Pinterest, Inc. (PINS) September 9, 2026 Conference Transcript

In the interest of time and for the webcast, we're going to get going. It's my privilege to kick off our first fireside chat of day two with Bill Reddy, the CEO of Pinterest. I'm going to read a quick save harbor, and then we're going to get into this. Some of the statements that Pinterest will make today may be considered forward-looking. These statements involve a number of risks and uncertainties that could cause actual results that differ materially. Any forward-looking statements that Pinterest makes are based on assumptions as of today, and Pinterest undertakes no obligation to update them. Please refer to Pinterest's latest Form 10-Q and Form 10-K for a discussion of the risk factors that may affect its results. Okay. Bill, thanks so much for being part of the conference again this year. It's always a great chance. Always great to have a chance to talk to you. It's been a few years now since you've joined Pinterest as the CEO. I think the anchoring theme I always consistently hear from you is about making Pinterest as a platform more actionable. Can you talk about the journey you've been on measured against that goal from when you came into the company?

Yeah, absolutely. I mean, you know, the headline is like, you know, Pinterest has become an AI-driven shopping assistant for 640 million-plus users. It's becoming one of the fastest-growing platforms in social media, consistent double-digit growth in users, strong engagement growth. And at the core of that has been the actionability that you just mentioned. You know, coming into Pinterest four years ago, it was pretty much a platform for, you know, visual discovery and browsing, but it wasn't a place to buy things. And it's now the case that more than half the users that come to our platform are there to shop. Gen Z is the largest, fastest-growing cohort, more than half our users, and primary use case is shopping. And at the core of that is making so that you can discover more of what you're looking for, make more of those purchases. And, you know, that has been at the core of the revitalization of the platform, both in terms of the user side of it, where we're getting, you know, much deeper engagement, particularly with those Gen Z users, you know, stated simply, Pinterest is where Gen Z goes to shop, and it's a place that they're thinking of as a first place to go search increasingly as well. So there's an Adobe study that we've cited previously that 39% of Gen Z thinks of Pinterest as the first place to go search. Why? 71% of Gen Z sees Pinterest as more personalized than other places to go search. So at the core of it is it's not just us using AI. It's us using really unique data that we get around the human curation on our platform. So AI doesn't have style and taste on its own. Humans do, but that's what people do on our platform is curate that style and taste, and we've used AI to really superpower our recommendations to users, giving them great recommendations before they even know what they're looking for that users will say, oh, my gosh, Pinterest just gets me, and increasingly make it so that they can shop and buy. And that's why now you see that from a platform that was almost entirely upper funnel ads four years ago, we've talked consistently about two-thirds plus of the business's lower funnel performance. We talked about more than 5x the number of clicks to advertisers over the last three years. Those things all get to the deep action ability on the platform. And so we've had great progress there. At the same time, in the course of monetizing a platform like this, we're still relatively early on. There's still a lot more opportunities. So one of the strongest parts of the story has been our growth and user engagement. And then the monetization, you know, I talked about the shift to lower funnel. We've made great progress on that, especially, you know, in our home market here in the U.S. But in international, where we have, you know, more than 80% of our users, still roughly 20% of our monetization. So a lot more of that to go there. So when I sort of look at the progress, you know, we've clearly proven out Pinterest as a shopping destination, Clearly proven out we can make it actionable. Clearly proven out that we have differentiated signal to train the AI on to give fantastic recommendations to users so they want to come back and shop more. That's a global phenomenon. And then the monetization always follows them behind the user behavior. And so we've made great progress in the U.S. We're translating more of that international, but there's a lot more of that to do. Okay.

Building on that answer about your future vision for the company, we got the news in the last week of Julia's departure from the CFO role. Can you talk to us when measured against where you want the platform to go, what you'll be looking for in a new CFO?

Yeah, certainly. You know, so, you know, having built from zero to many billions, you know, multiple times, you know, these things are like relay races, right? And you think about sort of, you know, what's the team and the things you need to do for each leg of the race. And I sort of touched on this a little bit in the last comment of, like, what were the things that we needed to address? And four years ago, this is basically a single product platform and a single geography, and we're now moving into multiple products, many more geographies, much greater complexity, and we've been building up a team for that leg of the race. And so if you look at what we've been doing more recently across the team, you know, started with a new chief product and technology officer that led shopping at Google, has been doing a fantastic job with what we're doing with the tech platform, bringing a great team there, recently a new, you know, head of product engineering for ads, new head of infrastructure, new head of sort of the internationalization of the platform on the product energy side, new chief business officer, new chief marketing officer. And so we're really building up the team for where we need to go next. And so similarly for the CFO, when I think about that, we have now proven out that we can monetize our audience beyond just one surface. We did the acquisition of TV Scientific. We've got multiple products within Pinterest now monetizing beyond the one surface, going to many more geographies. So when we look at that complexity of the next leg of the race, we've been building a team around that. And, you know, these are things that we'll be solving for, you know, with our next CFO and that, you know, we'll want a deep operational partner. You know, grateful to Julia. She ran her leg of the race well. You know, very good partner through that, and I'm sure she'll be great in the next thing that she goes and does. But if you look at where we were, you know, four years ago, there's a lot of what we were doing that was sort of, you know, addressing a lot of things that, you know, ideally probably should have been done before the company was public. And so we were really getting a lot of that operational rigor and hygiene. And now we're in this place of expanding to more products, more geos. That brings greater complexity. So we'll look for another great operational partner, but one that has done a lot of that kind of operational complexity at scale and across the rest of the team. That's what we've been building for. And we're seeing really great talent, really great candidates for our CFOs. So I'm quite confident that we'll get a really exceptional person in that role.

It will be a great partner for the next leg of the race. Okay. There's so many themes of convergence that we've been talking about over the last 12, 18 months, you know, search, social media, commerce, AI. When you think about the broader discovery process that consumers are going on right now against those themes of convergence, how do you think about positioning Pinterest against consumer discovery over the medium to long term?

Yeah, well, you know, I touched on this a little bit that, you know, we've made tremendous I think it's been the strongest part of our story, and it's a big change in the story. Four years ago, Pinterest was a platform that was rapidly declining, and users had missed the next generation. Now to a platform of 12 straight quarters of record-high users. Gen Z is more than half the platform, our largest, fastest-growing audience. We've talked about many times how we're growing across every geography that we track, across the generations that we track. And so it's broad-based. And so at the core of that is people are thinking about different ways to engage in their shopping experiences than what they did previously. I've said this before that the first 25 years of e-commerce in a lot of ways sort of solved buying but killed shopping. And the distinction being that, you know, if you knew what you wanted and you wanted to get it the cheapest and the fastest, you know, there's a lot of things built for helping you do that. But a lot of the rest of the shopping journey was about, well, I don't know what I want to buy yet. It's the sort of I'll know it when I see a problem. And if you think about how people shop in the real world, that's a lot of it, that, you know, whether they want to update the wardrobe for fall or they're thinking about what to make for dinner that night, They'll go into a shopping setting with a sort of loose idea, but if you ask them, like, hey, tell me exactly what it is you want to buy, they say, well, I don't know. I'll know it when I see it, and that's what Pinterest is solving, but we're solving it now in a way that is going from that upper funnel discovery all the way to basically sort of dream, decide, and do. So, like, what's that initial kernel of the idea? How do I refine that? How do I make a decision? And then how do I easily take action on that? And we're solving across all of those. And, you know, a thing I'll share, you know, one of the questions that we'll often get, especially on the strength of our Gen Z usage, and it's funny, I used to get this question all the time in the early days of Venmo, where people say, are these millennials ever going to, like, spend money? And I was like, of course, they're going to graduate college, and they're going to spend money and, like, all these things. And then we get that question about Gen Z, a lot of, like, well, can you monetize those users? And with Gen Z, not only are they our largest, you know, fastest-growing demographic at more than half the platform, they have the deepest engagement, the highest retention. They're also the most AI native, so they're building, you know, AI native, you know, capabilities, things like that. They are deeply versed in those things, like so our assistant capabilities we put out there, deeply versed in those things. But Gen Z on our platform, we now see the ARPU of our Gen Z users in the U.S. is roughly consistent with the ARPU of our older generations like millennials. And so I think that bodes really well for the future monetization of those Gen Z users that, of course, they're going to shop. They are shopping. They're shopping quite a bit, which is why their ARPU is now consistent with the next generation up with millennials. And I think that bodes really well for the future monetization of our platform. Because the purchasing power of those Gen Z users is only going to increase, and they see Pinterest as a place for them. Again, Pinterest is where Gen Z goes to shop, and so we see that even as their purchasing power is early in its growth, they've already equaled the ARPU of millennials on our platform. So, you know, we hadn't shared that previously, but, you know, that's what we're seeing on the platform, and it's exactly what I would have expected having, you know, built commerce experiences, you know, previously, these generations mature, but the depth of the engagement with our Gen Z users is just quite significant and outpaces, you know, the other generations on our platform. Okay.

Understood. You spent some time on the Q2 earnings call talking about your approach to AI as a company. Against the landscape of what's available to you, models, products, things you can bring into your ecosystem, talk to us a little bit about your AI strategy, both in terms of what you can pull into your ecosystem from the AI landscape, and then how you can take elements of your data and build personalized, unique experiences of interest in a cost-efficient manner.

Yeah, so this is, you know, from the time I came over from Google, you know, I've spoken about this consistently, that, you know, that in this coming wave of AI, that the sort of model capabilities will be available to many, but then there was going to be a lot of opportunity for places that had unique data, that, you know, AI thrives on feedback loops. So what are the places that had unique data, unique user feedback loops? And for Pinterest, you know, I talked about how we're using that unique human curation on our platform to train our AI to give really, really distinct recommendations. You know, that's at the core of the reacceleration of the platform, that basically everything you see on Pinterest is served by AI and has been for a couple of years. That's one of the first things I said about doing coming into Pinterest. But, you know, we use a combination of compact fit-for-purpose models and, you know, taking open-source, open-weight models and post-training on our data to really great effect. And, you know, we've been talking about this for years. Now there's a lot of discussion of, you know, open-source, open-weight. We were very early on that. There's a lot of discussion of, like, model routing. We were very early on that. There's a lot of talk about, oh, well, maybe you don't need the frontier for every task. Maybe that is over-solving the problem at much too great an expense. Well, check the tape. We've been on this for several years, and it's part of why, even as we have made it so that everything you see on Pinterest is served by AI, we've also had significant expansion of margins in our business over that period of time because we have been able to not only align those AI use cases with highly monetizable events that are very, very commercial, You know, it's also the case that we've been able to do it at very, very, you know, good cost effectiveness, and that's because, you know, we use compact models trained on our unique data. You know, we get great results from post-training open source, open way, and I think that's a really important thing as you look forward that, you know, in the initial sort of wave of adoption, sort of everybody just racing to make sure they didn't miss out, And particularly, you know, less sophisticated companies, things like that, where it's like, just give me whatever I can adopt. And as you now see token bills coming in and things like that, people are thinking a lot more about, oh, you know, I probably should think about the right tool for the job. And, like, using the frontier for everything, you know, might be a little bit like using a Formula One engine to, like, mow your lawn. You may have, like, not only massively overpaid, you may not have solved the problem as well as something that was built fit for purpose for the thing that you're trying to do. I think one of the most exciting things that has happened in the industry is the advancement of open source and open weight. I said on our last earnings call that, and I'll say it again because I think it's really important, any CEO that's not heavily leveraging open source, open weight models is almost certainly wasting a lot of shareholder money because you can get similar capabilities at dramatically lower cost. For us, we shared that when we use open source, open weight, you know, compared to a comparable size model, we are able to get, you know, costs at less than 8% of what the comparable closed models would be. But we also see greater effectiveness because you can't train the closed models on your own data. So when you can train on your own data, that's greater effectiveness. And it's actually greater safety as well because you can run it in your own secure environment, which you can't do with the closed models. And so you know where your data is going with the open-weight models when you're running in your own environment. And, you know, not only do you have a very good thriving open-source, open-weight ecosystem now, including here in the U.S. with U.S.-based models, you have the hyperscalers leaning in and doing what they've always done. If you look at, you know, how important open source software was over the last 30 years, a big part of that was that hyperscalers came and sort of packaged up open source to make it easy for others to use. Like, you know, most of the Internet runs on Linux. Well, Linux, most people would have thought it was, like, sort of harder to use earlier on. But then you had hyperscalers sort of package that up, make it really easy for others to consume. The same thing is happening with models now that you have, you know, these open source, open weight models that are just, you know, only a few months behind on any comparable size, you know, tend to be only a few months behind the frontier, and then you can post-train on your data, get better results, much lower cost, and now much easier and safer to do because you can do it in your own environment, whatever hyperscaler you use, you can do it in the environment where your data already exists so you know your data is secure. It's not going out someplace else. And so I think that's a really important trend. And it's a good thing for the thriving of the overall ecosystem, just as open source was. Most of the companies in the Valley wouldn't exist if not for open source software. And so I think that just says that, you know, this sort of world-changing technology will be in the hands of the many rather than the few. The innovation will be for the many rather than the few because of what's happening with open source and open weight and having, you know, chip makers like NVIDIA really behind that, having hyperscalers behind that. You know, I think just that, like, there's clearly a good ecosystem there, and it's what we've been doing for several years now, saying, like, right tool for the right task gets you better effectiveness, better costs, and you can do it safely and securely. And so I think that's a really, really good thing for the broader tech ecosystem. Okay.

I wanted to turn to the advertising landscape. You know, we're sitting here in early September. What are you seeing in the ad market today? And away from sort of the environment, how are you also thinking about the competitive landscape and where Pinterest fits in to the broader ad ecosystem right now?

Yeah. So as I get started on this one, I want to make sure I'm really clear that I'm not updating or addressing guidance with this. It's not our practice to give intra-quarter updates on trends. That said, the puts and takes are consistent with what I talked about on the earnings call. So I'll spend just a minute sort of going back through what we talked about on the earnings call with regard to the puts and takes in our business. So, you know, we've talked about the user growth a bunch. One of the really exciting things in H1 of this year is the reacceleration of our UCAN business. You know, there's a lot that we're working through with adjusting to tariffs last year, you know, being a shopping destination and sort of the impact on retailers. That was a lot that we were working through last year. So the reacceleration of our UCAN business is quite noteworthy through H1. We talked about how the, you know, AI-driven app platform, our ROAS improvements for advertisers, that we felt like those things were durable, you know, as we looked into Q3. And so that, you know, that's, you know, something we're really, really excited about. We also talked about, though, you know, on our international business that we were seeing pressure there on two fronts. One, you know, new regulation in Europe that, you know, puts limits on Asia cross-border sellers, so technically not tariffs, but you can think about it similar to, like, what happened in the U.S. with tariffs where you're putting up new restrictions on Asia cross-border sellers, and so it's a new thing in Q3, and so, you know, so that creates, you know, pressure in the international business. And the other thing is just, you know, we're restructuring on the international business with our go-to-market, just as we did in the U.S. So you saw in the U.S. that as we reaccelerated, it was the, you know, the AI-based ad platform improvements, as well as significant, you know, overhaul of our go-to-market and some of the early signs and benefits of that. We're basically taking that playbook from the U.S. of shopping in the U.S., the go-to-market in the U.S., and now deploying that internationally. Actually, we talked about, okay, that will create some near-term pain to restructure those things, but that we think is very good for us in the medium to long term, just as it was in UCAN. So those are some of the puts and takes in terms of what we see in the business, consistent with what we talked about on the call. Stepping back from sort of these near-term things like new regulation in the EU and sort of short-term restructuring of our go-to-market and things like that, The shopping behavior of our users is a global phenomenon. The AI-driven platform improvements are a global phenomenon for us, and we continue to be very excited about that because what we're proving out is that not only can we drive great actionability, great outcomes for advertisers, we're creating a truly full funnel experience where historically you have... Upper funnel, mid funnel, lower funnel happening in different consumer surfaces. We're bringing that together, and we're proving out that not only can we drive great lower funnel performance, but as advertisers are seeing more and more that, well, sort of discovery and research may happen in one place, the last click may happen in another, our ability to tie that together and drive great performance is really shining through with advertisers. And, you know, I'd shared previously that, you know, it's what great CMOs always knew, that the last click wasn't the only thing that mattered, but that when we had advertisers doing multiple objectives, thinking of sort of upper and lower funnel together, that was, you know, two times the conversion on that. That's now three times the conversion for us. So, like, that benefit of, you know, doing multiple objectives with us, because we can span upper, mid, and lower, that's now three times the conversion. And so we see that You know, as we get the sort of flywheel of more users curating their taste on the platform, our AI-driven recommendations get better and better off of that signal, more and more differentiated off that signal, drives better and better outcomes for advertisers, and lets advertisers engage, you know, across, you know, discovery, sort of decision-making, and then ultimately purchasing. You know, we think that is something that is quite unique. And, you know, as we talked about the reacceleration of our UCAM business, you know, in the AI-driven ad platform improvements, you know, retail is a strength for us, right? Shopping is a strength for us. And so we think that's something that bodes really well. And as we're rolling out new AI-driven capabilities to consumers, I think we're striking a great balance between the AI sort of powering their experiences, but in a way that the users feel like is very human-centric to them. And there's discussion of AI slop and, like, all these things. Pinterest is a place where users actually get to directly engage in how they're refining their style and taste. And the AI is there for the assist, but it's not sort of overpowering the experience. And so I think that is not only good for users, but advertisers, you know, are really liking how we're driving great results, but in a way that's, like, brand authentic to them across all those stages of the funnel.

Maybe just one quick follow-up on that. When you think about where you're going over the medium to long term, has your view with respect to agentic commerce evolved at all? Because I think that's been a big talking point over the first day plus of the conference, because you're trying to get to more shoppable. You're talking about conversions. But what role does agentic commerce, either on-platform or off-platform, have to play in that?

Yeah, so, you know, we've had some really exciting progress on this. I've talked about how Pinterest has become an AI-driven shopping assistant. You know, we took a different approach than others. In the same way that I've said the first 25 years of e-commerce sort of solved buying but killed shopping, as you saw a lot of others go after agentic commerce, they went after the buy first. And I said very consistently that, you know, clicking the buy button is one of the easiest things, not only for the consumer, but also technically one of the easiest things. The real promise of Agentic and where consumers wanted the help the most was making the recommendation before they knew what to ask. So that promise of Agentic, of the agent sort of out there shopping for me before I even know what to ask, before I know what to do, or I was looking for something and I come back the next day and like, oh my gosh, I found the things I was looking for. This is what we've been doing on our platform, what we've been focusing on, and what's driving the depth of engagement. So, you know, we've been consistent on that. It's at the core of how we're driving engagement, and we continue to progress on that. Our new assistant capabilities, where basically you can think about we've had AI in the background on a lot of these things, like making all the recommendations, every pen you see is served by AI, you know, the really powerful personalization experiences, all being driven by AI, and that was in the background. We've now brought that more to the foreground. So our new assistant has been made available to virtually all users in the U.S., and we're seeing some really exciting things there in that in the same way that, you know, we took Pinterest deeper into the lower funnel with actionability with, you know, clicking and purchasing, it's also the case that we see that we're driving so much discovery and decisioning that, you know, those Gen Z users are on a platform, like, they've all used AI, right? They've all used chatbots, and we'd see that they'd start with Pinterest, and then, like, oh, they'd find it on Pinterest, they'd purchase on Pinterest, but they maybe have, like, a research question of, like, oh, hey, I want to buy that pair of shoes, but I haven't bought that brand before. Do they run bigger? Do they run small? Well, now with our assistant, you can just ask that right inside of Pinterest. And the encouraging early signs we're seeing there, we've talked about how on our platform, overall, we now see over 80 billion searches per month, and more than half of those are commercial, which is a much greater commercial skew than you would see in other places to go search. So we are clearly a shopping-centric destination. With our assistant, we're seeing that while searches overall for us, more than half would be commercial with the assistant, it's 80% commerciality. So back to us aligning AI with highly monetizable use cases, that continues to play out as we bring those assistive capabilities more in the foreground in a way that still is very visual first, but lets you ask some of those follow-on questions right inside the platform. in a way that's really effective and very cost-efficient, so, you know, it's notable, we said this on the call, that at the same time, you know, we made our assistant capabilities available to all of our users in the U.S., or virtually all of our users in the U.S., you know, we also took up margin guidance, which gets back to, you know, using AI in a way that is very effective because of our unique signal, but also in a way that is very efficient from a cost perspective.

We have a few minutes left. I just want to try to get through maybe one or two big picture topics. One we've written about and we've talked about on our news calls is the evolution and the scale you've been building around Performance Plus. Can you talk a little bit about Performance Plus, how it fits into your broader advertising ecosystem, and how to think about what that does for the platform over the medium to long term?

Yeah, so, you know, this is a place that we've been very excited about, and we've seen really good results with advertisers. And, you know, obviously, you know, we were setting out on that journey sort of after some of the larger platforms, but we've seen really, really good progress. So we only went in GA with Pinterest Performance Plus, our AI-driven ad platform, at the start of last year, sort of a year and a half into GA, and still adding lots and lots of features. I think for us and other platforms, we'll be adding features to this in perpetuity, right? So you shouldn't think about it like, oh, there's a launch, and then, you know, did you get everything out of it, you know, from just that one launch? It's like you're building on that in perpetuity. But we shared previously 30% of our lower funnel revenue now going through Pinterest Performance Plus. Well, the pace of that adoption has been quite exciting, and I think when you compare it to the pace of adoption on the larger platforms that were at it for multiple years before Pinterest, I think that pace of adoption compares really favorably. I'd say roughly half the time of what that would have taken from the public commentary of other platforms. So we feel really good about the pace of that adoption and the fact that it's helping advertisers tap into really unique shopping behavior with really good incrementality and, again, meeting those users in a place that is not only letting the advertiser get great results but also really tell their story in a way that differentiates. I think this is something that every advertiser is struggling with is, you know, as, you know, sort of traditional sort of SEO-based web traffic declines, you know, which you can see in, you know, all kinds of data out there that that is declining and more of these decisions happen other places. Well, as those decisions are happening in other places, how much can the advertiser engage? And, you know, advertisers, you know, they've been doing a lot of things to try to figure out, like, well, if I do this thing over here, you know, does it eventually some way trickle into something that happens on the other side? Like, you know, in a lot of ways, you're sort of competing to be the salt in the soup on that. Like, how do you – and, like, for us, we're making sure they can engage directly, and they're not competing to be the salt in the soup. Like, they get to be the main course. And if they get to have their brand show up exactly how they want it to show up in a way that is actually really compelling for the user also, because since we're a shopping destination, ads can be great content on our platform. So, you know, so these things, you know, I think, again, we're quite excited about that in the, you know, the role of Performance Plus is just making it easier and easier for advertisers to take advantage. You know, we talked about cutting the time in half for them to create campaigns while also giving these, like, really great results in terms of how to meet the user, where to meet the user, how to engage with them across multiple stages of the journey, and then having the AI just figure that out for them versus them having to go do all that manually. Okay.

You also decided to acquire TV Scientific. Talk to us a little bit about how you're thinking about that asset now that it's inside your portfolio, amplifying some of the commercial proposition of the advertising offering, how it can improve signal, how it can drive more performance outcomes.

Yeah, so the basic premise with this, you know, so TV Scientific is a performance advertising platform for connected TV, and one of the things we've talked about is that as we made Pinterest a shopping destination, you know, that, you know, job one is certainly make Pinterest itself a destination, which it clearly is. It's more than 85% of our usage comes to our mobile app directly. Pinterest is a destination, and there's a lot more to go on that, but the audience that we have, I would say we believe we've got more direct knowledge of user commercial intent probably than any other platforms aside from Google or Amazon, and what you've seen in And those platforms is that you can take the value of that audience to build, you know, great ad products beyond one surface. And, you know, we've taken our highly commercial audience and our knowledge of tastes and preference and what users are looking for, and we're using that to show much more personalized ads on connected TV, and we're proving that in the same way that on our platform, we said doing multiple objectives leaves three times the conversion rate. When you do sort of upper and lower together, we're now showing it on connected TV. You know, the historical world was like you and I would watch the same football game on Sunday, and we see the same ads. Well, you know, we may have some similar interests. I'm sure we do. We get along pretty well. But I'm sure that at any given moment, you and I are shopping for different things. We should see different ads, and that's not just better for the advertiser or better return for the advertiser. It's better for the user. And so with TV Scientific, it's letting us show those much more personalized ads. But then the thing that we've talked about is that as we do that, we're able to show that adding our audience on top of what TV Scientific was doing with performance advertising and CTV, there is a more than 60% lift in purchases from doing that. So how valuable is that audience? Tremendously. So there's a lot more that we'll do in connected TV, but we think the value of that audience, you know, is quite noteworthy and lets us think about how we monetize across, you know, multiple other surfaces. We're at the very beginning of that. We think that's a big forward opportunity. And maybe just a place I'd say in terms of where we are in this moment, you know, AI is moving very quickly and sort of where people think the value is going to accrue keeps shifting. And, you know, there was a stage of this where people were saying, like, oh, all the value goes to the model creators, right? And, you know, there will only be two or three companies in the future because all the value goes there. And then, oh, well, wait a minute. Hyperscale is going to be pretty important in this. And, like, you know, chips and memory are going to be pretty important in this. And then now you see with open source and open weight, you have a lot of commoditization of model capabilities. And, you know, in the same way that open source software didn't lead to zero proprietary software, in fact, most proprietary software, much proprietary software leverages open source, you're going to have a mix of both closed models and open models, but, you know, that says, like, oh, with the, you know, advent of open source and open weight models, you know, that's going to allow for much broader value creation. And so a lot of people have been focused rightly on there's a lot of infrastructure build out and those kinds of things. I think the next part of this that people haven't paid enough attention to is how much value there is in distribution, how much value there is in unique data. And this is a place where, you know, at 640 million users, we are clearly one of the largest practitioners of applied AI, bringing, you know, AI-driven experiences to a huge mass of users, you know, in a very unique way. You know, search has been, you know, search has been this expansionary moment, right, where, you know, it's clearly the case that, you know, chatbots are a new form of search for most consumers that's expanding the search market overall. But search has also been fragmenting for years, and there were a few general-purpose ones, but many vertical-specific ones. We're driving that sort of vertical-specific around shopping and visual, but we think there's a lot more of that that we can do across multiple surfaces. And we're in a moment where I think that people are seeing that, okay, yes, there will be a lot of value in the closed models, but open models make it so that a lot more value can accrue to those who have distribution, who have consumer relationships, and those who have unique data, and TV Scientific is sort of a first step in us proving out the value of that audience across multiple surfaces.

Okay. I think in the interest of time, we're going to have to leave it there. I always love getting the opportunity to chat, Bill. Please join me in thanking Pinterest for being part of the conference.