Meta Platforms, Inc. (META) Earnings Call Transcript & Summary

All right. Good morning, everyone. Welcome to our next fireside chat here at the Morgan Stanley 2026 TMT Conference. We are thrilled today to welcome the CFO of Meta, Susan Li. Susan, welcome back.

Thank you so much for having me.

It's always good to see you in our conversations we have about the industry, the company and everything exciting going on in the overall ecosystem. So...

A very sort of even keeled humdrum period in which to be presiding over one of the most conservative planning cycles that we've been through as an industry.

Clarity is high. We all know ROIC. It all makes sense. Exactly next question. Let me start with the important disclosures, including the personal holdings disclosures and Morgan Stanley disclosures appear on the Morgan Stanley public website at www.morganstanley.com/researchdisclosures. They are also available at the registration desk. Some of the statements made today by Meta may be considered forward-looking. These statements involve a number of risks and uncertainties that cause actual results to differ materially. Any forward-looking statements made today by the company are based on assumptions as of today, and Meta undertakes no obligation to update them. Please refer to Meta's Form 10-K with the SEC for a discussion of the factors that may impact actual results. One day I'll have an agent that's just going to do that.

I know. Or are you the person reading really fast on the pharma ads?

Yes, that's right, exactly. Yes.

Okay. So there is a lot that's been changing. Let's -- I want to sort of reflect on the last year external investor conversations and the perception of what Meta is doing versus where we are now. A year ago, we were sitting here talking about drivers of multiyear durable growth, ROIC on the core platform, all of these call options like Meta AI, Meta was going to build. And the market said, Meta is the AI winner, and that's it. One year later, as you know from the discussions, they're different now. There are more questions about ROIC. There's more questions about the positioning of the company versus other tech peers. Maybe let me start with, as you look at it internally, what has changed over the last year about how you think about the strategy of the overall core platform and those call options that we used to talk about 1 year ago?

Yes. Well, it's funny because when you look back -- and actually, it's a very sort of a very apropos timing that you're asking this question, right now is our performance review season. So I've just delivered a lot of performance reviews. And when you look back 1 year ago, it seems almost clean. When you look back at what the questions we were just debating a year ago, the things we were -- the biggest challenges on the horizon and you look now what has transpired over the last 12 months, right? And more importantly, what you think will happen over the next 12 months. And when we look back at the last 12 months, okay, so we take stock of what's happened. The core business continues to perform, I think, extremely well. We have felt very good about our sort of -- our ability to run what has been now for many years, I think, an increasingly robust and measurement-driven process around how to evaluate and fund investments in the core business. That's both across the sort of organic content side, that's also across the ads ranking and recommendation side. I've been at the company for, gosh, 18 years now, and I have worked on ads the entire time. And when I -- sometimes I think back to when we were launching ads on mobile and then that was when we just began and then kind of the ad load ramp from nothing to 12.5%, that was like the first 4 years. And every half, we meet with the ad teams, and they come with kind of the list of here are the different ads initiatives we have. These are the front-end things. These are the ranking recommendation things. These are the kind of -- we have an internal metric called IREV, which is basically how we measure the performance of ads. And here's the list and here's what they add up to. And it is, I think, one of the maybe modern wonders of the world that we have continued to generate basically half after half a list of improvements that continue to generate IREV gains every half and those continue to compound on each other. And so I described the monetization a bit in more detail, but that's true on the organic side, too. And I would say the core business is very healthy. On the GenAI side or the AI side, we've done a lot of rebuilding in the last 12 months. We have built the MSL team and assembled, I think, an incredible leading basically cohort of talent in this space of AI researchers, but also AI leaders, product leaders, et cetera, to come together to complement the existing talent that we had. And that team, I've spent a lot of time with them. Mark has obviously spent much, much more time with them. They've come together really well. They are hard at work producing both the foundation models and also thinking about and building the product experiences that we're going to need. And as we said earlier, we expect the first models that come out of that team to be good, and we expect over the sort of course of the remainder of the year and next year for us to -- we hope to be part of pushing the frontier too. We've also -- the way we think about capacity, and I assume this is something that has been echoed by all of my peers sitting in this chair just continues to evolve. But we thought there was going to be enough capacity 36 months ago, 24 months ago, that continues to change. And we are continuing to build for what we both know that we need today based on what we need in the core, based on what we need to train, but also what we plan to need for inference, inference across a lot of dimensions, both because of the customer, consumer experiences we want to build, also inference for like personal -- not personal, sorry, productivity -- internal, that's what I mean, internal productivity use cases. And so that's a place where we have, frankly, been playing some catch-up through the year. We are still playing catch-up. We are doing a lot to grow our O&O infrastructure footprint. But as it turns out, data centers are a long lead time project and a lot of the stuff we're doing today won't come online until '27 or later. And so we've also started taking down some cloud capacity. So really, I think when I think about the last 12 months, core business, very healthy, very excited about the ongoing opportunities there. It also lets us fund the work in AI from a position of strength and confidence. And then on the AI side, we have been all hands on deck, assembling both the talent and the capacity, as I said. And I would tell you, in the way that only he can, I think, Mark is just like an incredible -- an incredible leader who responds with tremendous sort of focus to the problem at hand. And whether it is over the course of the last summer, if you had seen Mark, he was like the recruiter in chief, identifying and bringing folks on board, really helping the team come together. When we were looking at capacity and the fact that we didn't have enough data center capacity, frankly, to put servers in for the anticipated needs. Mark is the person pushing us to be more creative about data center infrastructure. And I think we've talked a little bit about some of the projects we have. We have some of the finest tents in the world. It turns out you can get tents that are rated to stand for 25 years and withstand tornadoes and all these things to get capacity up faster. So I think that we've learned a lot of lessons over the last 12 months, but I think we are also -- we are never a company that is -- that's not going to respond to the challenge at hand and kind of with the most focus and energy and attention we can bring to bear.

It's a good preamble for me to sort of dig into a little more. Let me go back to that IREV internal metric you talked about of all of the improvements you've been making to the algorithms on organic content, the algorithms on ad rank and the advertising content. I think my team counted 20 changes you listed in the fourth quarter alone. So yes, you've been IREVing. Can you give us a little more quantification about some of the products that are really driving better engagement, driving better conversion? Give us a little quantification of those. And how have all those sort of changed your visibility and confidence about revenue growth to come throughout '26 and even in '27?

Yes. So we have now for -- we run a budgeting process every year. And when we do, teams come to us across the -- especially the ranking and recommendation teams have now a very buttoned-up process, both on the organic content side as well as on the ad monetization side. They run a lot of experiments to identify what they think are the highest confidence experiments, and they're able to measure returns on those experiments, frankly, over both a 1-year and a multiyear basis, so we can kind of look at this over a few years. And I would say, roughly, those things fall into the bucket of -- on the organic content side, how do we basically continue to grow the sort of interestingness and relevance of what we're showing users. And I think in Q4, for example, some of the product ranking work that we did on Facebook resulted in like a 7% lift in organic content views. That basically -- that was the product launch that drove the highest revenue impact in the last 2 years. And we have a sort of -- we have a healthy pipeline of work ahead of us to basically to continue making the content more relevant through a couple of things. One is just scaling up the amount of data we can use that lets us increase sort of the history of content interactions, makes the overall corpus of data available to the recommendation engine larger. The second thing is we're really focused now on -- in the same way that we talked in the past couple of quarters, the way we are really trying to redistribute ad loads so that what we care about is right now, are you in a position where you're interested in engaging with an ad where you want to buy something where you're in a period of commercial intent. In a similar way on the organic side, we want to make the content recommendations most relevant and adaptive to the way you are engaging with content right now. Like what are you looking at right now and what's most interesting to you now? And we're also investing in using LLMs to deepen our content understanding. They are -- as the models continue to become smarter and the sort of understanding and reasoning capabilities become better. Using LLMs to kind of help us understand content helps with recommendations in part because the traditional recommendation engine relies a lot on engagement signals and then you need a lot of engagement to happen to get the engagement signals, but LLMs can reason in real time about whether this is a piece of content that would likely be interesting to you based on what we know. So that's on the sort of organic content side. On the ads ranking and recommendation side, we continue to do a lot of work across all of the -- we've talked a lot about our models, Andromeda, which is on the retrieval side, Lattice, which is on the model consolidation side, GEM, which is on the ranking side, how do we continue again to scale up models and make sure -- in a kind of a similar vein that the ads that we are showing you are the most likely to be relevant to you at this particular moment in time and that you want to engage with the ads in real time to the maximum degree that we can. We're also trying to -- that's kind of the organic and ads bucket. We're also trying to do work in part because we're compute constrained on compute efficiency, how do we use the compute we have today for the highest impact. One of the launches we had on Instagram last quarter grew, I think it was like a 3% conversion lift on Instagram by applying compute to the highest impact sort of ads problems. And so we have a lot of work in all of those pipelines. And that I would generally describe as like work that gets funded through a very ROI-driven process. That's not even the new more research stuff be like, hey, we've got to line up some big bets because we want over the course of the next years to have other things in the quiver. Maybe we have slightly less solid understanding of exactly how that will look today. That's some of the foundation -- the new foundation model work we're doing. We -- I think there was an announcement earlier this year where we're merging some of the research efforts across ads and across the discovery engine teams. The idea is to build sort of a unified foundation model and also doing work to, frankly, build some of our model architecture on top of LLMs and then fine-tune them with engagement data. That's sort of newer research efforts that we hope will pay off over kind of the longer run. But all this is to say, I feel, again, just very good about the pipeline of initiatives we have had, that we have. And the thing that I think now at some point I was up here talking about this, it used to worry me, and it still does, to be clear, I'm just like engineered that way, that if you added up all these initiatives, sure, you could measure the return on each one because of that individual experiment, but you didn't know where on the slope of the curve you were. And so maybe actually, if you add up these 20 things, then you need to discount them by 80% because like the slope becomes much steeper. That has not turned out to be the case. These -- the work that we have done has turned out to be more additive than we expected. And there is a virtuous cycle that you get into with advertisers, right? Like you make the ads perform better. That, in turn, drives costs down for advertisers. That in turn drives their budgets on us up. And then on the platform up. And then hopefully, that's good for their business, and that's a like long-term virtuous flywheel because now it's good for their business. They have more money to spend in the next cycle around doing this with us. That's really hard to measure, right? That's a multi-month, sometimes multiyear process. It's hard for us to measure that very directly. We have, in fact, done our best and run experiments that have been, I think, not statistically significant is probably the way that -- because you're trying to measure something that's so diffuse. But from everything we can observe, that appears to be happening on the platform. And our goal every day is to be the best place advertisers can come and spend their money relative to anywhere else.

You have a lot going on. A lot of pipeline. The one area you mentioned that I want to dig into because I think there is a little bit of a misperception externally about what you're doing now with LLMs on the core versus how you think about using LLMs in the future the core. So you mentioned it a little bit, but maybe just remind us how are you using LLMs now on the core? What does it look like 2 years from now? And what do you think that could do to signal and engagement things?

Yes. So today, LLMs are not a big part of kind of the work in core ranking and recommendations. It's not to say we don't use them at all. There are places we use them more than others, Threads, because Threads is text-based is a place that we're a little bit further ahead in terms of using LLMs to help with the ranking recommendations work on Threads. We are investing in using LLMs to help understand content today for the purposes of, again, better predicting whether the content will be relevant to you. But we are not, by and large, using LLM architecture to do ranking and recommendations work yet. And that is, I think, something that is a little bit again of a longer-term research effort. We don't know exactly what that will look like, but we think it's worth investing in. And we hope that there will be very meaningful gains when we're able to do that successfully in the future.

And it'll take a lot of capacity, which requires a lot of CapEx, which is a hot button topic. I'm sure you get a lot of questions about. So you talked about the pipeline and new products to come. You have Mark on the last public call referring to the current systems as primitive to what they will look like over time with a lot of new projects to come both on and off the core. What types of analysis are you doing as the CFO when you're thinking through this is the right amount of CapEx to spend. How do you arrive at these numbers? How are you sort of putting math to it just to ensure that there's going to be enough revenue in a reasonable amount of time to deliver ROIC for the shareholders?

Yes. So the process is -- the process is kind of -- there are 2 parts of the process. One is, again, on that core work. And there, I think I alluded to this, we have a sort of a pretty robust budgeting process at this point where we kind of take the -- again, the expected inputs that is both across headcount needs and capacity needs, right? Those are kind of the inputs and then we look at, okay, well, this is what we expect the 1-year return to be. This is what we expect the 4-year return to be. Does this make sense? And that is a process we've run now for many years, I think, quite successfully. And that's how we build kind of what the capacity needs for the core are. On the newer things, right, we are, I would say, at an earlier stage in the process. And so this is a little bit more -- if the prior process I described was very science, this is a little more art. And by that, what I mean is the teams that are working on basically AI training today, they have the most immediately sort of clearly defined buttoned-up road map for how much capacity, let's say, they think they need to train models for the next 12, 24 months. And then we need to be -- so that's still like -- that's kind of like a demand road map from the teams that they have more certainty into. The part I think that is the most challenging for us to have certainty around is inference needs because that's both -- you have to predict meaningfully into the future because of the lead time and getting capacity. And we don't know exactly yet how the product experiences are going to take shape, which are the ones that will have the most scale, how will we use inference capacity internally, where will it drive the most productivity. These are all sort of questions that we are like living every day and trying to make our best predictions for what will be true in 12 months, 24 months, 60 months, right? And so what we want to do there is we have scenarios that we are looking at there, and we want to make sure that those scenarios pencil out next to what we also think the returns from these models, but more importantly, the experiences that we build on top of the models can look like. And so that is a little bit more of a -- again, it's like a little bit of a -- we're fitting together a picture of, okay, this is how much inference we need. And if this is how much we think that we're going to be able to grow the core business because of, again, using AI to make content and ads vastly more personalized to you, vastly more interactive. I think this is just -- this area in particular, I think I worry almost that we will underestimate it because when we put things out, they scale so quickly if they're part of our existing FOA experiences, which then immediately billions of people are -- will have access to. And so I think that's a place where it's -- that's probably a place where it's easy for us to underestimate what our inference needs could be. And then you're thinking about new things again that don't exist at scale, and that's a totally different kind -- what should the trajectory of business agents out in the world look like? We certainly think the opportunity makes a lot of sense. We expect that in the not-distant future, every business is going to have at least one, if not many AI agents in the way that they have websites and e-mails and customer service and all of those things today, they're going to have agents that handle some number of these things on their behalf. But it's a -- but because that doesn't exist at scale today, it's like a little harder to know what the trajectory should look like and how we want to roll that out and make sure we're doing it thoughtfully and doing it well. So we're really trying to make predictions over all these different spaces and think about the returns from each of those things also and make sure, again, that the math works out. And that's an imprecise. That's not like, okay, in 2026, the ROI is this in 2027, the ROI is this and so on, which pains me to be clear. I really wish that, that were the world we live in, but it's not. And we have to be willing to sort of make temporal bets, and that's a big part of what we have to do in an intelligent and thoughtful way.

Okay. As a Morgan Stanley analyst, we love our scenario analysis. So it sounds as you're doing scenarios on the ROIC.

Yes. That's right.

And one of those that involves the scenarios around sort of the frontier models and how to think about the super intelligence efforts and the different models that Mark just talked about. So a few questions. First, Mark talked about having some models to show us in coming months. Anything you want to talk about today on new models or not yet?

That I am going to leave to Mark and the AI folks to unveil at the right time.

No avocados [indiscernible], fine. Then let me ask you more conceptually then. There's been a lot of discussion about open source versus closed models. What is the company's latest philosophy on the importance of open source? And how do you think about the main monetization nodes of an open source model 3 years from now?

Yes. So I mean, first of all, I think our approach to open source has been one -- I mean, there's nuance to it, right? And that's been true historically. We don't open source everything we do. And I think we believe a lot, obviously, in open source as kind of a driver of innovation and standardization. And standardization brings efficiency, all things I love. But as the models become more and more capable, I think each model is kind of going to require its own thoughtful decision-making and discussion about whether to open source. So now for -- in terms of, sorry, where we get returns from the models, it is really, we believe, going to be from the consumer experiences that we build. And I alluded to this a little bit in my earlier answer across the family of apps. How do we make the content that you engage with just like better, right? And what is possible today is different from what was possible -- what we thought possible 5 years ago, 10 years ago, 15 years ago for a lot of reasons, right? Part of it is the underlying technology infrastructure has gotten better. So when we first started, people were using this on their desktops, eventually, they moved to mobile. As mobile infrastructure got better, then the content moved from being more text-based to now visual and then there were a lot of photos being shared. And then we had things like photo tagging that made photo sharing even more delightful and you're kind of in this like great cycle of leveraging innovation from a lot of different areas and better infrastructure to make kind of the content experiences more engaging. Now we have seen over the last couple of years, I think a big shift towards short-form video as being one of the sort of most engaging forms of content, again, enabled by a lot of things, including the infrastructure that lets people see short-form video and the ability to kind of again, rank and figure out what to show you. I think that, that is going to continue evolving on all those dimensions, like what if you could interact with the video? What if you could watch a video and say, "Oh, I wonder what would happen next if X," right? And then that's able to adapt for you, right? So I think AI kind of -- I don't know exactly what the word will be, whether it's AI-assisted content or maybe AI-generated content, I think has the potential to -- like I watch like a lot of videos with my kids about science because we're nerds. And like I wish like at the end of the video, there are a lot of great like YouTube videos for kids about like elements. But often at the end, you're like, I have 4 more questions. My kid has more questions about this topic. It'd be great if they could just ask and then the next part of the video happens, right? Instead of I'm like, okay, sorry, let me go search for what this carbon allotrope is. And then -- but it would be better if you could just interact with it and it could give you what you're looking for. So I actually think the kind of intuitive extension of making content really interactive is something I'm, a, very excited about, and I think it's just like an obviously large adjacency to what we do today. And ads, I think, just to not belabor the point, are an extension of that. You get the individualized ad for you, I get the individualized ad for me from the same advertiser. The advertiser doesn't even -- as part of our ongoing continuous now multi-decade effort to make advertising as streamlined as possible for the advertiser where they just come tell us how much they want to spend on something and we deliver it, this is like the next step in that journey for them. I think that the opportunity that comes from those 2 things, neither of which requires us to launch a brand-new business off the ground that doesn't exist today is already extremely large. And I'm very excited about both of them, and they require 0 leaps of the imagination, I think, to understand why they would be big businesses. Setting aside those things, there are obviously more -- I mean, basically new AI experiences, business agents being one of them. Again, I think this is going to be something that will be very commonplace in a few years, even though today, we're not quite -- there's a funny -- there's a hotel that I was trying to book in Orlando. It turns out when you have kids, all the kid events are in Orlando for some reason, home of the greatest convention centers in the world. And I called this hotel and I thought I was -- like it seems very organic.

You called them.

Yes. I called them and I thought I was talking to someone. It took me about 4 minutes to realize I was not talking to a human. Like I was going through what was turning out to be a really confusing phone tree. But in the very beginning, I thought I was talking to a person for quite a long time. That experience should be way better over time, right? We bought Manus. We are excited to -- Manus already obviously exists as a very promising business, and we are excited to scale Manus and grow it and kind of have the notion of multipurpose AI, I think, is going to continue becoming more valuable for folks, and I think there will be a market there. And then I think there are things that, again, don't exist at scale today from like from a product perspective. But certainly, I think as we have those AI experiences that are built out, whether it's in Manus, whether it's in some future version of Meta AI, I think it will be I think monetizing those consumer experiences is not the hard part. I think it is growing the consumer experiences that is the hard part.

I actually -- you brought Meta at the very end. That's why I want to go because you didn't say Meta AI and then now you brought it up. So over the last 2 years, there's been a lot of ebbs and flows that sort of investors' confidence in Meta AI's positioning. I would say a couple of years ago, there was a lot more anticipation Meta AI could be a leading agent to compete against Gemini or GPT. I remember sitting up here in the past talking about booking your trip to Orlando through Meta AI. Now there is this external perception that Meta AI is behind, falling behind and not going to be able to catch up to the other players. What's your reaction to that? And how do you think about sort of the pace of product innovation on Meta AI throughout 2026?

Yes. Well, Meta AI has over 1 billion people who use it despite not being on a state-of-the-art foundation model. So while -- like I think I am totally clear-eyed in assessing where we are today, I am meaningfully more sort of optimistic than that framing, I think, about where this could go for a lot of reasons. One is, again, just the kind of scale of distribution. I think one of the playbooks we really have dialed in as a company is when you have a good product with consumer market fit, how do you leverage the distribution network we have to put it in front of a lot of people. But you don't want to do that until it's like a very valuable experience. The second thing is I think that our ability to personalize -- to personalize your conversational AI agent to you is going to be second to none. I think both based on just your deep history of interacting with the platform already and our ability to understand that information and sort of use it to make sure we're building a good experience for you. So I feel -- I think that when we have a frontier model, I feel quite confident that the combination of that, the combination of the distribution graph, the network effects, the fact that there are a lot of very natural places to have Meta AI interact with you. I can have -- I can be in a WhatsApp conversation with my friends, and we can be talking about going to dinner and it can just -- an AI agent in the chat can just book the restaurant, right? Or we're planning a ski trip and an AI agent can like tell us where there's snow, evidently nowhere. But if there is snow, like where should we go and when and like what kind of gear do we need. Like I think that -- I think kind of how -- I think the ways in which the family of apps as it exists today, I think, are a great scaffold for AI experiences to fit very neatly within them, I think, is -- are very intuitive. I'm excited for that, and I'm excited to see how that unfolds.

Okay. We're excited to see the product evolve. The other area that I want to sort of touch on before we get to the end is custom silicon. This is another part that sort of has been a multiyear evolving strategy, and it sort of has expanded. So maybe remind us where are you using your own custom silicon now? What have been the early learnings from that from an efficiency perspective? And how do you think about the next couple of years of expanding the use of custom silicon as opposed to using third-party chips?

Yes. I'll actually expand the question a little bit. Custom silicon is one of, I think, many -- I mean it is one and a very important part of an overall like how do we bring the cost of compute down strategy over time. And obviously, chips are the sort of most expensive piece of that. And again, apparently to date, the shortest-lived version because new chips come out and you want to leverage the better performance that you can get, but obviously, who knows over the longer term. And so for us, because we have so many -- the scale of our silicon needs across AI training, across anticipated AI inference, across the core ranking and recommendation work, across CPUs for keeping the site running in kind of the like bread and butter of running the family of apps. We are -- we're really focused on basically making sure that we are getting the optimal chip for each workload and each -- and that combination is still at a scale that like lets us do this in a sort of cost-efficient way, if that makes sense. And so you've probably seen a number of announcements come out between us and some of the different chip providers. And that's all in service of that effort, which is like based on what we know today and our current needs, what do we think is the best chip to use for each of these use cases and some of them are totally off the shelf. Some of them are somewhat customized, some of them are very customized. And can we negotiate like what -- the volumes we need at what we think are attractive prices for kind of for each of those chips. Custom silicon is a big part of that, obviously. Some of our workloads really are very customized to us. The sort of ranking and recommendations workloads have been where we have started, and that's the place where we have rolled out custom silicon at the most scale. But we expect and are hopeful that we are going to expand that over time, including eventually to training AI models. So that is obviously later in the road map. But we're feeling both -- I think we're feeling quite optimistic about the way those chips are performing today because, again, they really let us optimize for performance per dollar and like the total cost of ownership of the chips we need for each use case.

Great. Let me ask you one more. We talk a lot about the GenAI or the GPU opportunities and everything is sort of the growth in the pipeline. What is sort of the most underappreciated challenge or the factor that keeps you up at night when you're sort of thinking about making sure you execute on the right factors in this whole GenAI era in the next couple of years?

I think there are 2 dimensions to it. On the product side, I think I alluded to this earlier, but I think -- again, and I think this is so natural, but when we, including the industry at large, talk about kind of what the future sort of products and experiences are going to be. And clearly, there have been some great new products and experiences that have been built. We tend to think about new things. And I -- again, I think we underestimate how big the sort of taking like AI technology and making things that exist better, I think, is just going to be a -- that is going to be a massive opportunity. And I think I want to make sure that we are resourcing against that appropriately. I also think AI tools are changing the way we work. And I think clearly, if you were starting a company today, you would use a lot of AI tools very differently, and you might set up a lot of your workflows very differently and you might set up your teams very differently. And we don't want to -- we -- like as a company that has now existed for over 20 years, we don't want to find ourselves behind companies that are being born today and that are AI native from like the very day of inception. And so making sure that we stay on top of how would -- what would a team that is solving this problem like at a start-up, how would they think about solving it, right? And of course, we can't just perfectly do that because we have an inherently very large code base that already exists, and we've got lots of processes and things that exist for good reason. But I do think making sure that we're asking ourselves that question is very important so we don't get left behind. And what we have found, I think I mentioned this on the last call that like AI tools are making our developers more productive. They are making our most effective -- our developers who are most effective at using them much more productive. They -- I think we -- I think 80% was sort of the stat we shared in terms of increase in coding productivity. And so -- and then what does that mean for -- well, how should we think about like how you use these tools to not only increase your own productivity, but also make it easier for you to work with other people, what should teams look like in that future? How do we -- we've got senior executives of the company who are like using AI tools to build their own agents and stitch together data from different sources and things that you used to have to ask, you sent an e-mail into like a team and would wait several hours for multiple different data sources to get stitched together for you, like people are getting that information much faster now, and that enables them to make decisions more quickly and to make more decisions, right? And like the long-run goal of this is to do more things, right, and to do more things and build more experiences. And so making sure that we for a company as kind of at the size and scale that we are that we don't work any less efficiently than companies that are AI native from the start. That is, I think -- that's something that I think about a lot and want to make sure that we are as well set up to compete as any of them.

Great. Susan, thank you very much. We're excited to see all the efficiency and you guys do more and more things in the years to come.

Thank you so much.