has really seen a massive volume increase. And so 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-plus customers from over 90 countries, 11,000-plus participants. We had policymakers. We had hyperscalers come in and announce where they're providing their own power and as part of that dialogue. And 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 5 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?

Well, 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, and 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 12. And I wouldn't want to get in front of the management team on what their message is going to be. So I encourage you to listen in there as well next week.

Got it. And maybe just to focus in on the M&A aspect of your capital allocation. Obviously, you guys have shown us with the buybacks, the dividend. But how are you thinking about what are the areas of M&A that would attract you to doing some deals? It sounds like you have a lot of what you need, but just help us with what the white spaces or scale areas might be.

Yes. Look, our priority is always going to be on organic growth. And we laid out areas during Investor Day like private markets, energy addition, DeFi, supply chain. And 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 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. And 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 Cap IQ 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. And so 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. And I would say going forward, look, the lens and the criteria through which we might look at opportunities, tuck-in, et cetera, is -- I'd say the bar is raised because 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? So it's got to be something that is unique, hard to get, not anywhere else for it to really qualify, I think, as we go forward. And that's where we're putting a very, very fine-tuned lens on everything that we might look at. But 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, and thank you, everybody.

Okay, thank you.

Appreciate it. Thank you.