Operator: Hello, everyone. Thank you for joining us, and welcome to Pinterest's Second Quarter 2026 Earnings Conference Call. [Operator Instructions] I will now hand the conference over to Andrew Somberg, Vice President of Investor Relations and Treasury. Andrew, please go ahead.

Andrew Somberg: Good afternoon, and thank you for joining us. Welcome to Pinterest's earnings call for the second quarter ended June 30, 2026. Joining me on today's call are Bill Ready, Pinterest's CEO; and Julia Donnelly, our CFO. The statements we make on this call reflect management's view as of today and will include forward-looking statements. Such statements involve a number of assumptions, risks and uncertainties, and actual results may differ materially. We disclaim any obligation to update these statements. For information about assumptions, risks, uncertainties and other factors that could affect our results, please refer to our earnings press releases and the periodic reports we file with the SEC and are available on our Investor Relations website at investor.pinterest.com. During this call, we will present both GAAP and non-GAAP financial measures. A reconciliation of non-GAAP to GAAP measures is included in today's earnings press release and presentation, which are distributed and available to the public through our Investor Relations website. Lastly, all growth rates discussed today are on a year-over-year basis unless otherwise specified. And now I'll turn the call over to Bill.

William Ready: Thanks, Andrew. Good afternoon, and thank you for joining our Second Quarter 2026 Earnings Call. Our strong Q2 results reflect the progress we're making against our strategic priorities. We ended the quarter with 640 million monthly active users and delivered revenue growth of 18% globally and in UCAN, a 5-point acceleration in our largest market. We continue to build momentum across our 3 strategic priorities. First, continuing to build a differentiated visual search, discovery and shopping experience for users; second, keeping AI at the core of everything we do, from powering the user experience to our ads platform and our internal operations; and third, accelerating monetization through improved go-to-market and measurement capabilities. At the center of our strategy is a simple idea. Pinterest helps people discover what they want and then go do it in the real world. That idea comes through clearly in our new brand campaign built around the line, "the best thing you can find online is a reason to go offline." It reflects what makes Pinterest different. People come here with intent. They find inspiration that is personalized for them, refine their style and taste, and they can take action directly from our platform. The human curation that is so unique to Pinterest is the highly differentiated signal that trains our AI to make such exceptionally relevant recommendations to users. Today, I will focus my remarks on 2 topics that cut across those priorities, how AI is powering our user momentum and how we're turning that strong engagement into more durable monetization. With that, let me start with the user side of the story. Q2 marked our 12th consecutive quarter of record users and our 11th straight quarter of double-digit user growth. In our core UCAN market, users grew 4%. We're also winning significantly with the next generation of users as Gen Z continues to be our largest and fastest-growing cohort, representing over half of our user base. AI has been at the heart of this momentum. We've transformed Pinterest into an AI-powered shopping assistant. Effectively, every Pin a user sees is personalized and served by AI. We were early to move on AI and its enabling technologies, having completed our full transition to GPU serving more than 2 years ago, which materially improved relevance and personalization using larger models and more data. Our scale and the unique human curation that occurs on our platform have fueled our proprietary Taste Graph. With more than 80 billion monthly searches on Pinterest, the vast majority visual and over half commercial, and over 16 billion boards created on our platform, we have a uniquely valuable signal, human taste and curation at scale. This enables us to train our AI with a deep understanding of taste, context and intent that gets more personalized every time a user comes back. This powerful feedback loop is what helps users discover what they want before they have the exact words, brand or product in mind. That's why so many commercial journeys begin on Pinterest. Today, over 96% of text-based searches on our platform are unbranded, for instance, "cool running shoes" or "new fall wardrobe ideas." This is a great moment for marketers to meet prospective customers when they have clear commercial intent, but haven't yet decided what to buy. As users move from inspiration to a more specific decision, their needs evolve from discovery to research with questions like, "do these shoes run true to size?" Or can you compare these 2 brands? To handle this part of the commercial journey, we're bringing AI further into the foreground of the user experience with our AI conversational layer, Pinterest Assistant. This intelligence layer, which we made available to the vast majority of U.S. users as of the end of July, helps our users answer those later-stage research questions, enabling them to move from an idea to a finished plan or purchase entirely on Pinterest. Pinterest Assistant is now woven throughout the user experience. Users can engage with it where they already explore and plan with contextual entry points and prompt suggestions across the experience, and we'll be able to generate product comparisons, step-by-step instructions and visual forward guidance. For example, someone planning a living room refresh can ask what styles work in their space, get help finding the right rug and explore ways to pull it all together within their budget, all without leaving Pinterest. What makes Pinterest intelligence differentiated is that it is built on our Taste Graph, years of visual curation signals from user searches, saves and boards, giving a deep understanding of products and style that is highly relevant to how people shop and to each individual's taste and interest. Notably, it's also visual first, consistent with the experience users expect from Pinterest. We are also beginning to introduce memory and conversational history, creating an even more personalized experience. Importantly, we are building this capability in a highly differentiated, effective and cost-efficient way. Our approach to model deployment includes our own compact fit-for-purpose models built for Pinterest-specific use cases and suitable open source models post-trained in our own environment within our secure cloud infrastructure. When we leverage open source models, such as with Pinterest Assistant, we are seeing superior performance for our use cases when compared to closed third-party models because we are able to post-train open models on our highly unique data. With open models, we are achieving cost per transaction at less than 8% of the cost of comparable closed proprietary models. This gives us substantial headroom to deepen and extend these capabilities over time in a way that is differentiated, highly effective and cost efficient. Stepping back, this is the next iteration of work we've been doing for years, taking the discovery and intent that already exists on Pinterest and making it even more actionable for users, helping them move seamlessly from dreaming to deciding, doing and buying. With the launch of our AI conversational layer, we are taking the next step to enhance that actionability even further and making it even more seamless for our users to move through their entire commercial journey on Pinterest. This is what sets up the monetization story I'll turn to next. On the monetization side, AI is helping us improve advertiser performance across the funnel from better targeting and bidding to more automated creative and stronger measurement. As we shared last quarter, advertisers using Pinterest Performance+ campaigns see meaningfully better ROAS and grow spend faster than those who haven't adopted it yet. That uplift comes from automating more of the campaign setup, bidding, budgeting, targeting and creative optimization that advertisers historically had to manage manually while still giving them clear controls. As part of our efforts to serve a broader group of advertisers with Pinterest Performance+ campaigns, this quarter, we launched Smart Assembly to help more advertisers benefit from creative optimization tools. This is a new Pinterest Performance+ creative capability for advertisers promoting brands, services or content who do not have existing shopping product catalogs. Advertisers can upload multiple images and Pinterest automatically builds and serves the best performing ad for each impression. In early alpha testing, Smart Assembly delivered a 6% improvement in click-through rate on average, demonstrating that creative testing and diversification can meaningfully improve performance. Over time, our goal is for nearly every lower funnel campaign on Pinterest to start in an AI-powered best practice setup with added controls for more complex buyers who need them. Our near-term road map, including a simpler campaign creation flow, more sophisticated bidding and more automated creative, is designed to drive the next leg of Pinterest Performance+ adoption, including among advertisers that require both performance and increased control. We're also investing in Business Assistant, our conversational AI collaborator for advertisers, which is currently in beta. Business Assistant combines an advertiser's business context with Pinterest platform insights to surface actionable recommendations, including relevant trends, top-performing Pins and optimization opportunities. The goal is to help advertisers understand what is resonating on Pinterest, decide where to put the next dollar and make Pinterest easier to use and scale, especially for advertisers that do not have large dedicated teams. At the same time, we are upgrading our bidding and measurement systems so we can do 2 things better: help advertisers find the highest value impressions on Pinterest and prove that value through the metrics and measurement systems advertisers already use. With a small initial group of some of our largest and most sophisticated advertisers, we are continuing to pilot integration between their in-house measurement systems and our AI bidding systems, allowing us to optimize bidding toward their unique set of desired outcomes. That initial group is seeing strong performance, partially contributing to our strong Q2 results. We are now expanding testing to a small number of additional advertisers over the course of the third quarter. The learnings are also informing broader bidding enhancements across the platform that can scale to many more advertisers. We are always working on making our core ad delivery engine better through adapting new and better signals and driving enhancements in our AI modeling work. This quarter, we have made our shopping ad delivery systems better at deciding which products to show, when to show them and how to allocate budgets against the highest value advertiser opportunities. That includes improving product selection for advertisers with large catalogs and increasing the variety of relevant products users see. This package of changes are designed to improve lower funnel performance across both large enterprises and smaller advertisers. The early results are encouraging, helping drive stronger, more consistent ROAS. Finally, we are continuing to extend Pinterest's unique consumer intent signal and audience to connected TV through tvScientific, and early advertiser reception continues to be strong. In 2027, we expect to fully integrate tvScientific capabilities directly into Pinterest Performance+, turning Pinterest into a full funnel search, social and CTV platform performance solution and opening access to larger and incremental budget pools. We're pairing this progress across our ad platform with a more disciplined performance-oriented sales and go-to-market motion, helping us better monetize the strong engagement and commercial intent on Pinterest. Early progress on sales and go-to-market transformation was reflected in our strong Q2 results, particularly in UCAN. Our focus on clear seller accountability, better packaging of commercial moments and a tighter connection between product performance and advertiser conversations are supporting strong UCAN demand. We're also making progress on our mid-market and managed SMB go-to-market efforts, including restructuring account coverage teams and realigning incentives to better serve this cohort over time. We're now applying our UCAN playbook internationally, where we see significant opportunity to close the gap between engagement and revenue. We're earlier in this work, but our progress in UCAN gives us confidence in the opportunity. With new international leadership, we're sharpening our global go-to-market approach and bringing more global discipline to how we drive performance selling internationally. We're also testing the expansion of third-party demand into Europe. Taken together, the product-led improvements across our ad platform and a more disciplined go-to-market engine are strengthening monetization and helping revenue better reflect the value of the engagement we're seeing on Pinterest. In closing, Q2 shows that we are making progress on the priorities that matter most. We are building an even more differentiated visual search, discovery and shopping experience. AI is improving both the user experience and advertiser performance and stronger go-to-market and measurement capabilities are helping revenue better reflect the engagement we see on Pinterest. We are still early on many of these initiatives, but the initial results indicate we are on the right path. As we build for the long term, we remain focused on making Pinterest a positive platform centered on time well spent. As the global conversation around online safety and youth well-being continues to grow, we believe that foundation matters more than ever. We will keep making deliberate choices that put user trust and well-being at the center of the experience, and we remain confident that building a positive platform and building a strong business reinforce each other. With that, I'll turn it over to Julia to walk through the Q2 financials and our outlook in more detail.

Julia Donnelly: Thanks, Bill, and good afternoon, everyone. Today, I'll be discussing our second quarter 2026 financial results and provide an update on our third quarter 2026 outlook. All financial metrics, except for revenue, will be discussed in non-GAAP terms unless otherwise specified, and all comparisons will be discussed on a year-over-year basis unless otherwise noted. Q2 was a strong quarter. We've transformed Pinterest into a scaled AI-powered shopping destination for 106 million users in the U.S. and Canada and 640 million users globally. Importantly, we have a direct relationship with our users. 100% of our reported users are logged in and 85% come to Pinterest directly through our mobile app, meaning we are not heavily reliant on third parties for traffic. Revenue exceeded $1 billion for the fourth consecutive quarter, growing 18% year-over-year. This growth was led by strength in UCAN, our core market, where revenue growth accelerated to 18% year-over-year. This performance reflects a number of our initiatives coming together. First, our ongoing ad platform enhancements to improve return on ad spend and to advance our bidding and measurement capabilities are driving advertiser performance and spend. In addition, we are seeing the early results of our sales and go-to-market transformation take hold in UCAN, where increased rigor and discipline in how we go to market are beginning to show up in our results. As we discussed entering 2026, we have more work to do, so our revenue consistently reflects the strength of our user activity. While this will take some time to fully take hold, particularly internationally, our results in UCAN give us confidence that we are moving in the right direction. Now I'll move to the details of our second quarter results. We ended the quarter with 640 million global monthly active users or MAUs, growing 11% and reaching another record high. We continue to demonstrate user growth across all of our geographic regions. In Q2, our U.S. and Canada region had 106 million MAUs, growing 4%. Our Europe region had 157 million MAUs growing 8%. And in the Rest of World markets, we had 377 million MAUs growing 15%. Shifting to revenue. In Q2, our global revenue was $1.180 billion, up 18% or up 17% on a constant currency basis, with strength led by our conversion and consideration objectives. Across verticals, we continue to see strength in retail as well as smaller but faster-growing emerging verticals on our platform, including financial services, travel and health. Turning to our geographical breakouts for Q2. Revenue in the U.S. and Canada was $880 million, growing 18%. Growth came from retail and emerging verticals, including financial services, travel and health. In Europe, revenue was $213 million, growing 12% on a reported basis or 7% on a constant currency basis. Growth in Europe was driven by retail. Revenue from Rest of World was $87 million, growing 38% on a reported basis or 32% on a constant currency basis. In Q2, overall ad impressions grew 16%. The deceleration from prior quarters was partly driven by lapping the ramp in ad impressions from previously undermonetized international markets. Ad pricing in Q2 increased 1% year-over-year. This was driven in part by stronger demand in our UCAN region as well as the higher relative mix of UCAN ad impressions, which carry higher average pricing overall. Moving to expenses. In Q2, cost of revenue was $245 million, up 25% year-over-year and up 6% versus Q1, driven by the full quarter impact from tvScientific and our investment in additional GPU capacity. Our non-GAAP operating expense was $629 million, up 13%. The increase was primarily driven by sales and marketing due to our brand campaign and sales headcount investments as well as R&D to support our AI and product initiatives. In Q2, we delivered $311 million in adjusted EBITDA with an adjusted EBITDA margin of 26%, up 130 basis points versus Q2 last year. The higher-than-expected adjusted EBITDA was driven by flow-through from higher revenue. We also delivered Q2 free cash flow of $270 million. On a trailing 12-month basis, we've generated nearly $1.3 billion of free cash flow, representing 94% free cash flow conversion. In Q2, we allocated $58 million towards share repurchases. Separately, we entered into a capped call transaction for a total consideration of $99 million, which protects against dilution from our previously issued convertible notes up to a price of $30.59 per share. As a reminder, year-to-date, we've repurchased over $2 billion of stock, retiring nearly 111 million shares. We ended the quarter with cash, cash equivalents and marketable securities of $1.3 billion. Now I'll discuss our guidance for the third quarter. We expect Q3 revenue to be in the range of $1.190 billion to $1.210 billion, representing 13% to 15% growth year-over-year. As we move from Q2 to Q3, there are a few sequential factors to keep in mind. First, based on current spot rates, we expect foreign exchange to be a modest headwind in Q3 after providing a 1-point tailwind in Q2. Second, the shift of Prime Day from Q3 last year into Q2 this year resulted in an approximately 0.5 point benefit to Q2 and will represent a roughly 0.5 point headwind to Q3 as multiple brands and retailers increase their advertising spend around that moment. Lastly, in Q2, we saw a nearly 1 point benefit from World Cup-related spend that will not repeat in Q3. Moving down the P&L. We expect Q3 adjusted EBITDA to be in the range of $335 million to $355 million. We anticipate Q3 non-GAAP cost of revenue expense to be roughly flat versus Q2 2026 due to the accelerated recognition of certain contractual benefits in Q3 associated with our recently executed multiyear infrastructure agreement. In Q3, within non-GAAP operating expense, our primary area of year-over-year investment will be sales and marketing and R&D to support our AI and product initiatives. As we saw in the first half of 2026, we continue to expect modest headwinds from cost of revenue as a percentage of revenue in the second half as a result of the investments in areas such as additional GPU capacity as well as the impact from the inclusion of tvScientific. Given our first half revenue outperformance, we now expect full year 2026 adjusted EBITDA margins of approximately 30% versus our prior expectation of approximately 29%. In closing, I'm proud of our teams for yet another strong quarter of results as we execute against our strategic priorities. I'm encouraged by our performance in the first half of the year as we continue to deliver for our users and advertisers. With that, I'll hand it over to Bill for some final words.

William Ready: Thanks, Julia. I want to thank our teams at Pinterest, our advertising partners and all the people that come to Pinterest to find inspiration and take action. And with that, we can open up the call for questions.

Operator: [Operator Instructions] Your first question comes from the line of Mark Shmulik with Bernstein.

Mark Shmulik: Julie, you shared a little bit of color on that kind of revenue guide puts and takes, but hoping to get a little bit more color on perhaps some of the regional differences you might be seeing. I noticed a little bit of a slowdown in Europe. Is that just kind of tougher compares?

Julia Donnelly: Sure. Thanks, Mark. So yes, overall, we're really pleased with a very strong Q2. And on a global basis for Q3, the high end of our Q3 guidance range is consistent with what we just delivered in Q2 when you adjust for the 3-point sequential impact I called out in my prepared remarks from foreign currency, Prime Day and World Cup spend. At the core of our Q2 outperformance is our ongoing strength in users and engagement. We continue to win shopping demand from consumers globally and in UCAN, and that multiyear trend gives us the most conviction in the trajectory of our business. We're now making it easier for advertisers to tap into that consumer behavior through our AI-driven ad platform improvements and the early dividends from our go-to-market transformation. And that's really what's translating into the strong revenue performance that you saw in Q2, particularly in UCAN. So let's start with UCAN since that was the biggest revenue driver in Q2. We drove a 5-point acceleration sequentially to 18% year-over-year in UCAN, and this reflects a few different things coming together. So first, as a result of our ad platform improvements, we began to see pockets of large retailer spend accelerate with certain retailers leaning in as a result of our ongoing ROAS enhancements and AI-driven bidding optimizations as well as Asia-based cross-border retailer spend into UCAN. Second, in UCAN in Q2, the strong year-over-year growth outside of our largest retailers continued across mid-market, managed SMB and emerging verticals as many of these advertisers are benefiting from our ad platform improvements, including Pinterest Performance+ campaign specifically. Lastly, UCAN in Q2 also benefited from Prime Day, shifting from Q3 to Q2, World Cup spend, and a full quarter contribution from tvScientific. So overall, as we look to Q3, we expect strong growth in UCAN to continue. For Europe and Rest of World, we noted on the last earnings call that we expected growth to moderate in Q2 as a result of the deliberate leadership and structural changes we're making to our go-to-market organization and more difficult last year comparisons in certain markets. So in Q2, we were lapping the ramp of resellers in Rest of World last year and lapping a significant influx of cross-border spend into Europe last year following changes in the U.S. tariff environment. That moderation was generally consistent with our expectations. In addition, we faced incremental pressure mid-quarter from Asia-based cross-border retailers impacted by regulatory actions, particularly in Europe, and that pressure is continuing in the third quarter. We have more work to do to fully realize the benefits of our go-to-market transformation in Europe and Rest of World, and we expect some level of disruption to continue in Q3. As we look to the third quarter, it's also worth noting that Q3 represents the most difficult comparison of the year for our Europe region in particular. So stepping back, the underlying trends in our business remain healthy. We're pleased with the strong Q2 overall, led by growth in our core UCAN market, in particular, which continues to be underpinned by our durable user and engagement strength that we continue to see.

Operator: Your next question comes from the line of Colin Sebastian with Baird.

Colin Sebastian: Bill, you talked about the evolution of the Pinterest Assistant and updates there as usage takes hold. And I guess as the competitive landscape continues to evolve, what are you seeing in terms of early engagement signals there? And then how are you innovating on the broader Pinterest user experience side?

William Ready: Thanks for the question, Colin. Over the last 3 years, we've effectively turned Pinterest into an AI-driven shopping assistant. So even today, every pin you see on Pinterest is personalized and served by our AI. And that's been really at the core of the significant acceleration in our user growth over that 3-ish year time period. And what we're doing now is we're bringing more of those AI capabilities in the foreground of the experience for users to interact with directly. So I'll give you a little bit more detail about how we're doing that. We're leveraging AI to make Pinterest more personalized, more actionable and more valuable users. And that advantage continues to compound, particularly the human curation on the platform. So we've been applying AI at scale for years. We completed our migration to GPU serving over 2 years ago. And as I mentioned, every pin you see on Pinterest is effectively served by AI. But it's really that human curation that 640 million-plus users on our platform that are creating hundreds of billions of unique connections across our Taste Graph that really gives us proprietary signal that we can use to train our AI and personalize the experience with every interaction. And that feedback loop has accelerated over the past 3 years. As consumers are responding, that's what's led to the 12 consecutive quarters of record users, 11 straight quarters of double-digit user growth. And on the competitive landscape, we've long believed search will continue to fragment with a small number of horizontal general purpose platforms, but more vertical-specific experiences that solve distinct consumer needs. And that trend of the search market fragmenting has actually been happening for more than 2 decades. Think of how many product searches start on large retailers or travel searches at online travel companies versus a general-purpose search engine. So as AI becomes more widely available and open source models continue to improve, we believe there can be many winners, particularly in vertical-specific search. The user growth in our platform alongside the growth in AI over these last few years is very clear evidence of that. You're also seeing that the general purpose AI applications, they're discovering what those of us that have built much larger platforms have known for a long time that it's very hard to be all things to all people. And you've seen them pull back from many of their vertical-specific experiences. So at Pinterest, we're very focused on visual search, particularly around shopping, and we're using AI to make the experience more actionable across the shopping journey. People already come to Pinterest to discover products, ideas and aesthetics they love. And research shows that shoppers find AI chatbots most valuable when researching and comparing products and narrowing their choices. The Pinterest Assistant, our AI conversational layer that we've now made available for the vast majority of U.S. users, it brings those LLM capabilities into our visual, personalized and proactive experience. So it helps users move from inspiration through consideration and decision-making and allows them to complete more of their shopping journey on Pinterest. By combining our proprietary signals with cost-efficient open source models, that really allows us to run those securely in our own environment. So we're able to keep improving the experience, scale those innovations with strong ROI. And the result is a better product today and compounding advantage as more people use Pinterest. And lastly, I'll just say, it has been really, really exciting to see just how much the open source ecosystem is expanding. We've been very early on that. It's been woven into our experience. It's core to what we're doing, part of how we're doing that very effectively. But it's great to see that momentum building. And this is one good example of how we're using that, but I think you're going to see more of that across the ecosystem as well.

Operator: Your next question comes from the line of Brian Nowak with Morgan Stanley.

Brian Nowak: Maybe I want to ask about the UCAN. It seems like you've made a lot of progress on the improvement in attribution, the entire go-to-market on the UCAN. Bill, let me ask you, as you sort of look ahead the next 12 months, what are the next sort of sources of innovation or unlocks that you're looking for to even drive faster and more durable growth out of the UCAN business?

William Ready: Yes. Thanks for the question, Brian. First thing I'd say is just we've talked about this previously. We're still a long way from having fully monetized all the commercial intent that already exists on our platform. So while user growth and engagement continues to be one of the highlights of our platform, especially as you look across sort of the social media landscape where you're seeing platforms they're generally having a harder time growing, 11 straight quarters of double-digit user growth, that user growth and the fact that more than half of them are here to shop continues to be the strongest part of that. But as we've shared before, we shared on the last call, we 5x the number of clicks to advertisers over that last 3-year period, but we certainly didn't 5x the revenue. So the things that we've talked about in terms of aligning our AI bidding systems with measurement systems, which I talked about in my remarks, getting more integrated into a broader set of measurement systems so that the advertisers can measure those things. And then our AI-driven ad platform, where we just keep making it easier and easier and easier for advertisers to tap into that very high commercial intent on the platform. But I think the -- we see that flywheel continuing to spin both in the user engagement and the fact that users and the engagement there is coming in the way that we want it with high intent, with search, with actionability and that advertisers are leaning in more and more as we give them better ability to go access that commercial intent.

Operator: Your next question comes from the line of Ross Sandler with Barclays.

Ross Sandler: Bill, you mentioned the integrations with first-party measurement and the like AI-optimized bidding as a growth driver at some of these larger accounts. Could you just elaborate on where we are on that progression and what you're seeing thus far?

William Ready: Definitely. Thanks for the question, Ross. So we're still early in the work, but the initial results from the pilot are very encouraging and contributed to our outperformance in Q2. The small number of advertisers that were participating in the pilot, they're seeing meaningful performance improvements, which is giving them greater confidence to increase their spend on Pinterest. And so we're expanding this work to a limited number of additional advertisers in Q3. And by integrating with advertisers' measurement sources of truth, we're able to align our AI bidding systems to optimize for the hyper-specific outcomes each advertiser values most, whether that's customer lifetime value, incremental ROAS or another metric they use internally to evaluate performance. So the more we're able to line up precisely with what they're looking for, the more we can make sure that AI is delivering their exact outcome. And then so they see more value captured from Pinterest, and they're seeing that across the funnel, upper, mid and lower. And while we started this work with some of our most sophisticated advertisers because they have the data and the measurement infrastructure and the technical resources required to deploy that, our most customized solutions will remain focused on that relatively smaller number with that level of sophistication. But as we move beyond that initial group of advertisers, it's reasonable to expect the full adoption curve will take place across the rest of our advertisers, but it will take some time to play out. You're seeing a few things here like the industry shifting beyond last click attribution to recognize value earlier in the funnel where more than just the last click matters, great marketers have always known that. The ability to take action on that, it's the most sophisticated to do these things first, but we're seeing that take hold through a broader swath of other advertisers through third-party measurement platforms. And so as those capabilities become easier to deploy through third-party measurement systems, we're making sure that we tie in closely to those things. So again, those -- that adoption cycle takes time. Advertisers always have sort of cycles they go through in terms of how they adjust their measurement and they're cautious and very methodical about that. But we absolutely see that happening across the industry, not just with us, but with other platforms. And we think that general trend bodes quite quite well for us. So as I mentioned already, the paid clicks to advertisers on Pinterest are up well over 5x over the last 3 years. So all of these things help us to capture more of that value that we're already creating. And the feedback that we're getting is encouraging. Simply stated, when advertisers can see the full picture and we can tune our bidding to what matters most to them, they have more confidence and they increase their investment in Pinterest.

Operator: Your next question comes from the line of John Blackledge with TD Cowen.

John Blackledge: As you expand your AI product offerings, how are you managing token-spend usage and AI-compute costs? And what KPIs are you tracking to make sure those choices translate into real productivity gains for your teams?

Julia Donnelly: Thanks, John. I can take that one. So in 2026, we've expanded our AI capabilities across our product, both for users and advertisers and also for internal productivity-related use cases. We also just raised our margin outlook for the year. So I think that demonstrates our ability to invest in these capabilities while continuing to improve profitability by managing AI and compute costs efficiently. We've discussed our incremental investment in GPU capacity throughout 2026, which is reflected in our cost of revenue line item. We are already seeing returns through ROAS gains for advertisers and improvements to our user-facing experiences, as Bill just noted. In general, GPU investments have a relatively short payback period because increased compute capacity can be deployed quickly to enable rapid improvements to our AI models, which translate quickly into business outcomes. As we said, we use a blended model approach that includes our own compact fit-for-purpose proprietary models, open source models and in very limited cases, select closed third-party models. We evaluate models across the ecosystem constantly and leverage our centralized model routing layer to optimize production traffic for the right balance of quality, cost, latency and reliability. And this allows us to reserve more capable higher-cost models for complex work while using lighter, lower-cost options for routine tasks. In the second half, we're expanding this model routing infrastructure to even more internal use cases, including tools to increase productivity across engineering, sales and other functions. These costs appear in our OpEx line item. And here, we're tracking adoption, developers throughput, cycle time, incident rates and service uptime to ensure the greater speed does not come at the expense of reliability. For example, in July, weekly pull requests per engineer increased 45% versus last year, while incident rates and service uptime remained relatively consistent. So that gives us confidence that we're improving velocity without compromising reliability. As AI adoption continues to grow, we expect both AI-related compute and token spend to grow over time. We see that these investments are ROI positive, and that investment growth is planned and included in our outlook that we've provided. So we're scaling AI deliberately, expanding user capabilities, driving advertiser outcomes and improving employee productivity, all while maintaining a disciplined approach to profitability as well.

Operator: Your next question comes from the line of Justin Patterson with KeyBanc.

Justin Patterson: You've talked about broadening your revenue base beyond your largest retailers. Can you update us on that progress, particularly with the SMBs and discuss how Performance+ adoption and the product road map supporting this effort?

William Ready: Certainly. Thanks for the question. So we're really pleased with our continued progress in broadening the revenue base. As Julia noted, in Q2 and UCAN, we saw strong growth outside our largest retailers across mid-market, managed SMB and emerging verticals. And we've got ongoing ad platform improvements, notably Pinterest Performance+, that's been a key driver of this strength across those. So Performance+ gives advertisers an automated best practice setup across bidding, budgeting, targeting and creative. And so that's especially valuable for smaller advertisers that don't have large teams. So we're also adding the controls that more sophisticated advertisers need so they can take advantage of these things as well. As we shared last quarter, approximately 30% of our lower funnel revenue was running through Performance+ campaigns as of Q1. Adopters also grew their lower funnel spend more than twice as fast as non-adopters. So we're really seeing that be effective for our advertisers. And then I'd say more recently, the updates to our ad delivery models drove a 28% improvement in ROAS during testing for SMB advertisers using Performance+ with ROAS bidding. So these improvements continue to help smaller advertisers achieve better outcomes with less manual work. And as we look ahead, we continue to make our best-performing automation the default for lower funnel campaigns, doing more and more of that while also improving bidding, creative and measurement and adding controls for more complex buyers. So simpler campaign flow targeted for early next year will put best practice setup in place from the very start. So we want to continue to move towards that kind of experience where we're giving really, really simple setup with best practices baked in. And so as we've consistently said, this is a multiyear product and customer adoption cycle. And part of that is managing customers through not only their adoption, but how they think about leveraging these tools. But we continue to release new functionality every quarter, and we see good adoption happening across our user base. And other platforms that have deployed AI-driven automation suites saw adoption play out over several years. In fact, even some of the earliest to have done this are still working through their adoption cycle. And we expect a similar dynamic for ours as well, where we've created a lot of capability, but there's a lot more in front of us than behind us. And then lastly, just on the go-to-market aspect of this, we're also evolving how we go to market from mid-market and SMB advertisers. We talked about how we're restructuring account coverage, realigning incentives, so we can provide direct support where it can unlock growth and scaled support where that is a better fit. And so broadening our revenue base, including internationally, it's a multiyear journey, and we're still relatively early, but we're building the product foundation, coverage model and go-to-market discipline required to serve a much broader range of advertisers. And again, we believe there's significantly more opportunity ahead of us.

Operator: Your next question comes from the line of Nitin Bansal with Bank of America.

Nitin Bansal: Julia, can you provide some color on the increase in SBC expense this quarter? And as we -- as you continue to invest in AI talent, how should we think about the trajectory of SBC for the second half and next year?

Julia Donnelly: Sure. So we don't guide stock-based compensation expense specifically, but it's certainly an area we're focused on. Q2 had some unique factors and as a result, should be the highest quarter for stock-based comp expense in 2026. The increase in Q2 stock-based compensation expense was driven by the annual equity grant cycle for our employees and the lower stock price at the time of grant, which meant we needed to issue more RSUs to remain competitive on compensation. Equity is an important part of how we attract and retain our strong talent while also aligning employees with long-term shareholder value. We also saw some timing impact in Q2 as a greater portion of our annual grant expense was recognized this quarter. While stock-based comp will remain elevated in 2026, starting in Q3, we do expect both stock-based compensation expense dollars and the year-over-year growth in stock-based compensation to step down versus Q2. As in recent years, we also expect to be profitable on a full year GAAP net income basis. I'll also note that we've leaned in opportunistically to manage dilution, particularly this year at a moment of dislocation in our stock price. We've repurchased over $2 billion of stock at an average price of $18 per share. As a result, net dilution for Q2 was down 12% year-over-year, leading to growth in Q2 non-GAAP EPS of 30%. So we have a growing, highly profitable and cash-generative business, and we'll continue to be thoughtful in balancing employee retention and incentive alignment with opportunistic capital return and ongoing dilution management going forward, just as we've done in the past.

Operator: Your next question comes from the line of Jason Helfstein with Oppenheimer.

Jason Helfstein: So first question for Bill. You touched on the capabilities of open source AI models. Maybe how does Pins leverage that today? And how does that fit into your broader AI strategy? And then second, just, Julie, a quick one. Is MAU really the best KPI for UCAN? Or is there another metric that you could share with us over time to help us better understand what's going on in the business around engagement?

William Ready: Thanks for the question, Jason. So on open source, it's open source AI, it's an area that we're really, really excited about. I've been talking with you all about this since right after the DeepSeek moment, what, 1.5 years or so ago. And as we've discussed before, we take a model-agnostic approach. We use the right model for the task, whether that's one of our in-house compact fit-for-purpose models, an open source model that's post-trained on our own data or in some very limited cases, a selected closed third-party model. But we're one of the largest practitioners of applied AI at scale at over 640 million users. And we've been at the forefront of leveraging open source. It's become an increasingly important part of our strategy because it gives us 3 important advantages: cost, customization and control. So starting with cost. For our use cases, with open models, we're achieving cost per transaction at less than 8% of the cost of comparable closed proprietary models. And that's a substantial advantage. And it becomes even more compelling as open source models, including those from U.S.-based labs continue to improve. Our early adoption of open source has enabled us to scale AI efficiently to serve our 640 million users while also expanding margins and generating significant free cash flow. But it's the combination of those -- our in-house compact models and leveraging open source really effectively. And I would also just say, with the open source not only is it much lower cost, the fact that we can post-train on our own data makes it more effective. So as you're looking at what's happening in the open source ecosystem, those models continue to get better and better. It's not just how they do on the benchmarks. It's how are they going to work in your own environment. And when you can post-train on your own data, that makes them much more effective. So then you're looking at something that is more effective than the way you get from the closed models and at less than 1/10 of the cost, that really makes it so that at this point, any CEO that's not taking advantage of open source models is almost certainly wasting a lot of their shareholders' money. And especially that's true now that you have hyperscalers making it really easy to take advantage of that open source in their secure environment so that you can leverage the open source in your own cloud environment so that it's safe, it's secure. You don't have to worry that it's phoning home. And as others have called out, you don't have to worry that you're giving your company's most important data over to somebody that's going to potentially use it against you. So again, we're very, very excited about the progress in open source. That pairs with our own work on our own compact models that serve our use cases. But we're quite encouraged by what we're seeing in the advancement of the ecosystem there. And I think there's going to be a lot more of that to come. And it's, I think, a great democratizing agent. Most of Silicon Valley, most of the web, most of mobile was built on top of open source software. U.S. tech needs a thriving open source ecosystem. And I think with the hyperscalers showing up, with U.S. labs showing up, we're getting exactly that. And again, we've been one of the largest practitioners of it, but we think it's a really great thing for the industry.

Julia Donnelly: And then maybe just to take the second part of the question on UCAN and MAUs and engagement. I'd say we always internally look at sort of various baskets of metrics to measure engagement across the platform. We continue to see -- we're adding new users, and we're continuing to maintain strong engagement with those users that we're adding. So for example, in UCAN, for instance, searches and boards created continued to grow faster than users in Q2. But I'd say just sort of across the platform as we're looking at strength overall, we're seeing across a number of those basket of metrics, strength that gives us confidence that we're really resonating with users in UCAN in particular, with Gen Z.

Operator: Your next question comes from the line of Eric Sheridan with Goldman Sachs.

Eric Sheridan: Julia, maybe building on some of the commentary you've given so far with respect to EBITDA margins and given the performance of EBITDA in the quarter, maybe just put a finer point on 2 questions. One, how are you thinking about what the highest return areas are for incremental growth investments when you look out over the next couple of years? And when you think about flexing the EBITDA margin beyond 2026, how do you think about the scope for both headcount reductions, productivity gains and/or increased growth investments in the ad platform as different variables that could play into how margin evolves going forward?

Julia Donnelly: Sure. Thanks, Eric. I'd say, in general, we are internally just becoming ever more AI native company over time, and we're continuing to move further in that direction. AI is embedded now in how we build every single product, operate the business and allocate resources internally. So the restructuring actions we took in Q1 were really about aligning the organization with our highest priority opportunities and creating capacity to invest in the areas where we see the greatest potential returns, including AI. And so you're seeing that reflected in how we're rebalancing our cost structure a bit in 2026, where we're directing more investment capacity towards GPUs and internal AI tools while being thoughtful and disciplined about where we add headcount. Reported headcount was up 3% year-over-year in Q2, which includes employees who joined Pinterest through the tvScientific acquisition. So if you exclude that acquisition, headcount was down year-over-year in Q2 and is down 6% since the end of 2025. So to be clear, we'll continue to invest in specialized talent where we see a clear and compelling ROI, but an ongoing focus for us is finding the right mix of talent, infrastructure and AI-enabled productivity to [ be able to ] produce durable growth and expand margins over time. So for 2026, as I noted in my prepared remarks, we are increasing our full year 2026 projection for EBITDA margin to 30% given the strong performance, particularly in revenue that we saw in the first half. That's up from 29% that we had noted previously. And as we look forward to 2027 and beyond, I think it's still too early to talk through specifics, but I will reiterate sort of my prior commentary on the appropriate sort of near- to medium-term margins for the business that's sort of consistent with the long-term range we gave at Investor Day a few years ago of 30% to 34%. We still feel like those are the right ones to be tracking, but no specific commentary yet on '27.

Operator: Your next question comes from the line of Michael Morris with Guggenheim Securities.

Michael Morris: I wanted to follow up on the Pinterest Assistant comments and hoping you can expand a bit more on the path to monetization of the product. I understand how it further strengthens the user experience. It's very clear. But are all the pieces in place from advertisers to be on that monetization journey? Or do you need more there? And if I could ask one more on the -- just a follow-up on the comments on the deceleration in non-U.S. international growth that's driven by decisions that have been made by the new leadership team. Can you expand a little bit on what's changing in that go-to-market and when we will see the sort of fruit of that labor?

William Ready: Yes. So on the monetization for our assistant, one of the things I talked about on the call is that we've actually woven it throughout our existing app. And so that means it has monetization built in right from the start. So we don't think of this as a build it now and then later monetize type of thing with our assistant capabilities that are woven throughout the app. It's woven throughout the app right alongside of the monetization that is already baked in there. And I would just say, as you look at our journey over the last few years as we made Pinterest an AI-driven shopping assistant, you've seen us do that while having a pretty consistent margin expansion through that time, which has been twofold. It's been one about us using that AI for highly commercial moments where monetization, again, is sort of baked in right from the start as well as all the cost efficiency that we've talked about with compact models and using open source or open-weight models -- so those are the things that we've been doing to make sure that we've got not only the monetization, but the margin profile that we want from leveraging AI. And so that's exactly what we've been doing for the last several years in the background. Now to bring to the foreground, the same type of approach that is sort of woven right in right with the monetization there. And of course, there's always things that we'll do to keep adding to that. We've talked about like our Business Assistant for advertisers, things like that, where we're bringing more assistant capabilities to advertisers, all those things that will continue to enhance that. But we think about monetization from the beginning of our product design. And then on the second point, in terms of the go-to-market changes the monetization there. So these -- we expect -- we talked about these last quarter. It tends to take -- it's going to be a multi-quarter journey on these things. But the thing I'd point you to is we had work to do in UCAN as well. And when you look at the reacceleration of UCAN, the things that we did to reaccelerate in UCAN particularly around our go-to-market and sales approach, you saw those reflected in Q2. And that's what we're deploying, those similar strategies we're deploying internationally. So while it can take a little bit of time to take hold, you're already seeing that happen for us in UCAN, and that's what gives us really good confidence on that for Europe. And while there's still a little bit more of that for us to do and it can be a multi-quarter journey, we feel quite confident in the approach. I don't know, Julia, is there anything you want to add to that?

Julia Donnelly: Yes. I think that's spot on. I think just to add a little more color, our new Head of International Sales is in place now, focused on improving kind of market prioritization and coverage, deepening senior relationships with advertisers and agencies in the region and bringing out just greater consistency overall to measurement and cross-functional support across regions. We're also testing the expansion of third-party demand into Europe as we work to broaden demand. So just a little bit more color there. And again, as Bill said, I think seeing nice results here from the UCAN playbook and looking to extend those to international over time.

Operator: Our last question comes from the line of Shweta Khajuria with Wolfe Research.

Shweta Khajuria: I have one on engagement. What is -- specifically what is driving engagement strength? It's continued to remain strong, particularly among Gen Z. And I guess, what gives you confidence in these trends being durable?

William Ready: Yes. Thanks for the question. On the durability of these trends is 12 straight quarters of record high users. And through all 12 of those quarters, it's been the same things that we've been talking about in terms of really making Pinterest a highly shoppable platform with great personalization and with AI powering great recommendations. But not the AI alone, it's the AI powered on human taste and curation, that unique signal that we get. Stated very simply, the AI by itself doesn't have style or taste. People have style and taste. The 640 million people come to our platform really create a flywheel effect that we get really unique signal that we can train our AI or post-train open models on that then let us give better and better results to users. And as I mentioned in my prepared remarks, we see that flywheel continuing to accelerate. And when we think about how differentiated that is, I shared this on prior calls, that when we look at our latest multimodal models and the relevancy of the shopping recommendations we make, we talked about how we saw those outperforming closed models by 30 full percentage points on the relevancy of shopping recommendations. Not anything that it could do on the relevancy of shopping recommendations. So those are things that give us confidence in the durability, but I'd also just point to what's been happening over the last 3 years, where we're several years into AI chatbots. Gen Z is more than half our platform, our largest, fastest-growing demographic. They have all almost certainly used chatbots, and they see something different from Pinterest. And with Pinterest, it's not only the visual-first nature. It's how we are bringing that human taste and curation and which is completely unique to our platform and that we don't see happening any place else in the Western world. So there's a lot more for us to do to make that better and better. But we see that with Gen Z. They absolutely are thinking of it that way. And you see that not just in our user growth and engagement. You see it in third-party research as well. We've previously cited, Adobe did a study, independent of us, that 39% of Gen Z now thinks that Pinterest as a first place to go search. And the reason for that is more than 70% of Gen Z sees Pinterest as more personalized. So I think those are places where you can look and see that flywheel really taking effect. And as models commoditize, which they clearly are with open source, we're able to take those capabilities, train on our total unique signal and give really great recommendations to users. We've got great distribution to those users and have become really a beloved platform for those users, which is why, as Julia noted in some of her earlier comments, 85% plus of our users come to our mobile app directly. We're not dependent on others for that traffic. Pinterest is a destination that's beloved by its users. And I think that has proven out to be quite durable as well.

Operator: We have reached the end of the Q&A session. I will now turn the call back to Bill Ready for closing remarks.

William Ready: Thanks again to all of you for joining the call and for your questions. We look forward to keeping this dialogue going, and we hope you all enjoy the rest of your day.

Operator: This concludes today's call. Thank you for attending. You may now disconnect.