HubSpot, Inc. Aktie

Please welcome to the stage, Geoff Koegler.

Good morning, everyone. Welcome to HubSpot's Analyst Day here at UNBOUND. A big thanks to all of you that are joining us live here in Boston. It's really great to see you all. And thank you to those of you that are joining us on the webcast as well. We really appreciate you taking the time to join us today. I'm Geoff Koegler, Vice President of Investor Relations here at HubSpot. And this is my first UNBOUND. I'm incredibly excited to be here with the team and all of you.

Speaking of UNBOUND, I hope you had the chance yesterday to catch Yamini, Duncan, and Jon's keynotes. They did a great job walking through the latest trends across AI, marketing, sales and service and highlighting the amazing innovation that we're driving across our platform, including some exciting new product announcements that we made this week. So if you missed those, make sure you catch the replays. Also, make sure you catch the replay of Dharmesh's keynote on how to win with AI by becoming a builder. It was a really great session.

No Analyst Day would be complete without a safe harbor statement. You can find the full details on our Investor Relations website at ir.hubspot.com. We'll also be posting the full presentation to the website shortly after the event concludes. We've got a great agenda for you all this morning. Yamini, Duncan, Jon and Kate will join us up on stage. They're going to unpack our vision to win in the outcomes era. They'll walk you through where we're headed, our strategy across product, pricing and go-to-market and how all of this will drive long-term revenue growth and profitability for HubSpot. After that, we'll have Dharmesh join the team up on stage, and we'll have some time for Q&A. So get those questions ready.

And then for those of you that are live with us here in Boston, we'll have a short window to connect and grab some lunch afterwards as well. And then, finally, at 12:30, we'll all head back downstairs to the main stage session with Sunita Williams.

All right. With that, let's get started by hearing directly from some of our customers.

[Presentation]

Please welcome to the stage, Yamini Rangan.

Hello. Good morning, and welcome to Analyst Day. It is so great to be in Boston. And for those joining us here in person, it's great to see so many of you here. Look forward to the conversations that follows. And if you are joining us online, thank you so much for the support, and thank you so much for joining us here. Welcome.

All right. Today, let's get started by talking about HubSpot's vision and where we are headed. Now AI is creating a new era in software, not just defined by the features that we deliver, but by the measurable customer outcomes that we can drive. That's a big change. We are transforming every part of HubSpot in order to lead in this new era from our product to our go-to-market, to our pricing, to how we operate as a company. And we're doing this from a position of strength. Our connected customer data as well as trusted platform and the broad expansive distribution that we have and the ecosystem moat, they all give us durable advantages. And we're staying very disciplined, not choosing between growth and profitability. We are committed to driving both durable growth as well as expanding margin. Today, you're going to hear from the leaders in product, go-to-market as well as Kate, and we want to walk you through our strategy to accomplish all of this.

Now let's get started. Yesterday, I talked about the outcomes era, and the outcomes era is here. The last few years, we've seen one of the largest investment cycles in technology, trillions of dollars invested in building the foundation for AI. But the next wave of value is actually going to come from applications that take that intelligence and convert it into measurable customer outcomes and growth. That is the outcomes era, and HubSpot is built to win it. Now there's just been a lot of debate on what AI means for SaaS, who gets to capture the value in SaaS-pocalypse. I'm just going to address that directly. We don't believe that AI eliminates the value of platforms and applications. We believe it does raise the bar for how you convert that intelligence into real customer outcomes.

And to do that, you need context. So here is our thesis. Our thesis is that intelligence is necessary, but it is not sufficient. Context is what turns that intelligence into measurable customer outcomes within businesses. Now AI models are trained on vast amounts of publicly available information, and they're going to continue to get better. Absolutely no doubt about that. But no matter how intelligent a model gets, it does not know what's happening within a business, what's changing in the pipeline today, which deal is stalled, which customer is at risk. And all of that information is not just a static, dumb database. It is a dynamic, smart substrate with workflow logic that maintains state, rules, permissions, governance and audit trails that make all of that information trustworthy for businesses.

That is the context that HubSpot delivers. Now this is not just our belief. We actually quantified the value of context. We went across our customers that are using AI, and we measured how context impacts the outcomes. And this is what we found. We actually found that good context delivers great outcomes. We saw this with every one of the key metrics in marketing, sales and service improve with good context across our customers. You can see that. MQLs generated, up over 200%. Deals closed and won, up. The number of customer meetings booked, up. It takes a lot to actually move these metrics, but good context actually drives these types of outcomes.

But here's the part that actually really surprised us. We found that AI with bad context is worse than having no AI at all. This really surprised us. And you can again see every one of the metrics here deteriorate with bad data and bad context. Now this is because I think, as humans, we know how to control for this bad context, but AI does not, right? It might recommend a product that is no longer in your product catalog, might actually use an inaccurate definition of ARR, might route an approval to a manager who just changed roles 2 days back. And that is why we believe that even as models improve pretty significantly, the context within a business becomes even more valuable. And this is HubSpot's structural advantage. And this is why we are democratizing AI across go-to-market for scaling companies.

Now for just over 20 years, HubSpot has done one thing consistently. We have excelled at democratizing complex technology to bring it to scaling companies to help them grow. And in that process, we've become domain experts in both go-to-market as well as SMBs. In Phase 1, we democratized inbound. We helped tens of thousands of customers by giving them a playbook and a set of products to help them succeed in marketing. We created an all-new category called inbound marketing. In Phase 2, we democratized CRM. We helped hundreds of thousands of customers by giving them a unified platform with data across marketing, sales and service, and we made it easy to use. We went from a single hub company to a platform company with multiple hubs, and we crafted every hub organically.

Now we have the opportunity to democratize AI. Our goal is to help millions of customers grow by providing them an agentic customer platform and making it easy to get the outcomes that they want with AI. This is our biggest opportunity yet as a company, and we're bringing all of our domain expertise in go-to-market and within SMB segment to make this possible. Now we're doing this from a position of strength. We have consistently grown our revenue. We've generated strong margins as well as free cash flow over the past many years, and we'll continue to build on that solid foundation.

We also have a platform advantage that has allowed us to really bring the pace of innovation to our customers, and it has enabled us to grow our customer base to over 300,000 globally and drive multiproduct adoption across that customer base. And finally, we have exceptional distribution. We talk to millions of growth leaders every single month across our channels, across social, across academy, across the events that we do, and we have a powerful ecosystem moat. We have nearly 4,000 solution partners that extend the reach of our sales as well as customer success teams.

Our execution really helped fuel the growth and now is the foundation for the next phase of our growth. So the question becomes what's happening with customers? How are they adopting AI? The landscape with customers is changing pretty dramatically. We've now, over the last year, talked to hundreds of customers, looked at how they're adopting AI, and 3 big shifts actually stand out. The first is customers want to build. AI is democratizing building, and that's the fundamental shift. For many decades, businesses just had to adapt the way that they work in order to fit the software that they had.

Now AI, thankfully, is reversing that. And that means if you're a marketer, salesperson, serviceperson, you can actually build. You can build artifacts, you can automate workflows, you can create new experiences, all using natural language. No need to wait 4 weeks behind the developer's sprint. But there is a clear way to build well. And building well actually requires connected customer data. It requires growth context, permissions, governance, security. And that is the platform that HubSpot is building to deliver. We want to be the platform where go-to-market teams can build.

Next, what we're finding is that customers care deeply about outcomes. Now again, the conversation is changing really rapidly. A few months ago, it was all about token maxing. Now our customers don't talk to us about token maxing. They want and care deeply about the outcomes that they want to drive within their go-to-market teams. And we love that because the outcomes within go-to-market are just incredibly clear. We help our customers build demand, win deals and delight their end customers. That is the focus for us.

And third, I'd say that customers want proof of value as well as predictability in cost as they adopt AI, a huge shift again that we are seeing. In SaaS, the typical process was you evaluate features, you then implement the software, you drive change management and user training. And once the users begin to drive, they get the growth and the value out of software. But AI is changing that dramatically. You can just turn up a trial and look at what the value you get and the outcomes that you get within your environment. And that means that there is need for proof of value before the commitment to software. And these 3 shifts combined is really changing what customers expect out of HubSpot right now. And this is exactly what our product strategy as well as our go-to-market strategy are designed to deliver.

Now as we look ahead, we have a very clear strategy for AI transformation. We're not incrementally improving our business. We are transforming it across product, go-to-market, pricing and how we operate as a company. And this is what it means. It means that from a product perspective, we are moving from delivering features to delivering measurable customer outcomes. It means in go-to-market, we're not just selling software, we are proving value, and we are earning the right to expand and compound adoption from there. In terms of pricing, it's not about charging for access, it is clearly aligning to how customers get value and outcomes from HubSpot. And then as a company, we are scaling with people and AI. We are driving this full transformation from how we build to how we sell, to how we monetize, to how we operate as a company. Very exciting transformation.

Now Duncan and Jon are going to do a deep dive in terms of our product strategy as well as go-to-market strategy. So I want to take a few minutes to provide an overview across all of these 4 areas of transformation. Starting with our product. Our product strategy is to lead with outcomes. And that means every product decision now starts with a super simple question: Can we deliver trusted, measurable outcomes? And can we do that fast? We have 3 clear focus areas that you're going to hear about today that will accomplish this strategy. The first is that we are building an intelligence layer with trusted context. We're bringing together all of the Growth Context for customers in what you will see as a custom, Context Home. And this Context Home is what will provide our customers the visibility, the control and the trust that is needed in all of the information that powers every one of the AI and agentic use cases, really powerful. We believe this context is what will make every agent, every recommendation we make and every outcome that we deliver compound over time.

Then we want to drive measurable outcomes within marketing, sales and service using HubSpot agents and custom agents, 2 ways. Now HubSpot agents are domain-specific agents that we build. And at UNBOUND, we launched 4 new agents: Campaign Agent, Content Agent, Nurture Agent and Revenue Agent. You're going to see this in action today. And over time, we'll continue to expand the number of use cases that we support as well as the depth of outcomes that we deliver through HubSpot agents. At the same time, we are making it super easy for our customers to build on top of HubSpot, build workflows as well as their own custom agents.

And the combination of HubSpot agents and custom agents is what will drive clear measurable outcomes within go-to-market. And then finally, really excited about the all-new Breeze Assistant. This is the interaction layer. And our vision is that customers shouldn't have to navigate the screens, click on menus and learn software. They can have a powerful conversation with all of the value within HubSpot. We've completely rebuilt Breeze Assistant. It's no longer just a chatbot. You can ask questions, you can build workflows, you can build custom agents, you can build artifacts. This is why we are super excited about Breeze Assistant adoption.

Now that adoption has grown pretty significantly this year. The beginning of the year was 37%. And at the end of August, it was over 55%, over 55% of our Pro+ customers are using Breeze Assistant. That is great. And at the same time, customer satisfaction of Breeze Assistant has actually improved from 54% at the beginning of the year to nearly 68%. We are very excited about the progress that we are making in this powerful interaction layer. Now if you step back and you sum it up, we are the trusted context layer as well as agentic layer. And this is how we are transforming our product from delivering features to really delivering clear outcomes for our customers in this agentic era.

Now product transformation is great. It is absolutely the start. But in order to capture the full opportunity with AI, we are transforming how we go to market and how we meet customers where exactly they are. Customers are buying AI very differently than they bought SaaS. There's a fundamental shift that's happening here as well. They don't start by comparing feature lists. They start by asking a simpler question: Will this work for my business? And they want to see it work in their environment with their data. And when they see that, they want to scale with predictable costs that are transparent to them. And that changes how we think about going to market. We will land with focused use cases. We will drive outcomes very quickly and then continue to earn the right to expand from there.

And to do that, we are actually strategically leveraging our distribution advantage. We have millions of users who use our product every single day. We have hundreds of thousands of customers, and we have scaled go-to-market motion. We're using that reach to seed the right AI use cases with the best trial experiences, the best product experiences and very targeted pricing offers. That first use case, the agent use case, is strategically important to us, because what we find is that when customers get measurable outcomes in that first use case, the second, third, fourth use case becomes just easy for them to adopt. Now if you take a step back, SMBs are very early innings of adopting AI, and that is our biggest opportunity. So our strategy is to seed the market, is to prove value quickly and then compound adoption from there.

And to do that, we're also leveraging the strength of our partner ecosystem. Our partner ecosystem is our forward-deployed moat. Now delivering outcomes with an AI is not turnkey. You can't get results just by simply turning an agent on. You got to look at the data foundation and the context foundation that you're building, redesign the workflows, reimagine how a business operates. And that is exactly where our partner ecosystem comes in. And this is one of HubSpot's biggest structural advantages.

We have more than 4,000 solution partners, and they work with our customers every day to implement HubSpot but also transform their go-to-market. They actually drive 25% of our sourced ARR. They help us close nearly 50% of our ARR and actively manage about 40% of our entire customer base. So we are investing in this partner ecosystem by providing the platform and the tools so that they can custom build on top of HubSpot, by providing the right incentives to drive that motion, and also making sure that they're taking us into every industry vertical that they have the expertise in. We believe that companies that will win in AI will have a very strong ecosystem, and that's why we are continuing to build on the moat that we have here.

Okay. With that, let's actually talk about pricing. Now our pricing has also evolved alongside our platform. In Phase 1, we built inbound marketing and customers bought one hub, Marketing Hub. We had subscription pricing back then with an axis for contact tiers. Life was just simple. People knew exactly what to buy. Now in Phase 2, as we expanded into a broader CRM platform with multiple hubs, some had seats-based pricing and the others had subscription-based pricing. But now we are entering Phase 3 as we democratize AI and deliver an agentic customer platform. Customers are not just simply buying software anymore. They are actually buying outcomes from the software, which means our pricing needs to continue to evolve to meet where our customers are in AI.

So our vision for this next stage of growth is simple, a hybrid model of seats and credits. Seats provides access to platform, data and AI capabilities. And credits, they scale with the value and the outcomes that we deliver. And we want to make HubSpot easy to buy and easy to grow with. And we've been delivering against this vision. Many of you know that we started by simplifying our seats model back in 2024. And specifically, we did 3 things. We lowered the seat price. We removed the seat minimums that we had for Pro as well as Enterprise, and we introduced a new seat called the core seat to capture the value of the platform. And we saw clear results. We were able to add more customers. Those customers are healthier because they buy exactly what they want and continue to upgrade from there. And we were able to capture the value of the core seat through the platform.

Now more than 25% of our Pro+ customers have continued to add core seats. That's up 10% year-over-year. And at the same time, Pro+ customers have continued to buy more Sales Hub and Service Hub seats. In fact, the seat count has grown by 16% year-over-year and seat ARR has grown 23%. That's what we are seeing in terms of the seat adoption. And now we are -- as we look ahead, we want to be even more clear about our focus. We want to remove barriers to AI adoption. That is the focus in terms of how we are approaching pricing.

And in order to do that, we got to drive predictability in terms of the cost as well as flexibility in terms of driving the right adoption. This is a big change for application buyers, right? And so in terms of predictability, we are tying our pricing as close as possible to the value that we deliver as well as the outcomes that we deliver. And at the same time, we're providing visibility through dashboards and the ability to control spend. Our customers can set up thresholds right now. And in terms of the flexibility, we're making it super easy to swap between seats and credits, super easy to buy in chunks for credits.

And we are continuing to test and iterate and learn within this model. For example, we have a pricing test and experiment that was running in Nordics that makes it super easy to buy seats and credits, and we will continue to expand that pilot within just the EMEA market in Q4. And as we test and learn, we'll continue to share the progress with you. Over time, we expect seats and credits to become the primary source of our growth drivers. Speaking of which, let's actually talk about our growth model.

Our core growth levers that have powered HubSpot over the last many years remain solid and consistent. We're continuing to drive upmarket expansion, and the number of large deals continues to grow. We will deepen our platform consolidation as well as multi-hub adoption, and we will land and drive volume in downmarket. We plan to do that by focusing on single use cases that are powerful, drive value quickly, and then continue to expand to multiple use cases from there.

As we look ahead, we have very clear emerging levers for AI monetization. Seats monetizes platform and AI value when we assist the work that people in go-to-market teams that are doing. And credits will monetize the AI value when we deliver work. And as I talked about, we think about it as HubSpot agents consuming credits, custom agents consuming credits as well as the APIs that we deliver consuming credits. So if we put this all together, customers grow with HubSpot. And as the value that we deliver with AI continues to grow, we expect our monetization to continue to grow. This is our durable model for growth.

Okay. Let's finish by talking about how we are transforming as a company. AI is not just creating a new growth opportunity. It's really changing how we operate as an organization. And we've embedded AI into how we grow, how we build and how we operate and transform ourselves into being almost AI native with the speed of that. Our product organization is just completely all in on AI. And they are one of the most advanced in terms of using our own agentic harness, something you'll hear Duncan talk about in a few minutes. Now nearly all of our developers are using agentic coding. And as they do that, the lines per developer has increased more than 123%, and you'll see the interactions within our internal harness also continue to improve.

In terms of go-to-market, we are an AI-first go-to-market organization. From a marketing perspective, AI now handles almost all of our inbound chats. And from a sales perspective, Prospecting Agents help us set up the meetings as well as continue to help our teams drive more win rate. And in support, AI has enabled us to scale customer support without scaling the headcount. And as an organization, we have completely transformed. Everything happens in 6-week sprints with continuous experimentation. All of this means that we are committed to driving durable growth as well as profitability, not by choosing between one or the other, but by really transforming how we operate as a company.

Okay. And to bring this all together, we know that the outcomes era will really be won by companies that can take that intelligence from AI and convert it into clear, measurable outcomes for our customers. And we have clear structural advantages to win, a trusted platform with the right context that can drive customer outcomes, distribution that can accelerate adoption, an ecosystem moat that helps our customers transform with AI and an AI-first operating model that helps us balance growth as well as profitability. And the combination of these structural advantages as well as the deliberate transformation that we are doing is what will drive durable growth as well as long-term shareholder value.

Thank you so much for your support, and I look forward to your questions during Q&A.

[Presentation]

Please welcome to the stage, Duncan Lennox.

Hi, everyone. It's great to be here with you this morning. And Yamini just made one thing very clear: AI needs context to deliver outcomes. And that's where I want to pick it up. My job today is to show you what context-driven AI looks like inside our product and how we're building HubSpot to democratize AI for scaling businesses. And it starts with our vision for the outcomes era, build the best agentic go-to-market platform that delivers an easy, fast and trusted experience that customers love. And to do this, over the last year, we've rebuilt much of our platform from the ground up.

We've reinvented the CRM for the outcomes era. It's still the beating heart for go-to-market teams, but it needs to drive outcomes through people and agents, not just be a system of record for tools and workflows. So we added a brain to make it all work seamlessly together. And let me show you what our new platform looks like, starting with how we've rebuilt the CRM. The CRM is really 3 things: it's data, trust and intelligence. As always, it has to start with unified data at its core, bringing structured and unstructured data together. And this, of course, is what customers have always relied on HubSpot for.

Next, we built in security, governance, observability and quality, which is foundational to trust, which, of course, is more important now than ever, especially as agents and AI are taking action on someone's behalf. And finally, we've given the Smart CRM a new brain with 2 key components: our agentic harness that we call Aviator and the Growth Context graph. And this allows us to give customers what they've always wanted, a CRM that updates itself. On top of this foundation is the action layer. This is where go-to-market outcomes across marketing, sales and service are delivered.

Whether that's through the dedicated apps that HubSpot has always been known for, like Marketing Hub and Sales Hub, or the agents, both those that we build ourselves at HubSpot and custom agents that customers and partners are building on our platform. They all run on the Aviator harness in the Smart CRM. And then there's Breeze Assistant as the interaction layer, but this is not the Breeze that you may know. We've completely reimagined it as a go-to-market expert that does the work for you. It figures out the best way to achieve your outcome, choosing the right tools and agents to get the job done.

And this fundamentally changes how you work in HubSpot, bringing all of the power of agentic AI to users to drive the highest quality, best value, fastest outcomes, all without them needing to know anything about AI. And when we talk about democratizing AI, this is exactly what we mean. And finally, we want customers to be able to get the best go-to-market outcomes from HubSpot regardless of how they choose to engage with our platform. So we'll get into all of the details about how we optimize for agents running on HubSpot and agents running HubSpot as part of our headless strategy.

Okay. Let's start with a look under the hood of the Smart CRM. Now I want to make an important distinction here. The CRM is not simply a system of record. It's a system of context, the intelligence layer for your go-to-market. That's the bet that we've made on CRM for the AI era, and we rebuilt it with 2 defining elements: first, our new agentic harness called Aviator; and secondly, rich Growth Context built in. And I want to dive into both of these.

Let's talk about Aviator, our agentic harness. Now a lot of people are throwing around this term at the moment, but a few people are talking about what it actually takes for it to truly work. We've thought deeply about this, which is why we rebuilt the CRM with the agentic harness instead of bolting it on to an old system that wasn't built for AI outcomes. So I want to talk about what it is and how ours is different. At its simplest, an agentic harness is designed to let you run an execution loop calling an LLM to do work repeatedly until you get the desired result. But in practice, that's a pretty inefficient mechanism and delivers slower and more expensive results that often miss the mark. Our harness is deeply integrated into the heart of the CRM with quality, trust and safety and governance.

And how we build Aviator is key to our ability to deliver the best go-to-market outcomes. First, we made it model agnostic. We think of this as taking the Switzerland of AI approach. Not every model is good at everything. And it changes on an almost weekly basis. So we're highly opinionated on which should be used for which task. Next, we use our domain expertise in go-to-market to build evals that run continuously to pick the right model. That means we know what great looks like. But our customers don't need to know or care about any of this. They can just be assured HubSpot will deliver the best outcomes for them even as the state of models and AI continues to evolve rapidly.

The second part of our unique strategy comes down to context. In the outcomes era, good data is necessary, essential, in fact, but it's not sufficient. Building on our unified data model of structured and unstructured data, we developed the Growth Context graph. It computes insights and intelligence from across a customer's unified data. This intelligence then allows us to use precise high-quality context in real time for a given task or outcome.

And Growth Context has 3 dimensions that work together. The first is business context, what you do, everything that makes your company yours, your brand and voice, your product and positioning. Next is team context, who you are, how you and your team work and win together, your roles and goals, your workflows and processes, your methodologies and habits. And finally, of course, customer context, who you serve, the full history and nuance of every customer relationship, your ideal customer profile and personas, your communications, contract and revenue details.

A point solution might have one part of this, but that's why they hit a ceiling. But in HubSpot, all 3 live together, and now that's in the Smart CRM. The Aviator harness uses Growth Context to provide the right context for a specific task at the exact moment that it's needed. And this is so important because the wrong context adds noise and cost, which is how businesses find themselves wasting money on tokens with minimal ROI. Our unique combination allows us to deliver the highest quality at the lowest cost. And if at any time, the best outcome favors another model, we route accordingly.

Okay. So we pick the right model, we inject the right context, all without customers having to do anything. And it's all built into the trust layer of the Smart CRM, which has security, including things like role-based access, governance for things like permissions and approval workflows, observability for full visibility into what every agent did and why, and quality, including safety and evals. This is what makes us the go-to-market harness for scaling businesses. Now I want to show you how customers experience all of this in HubSpot.

Yesterday on stage, I unveiled our all-new AI native CRM. It's the CRM that you know and love now rebuilt for the outcomes era. This is because our agentic harness now orchestrates and manages action, which changes everything. This new CRM works on our customers' behalf and updates itself. This will save customers' hours of manual updating, the headache of forgetting to add details. But most importantly, it will give them accurate insights at the moment they need them. And those insights are stronger and more proactive than ever because we built Growth Context directly into the Smart CRM.

But we didn't stop there. Businesses need to be able to see and control their Growth Context to understand what it's powering and how it's delivering outcomes. So we built Context Home as their one-stop shop. This is a new capability that lets customers see everything HubSpot knows about their business and manage it directly. It also gives customers a score on how complete their context is along with recommendations for where to add additional context that will let us deliver even better outcomes for them. All they need to do is upload a file or point us at a source, and HubSpot will do the rest automatically. And all of this context is available to every agent and every AI feature across HubSpot the moment it's relevant. From our own HubSpot agents to custom agents customers build on our platform with Agent Builder.

And I want to show you how this comes to life for customers in something that they use every day, e-mail. This is what a generic e-mail written by AI that has no context might sound like. We've all seen e-mails like this. It's AI slop. Now this is what happens when AI has the complete precise context it needs. And let me point out the pieces of Growth Context that went into this e-mail. First, from business context, we knew the product that was trialed, how it's positioned against the competitor and even the Gartner comparison to show that.

From team context, we knew how the team typically follows up after a trial, the rep's unique voice and tone and even that UNBOUND was the right moment to push for a meeting. And from customer context, we knew this customer's specific usability and price concerns, the team member that they should loop in for next steps and that per-seat pricing was particularly relevant to their company size. Every sentence in that e-mail earned its place and helps our customers drive better outcomes. And the user didn't have to futz around with figuring out what docs or other context to upload or what prompt to write or which agent to use.

So let me recap what we've covered. We've rebuilt the Smart CRM as the foundation that makes everything else work. Our agentic harness delivers high performance at a lower cost. Growth Context delivers accuracy and control. This is the intelligence layer, the new brain of HubSpot. Now let's see how it powers action. When it comes to driving outcomes, agents are the most effective way to scale how businesses build demand, win deals and delight customers. In the past 6 months alone, we've built several new agents and tools in HubSpot aimed at driving these exact outcomes, and I'll get to those in a minute.

But first, I want to talk about another foundational update we've made to HubSpot, how our customers actually use those agents. Yesterday, I introduced the all-new Breeze Assistant. Many of you know this tool. It's been on HubSpot since we first started building with AI. But what we unveiled is a complete overhaul. It might look similar, but just like the all-new CRM, it's been rebuilt for the outcomes era. You now get work done just by talking to Breeze. Instead of navigating menus or learning workflows or even having to understand the capabilities of our agents, customers just describe what they're trying to accomplish, and Breeze takes action across the platform on their behalf, pulling in the right tools for the job. Customers tell us that they don't have time to become AI experts, and Breeze Assistant means they don't have to.

Now I know many of you watched the spotlight from yesterday, and I definitely encourage you to do that if you haven't because there's some really exciting stuff in there. But I want to highlight some of the new tools and agents we launched and how our customers use Breeze Assistant to drive outcomes. For marketers, the outcome is building demand, but this has never been harder. Organic reach is declining, buyers are asking AI instead of filling out forms, all while the volume of content needed to cut through the noise has gone up and ROI has gone down. Marketers need a dramatically new way to build demand, so that's what we created.

Our all-new Marketing Studio is their single home where they can just have a conversation with Breeze and then go from insight to a live campaign in minutes. To start, they just ask Breeze for a full campaign plan. It pulls in our new Campaign Agent to build it, everything from messaging angle to an asset plan and audience segmentation. Next, we've made it easy for marketers to create their campaign content just by asking Breeze. It calls on the new Content Agent, which takes the campaign plan and drafts the assets in the correct brand voice and targeted to the ICP.

And when marketers then have their campaign up and running and need to nurture the leads, they just tell Breeze. It pulls in our new Nurture Agent to write automated e-mails for every contact using CRM data and engagement history. With the new Home and Marketing Studio and Breeze Assistant as their guide, we're helping marketers build demand all from a single conversation.

Now let's see a sales journey where the outcome is, of course, winning deals. We know reps are spending more time than ever on admin and less time actually selling. Deals stall, follow-ups get missed, and context doesn't carry between conversations. So we're giving sales teams valuable time back to win deals, and they start, just like our marketers, by talking to Breeze Assistant. Let's say they want to break into a big prospect. They just tell Breeze, and it calls on Prospecting Agent. Immediately, it pulls the buying committee and drafts personalized e-mails for each person. We already saw the difference Growth Context makes in those e-mails.

Next, when the reps meet with prospects, they want to capture the details from the meeting. They can use our Notetaker to dictate updates so nothing gets lost. And then Deal Progression feeds those updates to the CRM automatically. So deals keep moving and keep selling. No other platform makes it easy to prospect, research, coordinate, update the CRM and land the deal. So just to summarize what all of this means for customers is a platform that works for them, a go-to-market harness that selects the right model and context, a CRM that updates itself and delivers insights and a single tool in Breeze Assistant that launches agents just by chatting. Together, these democratize AI for scaling businesses because they take the burden away from teams so they can focus on their customers.

Now in a world where customers are increasingly using agents, we're very aware of a different customer challenge, and that's agent sprawl. Leaders are losing sight of what's running, what it's doing and whether it's working. This leads to difficulty managing the agents, higher TCO and a poor customer experience, all for agents that don't actually work together. We believe there's a better way and customers will want a single view into it all. We built Agent Hub to help solve that. It's one place to see, manage and build agents across the entire go-to-market, and they all operate from that shared context, which a collection of point agents can't do. Agent Hub is where customers gain confidence that their HubSpot agents and custom agents are actually delivering the outcomes their business needs.

We're also seeing a new kind of go-to-market team member emerge, the builder. These are people who want to quickly build agents that extend what they can do on HubSpot, whether it's as simple as qualifying every lead before a rep touches it or as sophisticated as monitoring your entire pipeline and flagging at-risk deals before anyone has to ask. That's exactly what we're making possible with our new Agent Builder. This lets customers and partners build agents that run on Aviator and use Growth Context just like our own HubSpot agents, and you can build all of it just by chatting with Breeze Assistant.

Our last layer is all about extending the way customers and partners interact with our platform. And I want to spend some time here because I think it's one of the most important things HubSpot has built. There's 2 parts to our ecosystem strategy. Agents can run HubSpot using our data and intelligence as a building block. Think of custom agents businesses can build on our platform. And agents can run HubSpot, operating the platform end-to-end through APIs without a human ever logging in. This is what most people refer to as a headless CRM.

We're unique because we think about our API in layers. First is the data APIs. This is raw access to everything in the CRM, over 1,000 public endpoints covering contacts, deals, sequences, content. And we're committed to giving API parity with everything you can do in the UI. We want developers building on us, not hitting walls. The second are our context APIs. Instead of raw data, you get insight that we've already computed. Think of it as asking HubSpot a question and getting an answer instead of a spreadsheet. This is actually the same layer our own HubSpot agents run on. That's how good it is. And the third and most important, our work APIs. This calls either a HubSpot agent or a custom agent to drive an outcome end-to-end.

Let me give you an example of how this works. Imagine you're a rep and you want to know which deals in your pipeline are at risk. With the data API, you'd pull all your deal data, bring it into an external third-party tool and figure it out for yourself. That's a lot of data and a lot of work. You're also going to burn a lot of tokens. With the context API, you say, "Give me the 10 most at-risk deals in my pipeline," and you get that insight back directly. And with the work API, you can say, "Figure out which deals are at risk and give me an action plan." You'll get the 10 deals and a unique action plan, next best action for each one.

We believe deeply in our agentic ecosystem strategy, and we've been executing on it for the last 18 months. Over a year ago, HubSpot became the first CRM to support MCP, so agents can interact natively with our software, and we did this first with Anthropic. Then in June 2025, we were the first CRM to launch a deep research connector with ChatGPT. In July of that same year, we became the first CRM connector with Claude. And in May 2026, we shipped the Agent CLI, a completely new layer that lets agents automate bulk scheduled work on HubSpot with no human in the loop.

By mid-2026, we were the first CRM to have connectors with all 4 leading frontier models, adding Gemini and Copilot. And today, we're the #1 CRM connector on both Claude and ChatGPT with weekly active users on ChatGPT up 250% since June. And yesterday on stage, I announced that HubSpot is the first CRM to integrate with ChatGPT ads. And we launched the AI Growth Bundle that gives new customers 65% off HubSpot Starter and HubSpot credits, a free ChatGPT business seat when they buy one, and up to $750 spend match on ChatGPT ads.

By connecting to the deep context in HubSpot, ads become more powerful and customers get attribution that's only possible by integrating their CRM. This is what real momentum looks like. These aren't just announcements. These are real products with real users and real growth.

Now I want to wrap it up. We've heard a lot today, but I want to actually talk about what actually makes HubSpot different. AI that runs on a foundation rebuilt with the right harness, the right context and the right governance, and it has a compounding effect. Every action makes the next one better for a customer. That's what Aviator and Growth Context give us, and no one else has that foundation. Second, we made it easy for customers. Most agentic platforms give you power and then ask you to go figure it out for yourself. Breeze Assistant means customers don't have to. They just describe what they want. Agent Hub means they're not losing track of what's running. And the CRM updates itself, so go-to-market information is always relevant. We're not just making AI available, we're making it usable, and that's what it takes to democratize AI at scale.

Third, we don't believe in walled gardens. We believe in customer choice and meeting them where they are, and we've used these principles to build a 3-tier API strategy. And finally, trust, which is at the core of everything we do. Every company building AI right now is learning that customers are nervous. Businesses want to know what's running, what it has access to, what it did. Trust is about transparency and control, and that's what we've built into every layer of the agentic customer platform, from Aviator and Growth Context in the CRM, to Breeze Assistant and our own HubSpot agents. So customers can trust us as their go-to-market harness.

And here's where all of these things we've talked about add up, the outcomes. Customers using HubSpot with rich Growth Context across the platform are seeing real results from AI today. They're seeing demand built with nearly 4x more MQLs. They're winning deals at over 3x the rate, and they're delighting customers with twice as many tickets closed. We set out to build the best-in-class agentic customer platform that drives go-to-market outcomes that customers love. We believe that what we've shown you today and introduced to our customers this week not only meets the moment, but gives businesses the platform they need to grow well into the future. Thank you so much.

[Presentation]

Please welcome to the stage, Jon Dick.

Thank you, Duncan. So great to meet you all. How are you? Nice to see you. Thanks for coming to UNBOUND. I'm Jon. I'm the Chief Customer Officer at HubSpot. I lead sales and customer success. But I'm a long-time HubSpotter. I've been at HubSpot for about 10 years now, and I have worked across pretty much every part of our go-to-market. Most recently, I led our customer success team. And leading that team, I had the opportunity to get to know our customers deeply, their goals, their challenges and their hopes when they choose HubSpot.

And I think that our customers are going to absolutely love the HubSpot that we're building for the outcomes era. Not only do I think that the product that Duncan just talked to you about is going to absolutely blow them away, but the transformation of the business model will align HubSpot's growth with our customers' growth in a way that was just never possible before. It's good for customers, and it is good for us. So my mission as the Chief Customer Officer is to build a go-to-market that meets the moment for the outcomes era. It means democratizing AI, delivering exceptional results for customers and ensuring durable growth in the process. And that's what I'm going to talk to you about today, how our customers are changing, how our go-to-market is transforming and how we're going to drive growth, both downmarket and upmarket.

So let me just start with the customer. In the SaaS era, the customer journey was very well established. You've seen it before. It looked something like this. And it was a journey that was built on establishing upfront promise. What can this solution possibly deliver for my business? This has all changed with AI. In the outcomes era, the customer journey is built on upfront proof. What will this solution deliver for my business? And I think that new journey looks more like this. Let me just talk you through it at a very high level. So the customer journey used to begin when buyers started doing research on their own. They found a whole bunch of solutions for their problem. In the new journey, buyers are outsourcing this. They're outsourcing it to LLMs and to influencers. And so the whole thing looks very different.

They're being given a short list of solutions to consider for their problem, and they're just showing up much more ready to buy. But buying also looks very different now. Buyers are no longer interested in spending a tremendous amount of time upfront, evaluating if a solution works for their business. Instead, they want to jump right in. They want to start building for their business. They don't want a demo from a vendor. They want the vendor to help them actually build their first use case on the product in their environment. And that first use case then becomes the business case. Instead of developing a theoretical ROI and committing based on promised value, buyers are able to look at actual proof and have confidence in the outcomes that the solution will provide.

And with that confidence, they grow and their spend grows, too, not only by spending more in that first use case as it scales, but also by adding other use cases, because in this new customer journey, one use case leads to the next. So this new customer journey has really inspired us at HubSpot, and it's caused us to really obsess about 2 big things in our go-to-market. The first is how we drive distribution so that more companies consider HubSpot on day 1 and continue to consider us on day 100. Second is how we engage customers to help them actually build that first use case and see value immediately.

Let me start with distribution. So distribution has been an advantage for HubSpot since our founding, and it continues to be a real cornerstone of our go-to-market strategy. The big shift, of course, is that buyers increasingly rely on more trusted sources to make purchase decisions, especially when it comes to AI products. They bypass the traditional channels and instead, they look to the LLMs, the influencers, communities to learn what they should be buying. Now we've been ahead of this shift at HubSpot, and we've been diversifying into these channels for several years now. Our YouTube and newsletter reach, for instance, beats major business media publications.

And you can see that with the live stream of our spotlight from yesterday. We're already at 1.65 million views on YouTube, well ahead of other keynotes that have been posted. Meanwhile, our global aided awareness for HubSpot upmarket across core geos and segments is at all-time highs, and we continue to be #1 in AI share of voice for the CRM category across all of our major markets. So this is our playbook, and we are continuing to invest in this.

But distribution, of course, isn't only about new customers because the other place that buyers look are to the companies that they already work with and they already trust. With over 300,000 customers, of course, we have a substantial distribution advantage and a trust advantage within the SMB space. And as you know, we've been successfully leveraging this advantage when it comes to upgrade and multi-hub expansion for some time, and that continues to go well. But we're also now focused on leveraging our existing customer base for the emerging AI adoption opportunity. We are actively working to get as many of our customers building their first AI use cases on HubSpot, and we're making really good progress with that. 19% of Pro+ customers are using HubSpot agents as of August, which is up 2x since the beginning of the year.

So we're working with them actively, and so this brings me to how we are doing that. So we are transforming customer engagement at HubSpot, and we're putting our focus on helping build that initial use case, helping customers achieve value, and then growing from there. What this looks like in practice is fewer handoffs and more help. To do that, the lines between sales and customer success really start to blend in a whole new way. That's why we combined the sales and the customer success team under my leadership earlier this year, why we've aligned everybody in sales and customer success on customer outcomes and why we are rebuilding our motions with customers.

But we, of course, are not the only ones who help our customers at HubSpot. Our engagement model also includes our partners who are really an extension of our sales and customer success teams. They act as HubSpot's forward-deployed team. Our partners are a major advantage for HubSpot, and we are leveraging them to help our customers adopt AI. Partners have the industry experience, the technical expertise and the change management resources that our customers need in this moment. So accordingly, we are doing a lot more with our partners. We are doing more co-selling. We are doing more co-delivery. We are rewarding them for their efforts by providing incentives for customer AI adoption, and it's paying off. When a partner is involved in an upmarket deal, we see nearly an 8-point higher win rate, 75% higher ASP, and a 14-point lift in net revenue retention. So as you can see, when partners are involved in selling and expanding, it really works for driving value for our customers. So we are going to continue to do that.

So now let's actually talk more about our customers. And I want to start with the group that's been the foundation of the HubSpot story for our entire 20 years, which is small businesses. So what we're seeing right now downmarket is that customers have more options than ever, but the same challenges. They're more price sensitive, they're time constrained. They just need easy ways to grow. And that is what we have always delivered at HubSpot. And I think by what you saw from Duncan, what we will continue to deliver, because we've built them an even easier way to grow in the age of AI. I mean no more data entry, no more managing omnichannel complexity, no more learning the software. I've been using HubSpot's Mobile Notetaker on the HubSpot app all week in meeting with customers, and it's been absolutely great.

They can build and scale on HubSpot without friction now. And so this gives us an opportunity to reintroduce HubSpot to the downmarket customers in a whole new way. And so the first thing we're doing is we're positioning ourselves differently. We are obsessing much more about the outcomes that we drive for customers than the technology that drives it. And that's what our customers care about. They care about building demand. They care about winning deals. They care about automating all the stuff they hate. Second, we are ramping up our promotion in Q4, and we are pursuing new partnerships and helping more customers see this whole new and improved AI-native HubSpot.

And third, we're being more flexible with our customers. We are letting them shift their spend between seats and credits, just so it's easier to build, prove and grow. So better product, better positioned with the pricing flexibility that customers expect.

Now let me tell you 2 stories. I want to tell you 2 stories of downmarket customers who built and quickly saw value with HubSpot. The first is Phin. They're a security awareness and phishing simulation platform. They were an existing HubSpot customer who had a common problem. Their reps were spending more time doing prospect research than actually prospecting. So the reps were spending their time combing through thousands of e-mails and deals, searching for insights on who to reengage, what messaging worked, how to personalize their outreach, and they knew that they needed a better solution. And so they did what any modern buyer would do in the new customer journey, and they started building. But they didn't just build on one solution. They built with a frontier lab, they built with a point solution, and they built with HubSpot, 3 different experiments to see which was going to work for them.

HubSpot won. And we won because of our trusted context. Prospecting Agent went out and researched all the old deals by pulling notes, the contact details, the call history, pulled it all from HubSpot and drafted relevant reengagement messaging for the reps. Now you saw from Duncan the difference between an e-mail written by generic AI versus one written by HubSpot with Growth Context. So it should come as no surprise that Phin's e-mail response rates more than doubled on HubSpot, and they won multiple deals that had previously been written off. So these results were great for them. It led them to 8x their credit commitment with us, and they are now on track to continue to increase their spend this year. I just love this story because we won on proof, not on promise.

Now another example of a downmarket customer that I wanted to share is VisionWave Holdings. They're a microcap defense technology company. And their COO had a problem. He had a major defense trade show coming up, and he had no way to collect leads at scale. He needed outcomes in like literally days, not months. And so he didn't have time for the old traditional customer journey and to go through that whole process. So he just came to HubSpot, and he came to HubSpot ready to get started. He got on to our free product, and he began building. In 1 week, using only Breeze Assistant, he built a custom lead registration system for the trade show, he configured the sales processes and he ingested the company's existing leads and deals. The COO described the experience as the fastest and best CRM implementation in his 30-year career, and he upgraded to our enterprise product touchlessly because of it.

As for the trade show, according to him, VisionWave have got an overwhelming pipeline of deals from governments and Tier 1 contractors around the globe. I love this story because it shows the power of Breeze Assistant in action for our customers and how powerful it can be, especially downmarket. So let me tie it all together. With our new AI native CRM, our value proposition downmarket is easier to use software, faster time to value and better outcomes.

Okay. Let me switch gears now and talk about the fastest-growing part of our customer base, our upmarket customers. So what we are seeing upmarket is more deliberation than ever before. And honestly, I get it. It makes sense. It's a once-in-a-generation platform shift and companies want to make sure that they get it right. What this is showing up as for us is more scrutiny from buyers, larger buying committees and a requirement to just derisk the implementation.

Now the good news is that despite this, our product and our go-to-market have never been stronger for an upmarket buyer. As Duncan showed, we have a legitimate platform advantage. Our trusted context drives better outcomes at a lower cost, and our headless strategy future-proofs HubSpot for our customers. Customers, when they come to HubSpot, can be confident that we will scale with them despite a changing AI landscape. Meanwhile, as you saw the core marketing, sales, service and ops use cases, they have all gotten more valuable for our customers. On the go-to-market side, we have more credibility upmarket, more logos, more proof, more peer companies willing to be references for our new customers. And so all of this together means that we just have a real opportunity upmarket.

So here are the new ways that we are meeting the moment. The first place we're focused is the initial sale. We know these upmarket buyers are more hesitant and have higher expectations. So we have updated our sales playbook to match. We're doing more account-based marketing. We are involving C-suite decision-makers earlier, and we have built out a dedicated specialized sales team for service and CPQ use cases to help those buyers. To derisk the sale, we are investing in more resources to ensure that our customers see tangible value from their first use case as quickly as possible.

What this looks like in practice is funding more partner services, building outcome-oriented implementation plans and offering HubSpot go-to-market engineers to support their AI setup. And to help drive expansion into AI, we are partnering with our customers much more deeply than we ever have before. We are doing discovery focused on how their core go-to-market processes work. We're mapping their problems to agentic solutions, and we're granting them credits to actually trial our agents to solve those problems. We are embedding our HubSpot partners into the delivery and ongoing success of all of that as well.

So a better playbook, more protection upfront, more partnership long term, all guided by greater personal engagement upmarket. And we're already seeing the results. We are competing in and winning much bigger deals, deals over $120,000 of ARR, up 38% year-over-year, and our partner attachment to upmarket customers has grown as well.

Now let me walk you through 2 stories of upmarket customers that I really love as well. These are stories of customers who are driving incredible outcomes with HubSpot. So the first is Nations Roof. Nations Roof is one of the largest commercial roofing contractors in the U.S. They've got over 40 locations across the country. So they originally came to HubSpot for marketing. They needed more leads, and they needed to automate more of their marketing activities. And while the marketing team got going on HubSpot and experienced what it was like to engage customers with HubSpot and experience the ease of use of HubSpot, their sales team was on a different planet. They were on an incumbent platform that they referred to as a Franken-system.

They told us it was over-customized, it was over-expensive and it was so painful to use that reps simply avoided it. I hear this story like multiple times a week, by the way, from different companies. Within a year, the CRO decided that it was time to make a change. So he went to the market looking for something different. And I remember meeting him. It was very distinctly the way that he described what he was looking for and his hopes and dreams in a new CRM platform. He said, "I just want a CRM where the reps don't have to do data entry. That's what I want. That's what I need." And he got excited as I started talking to him about what we were building with our Notetaker and our self-updating CRM and our Mobile Notetaker.

He also got excited about the idea that his sales and marketing team would actually work out of the same system and could work together. And he got the most excited about the lower total cost of ownership that he would have if he consolidated all of it on to HubSpot. So that's what they did. What started as a marketing use case quickly turned into 4 enterprise-level hubs and 50,000 additional credits, bringing their annual commitment with us from $10,200 up to over $300,000.

The next example I want to talk about is a company called Eventus. So Eventus provides software that helps financial institutions detect market manipulation and meet regulatory requirements. The firm sells into one of the most compliance-intensive segments of fintech. The cycles are long, the buyers are technical. The addressable market is small and very high value. This means that prospecting for them requires extensive research, depth and personalization, but that doesn't mean it needs to be done by a human. They had a vision for building a prospecting system that would augment their sellers and not replace those sellers, one that could do the research, track the signals and work with their reps.

Now to their credit, they jumped right in. They pulled some engineers and we're like, "Let's just build this prospecting system." They went at it. But when they learned that HubSpot had built a Prospecting Agent in the same system that their sales reps already worked in, they decided to give it a try. They made a small credit commitment, and they ran a pilot on HubSpot. They love the early results. What they loved was that each rep got an agent that was trained on their voice and their selling style, and that agent did all the work.

It pulled the context on where a lead came from, it pulled the engagement history, it pulled the campaign goals to draft an outreach. All the reps had to do was review it and approve. Based on this, they made the decision that it wasn't worth the cost for them in dollars or engineering time to replicate what HubSpot had already built. So they went all in on their first use case, and they started to scale. Since then, their teams have seen a 27% increase in influence pipeline. They've got back 87 selling days across the team.

And because of it, they've also began to expand into new agent use cases. They are now enriching, tagging and routing good fit accounts with Data Agent, and they're building a big ABM program for their account management team for a top 50 ABM approach. So in the last 4 months, they've increased their credit commitment 3.3x with HubSpot, and they are still expanding. In both cases, our upmarket value proposition of an easy, fast, trusted platform with lower TCO delivered exceptional value to our customers. As I hope you can see, we have an immense opportunity upmarket and downmarket, and we are executing against both of these segments very thoughtfully.

So I want to close where I started. It is a noisy time in the paradigm shift, and there are lots of options, but HubSpot is positioned to win for the outcomes era. Against the incumbents, our AI native capabilities and our trusted platform deliver on buyers' expectations with a CRM that is easier, faster and provides better outcomes at a lower total cost of ownership. Against custom-coded tools and everybody wants to vibe code their own CRM, our message to customers is that starting on this path is easy, but scaling it is exceptionally hard, and HubSpot does the hard stuff really well.

We provide the governance, the compliance, the maintenance, the integration, the model neutrality, the ongoing innovation, everything you need to confidently grow. And we do it without the complexity and cost of doing it yourself, and we make it available in whatever way you want to access it. Against AI point solutions, we think history will repeat itself here. In the software era, nobody wanted a Franken-system. And in the AI era, we don't think anybody will want agents sprawl. Our unified platform has both breadth and depth. It delivers the trusted context that every agent needs, and it scales exceptionally well.

So this era is going to reward the platform that delivers outcomes the fastest. And that platform is HubSpot. And that's why we're leveraging our position to win more customers and drive more growth.

So Kate Bueker is now going to take you through our business performance. But first, I'd like to share another example of a customer getting outsized value from HubSpot. Thank you all very much.

[Presentation]

Please welcome to the stage, Kate Bueker.

Thank you, Jon. Hello, everyone. It is great to see you all back here in Boston. You've heard from Yamini, Duncan, and Jon about where HubSpot is headed as we transform with AI. I am excited to share how this is going to drive durable revenue growth and margin expansion in the years ahead.

All right. In terms of our agenda, I'm going to start by sharing how the solid foundation that we built as we democratized inbound and CRM puts us in a position of strength to democratize AI. Next, I'll walk through how that strategy drives new growth levers as we align our business to delivering outcomes for customers. And finally, I'll share how we'll drive margin expansion and earnings growth as we transform how we build, grow and operate. Over the last 20 years, HubSpot has built the leading go-to-market platform for scaling companies and a solid foundation for growth in the age of AI.

This foundation includes our financial scale, platform advantage and exceptional distribution. It is what sets us up to win as we democratize AI. Let's start with our financial scale. HubSpot has a consistent track record of delivering revenue growth, margin expansion and strong free cash flow growth. Since 2021, our revenue has grown at a compound annual growth rate of 23%.

At the same time, we've delivered consistent margin expansion, expanding our non-GAAP operating margin by 12 points. And our cash position is strong at $1.4 billion as of the end of Q2. In 2026, we expect to deliver $750 million in free cash flow, representing a 30% compound annual growth rate since 2021. Next is our platform advantage. We've grown our customer base at a compound annual growth rate of 20% over the last 4.5 years to over 300,000 today.

We've also driven significant multi-hub adoption with 70% of our installed base ARR leveraging 3 or more hubs. Across our 300,000 customers, the scale of our CRM and context is massive with 5.5 billion company records and 18 billion contact records. And we have nearly 2,000 technology partners that have integrated into our ecosystem. Finally, the platform is unlocking new ways for customers to engage with HubSpot's platform. I know Duncan shared, we were the first CRM to have connectors with all 4 of the leading frontier models, and we are the #1 connector on both Claude and ChatGPT.

Last, our exceptional distribution. We are one of the 10 largest business media networks worldwide with over 350 million organic views over the last 12 months. We have a sales and customer support team that is 4,500 strong, and we have 4,000 Solutions Partners that help extend our reach by helping customers build their go-to-markets on HubSpot. And our distribution extends across the globe with 50% of our ARR coming from markets outside the United States. Our strong foundation sets us up to win in the AI era. Next, let's talk about how we will drive growth. Our growth strategy remains strong. Our core growth drivers are upmarket expansion, multi-hub adoption and downmarket volume.

Our strategy also enables a set of emerging growth drivers across seats and credits that align our business model to delivering outcomes for our customers. Let's dive in starting with our core growth drivers, which should all look familiar to you. Our upmarket performance remains strong, reflecting continued demand from larger customers for a unified AI-powered platform. We saw this again in Q2 with large deals growing 38% year-over-year. As a result, deals with over $120,000 in ARR represent 18% of our installed base, up more than 3x since the beginning of 2021. Our multi-hub momentum continues to grow as new and existing customers consolidate their go-to-market technology stacks on HubSpot.

The share of our new Pro+ ARR that is multi-hub has grown steadily with more than 80% coming from customers with 2 or more hubs and more than 55% coming from customers using 3 or more hubs. As a result, only 9% of our installed base ARR is on a single hub today and more than 70% use 3 or more. Downmarket volume remains a key element of our strategy. You heard this from Jon. As of Q2, we had 150,000 starter customers on HubSpot, up from 50,000 in 2021 and representing about half of our total customers.

We are transforming our downmarket product to deliver an AI-native CRM that helps small customers achieve the outcomes they care about at a very low cost. We aim to capture customers early in their journey and grow with them, and we remain committed to this segment. So our current growth drivers are working. And as we execute our strategy to democratize AI, we are seeing traction with a set of emerging growth drivers. These AI growth drivers include seats and credits. Seats are about making human users of HubSpot even more effective and credits are how we monetize the outcomes we are delivering for our customers. Seat growth has been a meaningful driver for HubSpot with paid seats up 80% since our 2024 pricing simplification.

We had more than 2.5 million total paid seats at the end of Q2, including more than 1 million paid core seats. We're committed to making every go-to-market employee as productive as possible. As a result, we've continued to add value to our seats and notably our core seat with features like Breeze Assistant, Notetaker and now a self-updating CRM. Breeze Assistant adoption continues to scale, growing from 37% of Pro+ customers at the beginning of the year to 57% in August, and Breeze Assistant agentic actions are up 4.4x. We will continue to monetize seats through the -- monetize the core value of our AI and platform capabilities, and we're going to continue to increase the value of seats and drive deeper penetration within our customers. Now let's talk about agents and credits.

Duncan outlined our agentic strategy, which includes 3 opportunities: HubSpot agents, custom agents and APIs. Each of these will consume credits and drive monetization over time. Across all of these opportunities, we will measure our progress with a consistent framework, reach, depth, quality and growth. First, reach. How many customers are adopting our agents. Second is depth, how often are customers taking agentic action. Third is quality. What is the quality of the business outcomes we are delivering? And finally, growth, how is all of this translating into credit and seat consumption. Let's dive in. The first opportunity we see is with HubSpot agents. Our strategy here is to deliver best-in-class agents across marketing, sales and service use cases. In addition to Customer Prospecting and Data Agents, we launched Campaign, Nurture, Content and Revenue Agents this week at INBOUND. Let's unpack the early momentum we are seeing, starting with reach. 19% of Pro+ customers using HubSpot used HubSpot agents as of August, and this is up 2x from the beginning of the year.

Over the same time, we have seen the number of monthly agentic actions increase by 3.5x, reflecting the quality of the outcomes we're delivering for customers. And we've seen monthly credit consumption more than double, even with the change that we made in April to lower agent prices and shift to outcome-based pricing, which created a near-term headwind. And equally encouraging, we are seeing credit usage increasingly diversified across our agents. Customers are not just adopting a single agent, they are beginning to use agents across the entire customer journey. Let me give you an example. Sesame HR is a 400-person software company with more than 18,000 customers worldwide. As the company grew, its support team struggled to keep up.

At best, they could cover 70% of their tickets, leaving the rest unanswered. Instead of hiring more reps to handle the volume, Sesame ran a 1-month pilot with Customer Agent, and they saw results. Within a few months, they could -- they were able to cover 100% of the tickets with 60% of the tickets resolved through Customer Agent without any human escalation. Customers got a better experience and the support team had more time to focus on higher-value work. The value is translating directly into credit consumption. Sesame quickly used its initial credits, and they purchased additional credits that represented about 100x the initial amount. This resulted in $80,000 in agentic ARR. The company has been very encouraged by the success with Customer Agent, and they are testing additional HubSpot agents. Sesame is piloting Buyer Intent to help its sales team drive conversion rates and better outcomes just like its support team. Okay.

The second opportunity we see is with custom agents. As Duncan shared, the strategy here is to make it incredibly easy to build agents and go-to-market automation directly on HubSpot. Breeze Assistant and our Solutions Partners will help drive adoption. While early, we have seen strong initial interest and engagement. Over 3,000 custom agents have already been deployed since we launched in July. And the most common adoption in deals are things like internal workflows around deal management and revenue operations, places where customers can quickly save time and drive consistency in their process. We see a significant monetization opportunity with custom agents as our partners and customers build more agents and consume credits. Let me share an example. Cyber Advisors is a nationwide cybersecurity leader with over 400 employees. The company saw an opportunity to make its go-to-market more efficient while driving incremental growth, and they turned to HubSpot agents to help.

To start, they launched pilots with Customer Agent, Content Agent and Prospecting Agent, and they saw results. Prospecting Agent now sends a personalized e-mail to every new lead before the team member picks up the phone. And so far, more than $700,000 in closed revenue has been attributed to Prospecting Agent. But Cyber Advisors aren't just using HubSpot agents. They are building custom agents on HubSpot that suit the unique needs of their business. For example, the customer built a scoping agent that parses call transcripts in real time and creates and sends a ticket to the solutions architect to prepare a quote. This is a process that used to take days and deals would often go cold as a result.

Now it happens in minutes. Cyber Advisors also built an upsell agent that automatically analyzes customer accounts based on things like industry and company size, and then it recommends services that they are most likely to need to the sales reps, allowing the sales reps to have much more targeted conversations. Together, HubSpot agents and custom agents are transforming Cyber Advisors go-to-market. And they are mapping out plans to build additional agents on HubSpot's platform. The third opportunity we see is with APIs. This is all about extending the ways that customers and partners work with HubSpot's platform.

As Duncan shared, this includes accessing our platform through data, context and work APIs. We're confident in our ability to monetize the value we provide to customers no matter what work surface partners and customers choose to operate. All right. I'll end where I began. As we deliver AI value with HubSpot agents, custom agents and APIs, we expect credit consumption to scale as we monetize the work and the outcomes we're delivering for our customers. And as we see traction with these emerging growth drivers, we want to make it as easy as possible to buy and scale with HubSpot. This means lowering the barrier to entry to get started and giving customers more choice and control over their AI spend.

We took a big step in this direction in 2024. We simplified pricing for sales and service by lowering seat prices, removing seat minimums and introducing the core seat to give all go-to-market employees access to the platform. These decisions eliminated large upfront commitments and made it easier for customers to adopt and grow with HubSpot. What we expected was more customers, lower initial ASPs and better net revenue retention, and that is what we saw. The change drove approximately 2 points of benefit in net revenue retention in 2025, and we are seeing a similar benefit in 2026.

The decision to simplify seats was the right one, and it gives us confidence that further simplifying our pricing to drive AI adoption is the right thing to do for our customers and also for our business. So let's talk about where we're going. We want to make it easier to consume functionality across all of our hubs by giving customers predictability in their spend and flexibility in how they purchase HubSpot. We've made AI pricing more predictable by aligning agent pricing to outcomes by making credit usage visible across our platform and by letting customers set limits so they are not surprised by runaway AI costs. And customers have more flexibility than ever in how they choose to purchase HubSpot.

Customers can pay for credits by buying capacity packs or simply by paying as they go. And as Yamini shared, we have been piloting a simpler seats and credit-based model with new customers in the Nordics and Benelux. Our early learnings give us confidence that we're on the right track, and we are expanding our pilot to new customers in EMEA starting next month. We will continue to test, learn and evolve pricing in a thoughtful way. And over time, we expect that seats and credits will be the primary drivers of our revenue. Let's recap. Our core growth drivers of upmarket expansion, multi-hub adoption and downmarket volume remain large opportunities.

Our strategy drives new growth levers of credit and seats as we align our business model to delivering outcomes for customers. And we will continue to remove barriers to AI adoption and simplify pricing. Let's shift gears and talk about the meaningful margin expansion opportunity ahead. Yamini set the table well for me. As we transform how we build, grow and operate, we expect to deliver durable margin expansion and drive outsized earnings growth. Let's dive more deeply into how we think about delivering leverage, starting with COGS and gross margin. We plan to invest aggressively to drive agent adoption and usage across our platform. As a result, we expect that COGS growth will outpace revenue growth in 2027. That said, Aviator gives us the flexibility to leverage models from different vendors and route tasks based on which model will deliver the best quality at the best price.

This gives us a structural advantage. As model capabilities grow and prices change, we will continue to optimize. Turning to OpEx. We're transforming how we operate internally, and we see meaningful opportunities to deliver leverage across the organization. In R&D, we'll continue to invest to drive innovation, but our best-in-class internal agentic platform lets us do this more efficiently and deliver leverage. In sales and marketing, we use AI today to handle the majority of our support tickets to create and localize marketing content and to drive growth in AEO visibility.

We're going to build on this momentum and continue transforming our teams with AI. We're focused on standardizing usage and driving consistent adoption of AI-enabled processes. And in G&A, we'll see modest leverage by continuing to lean into automation, systems and data. We are confident in our ability to balance gross margin pressure with OpEx leverage and drive continued margin expansion in the years ahead. Looking ahead, our 2026 guidance for non-GAAP operating margin is 21%, representing a 2-point increase year-over-year. In 2027, we expect non-GAAP operating margin of 23% to 24%, representing 2 to 3 points of incremental margin expansion. And we are raising our long-term target for operating profitability.

We expect to deliver non-GAAP operating margins of 30%, GAAP operating margins of 20% and stock-based compensation as a percentage of revenue of 10% by 2030. Before wrapping up, I want to reinforce our long-standing principles of balancing growth and profitability and share more about how we intend to manage the business moving forward. First, we will solve for long-term revenue growth. We have a deep conviction in our strategy, and we're making the strategic decisions to set ourselves up for durable long-term revenue growth. Second, deliver operating margin.

We expect to continue expanding GAAP and non-GAAP operating margins. Third, we will deploy capital strategically, using free cash flow for organic and inorganic opportunities that accelerate our AI strategy and for opportunistic share repurchases. As of Q2 2026, we repurchased more than $1 billion in shares over the prior 12 months. Fourth, manage dilution. We'll manage dilution through a disciplined stock-based compensation strategy. If we take all of these together, we believe that the business can compound earnings growth at a very healthy rate into the future. Let me wrap with 3 key takeaways from today. First, the solid foundation we built as we democratized inbound, CRM and built a durable platform company puts us in a position of strength to democratize AI.

Second, our strategy creates emerging growth drivers as we align our business model to delivering outcomes for customers. And finally, we are unlocking durable margin expansion and earnings growth as we transform how we build, grow and operate.

Thank you for your time today and for your continued support of HubSpot. With that, we'll move into our Q&A session.

All right. Great. So we're going to move into the Q&A session now. I'm sure there's going to be lots of questions. So if you have one, please raise your hand. We've got a couple of mic runners that are going to be running around and so you can just stay seated. We would just ask that you please introduce yourself before you start your questions. We know who you are and try to stick to one question so we can get to as many folks as we possibly can. So I'll give it just a moment here for hands to go up, and we'll get going here. Yes, let's start with Jackson.

Jackson Ader at KeyBanc Capital Markets. Thank you so much for doing this. I guess we'll start with Yamini or Dharmesh, really. I mean the big question right now that I think I have and investors have are around the headless strategy. Can you just explain it simply as like why is headless good for an application software company? I feel like I've spent my entire life thinking that the higher up the stack you are, the closer to the user, the more value accrues to that company rather than being down the stack. Why is that different in 2026?

Okay. I'll start and then Duncan as well as Dharmesh can add if they have. So take a huge step back. I think software was always meant for users, and there's a way in which users got value, and that continues. But what is different with AI is that agents can operate on top of software and drive value as well. So there's a big paradigm shift that is happening. And the way we think about headless, and I think Duncan did a great job of like laying out what our headless strategy is, there are 3 ways in which now people can interact with HubSpot. The first is by having Breeze Assistant as the key way in which you get the value out of HubSpot. Think about that as users, not having to learn how to navigate, not having to learn how to move screens and really having a conversation to get the most value from an application like HubSpot.

That is a new interaction mechanism, and that's what is growing, and we are seeing that adoption within HubSpot. The second way is through connectors. Everybody is talking about connectors. We were one of the first ones to actually build connectors with all 4 of the frontier model companies. And we've been having that within our ecosystem for more than 15 months. And that is a new interaction way. Just like way back when the Internet came, there was a browser and applications became available on the browser. Now there is a new interface in which customers can actually go and ask questions about the software applications that they're using. And that's what connectors enable. Our connectors, we've seen nearly 350,000 users on connectors. When they leverage it, what they're doing is they're going to an LLM of their choice that they're working on and getting insights getting reports, maybe even creating artifacts to really get more value.

We've seen engagement go up. And the cohort of customers that are using those connectors, we've seen 12-point improvement in retention. So more users are using HubSpot. They're engaged more deeply with HubSpot. And because of that, the retention is going higher. So that's the second mode of interacting with software that is completely new with the age of AI. The third is actually an agent talking to software and getting more value. The first 2 are users interacting with software to get value. The third is really an agent that runs.

And we are now making it easy to get to the data layer, but more importantly, from our perspective, to the context and the work layer, which is what Duncan laid out in terms of the strategy. And why is that valuable? -- because the number of ways in which you can build with AI, you can automate more things with AI than you ever did before with just the software applications before, it is expanded, which means agents get to build. And that means our data, our context and the work that we deliver also continues to expand. And that's how we think about the strategy. So look, lots changing, and we feel really confident in terms of where we are going with our strategy. I don't know, if you want to add.

The only thing I'd add on top of it is if you think about that 3-layered API strategy, when you're interacting with HubSpot at the context and work layer, you're getting all the benefit of us doing our fundamental job, which is our job is to go out there and deliver the highest quality, best value, fastest go-to-market outcomes. So as part of all of that, you get everything running on the Aviator agent harness, leveraging growth context -- so at the end of the day, that's always our #1 job. The second thing is we want to be able to meet customers where they are. So if they're interacting with us via an LLM like Claude and ChatGPT, we want them to be able to do that really easily.

Okay. Great. Let's maybe go down here to Elizabeth.

Elizabeth Porter from Morgan Stanley. You highlighted the flexibility of being able to choose between seats and credit consumption. And just as that credit consumption starts to expand, how much of the opportunity should we think about it truly being incremental to the wallet versus just a shift from what would normally be priced into a seat or a hub? Just talk to some of the confidence that you're seeing that credit expansion really drives average revenue per customer higher than simply shifting the composition of spend?

Yes. Thanks a lot, Elizabeth. I'll start, and feel free to chime in as well. Look, the easiest way in terms of the framework that we think about is that if AI and our platform adds value to someone within a go-to-market team, it belongs to seats monetization. So you'll see us add more and more value through that, which is Breeze Assistant where people charge for copilots. We just include that within our core seat as well as Notetaker as well as a lot of the functionality. So if we help go-to-market team members get much more effective, that belongs to the seat.

And if we deliver outcomes or value that scale beyond people that are using a software, then that belongs to credits. That's the simple way in which we frame these 2. Now over a period of time, we do think that the agent use cases are going to continue to expand. Today, we laid out our framework for how we think about it. HubSpot agents are domain-specific agents will continue to expand both the number of these agents as well as the depth of outcomes that they deliver. And as we do that, we'll see it in our framework of reach, depth as well as the quality. Custom agents, pretty early, but we're seeing really good publish rates in terms of how many customers are deploying custom agents as well as custom workflows.

They consume credits, and then we laid out the structure for API. So the question you're getting to is how can you be sure that these are all additive? Well, we're very early in the stages, but the examples that both John as well as Kate gave today show the cases where it is additive. It is additive because on the Customer Agent side, we are adding to how our customers are resolving tickets. It's much bigger than the opportunity that we had before. Prospecting agent, we're continuing to see how we can drive even more outcomes by delivering qualified leads, that is additive.

So at least so far, we have continued to see. I think in the fullness of time, even if the seats are capped based on the headcount, we'll continue to deliver more agentic use cases. And as those agentic use cases expand, we think there's a huge opportunity for us. It is early stages, but lots and lots of opportunities.

Yes. I guess just to maybe restate and reinforce what Yamini had to say, like with small and medium-sized businesses, there are many, many cases where they just aren't getting to all the stuff that they can and should be doing to grow their business. And you, again, heard that from me and from Jon, -- and that the new value that we can deliver through agents or even just our AI-enabled customer platform allows them to do more work, and we're delivering more value than we ever have before.

All right. Let's go over to Matt maybe here.

Matt VanVliet from Cantor. I guess as we see more companies approach this sort of headless architecture and it seems great to unlock a lot of the value that's sort of trapped in the data model today. But how is more of the work maybe gets done on an interface that isn't native to HubSpot, do you ensure that the data gravity remains around the Smart CRM and you ensure that data continues to be written back into instead of over time, they start pulling in data sources from other areas, whether it's cloud data warehouses or even other systems, and it sort of eventually diminishes the value of HubSpot. And so how does that multi-hub approach and getting that into more customers' hands sort of fortify that?

Sure. I mean, look, the way we think about it is this is why Aviator and the agentic harness is so important to be built directly into the Smart CRM. What we're doing is focusing on that goal I was talking about is delivering those highest quality, best value, fastest go-to-market outcomes. And we're doing that by being able to always have that shared context that's available for all of our agents, our own agents or custom agents to be able to use on top of that. And then exposing that and making the ability to drive those outcomes available via things like the context and work APIs, even if that's headless and inside of another product like a Claude or a ChatGPT.

So that goal of us being able to always have that mix of structured and unstructured data, computing -- pre-computing insights out of that in the context layer and then using that to inject into our own work layer will mean that you'll always be able to get those best outcomes directly through HubSpot regardless of how you're interacting with it. And we think that's going to continue to be a huge opportunity for customers even in headless.

Yes. One thing I'll add just on the headless since it's a point of discussion is that headless exposes the data and work API through that mechanism. But the other thing we're going to start to see and this is evolving already is we will see interfaces coming across the wire as well. So we'll have a hybrid conversational interface. I don't think the future is everyone is just going to be chatting with their business software, and that's the only way to interact with it because the affordances aren't there, it's not that user-friendly.

So we'll start to see is like, okay, yes, we're going to pass some data across the wire. We're going to get some work and insights across the wire. But we're also going to have UI elements that are exposed from HubSpot because we've been doing this for a while to say whether within Breeze Assistant or within third-party AI application, it's going to be a combination of data and UI that's embedded in that conversation. So it's not just data traveling across the wire. And kind of final thing would be within Breeze Assistant, which we're seeing increasing usage of, yes, it has access to all of our context, but it also has access to the user context that says we know what part of HubSpot you're in, what we know what you're doing, and we can actually bring the power of both the Frontier model and HubSpot's domain expertise inside Breeze Assistant. So I think of the AI apps more as like a browser.

This is like a container for functionality. It's the new way to embed software. And Breeze Assistant is our embedded way. So it has the same frontier model. They can do model routing. It can do now UI embedding. So over the fullness of time, my sense is that yes, for ad hoc queries, you're going to go headless, maybe you'll sit within Claude or ChatGPT, there's increasing gravity just to Breeze Assistant simply by where it sits in the stack that has access to everything that's going to be more economical and efficient too.

Yes, go ahead.

Brian Peterson from Raymond James. So as you guys hit Phase 3 of this growth trajectory, I'd love to understand how you're thinking about the net new customer opportunity versus expansion with existing customers. And Kate, I know you're not guiding to that metric and you historically haven't. But if we look at what you outlined, it would seem to be that the NRR should be increasing. Is that the right way to think about it over the long term? And durably, what should that NRR look like?

Yes, I think it's a really good question. I do think that there are a number of opportunities that you should have heard throughout today to really drive NRR over the next couple of years. Like certainly, seats and credits are 2 big opportunities that will fuel net revenue retention over time. I think in the near term, we've been very clear about where net revenue retention goes, which is largely flat based on some of the dynamics we shared in Q2 earnings. But over time, if we can execute this strategy well, there are a lot of tailwinds to net revenue retention.

Yes. Maybe let's come down here. Tyler.

Tyler Radke from Citi. Great to be here. So a lot of great vision on AI, the things that are going to drive long-term revenue growth. At the same time, you've talked about the evolution of pricing in a more promo discounting down market, changes to what you're -- how you're monetizing and charging for the AI credits. Can you just talk about the puts and takes on revenue? Should -- does any of this have an impact in how we should think about the shape of revenue growth or ARR growth near term? And then I think you referenced some early good traction in the Nordics, for instance, that you're rolling out to Europe. Maybe just talk a little bit more specifically about what you saw there? And maybe is that going to extend globally, too?

I want to take the first part of it.

Go for it.

Okay. So I think -- what you should have heard from both Yamini and I is like we are experimenting broadly in pricing and packaging. Like this is certainly the new part of the transition to AI. And what we want to ensure is that we are removing as much friction to our customers adopting and really like growing their use of our AI tools over time. One of the ways that we are doing that is through the seats and credits simplified pricing in the Nordics and Benelux. I think what we like about that is it in many ways is a parallel to what we did in 2024 for service and sales, where you are lowering the barrier to get started by really just making it like super simple to buy a couple of seats and some credits and really get going and then just grow from there. And what we have -- I mean, it's obviously very early, but what we've seen in those early cohorts is a lot of those same mechanics, which is maybe lower initial ASPs, but larger growth trajectory from there, and it gives us the confidence to continue to learn and expand. And so we're moving into EMEA for new customers next month.

Yes. I think I would completely agree with what Kate said. And if we take a step back, there is a big shift that's happening within pricing, right? And customers, I would say, are in the spectrum of foggy to confused to worried about what the pricing changes are because suddenly, they're not just buying subscription seats, they're -- is it token? Is it credit? Is it usage? How is it tied to? And how can I control it? And so I think like as we think about how we help our customers navigate not only a platform shift, but also pricing associated with that platform shift.

Our focus is looking at what are the barriers, what are the -- what feedback points that we get from our customers and just make it easy. So I think that's what you will see us in terms of the different ways we are driving these pricing pilots and experiments. One of the other things that we think about is that everything that is not tied to a go-to-market team member should just be based on credits. Today, we might have contact tiers in one way, messaging app in another way and a different mechanism in another way. We just want to simplify it, and that's part of what we are trying out.

And look, we always focus on one, making sure that we are delivering clear value. And when we do that, we want to see repeat usage as well as the depth of usage. And when we do that, that has to drive the right outcomes. And if we do all of those, then it drives the monetization and growth. And this is why with all of you, we have shared how we think about the strategy working, which is reach, depth, quality and then that basically drives growth, and you should expect us to continue to drive that.

Billy Fitzsimmons from Piper Sandler. I have both a financial and technology question, so it's going to be applicable for a few of you on stage. I want to hone in on the sight line into the longer-term margin targets because one of the things we talked about today was that COGS will outpace revenue growth. And over time, that model routing and optimizations could potentially be a little bit of an offset. Can you talk about where we are today for some of the first-party products in terms of usage of frontier models versus open weight models and what that could potentially look at -- look like over time?

And one of the things some other application software companies have talked about is the ability to build first-party models, which could be more efficient on cost. Is that something you are thinking about? And then just lastly, on the OpEx lines, can we just talk about how your own use of AI internally has changed year-to-date?

Right. I think this is Duncan first and then Kate.

Sure. Yes. I'd say, firstly, we certainly see Aviator and building our own agentic harness as a strategic differentiator for us, primarily, number one, as I was talking about, to make sure we're driving those highest quality, best value, fastest go-to-market outcomes for customers. But certainly, as part of that, and that Switzerland of AI approach, that gives us a lot of control and flexibility about how we manage this. And we've built it in such a way that we can do this very much on a task-by-task basis. So there are hundreds of tasks across the go-to-market journey and building out this rich set of evals that we have, we have thousands of these evals that we've built out and continuously run.

And that lets us evaluate on an ongoing basis, a wide spectrum of model families, model sizes and so forth. And we do that across the full spectrum. We work very closely with the frontier labs. As you can imagine, we have looked at open-weight models and so forth as well. So it's changing weekly and having that flexibility, that dynamism, I think, is a real advantage for us. And at the end of the day, that lets us deliver the best results for customers. I would say on the notion of training models and so forth as well, again, we have a pretty sophisticated organization in terms of infrastructure and how we look at these things. So you can imagine that we're looking at and evaluating everything across the board, again, on an ongoing basis.

Yes. And I think there's like a ton of good news in what Duncan's team has built in terms of the sophistication and flexibility in sort of that core platform technology. In terms of where we are on the maturity side, like this is a huge opportunity, right? And I think we've always approached things as like first, deliver the best quality and then optimize.

And so as I think about where we are in that cycle, I think we're still very early. And like it's sort of quality number one, and then optimize cost over time. In terms of where we are as an -- sorry, that's sort of I'll sum that up in terms of where we are on the gross margin side. More broadly around where is the organizational maturity in adopting AI, I would say we were very early on in really enabling our employees to get access, experiment, try AI. And I think each of the functions has -- broadly, there is significant usage of AI across the organization. I think the functions are at different states of maturity in terms of the shift from the individual productivity gains that we've seen from just exposing and letting people experiment to like driving real organizational efficiency that will deliver leverage.

And actually, Duncan's team is probably the furthest along, and he can comment a bit more on that. But the internal platform that the team has used has allowed a real shift in how we build that is like broadly impactful across the product and engineering organization. So I would say they're leading the way in terms of driving real like innovation and sort of efficiency in how we operate. I think there are parts of Jon's organization that are really far along, like the support organization is one that is a huge call out for me where we've been deflecting tickets for, I don't know, 3, 4 years.

3 years now.

We have not added a net new support rep. But I think there's more opportunity there to like scale up the usage more universally around the sales and marketing organization. And I feel like there's a ton of opportunity for us to do that. And then as somebody who lives in the -- we call ourselves the company pillar and the G&A functions. I think, again, we have made nice progress, but there is lots more work we can do around systems and data to get us even more leverage on the G&A side.

Yes. Josh Reilly from Needham. As you think about the macro headwinds in 2026, including the extended sales cycles, how do you think about that in terms of customers figuring out what to do in their own businesses and their strategy versus actual economic uncertainty? And if there's greater visibility in 2027 on AI workflows, does that improve your operating environment?

Yes. Thanks a lot for the question. So just to step back, Q1 was a solid quarter for us. But as we came into the year, a couple of things that we saw. One is that customers are navigating a platform shift. I mean we take it as like everybody has been talking about AI. But when it comes to making a decision for a 1,000-person company, they want to make sure that it works in their environment, they have the data foundation ready, and they want to make sure that they're going to see the benefits of spending for AI. It is a big platform shift. And as they do that, there's a little bit of hesitancy in terms of making the right choice as well as making sure that the budget works within that.

So as we mentioned in kind of the earnings call in Q2, we said that for upmarket customers, it actually shows up as larger buying committees. And if the CMO was making a decision before, now it requires a CFO or a CEO approval or sometimes even a Board and PE approval. And I think we will see that continue. I mean this week, Jon, myself, we're all talking to literally tens, maybe even hundreds of customers within INBOUND. And they are navigating that shift. So I don't think this is a 1 quarter kind of an effect. We are driving that by engaging earlier, by really focusing on the outcomes that we can deliver to customers by making sure that our partners can deliver with the right implementation early on.

So we're changing our playbook to be able to drive that. And we will see some level of pricing sensitivity. So to take a step back, we understand that customers are navigating a big platform shift. We understand that there is pricing scrutiny as well as hesitancy with a new model. And so our job is to make sure that our playbooks and how we execute is really mapped to that.

And you asked about 2027. It's pretty early to say what happens in 2027. But as the outcomes become clear, as the customer stories that we share with other customers become clearer and people get much more comfort over a period of time, we'll see that type of navigational hesitancy of the new platform dissipate. And our job is to make sure that we are giving that level of confidence in our strategy as well as comfort in the pricing model that we are rolling out. Anything you would add?

You nailed it.

All right. Thank you.

Let's go back there.

Brett Huff from Stephens. Appreciate the time today. I had a question on kind of following up on the data gravity question in the multiple ways folks are -- you're meeting folks where they are in their AI journey. Some are just existing in HubSpot, and that's great. Some are extracting data in some meaningful way. Can you talk a little bit about how that data is getting back into HubSpot? I mean I'm a big believer that the system of record will still be there for a long time. But how does it -- what kind of behaviors do you see when people do extract it and how do they put it back in? And maybe it kind of links in with your -- this idea of self-updating CRM. Are you trying to kind of head that off at the pass?

Yes. I mean I think firstly, as Yamini talked about earlier, what we actually see greater engagement and retention from customers that are using our connectors to access the platform. So that means they're getting a lot of value out of HubSpot. And part of that is continuing to contribute data back into HubSpot because ultimately, it's growth context and the growth context graph that drives all of this work. And you want -- it is in customers' interest to have that shared context be the most relevant, be most up to date so that everything across the platform is leveraging that as part of it. So when they're using our own first-party agents, they're using other capabilities that are AI-powered within the platform, they're building custom agents in Agent Builder.

And then yes, of course, they're accessing in some cases, those agents within something like Claude or ChatGPT or even if they're building something custom within Claude or ChatGPT to help them orchestrate something. So we see data getting written back to the growth context engine as well as being used within those platforms.

Nick Altmann from BTIG. Kind of a philosophical question here. But just over the medium term, where do you expect the bulk of agentic engagement to be within HubSpot? Is it more on the prepackaged agents that you guys provide or more on the custom agent side? And maybe just talk about the implications of both. Like I imagine if you're a partner, maybe you have more incentives to work with customers on custom agents because that seems like a lucrative opportunity. At the same time, maybe your customer base isn't as used to buying or adopting sort of custom software. So maybe just talk about philosophically how you expect that to trend over the medium term, custom agents versus your prepackaged agents?

I'll start because we've literally been having this conversation of between HubSpot agents and custom agents, where would we see the mix over the long term. Look, we have domain expertise in go-to-market. And so we are at the cutting edge of what's happening in AEO, which means we can bring that to an agent. We understand what's happening with content, so we bring it into the Content Agent. So I think like over a period of time, we are going to bring our domain expertise within the segment that we serve for the go-to-market functions, and that is going to be the HubSpot agents.

And we'll see that adoption continue. The interesting part about custom agents is that it is workflow automation 2.0, right? And if you go back in the fullness of time and look at marketing automation and sales automation, yes, they used our features. But what did they do with our platform? They actually built custom automation and workflows on top of HubSpot, whether it was Marketing Hub or Sales Hub. And now there's an even easier way to build and extend. So custom agents are -- you can build new workflows, you can build custom agents on top of it, you can drive artifacts. So you can do a lot more with AI.

And so it's the second part of it. And you're absolutely right. One of the things that we're doing is with the partner ecosystem, they are going to love it because this is how they extend HubSpot into industry-specific use cases, and we can already begin to see how partners are engaged. In fact, today, we have a battle of the builders. We have a $100,000 competition for partners to actually build custom agents, share those use cases and really drive that. So we're incentivizing our partner ecosystem to do it. I don't know exactly how this is going to land, whether it's going to be more custom agents or not. I think both are really critical in delivering the kinds of results and outcomes that we want to within marketing, sales and service. But we've been having this debate. What is your take?

Absolutely. I mean, I think, look, if you look at it overall, we're going to continue to build out a set of HubSpot agents that can deliver across core go-to-market outcomes. We think that's incredibly valuable for customers to be able to just take those out of the box and get value quickly, whether it's Prospecting Agent or Customer Agent or any of these other capabilities like Campaign Agent that we've announced this week at the show. Agent Builder then is giving this incredibly easy-to-use tool that customers and partners can build on. And it can be the glue that connects a lot of these other capabilities together. It could be extending a HubSpot agent, building something brand new that's very unique to their business need and everything running on that same harness with access to the same growth context with all the same governance, trust and safety, quality guardrails around it just automatically applied as part of that.

And you just talk to Breeze in order to tell us what you're trying to do, and it will build the agent for you. So it's already been very exciting to see what customers have been building with Agent Builder, and it will be really exciting to see where it goes going forward. Ultimately, it feels to me like we're getting to this very exciting place where for a long time, companies had to adapt to the way the software worked. And now the software is adapting to the way their businesses uniquely work and putting that power in a very easy-to-use way into customers' hands with the help of partners often is very exciting.

All right. We've got time for 1 last question here.

He's had his hands up.

Martin Schneider with Constellation. You guys talked just now a lot about the Solution Partners, what you're looking at, the VARs and great opportunity there. But when you think about where headless and where context being king and then that auto entry, how do you see your ISV relationships needing to change and that focus where -- because, again, like features and there's little adjacencies, the agents can do that now. But how does that context -- where does that come from? Because a lot of that -- a lot of those surfaces, a lot of those places where people are going to want to work in a headless environment, they're your competitors.

Those entities are owned by people who you would see as competitors in CRM and in other areas. So how is that -- how are you going to have to play nice a little more or force that? And how is that working out when -- just to put it bluntly, small businesses like, hey, we use Slack, and we want to bring HubSpot into like a vice versa and create those surfaces and those artifacts and those cool things to both lower our token utilization of all of our AI and all that. How do you see that evolving, that whole ecosystem where a lot of these are your competitors today?

I think if you come back to what I was talking about earlier, we need to meet customers where they are. We're going to make sure we're delivering the best go-to-market outcomes for them and then engage with them where they want to engage with us. We already operate inside of Slack today. You can use Breeze Assistant from inside of Slack. Obviously, Breeze Assistant is there for customers that want to directly use that. We think that's the best way for most customers who are working in the go-to-market space. But we'll continue to evolve.

We've talked a lot this year about our open ecosystem, and we really think that's the road forward. So much is changing so fast. Customers are trying different things. But ultimately, we'll take that set of structured and unstructured data that we've had across the whole go-to-market. We'll use that to continue driving the best growth context out of that and be able to inject that into the agents and tasks that we're doing for them and then meet them where they are. And it's going to be really interesting to see how customers evolve and how they want to interact over time.

[indiscernible] for example, they wouldn't want to use that because they're inexpensive, and they don't know the context of the data model because with Salesforce it's not going to happen, right? So just would actually say how do you have...

Exactly. In that example, they're using Slack as effectively as an interface to access HubSpot and our entire platform via that, but we're able to meet them there in Slack where they are.

Yes. I would say like if we really step back, the paradigm for how customers interact with software is changing. Even within HubSpot in a year, even now, we would want them to just live and breathe in our Breeze Assistant. And that we're making it world-class. We just want to make that experience just exceptional. And if you look at how our customer satisfaction on just that usage has grown in the last 12 months, it's just been extraordinary to see how customers are adopting. If you are a serious go-to-market user, you're building campaigns that are multi-language, multi-region, you have like 10 million contacts. You're not going to sit in some other surface.

You're actually going to be in the software, interact with the power of the software. We will make that world-class, and we'll continue to build on that. The second part of it is that, yes, now there are new ways, whether it is [indiscernible] or another LLM that you can conversationally have interactions with the software, and we're going to make that great as well. We have all the connectors, and we can begin to see how that improves the engagement and retention that you get out of that. I think the third case is like other software that's using CRM, it is still valuable, too.

And over a period of time, I think the industry is still early in the cost optimization phase and performance optimization phase. it will be much better to leverage our Context API and Work API than pull the entire data and do inference outside and try to figure out which model to use outside. And so I think there's value for us in every one of those situations. And that's why we have confidence in the open ecosystem strategy that they're building.

[indiscernible] collaborative group in how people produce their on other products like Slack cover consumption, right, as well.

Okay. Great. All right. So we're going to conclude Q&A. For those of you that are live with us here in Boston, we're going to move out these doors now. We've got a few minutes here. We'll grab a quick bite to eat, and we can connect for a few moments. I just want to wrap by saying thank you to everybody for joining us here today. We really appreciate you taking the time to be here and for your continued support of HubSpot. Thank you.

Thank you, everybody.

All right. Fantastic. We will go ahead and kick off the afternoon day 2, Goldman Sachs Communacopia & Technology. Delighted to have with me on stage, Yamini Rangan, CEO of HubSpot. Thank you for being here again.

Thank you, Gabriela. It's a great conference, and thanks for having me again.

It's absolutely our pleasure. Yamini, I know how customer-obsessed you are. I think that's the best word to describe some of the leadership philosophy that you have at HubSpot. Help us bridge the gap a little bit between what we see on Twitter and some of the feedback we see on LinkedIn about AI and what you're actually hearing in the field from the mid-market customers that are core to the HubSpot base on what the vision is for where AI goes over the next 2 years.

Okay. Yes, there's a big wide gap between what is happening on X and what is happening on social post as well as what is happening from an AI perspective. I think the fundamental shift that's happening within software is that the prior generation of software, everything that we did was about delivering features that companies then implemented, and then they got their users to adopt it after the training period and then they got value from software. I think with AI, you fundamentally shifted that to delivering work for those users. And the work could be either a support ticket that you're doing for them or you are driving pipeline for them by setting up meetings, and that's the quality of the work.

So there's a big shift that is happening. And in order to do that, fundamentally, the platform and the product that HubSpot is delivering has transformed, right? It used to be that as a CRM, we had a structured database and that structured database had companies, contacts, tickets, deals, all of that, that we knew before. But now with AI, you can also process all of the unstructured information. This conversation that we're having, e-mails, text, a Slack, all of that can be in that unstructured conversation. And so the fundamental thing that we're doing is to build an intelligence layer, and we call that as context. Of course, context is a word that is being used across the industry, but context actually means what is the business information about the business, about the customers within your business, about your pipeline, about your teams, what is that information. That is what is context.

And why is that important with AI? Well, it's important because AI models are trained on everything that is available publicly. Lot and lots of vast amounts of information that's available publicly. The only thing it's not trained on is what's happening within a business, and that is the context of the business. So for us, we're building that context layer and the context graph. And what that enables from an AI perspective is to drive the right kinds of outcomes. And of course, HubSpot is focused on go-to-market, marketing, sales and service outcomes. And so we take that context and we've built a set of domain-specific agents.

And our customer agent resolves support tickets, prospecting agent qualifies the pipeline, our data agent enriches contacts and gets an audience ready. And so we take the context that we have built through that intelligent layer and we enable it through HubSpot agents as well as custom agents, agents that you can build based on our platform. And that is the action layer. So that's the second part of what we deliver. And then the third is really the interface to software, which is, in our case, Breeze Assistant. It is an interaction layer. And the pattern here is that you don't have to click on software, you can just have a conversation and have the software. So I think fundamentally, what is happening within software is that you're not delivering features, you're delivering outcomes. And in order to do that, we have an agentic customer platform that does that.

One of the debates that we started having yesterday is on how the nature of a marketing stack, and I think this includes B2B marketing, when you include agents, has to be somewhat infinite from a personalization standpoint. Before, if you think about marketing, we were making broad cohort analysis across different types of customers. Now with AI, you can actually be much more granular in that approach. Talk to us a little bit about how you level up. And I think it has something to do with the structured data that you're talking about. How do you level up to be able to give customers a much more granular way to go to market?

Yes. I think there is a broader change that's happening within marketing. And if you looked 5 years ago, there was a very clear playbook, especially for B2B marketing. And it started with your content being searchable on the net. When your content was searchable, you clicked on someone's blue links and then that brought you to a website. When it got to the website, you captured the e-mail and then you nurtured it through e-mail campaigns. And those e-mails were not necessarily personalized. They were much more generic e-mails that you would sequence, and that's B2B marketing of the last decade.

What is specifically changing is that, of course, nobody is clicking blue links because AI overviews, AI mode, LLMs have actually replaced all of that with by giving answers. And so first of all, you've lost a pretty big top-of-funnel source, which is just content marketing. Now that is replaced by omnichannel marketing. So the big shift that's happening in B2B market is that it's not just about the content that you generate. You got to show up in social. YouTube is one of the fastest-growing sources for B2B marketing, same with TikTok and same with a lot of these social channels. And so you have to be now present across social channels.

In addition, there is a new source, which is AEO, which is how do you show up in the answers that LLMs are actually generating and where do you have the share of voice there? And then how do you kind of expand on that? And so the big playbook change that's happening right now is you got to be able to enable omnichannel marketing. Within that, there is personalization that you asked. The question that you were specific about is how can you use personalization. Look, what is happening is that if you use an LLM to generate a generic e-mail and send it to the set of contacts you have, the click rates and the open rates are just going down. There is no room for just AI that is generating e-mail that is not personalized.

And so it becomes really important to get the intent signals of your buyer to know what they're interested in, what they're researching, what would actually drive conversion and use the context that I talked about earlier to generate an e-mail that is very specific to what they are looking to do. And that is what drives much better marketing campaigns. So broadly to step back, playbooks are changing pretty significantly, and you really have to be omnichannel marketing, and that is the vision that HubSpot has. We want to drive the -- and be the platform for omnichannel marketing for B2B.

Well, let me pick up on the change in strategy that you're talking about going from SEO to AEO. I want to better understand going through a transition like that can sometimes be disruptive, not just for your customer base, but also for your -- for HubSpot, your pipeline, if that makes sense, customer pipeline. So give us both sides of that coin, how do you help smooth out the disruption for your customers' customers? And then how do you do it for your own business?

Right. And I'd say one thing, which is that it is not a simple replacement of SEO with AEO. I think SEO was a broader playbook. And if anything, it is getting replaced by omnichannel marketing as a playbook. You just cannot replace it with AEO because even though AEO is nascent, it's -- the volumes are not there to completely make up for the volume that you lose in SEO. So that's a point I'll make. But having said that, maybe I'll start with HubSpot. In 2022, a pretty significant portion of our top of funnel was generated through content leads and since then, obviously, it has declined. And we have really embraced having a multichannel outlook in terms of the top of funnel.

Back in 2003 -- sorry, 2023, we acquired a podcast network, and we are the #1 podcast network for business in go-to-market. That has been a source for us. We've expanded pretty significantly in terms of social channel. YouTube, we have 10 different channels within YouTube. They all generate leads significantly and that has consistently grown since 2023. And the same thing with a number of social channels. In addition, I'll go back to what you said before, which is we have used e-mail personalization in order to drive much more conversion within the top of channel. So from 2022 to now in 2026, the composition of our top of funnel has changed pretty dramatically, and it is now a multichannel playbook that we leverage in terms of our marketing.

Now that is exactly the playbook as well as the set of products that we are leveraging for our customers, and we're helping them become multichannel B2B marketeers. And obviously, part of that is providing visibility into social channels. We added TikTok. That's one of the fastest-growing channels for our customers. We've added Reddit, Instagram, YouTube and all of the visibility across those social channels are important for our customers. At the same time, we also are enabling AEO. And many of you know that in April this year, we launched our AEO product, and the product does 3 things. One is that it gives you the share of voice that you have across LLMs compared to your competitors. Then it makes recommendations on what you need to do to get your content strategy up. And then it takes those actions.

And we're one of the very few marketing solutions that can actually do that full cycle between visibility to recommendations to content actions. And we're seeing traction within our customer base, nearly 32% of Marketing Hub Pro Plus customers are using our AEO. And so we're on this mission to really help expand marketing into an omnichannel marketing strategy, and that's really what we're executing for our customers.

I really appreciate the context of how over time the strategy evolves and layers. What I want to understand better is we can get a little bit into some of the near-term headwinds, specifically on your new customer adds numbers. Do you think that HubSpot as a company is now in a position where the headwind from an evolving SEO to AEO type market, the worst is behind you? Or is it just a period of time where it's going to be uneven? And maybe we isolate just that piece and then we can talk about.

Yes. I think specifically in terms of the net adds that we talked about in Q2, I think that there are a number of dynamics. I don't think it would be just fair to say that it is the SEO to AEO transition. I don't think that is the case. I mean you will -- as the composition of top of funnel is changing, you might hit air pockets in a quarter where one source is declining and other source is not picking up. But I think overall, we're really comfortable with the broad strategy that we have adopted since 2022, and I think we'll continue to do that from a marketing perspective.

I'd say specifically on net adds, we took some deliberate actions in Q2. When we came into this year, the first quarter was solid, and we looked at our AI adoption within SMB, it's still early days in terms of AI adoption there. And we looked at what are the blockers or what is creating friction in terms of AI adoption. A couple of things. One is that as we've just been talking about, they're not buying features and evaluating features anymore. They're actually buying work.

And a lot of times, they want to make sure that, that work works within their environment with their data. What does that actually mean? If we go and tell them that, hey, we're going to qualify leads for you or we're going to have support tickets resolved for you. They want to see that with their knowledge base, with their history of support tickets, it's actually going to work in their environment.

So one of the things that we did was to increase the trial period and give software during the trial period so that they can make an agentic use case work in their environment. And that obviously slows down in top of funnel. It has headwinds in a couple of areas. But we think it's the right motion. Again, early in a platform shift, the more confidence that you build in terms of your capabilities, the more you use your distribution and reach to really seed the right use cases, you can then compound the adoption later on, and that is exactly what we did, and that obviously has an impact in terms of near-term headwinds.

Remind us when did you switch to the longer trial?

In April.

Okay. Perfect. And the longer trials are how long?

They are -- in terms of agentic use cases, it's between 14 days to 30 days depending on.

Sure. So you already have data from those initial cohorts from April and May. Share with us a little bit how that strategy is working. When you look at the data for the folks that have had longer experimentation, what does conversion then look like?

Right. I think it's still early days because I don't think that in one cycle, you can look at all the conversions and say that is what you're going to get. I would say that in terms of the number of trials for agentic use cases that has increased, conversion really depends on whether the customer has their data already in one place. The level of data and the consolidation of data has to do with the conversion of the trials. If not, our partner ecosystem as well as our teams are helping them get the data in the same place and then driving the conversion of the trials.

So I don't think that this is an inflection that we see in one quarter. It's the foundation that we can put in place to make sure that customers get comfort and confidence to be able to do that. Now I think the broader question is what are we seeing in terms of agentic use case adoption. And we shared a lot of the trends that we are seeing there. I would start with the way we look at it is what percentage of our customer base using agentic use case at any given point. And we call that the reach, what is the reach of agentic use case. And I'd say at the beginning of the year was about 37%. And now across all of the use cases, it's well over 55%.

And specifically within that, if I look at the agent use case, we have a number of HubSpot-driven first-party agents, and that has grown from 9% to almost double that over the past 6 to 7 months. And so we're seeing the reach of agentic use case increase. Then the question becomes what happens with the depth? Are they using it every day, frequently or not at all? And that is where a lot of the focus is in terms of our ability to drive adoption. And we've seen the depth increased threefold, which means the number of agentic actions our customers have taken has grown over 300% in the same period of time, which is a good leading indicator. Both of these are early leading indicators, and then this is what drives the emerging lever for us in terms of credit monetization in the future.

Let's talk about credit monetization. How do you pick the value of a credit?

It really is simple and easy for point solutions. It's harder when you're doing it across a platform. And let me give you a couple of examples of how we think about actual credit utilization. So our customer agent is a pretty easy example because it resolves the support tickets. There are a number of companies that are providing customer agent. And so you can actually look at the market and you can look at the metrics. And there are broadly 2 ways in which we look at this. Many of the companies out there are charging per conversation and some of them are charging per resolution.

So one of the first decisions we made early this year is to tie it very closely to the outcome that agent delivers, which happens to be the ticket resolution. So that's how we picked it because it's much more customer-friendly. Then you pick a price point, which is not super competitive. We don't want to be always the top player, but we want to be a premium player in the market. So you pick a number. So I think like the way we think about agent rate cards depends on the outcomes that we drive and the number of channels that we drive and what is happening within the market. That is not the same with a data agent, which is enriching records and preparing the audience and the prospect match, that could look very, very different.

But to simplify this back for a customer that is today buying apps. In the past, it was pretty easier. They were able to buy a seat. And now because software is delivering work, it has to be not tied to the seat, and therefore, there's a credit mechanism that is growing. And the industry now has a lot of conversations about, you call it tokens, you call it usage, do you call it credits, you call it outcomes. And we believe that there is just a clear model, which is a hybrid between seats and credit and credit consumes anything that is usage-based and tied as close to the value. And that's the philosophy that we have, and that's what we are driving in terms of how we position back to our customers.

I want to run a couple of hypotheses by you on the compare and contrast between larger customers and smaller customers? So one school of thought is enterprises have the resource and the talent. If they want to build stuff in-house, they can. If they want to put together complicated marketing stacks on top of Databricks, they can go ahead and do that. SMBs, they really need their trusted front office vendor to guide them a little bit more because they don't have the resources. Therefore, SMBs are more insulated from the DIY threat. Now the other school of thought is SMBs much more so than enterprises are more willing to take risks. They're more willing to experiment. They're more willing to try things with new vendors. Do you have a view on what's actually happening in practice?

Yes. I'll start with your first hypothesis, which is SMBs, in general, want it to be easy and want it to be fast. And we've seen this consistently throughout our 20-year history. When we got started as an inbound marketing company, it was easy. When we actually moved from that Phase 1 of being a marketing solution to a CRM platform, almost everybody said, well, it's going to be really difficult because why would an SMB now want to buy a platform and there are other platforms out there and other solutions out there. I think the approach that we took is to simplify a very complex technology and make it easy for SMBs to adopt because a typical company with a 50-person company or a 200-person company, they only have limited number of resources, both people as well as dollars, and they need to get to their growth goals.

And so we got to be really good at democratizing complex technology and making it available for our customers. And that fueled the last 7 years of our growth, where we have gone from a single hub company into a platform company driving multi-hub adoption. So we proved that model. So the question is, is that what happens and plays out within AI? I talk to customers every single day and the ability for them to assess every new model that happens to come out every week and put together a whole series of agents is quite complex. And that's why we believe that we have a huge opportunity to take complex technology like AI and make it super easy for SMBs to access and get value from. And that is the value proposition that we have, and that resonates within our customer base.

Having said that, I can see why you're asking the flip side of the question, which is DIY, it's pretty easy for people to string together a set of things. I think in the early stages of a platform shift, you will see a lot of fog and noise. And so there will be a level of experimentation. But beyond that phase, we do think that we deliver a value for the SMBs by making everything simple and easy, and that is going to continue to resonate.

Fantastic. Okay. We got a lot of questions on Salesforce and Qadforce. The question I want to ask you for your business is, what do you think a healthy relationship with the frontier lab company looks like? And is there a scenario where that type of agreement could also make sense for HubSpot.

Well, we've had that connector for over 15 months. We are the first CRM to have a connector with OpenAI that we launched in June 2025. We launched our connector with Anthropic and Claude in July 2025, and we have a connector with Gemini that we launched in September of 2025. We're the first CRM to have connectors across all 3 frontier models. So I can give you data based on the last 15 months of what the interaction patterns look like. And simplistically speaking, you have a conversational interface in any one of these frontier models that is now deeply connected to the go-to-market skills within your applications that enables the end user to just use natural language to ask about trends and also take actions within the application layer.

And that is exactly what we have seen over the past 15 months in terms of the data. We now have over 350,000 users using each of these connectors every week. And so that has grown. And what we see in the data and the patterns of these users are that, one, they're using it for a number of insights, right? If I'm a campaign manager, and I have 10 million contacts and I want to drive a multi-language, multi-region campaign, I'm going to work in HubSpot all day long. But if I am a CMO and I'm preparing for a meeting and I want to create an artifact based on the campaign trends and the pipeline trends that I want to share with someone, then I'm going to use one of these interfaces to actually grab that information.

And what we have found in the 15 months of data that we have collected across these connectors is that users of these cohorts engage more and they use HubSpot more and the retention is about 12 points higher than anybody else that is not using these interfaces. So there's a lot of dialogue in the industry of is this a good, who gets the value, if you lose the interface layer. And the way we think about it is that way back when browsers came on board, you needed to get your applications to show up on those browsers and you did. And right now, there is a new interface layer, which is any one of these conversational interfaces. And so your applications actually show up there, and it actually drives better engagement and better retention. And of course, as models and these layers get better, your job as a company and our job as a company is to provide additional value on top of that, which is exactly the pattern that we're seeing.

This is a really good point. So you have 15 months of data on some of these use cases. So if you were to think about the sweet spot of customer, let's say, 200 employees through to 2,000 where historically you've been able to take share from Salesforce. Based on the functionality you already have, and what you can see from your market intelligence, what you're hearing from customers, it sounds like you already have essentially the same functionality as what a Qadforce would offer. Is that connecting the dots too explicitly?

I mean we've had that. So I can't speak about a competitor product, and I'm not going to do a tear down of a product. But I will say that connectors do something very simple. They take the skills within your application and make it available to a new interface, and that is exactly what we have had. We have thousands and hundreds of thousands of users that are using it. And so I know that in every one of the conversations I've had today, I've gotten this question, I'm like, let me give you the data over the last 15 months of what we have seen because our product is generally available and being used every single day.

I would love to pick up on the conversation you and I started having on the earnings call and look, there is a lot of different things happening in terms of buying cycles right now, experimentation, et cetera. And there's a lot that HubSpot has already done to drive outcomes in this new business environment. If you were to put on your prognostication hat for the next 6, 12 months, how long do you think we're going to be in this type of environment that feels like it's pulling teeth a little bit versus when the momentum actually starts to compound?

Yes. That's a very difficult question to answer because I don't think that anybody here has like a crystal ball. But what I will say looking across our customer base is that we have seen some patterns of adoption. There's -- I'd say, early adopters within the customer base that are driving adoption from experimentation to full on scale. They turn on every agent, HubSpot delivers. They build custom agents on top of HubSpot's platform, and they're consistently beginning to use HubSpot. And so there's a whole portion of the customer base that is early adopters.

I'd say there are what I would refer to from the technology adoption cycle as early majority and late majority. Now those customers are a bit more cautious, and they are experimenting. They are turning pilots on, and they will continue to move into kind of the spectrum of large-scale AI adoption, but it will take time, right? It's not even across the industries that we serve, and it's not even across the segment of customers that we serve. So as we look at it, and this is what happens during every platform shift. Across every platform shift, you don't get uniform distribution day 1 across your entire customer base. You do get pockets that become early adopters and then you get pockets that are waiting to see where it goes.

And so our job is to look at that majority of customers that are cautious adopters and say, what's the friction point? What are the barriers in terms of large-scale adoption of AI within the segment that we serve? And it comes down to a few things. Number one is data. If they have the data consolidated in one place, if they have really high-quality data, then they are faster adopters of AI. So we spend a lot of time with our partner ecosystem to drive that type of data consolidation and cleanliness within the data. The second barrier is really the comfort with the new technology in the platform. Is this going to work in my environment is the #1 question we get. This is exactly why we took a number of deliberate actions to drive trials and then to seed those use cases so that the comfort level increases.

And then once we've been exposed to agentic use cases, the third question is, is my cost going to be predictable? And can I get visibility and control over the spend? And there, again, we're driving a lot of customer-friendly policies. Every customer of ours can set thresholds and they can do it by use case, by the agent use case, and they can track their credit spend across their different use cases. And we have made it pretty friendly to really look at the outcomes that we are delivering to the cost and tying that as close as possible to that. So I think every cycle is going to go through this process. And our job is to help navigate the customers, look at the barriers and remove the friction in terms of AI adoption.

We're big believers in this idea of taking AI and applying it to business-specific context. I would love to understand a little bit of the nuts and bolts on how you do that. What is your internal AI strategy? How do you think about mix of frontier model versus open source model? How do you think about applying SLM to context data in a way that is not a headwind to gross margin long term?

Yes. That's a great question. So just to peel back the question, how are we thinking about the internal LLM usage? And how does that impact the gross margin of our business? I will say that we've talked about our platform as being an advantage for many years now. And you've asked me many questions about primary colors within platform and how we actually leverage.

I have them here.

You do. And I will bring that back into context, Gabriela, because one of the things about HubSpot is that we have a platform-centric approach to building. And what we have done in the last 3 years is we have created an internal agentic harness for go-to-market context. What does that mean? Lots of words there. I mean, context, everybody understands. It is what is specific about a customer's company, business, their end customers as well as their teams. That is the context graph that we can construct for each of our customers.

What we have then been able to do is to actually take the agentic coding available outside and create an internal go-to-market harness that becomes our infrastructure. That means the evaluation loops that we run are much more efficient than off-the-shelf agentic coding platforms that you can build. That has our libraries, our skills, our connectors that is optimized for our developer environment. And the reason this type of a platform approach is important is that we can be the Switzerland of AI, and we are the Switzerland across LLMs. What that means is for a specific use case, we can optimize the use of an LLM.

We might decide that a content use case, there is a certain LLM that performs better, and we are able to switch across LLMs. And the way we do that is to focus on 2 things. What is the best cost optimized approach and what is the best outcome optimized approach. So we look at 2 things across each of the use cases, and we're able to switch. The reason that's important is also why we can optimize gross margins, right? Because we have the infrastructure and we have the go-to-market harness that can go across LLMs. We're now able to optimize in terms of gross margins. For example, we've switched to open weight models, and that has brought the cost down across multiple use cases while keeping the quality of the outcomes high.

In the past 6 weeks, there are a number of models that are now at 80% of the cost of other inference models, and we've been able to switch that. And so I think the industry is still very early in the cost optimization phase across LLMs, but we have now the capabilities and the neutrality across the LLMs to be able to drive that. And that helps us to really change the gross margin trajectory of the business, even though, of course, everybody is going to have some level of pressure because of how the transformation works within AI.

Any clues you can give us into INBOUND a (sic) [ UNBOUND ] next week that we should be paying attention to?

Well, it is going to be exciting. As many of you know, next week is our conference. We call it UNBOUND, because we've gone to the new era from SEO all the way here. And so yes, it is exciting. It's fully sold out, 15,000 customers. And of course, we have an Analyst Day. And there are probably a few areas. I think in terms of our product we talked about the intelligence layer, the action layer as well as the interface layer. And we are clearly driving that vision of being a system of context as well as system of agentic action. So a number of launches that furthers that vision and accelerates that vision.

We talked a little bit about how marketing is changing for B2B. And we think that we have a huge opportunity ahead in terms of being the omnichannel marketing platform for our customers. And so we'll share vision in terms of how we are accelerating that. And of course, things are changing from a core CRM and how users get to interact with CRM. So we'll talk about that. So yes, it will be action-packed.

I still remember your Dario interview from last year. Yamini, it's always a pleasure. Please join me in thanking Yamini for her time.

Thank you so much.

Good day, everyone. My name is [indiscernible, and I will be your conference operator today. At this time, I would like to welcome you to HubSpot's Second Quarter 2026 Earnings Call. [Operator Instructions]

At this time, I would like to turn the call over to Vice President, Investor Relations, Geoff Koegler. Please go ahead.

Thanks, operator. Good afternoon, and welcome to HubSpot's Second Quarter 2026 Earnings Conference Call. Today, we'll be discussing the results announced in the press release we issued this afternoon. With me on the call this afternoon is Yamini Rangan, our Chief Executive Officer; Dharmesh Shah, our Co-Founder and CTO; and Kate Bueker, our Chief Financial Officer.

Before we start, I'd like to draw your attention to the safe harbor statement included in today's press release. During this call, we'll make forward-looking statements within the meaning of the federal securities laws that are subject to risks and uncertainties and including statements regarding our financial guidance for the third fiscal quarter and full year 2026, future financial performance, business outlook and strategy.

These statements reflect our views only as of today and, except as required by law, we undertake no obligation to update or revise them. Please refer to the cautionary language in today's press release, our Form 10-Q and our other SEC filings for a discussion of the risks and uncertainties that could cause actual results to differ materially from expectations. During the course of today's call, we'll refer to certain non-GAAP financial measures as defined by Regulation G. Reconciliations to the most directly comparable GAAP measures can be found in today's press release.

Now it's my pleasure to turn the call over to HubSpot's Chief Executive Officer, Yamini Rangan. Yamini?

Thank you, Geoff, and welcome, everyone. I'll start with our Q2 results and what drove them. Then I'll walk through what we learned in the first half the deliberate choices we are making as a company in response and how we are accelerating our AI transformation across the company. Let's dive in. Q2 revenue grew 17.5% year-over-year in constant currency.

We delivered 3 points of non-GAAP operating margin expansion year-over-year, bringing our operating margin to 20.3%. Our total customer count reached over 306,000 globally with 7,000 net additions in the quarter. I'm pleased to announce that our Board of Directors have authorized an additional share repurchase program of up to $1 billion, a clear signal of the confidence we have in our business and the growth opportunity ahead. We are still in the early stages of a massive shift with AI, and this quarter reflected that reality.

Let me be direct about what happened. April got off to a slow start and the quarter we expected did not fully materialize. Two factors drove the headwinds. The first was deliberate. As we discussed last quarter, customers adopting AI want proof of value before they commit and predictability in what it costs. So in April, we leaned into those trends and made changes across product, pricing and go-to-market. On the product side, we introduced trials so customers can turn on agents and AEO in their own environment with their data for their workflows. The goal is simple, let customers experience real outcomes before they buy. We knew this would create some near-term headwinds by extending the buying process, but we believe it's the right long-term trade-off, lowering the barrier to adoption and building customer confidence and outcomes will ultimately accelerate AI adoption.

So far, this approach has worked best with upmarket customers, where our teams and partners provide the support needed to make these trials successful. Our focus is to scale that experience so every customer can adopt AI with the same confidence. On pricing, predictability has become a defining theme in AI adoption. Businesses have been hit with unpredictable token costs. And they want pricing that is transparent and tied to value. In response, we introduced outcome-based pricing for several of our HubSpot agents, lowered entry price points and are providing customers clear visibility and control over usage and spend, including the ability to set thresholds that fit their budget.

The second factor was a shift in the demand environment in Q2 with increased budget sensitivity. Businesses want greater confidence that their investments will position them for the AI platform shift. As a result, purchase decisions are facing greater scrutiny, buying committees are larger and more deals require C-suite and board approval, leading to longer sales cycles. For existing customers, unpredictable AI costs across the broader landscape are impacting budgets, leading to budget optimization and downgrade pressure.

In response, we've evolved our execution playbook by engaging the C-Suite earlier, accelerating time to value with our partner ecosystem, and demonstrating our pace of innovation that future proofs their investments. These trends reinforce our conviction in accelerating our strategic pivot to deliver predictable pricing and clear value tied to AI.

Now despite the headwinds in Q2, we continue to see real momentum in AI adoption, upmarket wins and multi-hub growth. On AI, customer adoption is accelerating. Data agent has over 16,000 customers activated, up 80% quarter-over-quarter. Prospecting agent has almost 17,000 customers activated, up 28%, and customer agent reached over 10,000 customers. We launched HubSpot AEO in April, both within Marketing Hub and as a stand-alone product. And since then, 32% of Marketing Hub Pro Plus customers have activated AEO and nearly 16,000 customers have activated a stand-alone AEO trial in Q2.

More importantly, customers are seeing real business outcomes. Sesame HR, a 400% HR software platform could only respond to 70% of incoming support tickets before deploying customer agent. Now they cover all tickets received with 60% fully resolved without human escalation. After their initial trial, they have now purchased more than 1 million credits and are expanding into other agents. RevenueWell, a 250% dental software company, combined HubSpot's buyer intent with prospecting agent to identify high intent buyers and reach them with personalized outreach. Meetings booked increased from 8% to 28% and conversion rates climbed to nearly 10%, well above industry benchmarks. They have since grown their usage to more than 350,000 credits across 6 HubSpot agents. Upmarket momentum continued to be strong. deals over $120,000 ARR grew 38% year-over-year, reflecting continued demand from larger customers for a unified AI-powered customer platform. And multi-hub momentum continue. 64% of new Pro plus customers landed with multiple hubs, up 3 points year-over-year. Taken together, these results reinforce our conviction that we are making the right strategic choices.

Now let me step back from the quarter and talk about the bigger opportunity. The last 20 years, we're about helping teams do more work with software. The next 20 will be about helping them achieve better outcomes with AI. That's the opportunity we're building for. And that's why we are evolving every part of HubSpot from our products to our go-to-market to how we operate as a company. Our AI strategy is simple: drive growth for scaling companies. And that means delivering real outcomes across the full customer journey, and that is exactly what our HubSpot agents do. Customers don't want a chaotic agent's draw. They want a controlled cohesive way to build demand, win deals and delight their customers. In Q2, we expanded our agent portfolio and added both breadth and depth in agent capabilities.

We also added two new products: AEO, which shows marketeers how their brand appears in AI search engines and tells them what to do about it and Revenue Hub, which brings quoting, contracts, billing and payments into one place. Beyond HubSpot agents, we want to make building easy AI is democratizing the ability to build workflows, agents and automation. And we want every go-to-market builder on our platform to take advantage of that.

In July, we launched Agent Builder and Agent Hub. Agent builder, let's anyone build custom agents, agent workflows or mix and match both. What makes it powerful is that it connects deeply to HubSpot CRM in context, but it can also automate anything outside HubSpot. Custom agents can take a rich set of actions from sending a WhatsApp message to invoking an LLM to calling a custom API, and Agent Hub gives customers one place to manage all their agents, HubSpot build, customer build and partner built. Over 2,700 customers have already activated in beta and partners are leading the way here. Smart Bug Media, a HubSpot Elite solutions partner built a library of 20 custom agents on HubSpot spanning sales research, content creation and proposal compliance.

We have expanded partner incentives to encourage this motion and expect a significant portion of customers and partners to build on top of our platform, given the trusted context we deliver. The biggest adoption jump this quarter was in Breeze Assistant. Breeze is no longer just a chat interface. It is how customers interact with the entire HubSpot platform. They use it to build reports, create automations, invoke agents and generate artifacts or without writing a single line of code. More than half of our Pro Plus customers are using it, and weekly active usage has doubled since the start of the year.

What makes it powerful is what's behind it? Every action and every artifact is grounded in real CRM data, growth context and HubSpot's governance model. Customers get AI that actually knows their business. So how will you know whether our AI strategy is working? We measure it through four lenses: reach, depth quality and growth.

First, reach. Our customers adopting AI. Today, more than 55% of our Pros customers use our agents or Breeze Assistant, up by double-digit percentage points since the start of the year. HubSpot agent adoption among our Pro Plus customers has grown from high single digits to mid-teens this year.

Second, depth. Our customers using more over time. The total number of monthly agenetic actions across our customer base has increased more than 3x this year. Customers are not just experimenting with AI, they are making it part of their daily workflows.

Third, quality. Are our agents delivering real outcomes? Customer agent now resolved 72% of support tickets without human escalation. Prospecting agent is generating response rates on par with human written outreach, leading to more meetings booked and more deals closed.

Finally, growth. Is AI driving growth of our business? Total credit consumption grew in Q2 despite the pricing changes we made in April. Credit usage is now evenly distributed across data agent, prospecting agent, customer agent and buyer intent. That is an important signal. Customers aren't adopting a single AI use case. They're using HubSpot agents across the entire customer journey.

Transforming our product and pricing is only part of the story. We're also transforming how HubSpot operates. And that internal transformation is driving leverage. We have reorganized into smaller, more focused teams operating in 6 weeks prints. This lets us move at the speed of AI, experiment continuously and quickly scale what works. AI is now embedded in how we build, how we sell and how we run the business. We're doing more with less and getting faster as we go, and it is showing up in the numbers.

Operating margins are expanding in 2026 even as we invest aggressively in AI innovation. In addition, we expect to deliver 2 to 3 points of operating margin expansion in 2027, a meaningful step-up that reflects the operating leverage we are building as an AI-first company.

I want to close with what I keep coming back to. We are in the middle of a real transition to AI, and we are making deliberate choices to lead in it. While some of these choices create near-term headwinds and they will help us drive long-term compounding growth. Our core fundamentals are solid. Our AI momentum is real and accelerating. And the adoption indicators are moving in the right direction. We have a clear strategy, a focused team and high conviction in where we are headed.

With that, I'll hand it over to our CFO, Kate Bueker, to walk you through our financial and operating results. Kate?

Thanks, Yamini. Before diving into Q2 results, I want to reiterate the business trends that Yamini shared in her remarks.

April got off to a slow start, and the quarter that we anticipated did not fully materialize. We faced two headwinds this quarter. First, the delivery changes we made across product, pricing and go-to-market were a headwind. We believe these are the right long-term decisions for HubSpot to win in the AI era. Second, we saw a shift in the demand environment in Q2 with increased budget sensitivity. These demand trends reinforce our conviction in accelerating our strategic choices to better assist customers through this transition.

With that, let's turn to our second quarter 2026 financial results. Q2 revenue grew 20% year-over-year as reported and 17% in constant currency. Q2 subscription revenue grew 20% year-over-year, while services and other revenue increased by 8%, both on an as-reported basis. Domestic revenue grew 17% year-over-year in Q2. International revenue growth was 23% as reported and 18% in constant currency, representing 49% of total revenue.

We added 7,000 net new customers in Q2, bringing our total customer count to over 306,000 growing 14% year-over-year. This was below our 9,000 to 10,000 expectation and driven primarily by weaker conversion rates and increased buyer hesitancy. Average subscription revenue per customer was $11,800 in Q2, up 4 points year-over-year as reported and 2 points in constant currency.

Customer dollar retention remained healthy in the high 80s, while net revenue retention was 102%, down 1 point year-over-year as continued seat and credit expansion was offset by other net upgrade headwinds as a result of customer budget optimization. Q2 calculated billings were $930 million, growing 14% year-over-year as reported and 17% in constant currency. Non-GAAP operating margin was 20%, up 3 points compared to the year ago period. This expansion reflects our continued disciplined approach to head count spend, partially offset by AI costs.

GAAP operating margin was 5% in Q2 compared to a negative operating margin of 3% in the year ago period. This 8 points of expansion reflects our non-GAAP operating income expansion and a more than 4-point reduction in stock-based compensation expense as a percentage of revenue.

Non-GAAP net income was $165 million, and non-GAAP net income per diluted share was $3.26, up 40% and 49% year-over-year, respectively. GAAP net income was $43 million in Q2, and GAAP net income per diluted share was $0.86.

In the second quarter, the company generated $168 million of free cash flow or 18% of revenue. Our cash and marketable securities totaled $1.4 billion at the end of June. During the quarter, we bought back more than $500 million of stock under our current $1 billion share repurchase program. Our Board of Directors has authorized an additional share repurchase program of up to $1 billion, reflecting the confidence we have in our business and the growth opportunity ahead. Our continued strong balance sheet and free cash flow provide us with the flexibility to return capital to shareholders while maintaining our focus on investing in organic innovation and opportunistic M&A.

Before we dive into guidance, let me share our current thinking on the second half and our expectations on the near-term performance of our KPIs. Our Q3 and full year guidance reflects our expectation that the headwinds we saw in Q2 will persist throughout the remainder of the year. We now expect quarterly net additions to be approximately 5,000 to 6,000, and ASRPC growth to be in the low to mid-single digits in constant currency.

For the full year of 2026, we expect net revenue retention to be roughly flat year-over-year. We expect customer dollar retention will remain strong and stable in the high 80s, and we continue to expect net upgrade rates to be pressured in the second half as a result of customer budget optimization. We now expect net new ARR growth to be below constant currency revenue growth for the fiscal year of 2026.

With that, let's dive into our guidance for the third quarter and full year of 2026. For the third quarter, total-as-reported revenue is expected to be in the range of $924 million to $925 million, up 14% year-over-year on an as-reported basis and 15% in constant currency. Non-GAAP operating income is expected to be between $187 million and $188 million, representing a 20% margin. Non-GAAP diluted net income per share is expected to be between $3.25 and $3.27. This assumes 49.3 million fully diluted shares outstanding.

And for the full year of 2026, total-as-reported revenue is now expected to be in the range of $3.678 billion to $3.686 billion, up 18% year-over-year on an as-reported basis and 16% in constant currency. We continue to expect non-GAAP operating income to be in the range of $762 million to $766 million, representing a 21% margin. Non-GAAP diluted net income per share is now expected to be between $13.23 and $13.31. This assumes 50 million fully diluted shares outstanding.

Before we turn to some modeling notes, I'd like to provide additional color on our margin expansion trajectory. As we transform how HubSpot operates, our internal transformation is driving leverage. Our guidance contemplates 2 points of non-GAAP operating margin leverage this year. Looking ahead, we expect to deliver 2 to 3 points of incremental non-GAAP operating margin leverage in 2027. In addition, we remain committed to driving stock-based compensation as a percentage of revenue down year-over-year. We'll share more detail on this at Analyst Day next month.

As you adjust your models, please keep in mind the following. We continue to expect CapEx as a percentage of revenue to be 5% to 6% for the full year of 2026 and continue to expect free cash flow to be about $750 million.

Before we open the call for questions, I want to invite you to join us at our Annual Analyst Day at Unbound taking place on September 17 in Boston. We look forward to seeing you there.

With that, I will turn the call back over to the operator for questions.

[Operator Instructions] Your first question comes from the line of Samad Samana with Jefferies.

So look, Yamini, I appreciate all the details that you gave us. I know in the press release, you referenced the deliberate choices -- and I think we all understand about the pricing model change and the sales training, then there is also the terms of service change during the -- and then the roll back.

I guess the question that I have just processing Kate's guidance and all the information that you gave, were there additional choices in the quarter that disrupted the results and influenced the outlook beyond what we've already talked about. And then maybe looking ahead, -- are there more aggressive choices the company needs to make in this fast-moving AI backdrop. I appreciate all the progress that you guys have made. But is there more work left to do and more aggressive moves need that need to be made?

Samad, thank you for that question. Look, the simplest way to describe what we observed in the quarter is that as customers are navigating both platform shift and budget pressure they're cautious. And I mentioned two specific headwinds, but let me just be very, very specific and break those apart and unpack what those mean. Now when I look at the first half, Q1 was solid.

But as we move through Q1, it became very clear that customers are evaluating and buying AI differently. So in April, we leaned in and made a set of deliberate changes across product, pricing and go-to-market that you referenced. And specifically to unpack those, customers want proof of value before they buy. This is a different buying ocean than SaaS and where they bought features.

And so they want to see the agents work within their data, within their workflows and their environment. So we shifted to providing trials for several of our HubSpot agents in AEO. We knew that evaluations will slow deal cycles, but we would provide higher confidence for customers as they adopt AI. And that would allow us to see as many use cases as early on as possible with customers because once they buy the first agent and the second agent and they begin to see clear outcomes, they are much more progressive about adopting the third, fourth, fifth agent. That is exactly why we did that.

The second thing that we did is that we reflected on customers wanting predictable AI costs. That is an industry-wide concern today. And so we lowered the price for many of our key agents. We introduced outcome-based pricing so that we could tie the agent value to the value that they're getting and we gave customers much better disability and spend control. They can set thresholds for what they want to spend.

Now these decisions create a near-term headwind, and the decision we made is we'd rather remove friction and build customer confidence at the beginning of the AI journey and then help them drive much more adoption as they continue. We believe those are the right trade-offs to becoming the long-term winner. And as I mentioned, we are seeing encouraging signals in terms of AI adoption, and July continued that adoption in AI.

And specifically, prospecting agent, we're now seeing 17,000 customers. Customer agent, 18,000 customers that grew by 80%. Data agent, similarly -- sorry, data agent was the 16,000 that grew pretty significantly and intent signal. So you can see that customers are getting more and more comfortable, and it's balanced across multiple agents in terms of what they're adopting. And that is exactly what we want to see.

Those are all the deliberate changes that we made, and we feel very good because, yes, we take half a step back, but we are doing that so that we can take multiple steps forward.

Now the second trend that we mentioned is that in Q2, we saw a shift in the demand environment with increased budget sensitivity. And where that showed up is for prospects we're seeing greater scrutiny. We're seeing larger buying committees, and more of the deals that we are participating in require C-suite approval or Board approval. And that means the deal cycles are longer. Now having said that, we are seeing a lot of large opportunities within the pipeline, larger than what we've seen in the past, and they are closing, but they're just closing a month later or a few weeks later.

And so with all of that change that we saw in the second quarter, we understand that the buying environment is changing. So in response, we are engaging with our customers earlier in the process. We're providing them clarity in terms of the outcomes that we deliver. We're rolling out place to support flexibility for customers in terms of their spend and we're working very closely with partners to give confidence to work through and deliver outcomes.

So those are the two very specific trends that we saw and how we are responding to each of those within the quarter. Now, the factors that I talked about resulted in slower customer acquisition and net new ARR headwinds, and that is what we are contemplating in the guidance for the rest of the year, but we're very confident that we're making the right trade-offs to position the business for the much larger AI opportunity in front of us, and that is automation.

Our next question comes from Rishi Jaluria with RBC.

Wonderful. Look, I appreciate all the detail. And I think a lot of us are willing to be patient and long-term focus. But maybe would love to understand in terms of what you're seeing from a buying behavior, I totally understand kind of some of the business model shifts and a lot of the noise out there. And you talked a little bit at the beginning in your prepared remarks about how a lot of companies are struggling to manage their token bills and we obviously saw the trend of token maxing basically be DOA. So my question for you is, has that kind of token burn impacted budgets and how companies both new and existing, are thinking about deploying HubSpot? And maybe related to that, is there any kind of confusion or noise in the market from adjacent vendors that are relatively new to marketing themselves is more AI native, that's leading to that higher level of scrutiny that you talked about? Any color there would be helpful.

Yes, Rishi, thank you so much for the question. So there are two parts of your question. One is, what are we seeing in terms of the spending environment? And is that because of token maxing? And then the second part of the question is really around our competitive positioning within the market. So I'll take both of those separately.

Look, in Q2, we saw a more cautious spending environment. And look, there will be a sorting phase, I'm sure. And there will be clarity in terms of not token maxing but really driving value maxing, and that is why we are leaning really into delivering outcomes. Now what that cautious spending environment translated to for us is twofold. For new customers, we're seeing larger buying committees and more C-suite involvement. Many times, boards and firms are involved in the final approval. And that's not what we saw a few quarters ago where things that would get like approved at the VP of Marketing level now requires a Board approval.

We are definitely seeing more pipeline and larger deals within the pipeline, but because of the scrutiny, it just takes a month longer. And I've been working with a number of customers. We talk about like replacements, one of the larger deals where we're replacing an incumbent and consolidating CRM across 500-plus seat. It just took 2 more weeks than normal to go through the budget approval process at the CEO level, at the Board level to get it approved.

So that is what we are seeing. And it's not surprising that customers who are navigating a platform shift, they want clarity in terms of the outcomes that they can get, and they want predictability in terms of the cost. And we've contemplated that, and our execution now meets customers where they are.

In terms of how we are responding to what we are seeing in the environment is we are engaging with C-suite earlier in the process we are making sure that the value is clearly articulated. We're making sure that the migration and implementation with partners delivers past outcomes. And so we've changed playbook to meet where our customers are. Now, the second part of your question is really around the competitive environment. Look, the market is competitive. It has always been very competitive, and it's never a winner-take-all market. But I will say that where we continue to win and which is why we see the strength and the number of conversations that we're having is that we are, one, pretty easy to use; the second is that we provide a platform a number of times that I have conversations with customers they don't want agents [indiscernible].

They don't want like 10 agents from 10 different vendors because, one, the predictability of cost issue becomes even worse and the ability to see the outcomes in all in one place also becomes worse so customers look to us for, one, making sure that all of the agents are in a single place and our platform provides the context the CRM data, the governance and the workflows to be able to get them to do their work. So I think our overall position in the market is very solid. We see that in upmarket win rates. We see that across the board, but its deals are just taking longer to be able to close, and that's because of the environment we are in.

Your next question comes from the line of Brian Peterson with Raymond James.

I appreciate all the detail. Yamini, I'd love to understand the demand environment maybe from a customer size perspective, I know you're talking about CEO involvement and bigger committees. I think we normally associate that with enterprise I think you're saying that, that maybe is a stronger area of the market. I just want to make sure I understand that. And how can we think about large customers versus maybe SMB adoption through 2Q.

Brian, yes, thank you so much for the question. Let me just unpack what we are seeing upmarket and downmarket. In down market, we just saw a more cautious buying environment. And that is leading to higher scrutiny when it comes to making the decisions. And our priority is to make it super easy for down-market businesses to adopt hubs, adopt agents and deliver measurable outcomes. And all of the things that we did deliberately within our Q2 to lean in and deliver trials and make the cost predictable, those are going to help from a down market perspective.

Now upmarket is exactly where we saw a lot of the larger buying committees and more approvals that are needed. So again, to kind of like repeat what we are seeing upmarket is that the biggest change is the number of people that are involved in the buying process. And as we move to larger deals, we're definitely seeing more involvement from CEO, CFO and having the right conversations with them and making sure that they're comfortable within the process. Many of these deals close and the win rates are staying very solid upmarket, but it's taking longer.

Now I will say that relative to all of our business, the upmarket business continues to remain very strong. We saw that in the number of bigger deals. In the prepared remarks, I shared that the number of 120,000 ARR deals grew by 38%, and that means strong interest, increasing interest in a number of conversations. And when upmarket customers talk to us, they are talking to us because we consolidate their platform, and we are able to reduce their TCO cost and provide clarity in terms of the AI road map. So the clear distinction is how many people are involved in the process, which elongates the evaluation time. And in down market, the higher scrutiny involved within the decision-making process, which is where trials and giving real comfort around the predictability of costs are really helping.

So I hope that helps understand the environment that we are navigating. Having said all of this, look, we are leaning in. We understand that customers are navigating an environment where they are thinking about the platform decision as well as the cost and we're leaning in to make that easy for them. These are the right long-term decisions to set a HubSpot to win in the longer term within the AI era.

Your next question comes from the line of Raimo Lenschow with Barclays question.

Great to see that the credit consumption continues to grow healthily despite April pricing changes. And I recognize that it's very early, but is there any way to think about how much of total credit consumption today is coming from paid usage first bundled or trial usage?

Yes. Thank you for the question. So as we were walking through, the way we look at credit consumption is that it starts with breadth of AI usage, which is how many people within HubSpot are using AI features. Then it's the depth of usage then it's the number of outcomes that they're driving that then drives the credit consumption, which includes both they included as well as additional consumption. And what we saw are very clear leading indicators that are positive.

With reach -- I mentioned this. So the question that we have to ask ourselves is how many people are -- how many customers are using it. 55-plus percentage of our customers are using Breeze Assistant as well as agents. And I mentioned that agent usage went from 9% to much higher in the mid-teens and then continue to like accelerate to high teens in July. And I also mentioned that Breeze Assistant usage jumped pretty significantly. And Breeze Assistant usage is important because it's no longer just a chat surface. This is how customers interact with HubSpot. This is how they take agent action. This is how they build reports. This is how they build artifacts. And so Breeze Assistant is really important in terms of the reach.

Now the second metric that we look at is depth. If they're using how deeply are they using, how consistently are they using? We measure that as agentic actions taken and agentic actions taken grew 3x from the beginning of this year, which means it's not just experimental, they're using it much more. And then the outcomes, the quality of outcomes is what will enable more small, medium businesses to adopt AI. There, our customer agent resolution rate is at 72%, which is industry-leading, and the prospecting agent is actually delivering results as much or better than what humans would deliver in terms of outreach signals.

All of those are the pre leading indicators in terms of credit consumption, and as I mentioned, even though we reduced pricing, we've seen credit consumption increase, and we are continuing to see that trend go into July. So again, part of this is we're very, very early in the AI adoption cycle. If you look at businesses adoption, mid-market and small business adoption of AI is pretty early stages, and the more comfort that we give them, the more confidence we give them in terms of the quality of output as well as the value that we are delivering with the predictable cost, we are going to see it increase, and that is exactly what we are doing to make sure that we set ourselves up for the future.

Your next question comes from the line of Terry Tillman with Truist.

It's John Carlo on for Terry here. Just on the product road map, you had mentioned that there's a lot of progress moving forward. But how do you guys actually train the sales force to sell those new products?

Yes. Terry, that's a good question. Look, I think that just to kind of maybe step back from a product road map perspective, we are very, very clear. We start with HubSpot agents. We have deep domain expertise in marketing, sales and service. And those actually show up in HubSpot agents that we deliver outcomes for customers. The second, which we talked about is agent builder. We see a huge opportunity for customers and partners to build on top of the HubSpot platform and continue to extend workflows build automations, build custom agents. So we have a very clear product strategy.

In terms of how we're thinking about our teams and how we're training them -- look, first of all, they all live day in and day out on HubSpot. They're using all of these capabilities, and they're understanding what this means to their own sales and marketing processes. Having said that, we are training them to lead with outcomes and value that we can deliver to customers communicate the value of the full genic platform and then help customers understand AI pricing and credit. That is a new motion.

Customers want to understand how this all works, they want to adopt it, but they want to make sure that it is economical given their budgets, and that's where we are training our sales organization to be able to do. Now if I step back, the bigger change is really with customers and how they are buying and the go-to-market changes in the training reflects what we are seeing with customers. And to make it pretty simple, look, traditional software used to be about customers looking at features, implementing features and then making sure that users got trained on those features to drive outcomes.

But with AI, the real change is that we deliver outcomes. And they got to make sure that it works within their environment with their data, and they got to understand the ongoing economics before they commit. And so it is a different buy motion. Our sellers are stepping into it. Every week, I see the way that they communicate the value, and they help our customers navigate a big change in terms of what they're buying improve, and we have the right strategy and the confidence that we'll help our customers navigate a set of changes here.

Your next question comes from the line of Gabriela Borges with Goldman Sachs.

Yamal your commentary makes sense. My question for you and Kate. I know you said you're anticipating the environment to stay the same in the second half. My question for the both of you is when does it get better? Are we -- I would imagine there's a period of time where our customers are exploring. We're in this period of expiration and the products are becoming more mature. Do you envision getting to a more steady state environment? Or is this like the new normal, like what we were talking about is COVID, where we kept waiting for things to get better and it just turned out this is the new baseline. So maybe just give us your hypothesis for -- what do you think it takes for customers on a time line standpoint to get more comfortable and for the products to mature and for the whole bioprocess to come to us a little bit more comfortable for everyone.

Yes. Gabriela, that is a great question. And I will tell you that the moment that we are navigating right now is when customers are thinking about how they adopt AI and how they navigate through that? And the second thing is like how do they get a sense of where the budget needs to be as they adopt AI? And I think this is where we find ourselves right now. And if I go back and talk about what we observed and therefore, the changes that we're making, it really starts with helping customers through this environment. And as I've now talked about [indiscernible] again in terms of the changes that we made, if customers want comfort, in terms of the outcomes that they want to see within AI, we're there for them.

We're providing them trials. We are supporting them with solution architects as well as partners so that they can understand the outcomes that they can get within their environment. and we're helping them with predictability of cost, both by lowering the price and tying it to the outcomes, but also giving them the comfort of set up your threshold and make sure that you understand how the budgets work.

And so look, that part of it is the moment that we are in. I haven't lived through previous transitions many times. You start in a place where you want to understand how this new technology is going to work and how the budgets are going to work. And then going forward, when you begin to see others get the value when you begin to see the adoption across the industry, then you just get comfortable with it, and then you move forward. I think -- and I look at this and say, this is the moment that small, medium businesses are in the adoption cycle and understanding what this means for their budget.

As we give them confidence, both in terms of the predictability of cost and the clear outcomes that we deliver, there will be a level of comfort in all of this. So that's how I think about it. Now we are going to assume that what we saw in Q2 is what we will see in the second half, and that is what is contemplated within the guidance. But we're doing everything from our response perspective to provide the comfort for our customers as they navigate this transition.

Your next question comes from the line of Aleks Zukin with Wolfe Research.

This is Yvon here for Alex. Maybe one question on sort of the product side since HubSpot has always known as the company with most beloved products -- and this 1 is about the agent. So what do customer agent prospecting agents AO in these other identic products, what do they need to get right over the next two quarters for the H2 numbers to land? And where these products still maturing today. Our sense from the field is that most ad products, not just hubs, but really all products are still very, very early. I'm just wondering, from your perspective, what closes the gap on the go-forward basis? And then kind of connected to the previous question is, when do these products start impacting revenue and offsetting some of the other weaknesses?

Yes. That is a great question. And I will say that the SMB adoption is still pretty early stages. And we feel that we're leaning in and helping them navigate and adopt AI, but let me specifically talk about the agents that you mentioned and our strategy.

I'll start with HubSpot agents. Pretty simple. We want to take our deep domain expertise in marketing sales and service, and we want to deliver outcomes for our customers. And what are the outcomes that we deliver. It is to help them build demand. It is to help them win deals, and it is to help them delight customers. And so the set of agents that we now have in the market includes AEO agents to show up in LLMs. data agent to build up your audience or TAM, prospecting agent to outreach to your customers so you can build pipeline, smart deal progression so that you have a self-updating CRM and a customer agent.

So we have agents throughout the customer journey in the critical points of the customer journey. What we see in terms of the pattern of adoption is this. Customers start with internal facing agent, which means that data agent, the reason you see that very high adoption of data agent is because they're comfortable using an internal use case. And then they're also very comfortable with smart deal progression, which is use a note taker and update the CRM records and get sales productivity. They're all starting with those internal use cases because it's pretty easy to drive that within the teams.

What takes one more jump is really making sure that the outcomes of any agent that interacts directly with customers is solid. And that is the prospecting agent and the customer agent and so on. So what are we doing about it? As we think about every one of these agents, we're looking at how many customers are discovering agent, how many are activating how many get the first aha! moment of value and then how many continue to double-click and use each of these agents? And we are looking at every one of those steps for every single agent to be able to optimize. And that gives us a lot of control of like what do we need to do to drive customer agent adoption versus prospecting agent adoption?

That is where the customers are in the cycle of adopting AI. From a product perspective, that means doing more to drive that first moment of value and driving that clarity in terms of outcomes that we deliver and helping our customers navigate that process. I'd say the same thing with Agent Builder. That's the second part of the strategy. We launched Agent Builder so that our customers can build workflows, agents, automation on top of HubSpot. Why would they do that? Well, they do that because there is context, there's CRM data, there's permissions and all of the governance that's associated, and that helps them extend AI within their businesses and workflows.

And again, we have 2,700 customers already and 1,000 agents that are already adopted. So look, from a product cycle perspective, this is -- if you think about a mid-market company with 50, 100 employees, they're getting used to what this means to have agentic technology, and we are right there with them. We are looking at the points of friction. We are enabling block -- getting through those points of friction and driving that from a product strategy perspective, we are very clear that the strategy is going to help customers deliver outcomes with AI. It's just taking the time as we navigate that shift.

Your next question comes from the line of Jackson Ader with KeyBanc.

Great. I really just have one around the -- not so much on the AI disruption or your -- what you feel like you can control, Yamini, on making some of the changes, but the budget scrutiny, what is it -- when -- I'm just kind of struggling to think, okay, customers are scrutinizing their budget more, what is it that they are opting to spend their money on instead of HubSpot at the moment? And what gives you the confidence that this is -- it's something temporary? And how long does that temporary last? When do you expect the budget scrutiny to loosen up a little bit?

Jackson, thanks for the question. Look, I wish I had like a very clear crystal ball to give you exact answers there. But what happened, as you all know, in the first half of this year is that there's just a lot of token maxing. And that phase is not very healthy, and it is not tied to value and outcomes of what customers are getting. I think there will be a sorting phase. And one of the reasons why we've been leaning into clear outcomes, agent pricing tied to outcomes and clarity in terms of how they can set thresholds is to give comfort within that process.

The second thing that I would say is the early pace of technology adoption, there is a ton of experimentation. But as you, again, ask the question of where is my spend leading to clear impact in ROI, we think that, that is where HubSpot will win. And that is because our platform is clearly connected to the business outcomes that we are delivering. And second, because we actually have the flexibility to pick and choose amongst the AI models, and we can provide the most cost-efficient way to be able to deliver the outcome.

So look, there is a phase of sorting through where do I spend money and am I getting value? But I think that the industry is also getting to the point where they're now asking the right questions on how do I optimize spend across and what specific spend is leading to clear outcomes? And I think that's where HubSpot wins. I can't tell you an exact month or a quarter where that happens, but we are doing everything possible to be super clear in communicating the value that we deliver and the predictability of costs that we provide for customers that will help them through the shift.

Your next question comes from the line of Siti Panigrahi with Mizuho.

Thanks for the question. Yamini, really appreciate all the color here. Just to follow up to some of this question. among the customer base we're upticking your AI product, are they asking for more discount from their traditional core hub? Or basically, I want to understand how they're funding that spending? And in terms of NRR, what gives you that confidence that NRR be flat? Are you seeing any kind of churn for the customers, those who are not upticking AI?

Siti, why don't I start a little bit -- I'm hearing some feedback. But I'll give you Yamini a chance to breathe and talk a bit about net revenue retention, what we saw in the quarter and what gives us the conviction that it will be flat this year. If you think about the net revenue retention in the quarter, I think it's helpful to think about the components of net revenue retention, and it starts as it always does with the customer dollar retention.

In Q2, customer dollar retention remained really strong and stable in the high 80s. That's the foundation that we always count on for a strong customer dollar retention -- or for a strong net revenue retention. Seat and credit expansion, which, as you know, has been a key driver of net revenue retention continued to benefit net NRR this quarter. But as Yamini talked about, the agent pricing reduction, the introduction of trials created a near-term headwind for credit expansion. That's an outside of seats and credits. We did see pressure in other upgrade motions and how that's manifesting itself is that as customers come up for renewal, we're seeing some downgrade pressure as they sort of optimize their overall spend.

Given those trends, we took down our full-year view for net revenue retention to be basically flat to 2026. If you look at where we are year-to-date, we are flat. Net revenue retention does tend to be higher in Q3 and Q4 with UNBOUND and Q4 is obviously a big quarter for us, both from a new business perspective, from a renewal perspective and also from an upmarket perspective.

All that said, we do think that the actions that we're taking are positioning us really well to drive stronger net revenue retention over time, right, lowering the friction for customers to get started with AI use cases, provides an opportunity for expansion over time that will become a headwind to NRR.

Yes. And Siti, in terms of the first part of your question, patterns of AI adoption and how are they kind of finding the budget for that? Look, there are a couple of patterns and pools, I would say. One is that customers want to experience an agent outcome in their environment trial helps them, and they continue to grow. I'll give you an example of this customer, great customer called RentSpree. They started with customer agent and conversations are now being resolved using that, that was what they validated in their trials. And then from there, they expanded to another 350,000 AI credits, that is one pattern that we see. The other pattern that we see is having flexibility to be able to swap seats for credits and managing their budget, wanting to drive AI adoption, but managing the budget across seats and credits. And we have been very flexible to help our customers do that.

Both of those things, we think, are the right things. trials, give confidence that then lands with use cases and then expand from there to more use cases. and then flexibility in spend gives them the confidence that they can control their budget while adopting AI and both of those motions are what we are leaning into with what we are seeing in the market and meeting customers where they are.

Your final question for today comes from Keith Bachman with BMO. Keith, your line is open. You can ask question.

Operator, we'll take the next question, please.

Your next question comes from Parker Lane with Stifel.

Kate, just one for you on the two pieces that impacted the quarter here, the delivered actions we took and then the shift in the demand environment, was the demand environment fairly level and the pressures that you saw consistent throughout the quarter? Or is that something that started to become more pronounced as you exit the quarter?

I think that there's not a distinct difference between the pressure at the beginning of the quarter and the pressure at the end of the quarter. And just to be clear, we did see it continue into July.

Thank you. This concludes today's call. You may now disconnect.

Well, we will kick it off. And for those that are listening in, really excited to have Yamini Rangan with us from HubSpot. Thanks very much for being here. I am ecstatic to have a conversation about what's going on at HubSpot with -- around AI. We were just talking about -- there's so much -- the velocity and the scope of this cycle is pretty remarkable.

I don't know, actually, we -- before I get into AI, why don't we just recap the quarter maybe really quickly with you. It was a really good quarter, 18% growth, net adds were above 10,000. Can you just talk about sort of what's driving sort of the business right now? Because I know everybody wants to just get to the AI upfront, but maybe we'll cycle back then jump forward.

Yes. Well, thanks a lot for having me. I really appreciate you taking the time. And yes, we'll start with Q1. Q1, it was a good quarter. From a revenue perspective, we beat guidance. From an operating margin perspective, we beat that, as well as like net adds.

And look, we are seeing the same trends and patterns that we have seen for many quarters now, which is that a lot of our customers, our customers are in the 2 to 2,000 to employee range. They are consolidating on fewer customer solutions. And we've seen this now, and we continue to see this going into this year, where if you look at a typical mid-market company with like 1,000 employees, they have maybe a bit of a homegrown system, they might have a legacy system or they might have a point solution. And they cannot get into AI without actually consolidating, bringing all of those insights into the same place.

And so I think like we've basically seen that same trend continue, and that strength is both upmarket as well as multi-hub. And again, the trend that we are seeing is that you can't just have marketing and sales and service in very siloed locations. You just have to have all of the data to start leveraging AI. So we continue to see consolidation and multi-hub adoption.

And then over -- what our customers are looking at is where do they get started in terms of their AI journey and what is that like -- road map look like. And we begin to see the emerging levers that we have talked about in the business, which is core seats and credits. And the combination is what we saw in Q1. And the combination of all of that gives us this very clear view of what the growth levers are and how we are continuing to drive growth.

Okay. And can you just talk about on the pricing and packaging? You guys have made some changes. Just obviously, you're trying to drive adoption. Customers are asking you, let's see the value. Just talk maybe holistically about how you're viewing that right now and some of the changes you made and maybe why?

Yes. And Kirk, I think like we were just talking about this. If you think about AI, the fundamental shift within AI is that before AI, as a SaaS provider, you provided access to your technology so that users could then deploy it, point, click, navigate, get work done. And therefore, the pricing model that we had in the SaaS world was pretty simple. You either get a subscription fee to your technology or you provide access to a specific role like sales or service, and that came in seats.

The fundamental shift with AI is that you are now delivering work, and the work looks like either a ticket that is resolved or it could be a qualified lead that you deliver or it could be a target prospect that you're delivering. And so you're delivering something completely different, which is not tied to the value of the seat that someone has. And therefore, it means we have to have credits.

And so you asked, like we're making some changes. I think the industry is making changes. If you ask 20 different vendors what their pricing strategy is, I think the strategy is experimentation. It's not like, oh, we figured it out and we know exactly what we're doing.

And so where we are in the process is that we believe that the future of pricing is still going to be hybrid. There will be a set of just table stakes AI capabilities that we will deliver per seat. And that is going to always be the case. And then there will be work that we are delivering that is not based on seat, and that will be credit-based.

What we did in Q2 is this. We took our agents -- not all AI features because we have hundreds of AI features. We specifically took agents, which is Prospecting Agent, Customer Agent, and we made those outcome-based because we believe that AI should always be about not outputs, it should be based on outcomes. And so we took our Customer Agent and we said we were going to cut the price, but we are also going to make it based on the number of resolutions.

So Customer Agent resolves tickets. It used to be based on the number of conversations. We moved it to resolutions, and we cut the price in half. Why did we do that? Because we truly believe that we are in the very early stages of technology adoption and we want to remove friction. We want people to say, "Oh, this is like a no-brainer for us," and they start adopting. The same thing with Prospecting Agent.

Now the combination of the moves that we made will have short-term negative headwinds in terms of net new ARR, which we have been explicit about. But we also believe that these are the kinds of moves that like actually provide the right foundation for consistent adoption as we look at the next few quarters, and that's why we did it.

Okay. That makes tons of sense. How does this impact the sales motion for you all? I think driving adoption is just key for anybody. We were talking about earlier, the only way to push back on like existential narratives is adoption and value being delivered. So what's the sales motion that now goes along with sort of a more almost value-based approach to pricing? What are you changing on that? What are you leaving alone?

I love the way you framed it, which is it is value-based sales, right? It is much more about value-based sales. So a couple of things that we're doing. The first thing is that customers want proof of value. Because, again, if you are buying a piece of application, that application needs to be deployed. You train your salespeople. Those salespeople use the product, and then they build pipeline. It's -- that was the old SaaS world.

Right now, you're actually deploying agents. And the week 1 that it's deployed, it better write e-mails like your best sales rep. It better qualify leads like your best BDR. And so the difference is that customers now evaluating agents want proof of value that it actually works in their environment given their conditions and their value proposition they need to do.

So what we did, again, we saw this in Q1, and we provided trial periods. And during the trial periods, we activate the agents, we make it work within their environment. They can play with it, tweak with it, understand the kinds of outputs that these agents are delivering, and that gives them a lot more confidence that they can scale with it. So that's the change.

But if you step back, Kirk -- and you've known this industry for a very long time. I think what you're just saying is that the sales motion itself is changing. It used to be that salespeople go, sell, demo, product, and then they close the deal. And then comes the implementation, and GSIs, partners implement it. Then throw it over to customer success, and customer success will come 6 months later and say, what have you adopted and kind of like improve that model.

That model is changing real time. You -- during sales, you have to prove the value. The first week that you implement, you have to show the value. By week 4, that value better have compounded. Otherwise, why pay for tokens, why pay for credits, why pay for any of the things that we're delivering?

And so one of the other things that we did in Q1 to set the stage for this is like we pulled our sales and customer success teams together under one leader. And what that enables us to do is to make the handoffs easier, think about incentives that drive the value sales motion, helps set up like the whole organization in terms of delivering value, which is a completely different motion.

We're going to look back and say, wow, like the AI didn't just change the technology and the platform. It's actually having pretty significant changes in terms of how you reach customers and how you communicate the value and how you showcase that value. And that is what is happening, and we are like leaning into it. So it feels like a set of changes that we are aggressively pushing, which we believe will have medium-term, long-term impact, but will cause a bit of a short-term headwind for us.

Yes. And when you think about customers getting value, I mean, to some degree, their data has to be in a place where they can actually use it correctly or drive the agents to get the outcomes they want and see the value. So I guess when you're talking to your sales and your go-to-market about this, is it really about, look, you have to understand where the customer is at from a data perspective? If they're not there, let's not push something that's going to have suboptimal value for them in the near term?

Is that something you feel like you're -- like when you look at your customer base, you've sort of segmented it so that you understand like who's ready to go, who has more care and feeding to do before they even get to the point where they can take advantage of agentic? Because to some degree, you're not -- like if their data is all over the place, no matter how great your agents are, it's not going to have the outcome they want.

Absolutely. 100%. And the two of us, we were talking about this. Like if you think about the classic adoption curve, you have innovators, and these innovators are okay if the technology is not there. They can string it together. They can work on weekends, they can Claude Code. And those innovators also happen to be on X and LinkedIn, being very loud about what they built over the weekend. That tends to be 2% of the entire customer base.

Then you have the early adopters of technology. And early adopters of technology will turn an agent on, will work with what they have, and they will also give us feedback and iterate with us. But then there is actually the early majority and late majority. The late majority folks don't have the data ready, don't have -- they're still in fragmented applications. And AI is not a magic wand that you can wave and say all of the data get to the same place.

And so for those folks that are somewhat later in the cycle, we talk to them about data readiness. We talk to them about getting a consistent view of all of their customer information, and that is absolutely critical. Without that, they cannot even get on AI. So I think what is even going to happen is that even if you're today not ready for AI, you know that you have to get ready. And the first step that they're taking is data readiness and making sure that they have the right foundation.

So we talk to a lot of customers there. And then for the customers who are ready and willing to kind of like adopt, then we have a very clear road map of where to start with agentic use cases, the ones we've talked about, Prospecting Agent, AEO, which we haven't talked about, as well as Customer Agent, and we get them on that.

So I think what is different this cycle is that it's moving faster, right? If you compare this to the cloud adoption cycle. 1999 is when like a lot of these companies like were found and formed. 2009, we were still having conversations about like cloud adoption and getting CIOs comfortable of moving from data centers into like public cloud. I think what's going to happen this cycle is that it's going faster. And so the conversations are really based on the maturity of the organization and where they are in the AI curve, but we are actually working with all of our 300,000 customers to help them navigate the big platform shift.

Yes. And when you think about your customer base, obviously, more -- a little bit more mid-market. If you're talking to a big enterprise, like we got to get our data in order, that could be like a 2-year project or take forever. Can your customers get there if they really focus on it in 6 months, 9 months so that late majority starts to become a targeted customer for you from an agentic perspective within 12, 18 months? Whereas I think if you're talking about big enterprises, that data sort of unification could take a long time.

You're talking about it in the right way. So the -- one of the fundamental reasons why HubSpot didn't acquire through the growth is because when we went from marketing automation to sales and service, we said the fundamental thing is that the customer data has to be in a single record. And so we made a choice a while ago that we were going to invest in platform primitives to help us build faster and innovate faster.

But the resultant benefit for customers is that when they adopt HubSpot, they don't have to do data unification across marketing, sales and service. And this is also why we've seen consistent multi-hub expansion over many years. It's because you now have data in -- at least the core customer data in a single place, and that helps.

Now if you're not already a HubSpot customer, it certainly is not like a year implementation. It mostly is weeks and months before they can implement, get the data ready in one place and then begin to adopt. So the cycle is much faster. I do think that this provides an impetus even for late majority from the previous cycle to really start getting their data ready so that they can be in a position to adopt AI. And we're definitely seeing and having those conversations with customers now.

Okay. And we talked about it a little bit earlier, but your view on pricing being a little bit of a mix or a hybrid of seats and consumption. Is there a right answer to that longer term for your customer base? Because it's not where you might want to be. It's really where the customers -- you want to meet the customers where they're at. Is there -- in 2 years or 3 years, like is there a way you think about that to be like, ah, it might be like 50-50? Or is it just -- it really just depends on adoption of these agents and how fast it goes?

I think that even 2 years, 3 years, who knows, 5 years, but 3 years out, pricing needs to be hybrid, and it needs to have a seat-based component and a credit and usage and outcome-based component. The reason is that there are so many things within a customer platform like HubSpot that is just table stakes. That just comes with the core platform, and we would not want to nickel and dime or make that all usage-based. So we'll have core seats that every go-to-market employee needs or a specialized sales or a service seat that a specialized team might need and provide a lot of the functionality as part of that.

And then we see what we deliver will become more usage-based. I actually don't know the exact split between seats and credit, but we will think that -- the way we think about the growth levers is that there will be core growth levers. The emerging growth levers are core seats, which have a lot of data and AI value, and credits, which will be what we deliver through work, and it will continue to kind of build in that. Again, a lot of like seat compression and how does this actually impact how you think about the pricing.

I think in the longer term, there will be some level of seat compression, but that will come from delivering even more value. Like if you take a look at our business, the teams that we have actually held in a headcount are support, for example, where we have deployed Customer Agent to augment our Tier 1. But then we are delivering much better like value through the same individuals.

And so I think in the fullness of time, there will be some seat compression, but the narrative of it's going to happen tomorrow it's going to happen in the next quarter, the timing might be off. I think it's like we believe that there is this hybrid world of seats plus credits.

Okay. That makes a ton of sense. Let's talk about AEO a little bit. You're seeing some benefit from it at your top of funnel already. Talk to us about how this sort of helps the broader conversation with customers, what the sort of opportunity is for that and what you all have learned?

Yes, absolutely. Look, there's a huge shift that is happening. People are not searching for information, clicking on blue links, coming into website. They are, in fact, going to LLMs and asking questions, but those questions tend to be much deeper. The average search was like 4 to 5 words, and the average question or prompt within LLM is like 23. So it's a deeper search. It's much more intentional. And when you get a very targeted answer with a citation, then people convert more. So that's the broader shift that's happening.

If you look within HubSpot's customer base this year, the content leads or the leads that you got from searching to your website, that's down 27%. That's like big. Now we've seen this because we were even more inbound-focused. We've seen this for the last 2 years. And certainly, from a HubSpot perspective, we have gone through a diversification of lead sources from content leads to social. We are much bigger on YouTube as well as podcasts as well as newsletters. So we've diversified the sources and we have experimented with AEO.

So it's not like sometimes people confuse that, oh, SEO is getting replaced by AEO. SEO is getting replaced by a diversification strategy across multiple channels where your content needs to show up. So content is important, but where it shows up is much more diversified.

And that's what we've seen internally. And we've built all of that into a playbook called the Loop that we launched at our conference last year. We've built that into our products. And at Spring Spotlight, which was in April for us, we launched an AEO solution. And that AEO solution basically can give you visibility. So you want to know how your brand shows up and how does that show up compared to your competitors, the share of voice, it can do that. The second thing it will do is to say, look at your share of voice and say your share of voice today is 62%. In order for you to get it to 70%, here are 5 actions that you need to take from a content perspective.

And where we are going with it is that you can seamlessly take those actions. And so at some point, in the next couple of years, if a B2B marketer does not have an AEO strategy, they're going to be left behind. So we do think that this is early and there's just a lot that is happening in the industry, but it's an exceptionally important part of your marketing channel strategy, and that's why we are leading the market with our AEO solution.

And what's the -- remind me, I probably know this, but what's the monetization strategy around that? Is this become just a central part of sort of the Marketing Hub in general? Or -- and this is sort of becomes more -- something that just brings more people in, you're helping them evolve? Or is there an opportunity to sort of take price a little bit as part of this as they get better, frankly, hopefully, better outcomes to go around it?

It's two parts. The first is that we launched a stand-alone solution, which means that if you don't have anything else and you don't want any other marketing solution from HubSpot, you can start with it. And the good news with that is it will start showing your share of voice and give you recommendations and you can just adopt it to get going. And we think of it as a front door if someone wants to look at this and they can start with that.

Now if you want to take content actions and you want to be able to drive much better multichannel content, then you do need Marketing Hub. So you would buy stand-alone and then upgrade into Marketing Hub Pro. That's the path. So it is a stand-alone solution.

The second way we think about it is it comes included within Marketing Hub Pro as well as Enterprise, which means it increases the value of our current solutions. But what we are finding early adopters do is that the base case, it will come with 25 prompts in 3 LLMs. Now if you look at our AEO strategy and how we've been able to get our visibility up, we have hundreds of prompts that we are tracking. And those hundreds of prompts give very different share of voice metrics and very different recommendations.

And so as Marketing Hub Pro+ customers begin to use AEO solutions, they are going to need more prompts and more visibility, and that will also consume credits. That's the same mechanism of included credits plus packs that you can buy. And so that's the second form of monetization. So that's -- you buy it stand-alone or you use it within our current products, and then you continue to use more of the prompts, then it will consume credits.

But you're aligned very closely with value to this?

Absolutely. I mean, I think that's -- it almost goes without saying, but in the prior cycle of SaaS, you were providing access to a technology that could add value. In AI, you're providing value. So everything has to be based on products delivering outcomes. And then you can monetize it, but you have to start with a very clear view of how you're delivering outcomes and value.

Yes. Nice thing about AI is it's very binary. As we were talking earlier, if you -- either deliver value, people will use it. If you don't, they don't have to turn on. It's -- I think it's very -- it's a positive over the long term for a lot of people that are delivering value.

Yes. Absolutely.

Obviously, a question I'm sure you get all the time is -- talk to us about sort of the interplay between the HubSpot platform and the model intelligence underneath it that will help power some of the answers and frankly, be sort of the brain behind sort of the orchestration layer that you have. What's your view on sort of the interplay between you all in Anthropic or an OpenAI? I think there's a better together scenario for most of these -- for these companies and you all, but can you just explain that a little bit or go through the way you think about it?

Yes. I mean, look, you know the narrative better than I do. It's like there is a very, very powerful technology which is LLMs, but the narrative has been like it wipes out all categories of software. And from the beginning, we've been saying that we think that LLMs and HubSpot are complementary. And now 3.5 years into it, our conviction is even more deep in terms of how complementary they are.

And you can look at this in multiple ways. The first way is like LLMs are powerful technology, but completely trained on publicly available information. And we have private information about a company, about marketing, about sales and about service. And so you have to make those two work. We can take the capabilities of the LLM, apply the context that we have, which is what do you do within marketing, sales and service, and then train the output so that it can be like much more targeted. So that's like one.

The second is everything that you get from LLMs are probabilistic. And everything that we need to do is a combination of probabilistic and deterministic. I just did my sales forecast. If they gave me a probabilistic answer, I would not be happy with a forecast that is probabilistic. I need a deterministic answer. I can go through that for many, many things within sales and marketing. So you need the combination of something that's probabilistic with deterministic.

The third thing I would say is LLMs are today, very, very single player. I can write an e-mail, I can write a blog. But if it's a marketing team that's working on a multi-language, multi-region campaign, it is a multiplayer mode. It's not a single player mode. And we bring that technology and make it available for multiple players.

And so for many of those reasons, we take a powerful technology and we apply it to the domain of marketing, sales and service and really help our customers get to outcomes. And that's why I think that we'll continue to work. And one of the leading indicators we see, Kirk, is that we were one of the first ones to launch connectors, connectors with ChatGPT, with Claude, as well as with Gemini.

And what we are finding in the patterns of users that are using those connectors is that they ask a lot of questions, but then they take a lot more actions within HubSpot. And the level of engagement in users with connectors is actually going up, which is a positive, which shows that there is like value in both. And I think that that's the pattern that we are beginning to see consistently.

And I think you guys have always been -- you've always been fans of sort of an open framework that you're going to have agents that want to come in, access some data on HubSpot through MCP or APIs, whatever it might be. How do you think about -- what's sort of the fair monetization strategy on that front? And I think a lot of companies are still figuring this out, frankly.

But I guess if a CFO wants to go get data, HubSpot to make a decision from a CFO perspective, you might not have a sales you might not have a Marketing Hub or a Sales Hub for them. If he want to bring information from HubSpot up to a dashboard to do his job, how do you think about the value you're bringing to that scenario? Because I think a lot of the incremental new use cases that are being built on Claude that you see are things that aren't necessarily -- they're not trying to replicate or replace what you are bringing. It's sort of a net new workflow. And I'm sure you want to be a part of that, right?

I mean, multiple thoughts there. First, we always care about being an open ecosystem, and that's been the posture from day 1. The second thought is that, again, contrasting between the SaaS world and the AI world. In SaaS, we delivered great user experience and great developer experience. So if you're a salesperson, you log into HubSpot, you had great experience on the graphical interface. If you are a developer, you had APIs to basically connect with HubSpot, extend, integrate all of those.

I think what is different with AI is that now we need to have agent experience. That means an agent can sit on top of HubSpot, either talk to HubSpot using MCP or talk to HubSpot with APIs or talk to HubSpot with CLIs. And why CLI? It's not like anything new. It's just that agentic coding was trained on command line interface. So it actually does a more efficient job with command line interface than actually APIs and other things.

So the fundamentals are the same. It's like better user experience, better developer experience and now better agent experience. So then the question becomes, how do you monetize it? We think about it in twofold. One is that we deliver data. And we've always had APIs that deliver data. We monitor the API usage. And unless you are kind of like bulk extracting every minute in real time, you don't trip that. And so if a CFO wants to really extract data, go ahead, do it.

We think that, that is not going to be the place of value as AI begins to develop. Here's why. You can, from HubSpot, basically say, I want all of my company information and I want to compute which companies have a propensity to close deals this month. You'll take company data, deal data, sales conversation data, you'll compute all of that outside of HubSpot, and you'll come up with a metric, which is called propensity to close. That's what you do with our data. Go ahead, do it. It will be inefficient, and you'll burn a lot of tokens and inference [ cost ] doing that.

The better way is that you can come to HubSpot and say give me accounts that are -- can have a high propensity to close, give me accounts that are high propensity to churn. And we would have done all of that because we have an insight layer, what is our growth context, and you can extract that and make a better decision as a CFO. What are you going to do as a CFO? You shouldn't be doing the first one. You should be doing the second one.

And the way we think about our API monetization is if you take data, go ahead, take it. And you'll get charged the same way, but we will be able to extract more value if we deliver more value through the insights. And that's what AI will do, and that's what our strategy is becoming. It's like 2-part, data API and insights API. And when we deliver the insights API in high volumes, we're going to be able to extract more. And that becomes a newer way in which we can monetize the agents interacting with us. And that's how we're thinking about it.

Look, this is all evolving. We're thinking deeply. We're experimenting. We're investing in all the right places, and then we'll see how this develops.

Okay. All right. Yes, please.

Can I throw off? How do you think about kind of the headless approach, right? I guess, subquestion to what you were talking about. You're now opening your existing product [indiscernible] you see in the future, you're leading the headless program, right? And maybe the problem is [indiscernible], so kind of flipping it?

Yes. I mean, look, headless is exactly what I described, which is an agent that can now have an experience running on top of HubSpot either through MCP or through API or through CLI. That is exactly what it is.

Now the question you're asking is, are you okay with that? I think absolutely. Like I said, we think that the future is our users using our agents or developers or agents developing on top of HubSpot and building, extending other agents. And we'll be able to monetize it either as data API or insights API and access to our platform.

And so our job is to deliver just best-in-class first-party agents, the agents that are in core marketing and core sales and core support that we have the domain expertise to, like our Customer Agent, Prospecting Agent, we want to deliver best-in-class agents there. And then we want to deliver a platform where other agents can sit on top and build additional automation, additional workflows, ability to extend to other agents. And when we do that, we have -- our thought process is like we'll monetize it in a very different manner.

And so there will be a balance of this. And again, think about the customer adoption journey that I talked about. If you are on the bleeding edge, then you're using agents and you're building on top of platforms, and we get to monetize it differently. If you are in the later majority, then you're not doing the headless development. What you're doing is actually adopting first-party agents that have been tested over and over again.

And I think that's our strategy. That's why the simplest way that we think about it is like agents run HubSpot and agents run on HubSpot, which is like first-party as well as the headless ecosystem approach that we're just talking about.

Can you talk about -- actually, I'm curious, how does that sort of concept start to impact like the thought process around like Starter customers? Meaning, if you think about it, you could start having Starter customers just sort of have an agent that they build, but then plug in a hub. Does it actually open the aperture? I'm kind of curious, you guys have done an amazing job bringing small customers in and then helping them grow with you.

Does AI, I guess, change that playbook at all at the low end of the market in terms of sort of that adoption? You guys have had lower-priced products that have brought them in, given them quick value. Just talk about that playbook, I guess, in a more agentic world.

Yes. I think that it's very -- AI is making more go-to-market folks builders. That's really the shift that we are beginning to see, right, which means that if you adopted HubSpot as a smaller team, a 5-person team and you started with marketing automation, you started with sales, the fundamentals of what we've provided are workflow sequences. Now it's like agentic automation and extensions, right? And you can continue to use those and grow.

I still think that there is a certain level of scale that you get to where the Starter is not going to have the scale that is required and you need to like upgrade, and that's been the path for us. We've had a play of bringing -- having a very compelling free product and then bringing it into Starter, converting the Starter volume into Pro. And I think what AI is going to allow us to do is that you can have a much more smoother curve because you don't have to jump from Starter to Pro. You can actually use a lot of the agentic capabilities, pay credits and kind of like extend the power of the platform before you kind of like jump to Pro.

But I think what it's also doing is that your ability to hone in on a use case and deliver value in minutes is really critical. And what we have been talking about, which is SaaS, much more about delivering a product that people begin to use and then get value. AI deliver value immediately, and that's the transformation that's happening within our Starter product as well, how quickly can you get to starter use case with an agent and begin to get value.

You guys are obviously using AI a ton internally, showing some good operating leverage. What's the thought process there? You all have always sort of really focused on and invested in R&D. You've always been a heavy R&D company. Can you kind of do both now? Is this sort of the perfect playbook for you to some degree where you can invest in AI, but also continue to get that kind of operating leverage out of AI internally?

Absolutely. And look, I think that we got on to this internal transformation as early as we got on to kind of like the product transformation and delivering value for customers. And what is not very obvious is that the way we build an AI product is like fundamentally different from the way you build a SaaS product.

And in order to do that, what the journey that we've gone through is started with copilots, GitHub, that kind of stuff [indiscernible] coding. Even with the technology and the level of capabilities that we see within the market, it hits a limit very quickly because it's not optimized to your developer environment. And if a HubSpot developer needs to build with the agentic coding, they need our libraries, our skills, our ability to test code and review code and get it through that process.

And so what we have done is we've optimized it. The industry now calls it like harness. Everything is like a cool harness. But what we've done is containerized it, optimized it for the internal developers. And so that gives us a lot more in terms of the pace of innovation. And we saw from the time that we actually started leveraging Cursor and Claude Code and the jump between that and our containerized optimized environment for HubSpot developers, the productivity jump is like massive. And that is because of all the things that I talked about.

And what we believe in is like we have now an agentic execution platform internally where building the next agent and the next agent becomes like much easier and faster because they are pulling from the same libraries, from the same skills, from the same optimized environment. And as the number of skills that our developers build increases, the ability to build an agent on top of that becomes easier. And for our customers, it results in a very consistent interface with agents, agent training. Like if you train 5 agents, you can see it in the same place rather than adopting 5 different agents from 5 different companies, training it and looking at the context differently. So I think like the internal transformation that we have done in the R&D will pay off in multiple years, but that is the leverage.

The other thing that you asked about is like how does that change our own operating leverage. And on the go-to-market side, any and every one of this, we've been like bleeding edge adopters. We have trained, we've experimented and we have scaled with that. And we are seeing certainly, areas where we are getting efficient. I talked a little bit earlier about Tier 1 support, where we've not hired anybody since 2024. And we have taken that and used that headcount in other places to get like leverage.

And that's one of the reasons why last year at Analyst Day, we provided a midterm target for our operating margin of 20% to 22%, and we reached that a year earlier, and we've increased our operating guidance this year. And I think when you come to our conference this September, we'll provide you the midterm and long term. And we feel that even though we're spending a lot in terms of agentic coding and our -- you'll see some pressure in gross margin, we feel confident that we can continue to drive operating leverage as a business. And that's because of how much we have done internal adoption and transformation with AI and how we continue to do that.

Okay. Any last questions? I have one more.

I guess part of the bad thesis behind the SaaS [indiscernible] is customers will start [ backloading ] their own solutions. Are you able to track when you have a customer churn? [indiscernible] Are you able to see like what percentage of your churned customers are going [ in-house ]? Is it something you're tracking or [indiscernible]?

Absolutely. And no, this is -- again, you cannot prove otherwise until it gets proven by itself, right? And so -- but we track our customers, why they buy, why they don't buy, and vibe coding is not coming up in conversations. I talk to customers every day of the week, and no one is saying, I'm just going to start like vibe coding.

I'll tell you the 3 customer conversations I had today. One was a transportation company, which is like all transportation logistics. Another one was a homebuilder, and the third was a skin clinic. None of them have the capabilities, nor do they have the interest to vibe code. That's number one.

The second thing is vibe coding is all about coding becoming easier, but not integrations. We have integrations with 2,000 other providers. So you vibe code CRM, and then who's going to connect it with the ERP, with the accounting system, with the project management system, with name your next tool? No one has that capability to build all of those integrations. And then that vibe coded developer now gets like an even better offer from another person and leaves, and then it's all gone.

And then finally, the vibe coding person has not enough domain expertise. We know what's happening in AEO. I can tell you that our best people in AEO will learn so much this month compared to the last 6 months because the industry and the domain is changing so rapidly. So how is the vibe coding person going to be able to do all of that? So look, I know the narrative, but coding has gotten easier. Delivering value out of applications is still very, very challenging, and we don't see that within our customers.

[indiscernible]?

I mean, I don't know if that...

[indiscernible]?

Yes, it's minimal. It doesn't register is the way I would say it.

And no change over the last 3 years?

No.

Do you think the lack of that is also a factor of -- like you have so many B2B customers, meaning these aren't sort of B2C companies, these are -- if you're a law firm delivering that -- like the time it takes to go vibe code something and if you deliver something to your own customers that doesn't work, there's reputational risk for a lot of your customers?

Certainly. I mean this week, I had a conversation with a midsized bank. And I was talking to their CRO and their CIO. And the midsized bank is like, first of all, vibe coding did not count. But most of what their conversation was how can we deliver trusted output that we can scale and grow with and how can we make sure that our data is safe and it's governed and it passes all of the security compliance and SOC. And how do we make sure that we are future-proofing this.

Even if they have the capabilities, like a midsized bank is not going to like want to vibe code it on the side and custom build it. I mean, look, throughout the history of technology innovations, there's always this moment of like custom building can replace something else. It's turned out that it is harder than just custom coding and building. There's a lot more to going beyond that.

Just because you can do it, doesn't mean you should in many cases.

Absolutely.

So we'll leave it there. Thank you very much for your time. Appreciate it, Yamini.

Thank you.

All right. Hi, everybody. Thanks for joining us. I'm honored to have the CEO of HubSpot, Yamini Rangan with us. Yamini, thanks so much for making the time to come down to beautiful Newport Beach, and appreciate you joining us.

And I think I'll kick off with probably the question everybody has been asking you, which is the AI strategy at HubSpot is resonating. You had the Spring Spotlight. You showcased a lot of new products. What's the main message or two that you want us to take away from that -- from the Spring Spotlight and what resonated most with your customers?

All right. Samad, thank you so much for having us here, and it is a wonderful day. It's turning out to be beautiful outside. So thanks a lot for having us here.

Spring Spotlight for HubSpot -- and maybe just a step back, our AI strategy is to help our customers become much more productive with our platform and also deliver work for customers. The second part, which is delivering work for customers, comes in the form of many first-party agents that we deliver for our customers.

And at Spring Spotlight, we launched AEO. We'll talk about AEO Agent. We'll talk about Prospecting Agent and Data Agent. And just to kind of like step back, the set of first-party agents that we are delivering to our customers are: one, to help them resolve their support tickets; two, on the sales side is to help them drive much better outreach so that they can continue to build their pipeline; and three, on the marketing side is to build awareness using new tools like AEO.

And that -- one of the things that we have learned is that you need to have great context. Context delivers better outcomes. Otherwise, AI is just going to deliver an output that looks like a slop. And there's a big difference between AI outputs and AI outcomes, and that's the set of innovation that we delivered at Spring Spotlight.

Where are we seeing adoption and initial kind of reaction? I think AEO, we'll probably talk a little bit more about AEO, but we announced a stand-alone product as well as capabilities of AEO built into Marketing Hub Pro and Enterprise, and we're seeing a strong adoption and early trials there and continued adoption of the agents that we talked about, Customer and Prospecting Agent.

Great. And then look, I think there's a lot of either confusion or still learning where we are in terms of the relationship between the LLMs and between existing the existing software platforms. What relationship does HUBS have with the LLMs? Sam mentioned you by name on stage, Dario was at INBOUND last year. This year's lineup for INBOUND, hopefully, everybody is there. I think it's now -- I forgot what the rebranding of the name is.

UNBOUND.

UNBOUND. There we go. I just didn't want to screw that up. But -- so you have all these disruptors. Is it a complementary or competitive relationship? And just help people understand what they're misunderstanding?

Absolutely complementary. I think for the last couple of years, every single time we've gotten the question about LLMs and HubSpot in particular, applications in general, we've maintained that it is a complementary relationship.

And here's how I would say. If you think about an LLM, LLMs are trained on publicly available data on the Internet, petabytes of publicly available data. HubSpot has the data and the context of how go-to-market teams operate in 300,000 customers. That is why it's complementary. So you need the combination of LLM capabilities with the context of how sales or marketing or service and support functions within a company in order to deliver more growth and better leads. Because ultimately, our customers don't care about AI for the sake of AI. They care about more leads, better awareness, closed pipeline and better customer outcomes, and you need that context from HubSpot to be able to drive that.

Here's the thing. We are partners with all of them, right? We have a deep partnership with Anthropic, with OpenAI, with Gemini, and we continue to work very closely with them. We understand where they're going in terms of their road map. They talk about us as the SMB partner. And so it is a very complementary relationship.

The question in the market and the broader narrative is, are LLMs going to get like so good in terms of capabilities that they just destroy the value of the application layer? And this is where we have a point of view. The point of view is that you need probabilistic output of LLMs, but you need deterministic output within sales. If you are running sales and you are a CRO of a company and you just like run an LLM and you get a probabilistic answer of what your next forecast is going to be, it's not going to cut it. You're not going to be able to like drive that. So there's a combination of having both probabilistic as well as deterministic work.

The other part is all of the workflows, the logic is within applications. What do I mean by that? You can go to an LLM and say write me a blog. Pretty easy to write a blog. But if you go to an LLM today and say draw a campaign for me across 5 different channels, YouTube, LinkedIn, pick your channel. And I'm going to give you $1 million and I want you to run this campaign across 10 million contacts that I have and then make sure that the campaigns are attributed with the right revenue that they're driving, it has none of those capabilities. Those -- that's like the logic that exists within applications, LLMs can call on those logic. They call it really well, but they don't have that logic, and that's, again, why it's complementary.

The third thing that I would say is LLMs are single player. It's easy for one person to use an LLM and to share that output, but applications that drive growth are inherently multiplayer. If you have a sales team and the sales team has like 50 reps, every rep's pipeline change every single day needs to be represented as the team pipeline roll up, and that is not what an LLM does. And so again, very, very complementary.

Where I think is that LLM capabilities will continue to improve, but what HubSpot delivers on top of that is a growth context, knowing deep domain expertise within marketing, sales and service, but also contextualizing it to the business that we serve. That's why it continues to be very exciting and also complementary.

There is another -- bless you. There's another thing that you had mentioned in a meeting I heard earlier, and I think it's important, which you were asked about the pain points that HubSpot solves that maybe are underappreciated that an LLM can't. And I know you mentioned the contact side, but I was wondering if you could dig a little bit deeper into the pain points that HubSpot uniquely solves that maybe an LLM can't today like permissioning?

Yes, absolutely. And the conversation that we were having is like what are inherent value that like HubSpot customers buy us for. And the example that I would give is a marketing automation, right? Like think about a team within marketing that has to figure out what are the set of campaigns that they need to drive in a particular quarter across what channels, by allocating how much resources and how many contacts is that going to reach in multiple languages across multiple regions. Now think about the complexity of all of that. That is exactly what our solution provides, and that is not something that is easily extractable or easily done by an LLM.

The second thing that I mentioned is also the permissions. Now you think about a sales rep that has access to run certain sequences and is part of a sales hierarchy, you need to know their role. You need to know their permission. You need to know which team they belong. If they change roles and go to another team, you need to know where they are now going to roll up to. All of those permissions, the security, the governance associated with the actions they can take, that belongs to an application layer.

So you think about the content, the conversations that applications have, that's the growth context. You think about the workflows and the logic that applications have, that is not immediately replaceable. And you think about the multi-tenant, the multiplayer mode that we support with the right permissions. You put all of that together, this is why we -- again, going back to the point of like is it one day that there's just one application which is an LLM. That does not seem like the right approach to think about what is happening within the space.

Look, I'm old enough to remember when the hyperscalers are going to do everything. And I think we have more software than ever out there.

Exactly.

So I think another area that's been a big focal point, and I think it was maybe even more so early last year is on the disruption to SEO from Agentic search. And I know HubSpot recently, we mentioned AEO earlier. I was wondering if you could double-click into that, explain why that's important and how maybe customer behavior is changing and what the value of AEO is now in an LLM-centric world?

Yes, absolutely. Look, the leads that used to get generated were from people searching on Google, clicking blue links and coming to the website. The industry calls it content leads, leads based on content. That as a source for the top of funnel is actually declining.

And an interesting statistic for HubSpot customers, we track all of the content leads. This year alone, the top-of-funnel content leads have dropped by 27% across HubSpot's customer base. So you can begin to see that the content leads declining, which is basically SEO is having a huge impact. And every B2B marketing person out there is now looking to diversify their sources to other areas.

That is the same playbook that we've been on. We've diversified our set of sources. We had even more content leads, inbound leads that were part of our top of funnel. And for the past 2 years, we've been on the journey to diversify our lead sources from INBOUND to using social media with YouTube presence, with LinkedIn presence, to newsletters, to podcasts and now to AEO. So the first big trend that's happening in marketing is diversification of top-of-funnel lead sources. And that is the playbook that we launched with Loop. That is the set of products that we have within Marketing Hub.

What is new and interesting is Answer Engine Optimization, or AEO, which is how does a product or a brand show up in an LLM with citation. That is what AEO is. And what we have found is that if you show up within an LLM answer, the conversion is 3x better. Not 3% better, like 3x better because the person that's actually doing the prompting is deeper in their search. They have much more specific questions, and their -- the questions -- whatever the answers they get, they tend to act faster from that, and so the conversion rate is higher.

That is the reason why we launched a stand-alone tool for AEO. This was part of our Spring Spotlight release. And what it does is for our customers, it will say, what's their AI visibility? So basically give it a set of prompts and it'll say, what is your share of voice? If someone asked this question in an LLM, how many times do you show up? That's the first thing it does.

Then the second thing it does is it will give you recommendations. Well, you're not showing up in Reddit or this community. So make sure that your content is actually available in that. That's the second. And the third, which is coming pretty soon, is you can immediately use our content management tools to be able to drive content actions based on those recommendations. That's the AEO solution.

There's going to be a time where every B2B company has to have AEO solutions. There's no other way because that's where the industry is going. And so we have a stand-alone tool so customers can start with that, and it's also available as part of our Marketing Hub Pro and Enterprise. And we think that this could be a new front door where people get started with AEO. And then when they begin to need to take like content actions, they can upgrade to Marketing Hub Pro and Enterprise. And so pretty excited about that opportunity.

So we've had all this technology transformation, you guys are driving the charge, but it's also led to some bold choices on the business side, business decisions. So let's talk about the pricing model change. There's been a lot of conversation around that. What were you seeing that drove you to move to outcome-based pricing for your agents? And what are the early results that you're seeing from the change?

Yes, absolutely. So just to provide context to our pricing model, we have a combination of seats and credits. That's our pricing model. What we do is for anybody that gets a core seat or a sales or service seat, we provide included credits, which means you begin to start using AI features as well as agents. In addition to that, they can buy packs of additional credits. Credits could be bought in packs of like 100, 1,000 and beyond.

And the way we think about credit consumption is that our first-party agents, the agents that HubSpot builds and we deliver work as outcomes will be based on outcomes that we deliver. Makes logical sense, right? If our Customer Agent is resolving tickets, then we'll only charge when we resolve a ticket. That is literally the change that we made. And if our Prospecting Agent is delivering a qualified lead, we will only charge when we deliver a qualified lead. I think that basically shows the confidence that we have within our product strategy and the level of outcomes that we are delivering for our customers based on that product strategy. And so we made those changes to really align our pricing mechanism to the product strategy and where we are going.

In addition to that, there are other parts of our product as well as platform that will consume credits, like a number of our customers build custom agents on top of HubSpot. Those are all just going to be based on credits consumed. So again, you'll have seats that have included credits. Credits can be consumed by first-party agents that deliver work. They can also be consumed through second party and custom agents and other things within the platform. And the combination of core seats and credits are kind of the emerging way in which we are monetizing AI across HubSpot.

Yes. Along with the model change, and I think there's just a lot of movement happening. You guys made some choices on the go-to-market side concurrent with that. So why was April the right time to invest in that sales enablement? And how should we think about that helping to accelerate AI traction in the quarters ahead?

Yes, absolutely. Let me unpack what we did and why that has a near-term impact in terms of net new ARR, but sets us up. So coming into this year, we -- in general, we plan for a virtual kickoff in the first quarter and enablement in the second quarter. That has always been the model. So it's not a new timing in terms of April.

But what was different this time is that we spent a lot of time transitioning our go-to-market teams to be able to position agents and therefore, credits and really driving the AI adoption. So there is a sales motion change that's happening, and we're ahead of that.

Specifically, if I unpack it, in Q1 this year, we saw our customers really wanting proof of value of agents that are delivering work. What does that mean? It means that if they want to use Prospecting Agent, they want to make sure that the e-mails that the Prospecting Agent are writing is really what their BDR or their sales rep or their team would actually write, and they want to feel comfortable with that output. So what we did was we actually gave a 28-day trial period for those types of agents.

And AEO Agent, Prospecting Agent, Customer Agent, we've given them trial periods. Now it has some impact of elongating sales cycles in the short term, but we intentionally made that choice in order to seed our customers with these agents so that they can continue to grow. That's like choice number one.

The second thing is we did take our sales team, we drove a lot of enablement and helping them communicate the value of agents and credits. And that has a short-term impact in terms of the quarter, but also getting them to understand the pricing model and where we are going with outcome-based pricing and getting comfort in that.

Now look, it has an impact near term, and we are very transparent and clear about that. But the rationale is we are in the very early stages of AI transformation. It's like mile 3 in 26 miles of like a marathon race that you're running. And what is important at this stage is removing blockers for customers, seeding the right Agentic use cases and making sure that the outcomes that we deliver are super clear. And when we do this in the early stage, we know that the right adoption will result from the changes that we're making. So a set of intentional changes to drive AI adoption over the following quarters.

Very helpful. And I know we've talked a lot about the innovation that you're doing on behalf of customers, but the company itself is internally transforming how you operate. And I know Dharmesh also speaks a lot to this in different forums, as have you. So I'm curious just as you guys are thinking about drinking your own champagne, I'll use that terminology, what type of transformation are you seeing from an internal perspective from AI? What tools is HubSpot unleashing for its own workforce?

Yes. As much as we have driven AI transformation within the product and the platform, we are driving internal transformation with AI, and we're probably bleeding edge adopters of AI. And that has two benefits. One is as we experiment, iterate and scale with AI, those translate into product in terms of the feedback directly in go-to-market. And second, we are much more authentic in communicating what works and what doesn't work with our customers. So that's why we are constantly on the bleeding edge.

Specifically in go-to-market, I would point to a handful of areas where we have seen really good results. The first is on -- in the marketing side, if you go to our website, 82% of web chats are handled by AI. And we have increased that percentage pretty significantly over the past few quarters. And that is something that again translates into product that we can deliver for customers.

The second is AEO, which we talked about. Even before we launched this AEO product for the last probably 6 quarters, we have experimented with AEO. Our leads coming in from AEO has really grown. Pretty small base, but grown significantly over the past few quarters, and we have continued to be #1 in CRM in terms of AI visibility. So those two are areas.

In sales, Prospecting Agent using intent sources and then driving e-mails so that our BDR teams and sales teams can actually have broader outreach as well as assistance in terms of deals, deals closing as well as summarizing, all of that we are doing, which has increased the productivity on the sales side pretty meaningfully over the last few quarters. And then on support, I'll say that we have not hired a single support -- Tier 1 support agent since 2024, and we've been able to use that productivity in other areas of the business.

And so in go-to-market, we are really in the bleeding edge of leveraging both agents as well as assistants on top of HubSpot's Agentic platform to drive our own productivity. And I'd say more importantly, 3.5 years into leveraging AI on the bleeding edge, it's one thing to get individual productivity with AI, right? And we are seeing that. You're using an LLM, I use LLM to write a whole bunch of things, and of course, that improves it.

But there is a difference from going from individual productivity to institutional productivity. And that requires reimagining teams, reimagining workflows, providing a common context and a set of areas that we can go deep. And that's the learning. And that's kind of the vision of where we are going next. We are driving the next level of transformation internally within HubSpot to be able to reimagine how we get work done.

Listen, it's something that we're grappling with every day. I know we had a little side chat about we've gotten 5 new tools, but the hours of my day haven't increased. And I think that's something we're all kind of struggling with right now to unlock that productivity.

And so -- but we are seeing -- you mentioned not having hired in support for some amount of time. And certainly, the leverage is there, right? Recently, the company took up its full year margin forecast. How are you thinking about the productivity gains and letting that margin flow through versus reinvesting given just the big opportunity ahead? How do you balance that from the CEO's perspective?

Yes. You balance it very carefully. And I think the way we think about it is that our revenue growth continues to grow, and there's a gap between net revenue growth and headcount growth. We've been very, very disciplined in terms of where we hire and what kind of talent we hire and how do we drive a level of AI fluency within the talent so that they can drive higher productivity. And that's exactly what you saw.

At Analyst Day last year, we provided a midterm target range in terms of non-GAAP operating margin of 20% to 22%, and we reached it a year ahead. And that is at the same time when we are investing in Agentic coding and our gross margins have gone down, right? We mentioned in Q1 that our gross margin went down. At the same time, we increased the full year operating margin guidance for the year, and that is what we are doing.

So the way we are accomplishing it is we'll continue to invest in AI innovation. We'll continue to invest in Agentic coding that drives our developer productivity. At the same time, we're going to find through disciplined hiring as well as transforming our internal productivity and tools to get the operating margin leverage. And we're more confident now in terms of setting better like operating margin targets for both the midterm as well as the longer term, and you'll hear more about this at our upcoming Analyst Day at UNBOUND.

Awesome. Well, Yamini, thank you so much for joining us. Like you mentioned, I look forward to seeing you in a couple of months at UNBOUND. But meanwhile, we hope that HubSpot does really well. We're big fans. So thank you for joining us.

Thank you. Really appreciate the support. Thank you, everyone.

Good afternoon, and welcome toHubSpot's First Quarter 2026 Earnings Call. My name is Liz, and I will be your operator today. [Operator Instructions]

I would now like to hand the conference over to Head Director of Investor Relations, Chuck MacGlashing. Please go ahead.

Good afternoon, and welcome to HubSpot's First Quarter 2026 Earnings Conference Call. Today, we'll be discussing the results announced in the press release that we issued this afternoon. With me on the call this afternoon is Yamini Rangan, our Chief Executive Officer; Dharmesh Shah, our Co-Founder and CTO; and Kate Bueker, our Chief Financial Officer.

Before we start, I'd like to draw your attention to the safe harbor statement included in today's press release. During this call, we'll make forward-looking statements within the meaning of the federal securities laws including statements regarding our financial guidance for the second fiscal quarter and full year 2026, future financial performance, business outlook and strategy. These statements reflect our views only as of today and except as required by law, we undertake no obligation to update or revise them.

Please refer to the cautionary language in today's press release, our Form 10-Q and other SEC filings for a discussion of the risks and uncertainties that could cause actual results to differ materially from expectations. During the course of today's call, we'll refer to certain non-GAAP financial measures as defined by Regulation G. Reconciliations to the most directly comparable GAAP measures can be found in today's press release.

Now it's my pleasure to turn over the call to HubSpot's Chief Executive Officer, Yamini Rangan. Yamini?

Thank you, Charles, and welcome to everyone joining us today. I'll start with our Q1 2026 results and share what's driving our performance. Then I'll walk through our strategy and the progress we made with our Spring Spotlight product update and how they're delivering real outcomes for our customers. I'll close with how we are balancing growth and profitability as we transform as an AI-first company.

Let's dive in. Q1 was a solid quarter for HubSpot. -- with revenue growing 18.2% year-over-year in constant currency. We delivered 4 points of non-GAAP operating margin expansion year-over-year, bringing our operating margin to 17.8%. Q1 marked a meaningful milestone for HubSpot as our total customer count reached nearly 300,000 globally, driven by 10,800 net customer additions in the quarter. I'm pleased with how our AI strategy is translating into measurable growth outcomes for our customers.

We came into this year with clear levers to drive growth, and they're working. Our core growth levers of upmarket, multi-hub and platform consolidation and pricing tailwind remains solid. At the same time, our emerging AI monetization levers of core seats and credits are gaining traction.

Let me walk you through each one and how they drove Q1 performance. Our market momentum continues to be strong. Larger customers are consolidating on HubSpot to drive AI innovation and reduce total cost of ownership. In Q1, deals over 60,000 annual recurring revenue grew 37% year-over-year and deals over 120,000 ARR grew 64% year-over-year. Our partner ecosystem remains a core competitive moat with partners, sourcing and core selling many of our largest deals. AI adoption in B2B starts with clean data and unified context. That is what is driving our multi-hub and platform momentum.

Customers who bring together marketing, sales and service on HubSpot, get a single connected view of their customers and unified growth context that AI can act on. That value proposition is resonating. In Q1, 63% of new Pro customers landed with multiple hubs up 3 points year-over-year and 42% of our Pro+ installed base by ARR now owns 4 or more hubs, up 6 points year-over-year. Bottom line is this. customers are choosing HubSpot as the data and AI foundation for their go to market. In addition, our pricing model changes from 2024 continues to benefit overall growth. We lowered the price to get started and removed seat minimums to give customers a frictionless path to upgrade as they see value. That shift is largely complete.

About 90% of our installed base customers have migrated to the new pricing model, and 50% of our ARR has gone through their first renewal. We expect this pricing tailwind to continue as remaining customers come up for renewal and new customers upgrade based on the value we deliver. Now beyond our proven core levers, our AI monetization with core seats and credit is picking up pace. In 2025, we added significant AI data and platform value to the core set. Breeze assistant, smart starts, projects and company enrichment data are all now included. We also unbundled the Smart CRM, so customers can start with just a core seat. Our vision is to make Core Set an essential foundation for every go-to-market employees and the momentum back up that strategy.

Active core seat users grew 90% year-over-year and over 25% of Pro+ customers have now purchased additional core seats, up over 12 points year-over-year. Credit consumption is accelerating. Total credits consumed grew 67% quarter-over-quarter. The top use cases in Q1 were customer agent at 53% of credits consumed, Prospecting Agent at 17%, data agent at 16% and intent monitoring at 12%. Customer agents we found clear product market fit and now Prospecting Agent and data agent are gaining momentum, broadening the base of how customers get value.

Customers are not just trying AI. They're building it into how they work. Core seats and credits are becoming real growth levers. And as more use cases mature, we expect boats to compound. Now let me shift to the momentum from spring Spotlight and the progress on our strategy. Our AI strategy is simple: make AI work for growth companies. We have always won by deeply understanding the customer segment we serve and democratizing sophisticated technology for them. And that is exactly what we are doing with AI.

Today, companies are not struggling to find new AI tools. They're struggling to drive real growth outcomes. The difference comes down to context. AI without the right context produces output. AI with the right controducers outcome, and that is the gap HubSpot is built to close. The foundation of our platform is growth context. The specific knowledge that makes AI useful for go-to-market teams. It knows who the best customers are and why they buy. It knows how the best reps work and how deals close. It knows what progress pipeline looks like and where deals get stuck.

HubSpot captures all of this, business team, process, customer context across nearly 300,000 businesses in every industry. This becomes the shared foundation for agents to do real work and drive growth outcomes, as many of you saw at our investor webinar last month. We're not building AI features on top of CRM. We're building an agentic customer platform where growth context is the engine, agents can run on HubSpot and agents can run HubSpot -- running on HubSpot means any agent, ours or anyone else's can plug into HubSpot's data, context and capabilities as a building block.

Running HubSpot means agents can operate the platform end-to-end through our APIs, MCP server and whatever access methods come next. This openness is a strategic choice. The more agents that run on HubSpot, the more valuable our context becomes and the more valuable or context the stronger a platform gets. At Spring Spotlight, we launched key innovations to help customers drive outcomes with AI. Let me share the momentum we are seeing with our top agents.

Prospecting Agent handles the full prospecting life cycle, monitoring, buying signals, identifying high-intent prospects and crafting personalized outreach. Nearly 14,000 customers have activated it, up 33% quarter-over-quarter. Jotform an online form builder used by over 35 million people worldwide, train prospecting agents on their brand positioning and messaging and move to a fully automated setup, purchasing 625,000 credits per month to power it. In a direct test at Jotform, Prospecting Agent qualified leads on par with human reps, freeing the team to focus on customer meetings and closing deals.

Next, smart deal progression brings to life our vision of self-updating CRM. It listens to conversations, suggests CRM update, draft follow-up e-mails and recommends next steps so sales reps can focus on closing deals, not updating records. Customers are seeing a 10x improvement in CRM update accuracy, and we are seeing 75% repeat weekly usage. Data agent, which we launched last fall and updated at Spring spotlight is gaining significant traction. It enriches customer records, surfaces buying intent signals and prioritizes best fit accounts, giving marketing a better foundation for campaigns and sales a clearer view of prospects.

We're seeing significant growth in adoption. Over 9,000 customers have activated data agent, up 122% since last quarter and weekly usage is also up. We also enhance Customer Agent and expanded it to e-mail to help customers scale support with AI. We now have over 9,000 customers and the average resolution rate has climbed to 70%, up 5 points from last quarter, with some customers exceeding 90% resolution rates.

Now at the same time, we are reimagining marketing for the AI era. We launched HubSpot AEO at Spring spotlight. -- to help marketers see how their brand appears in AI tools like Chat, Gemini and perplexity and take actions to improve it. Early momentum is strong across paid, earned and owned with campaign activities earning millions of compression. This is beginning to drive trials and purchases of both stand-alone AEO and Marketing Hub Pro.

Customer outcomes across all of our updates this year speak for themselves. Limelight is booking meetings with prospecting agents at the same rate as their SDRs. Synergy is resolving 85% of support conversations autonomously, and Sandler grew leads 160% with our new AU tools. across sales, service and marketing. Our agents are doing real work and driving outcomes, exactly what we want to see. The confidence we have in our product strategy is also reflected in how we are evolving pricing. We believe AI value should be measured on outcomes. So we recently updated our pricing for agents to match.

Customer agents has moved to consuming credits based on resolved tickets and prospecting agents has moved to qualified leads recommended for outreach. Both agents now come with free 28-day trial so customers can see the value before they commit. This is outcome-based pricing in its simplest form. Customers pay when the agent works. We expect both our product updates at Spring Spotlight and pricing changes to accelerate adoption because when value is easy to observe, the decision to expand is easy to make.

Let me close with how we are balancing growth and profitability as we transform as an AI-first company. We are transforming how we build, how we grow and how we operate, and that transformation is showing up in our results. On how we build, 100% of our engineers now use AI tools, and we've seen a 73% increase in lines of code updated per engineer. We are shipping better products faster because we built a shared platform underneath our agents. Every new capability or skill we add makes the whole platform more powerful and our advantage compounds. On how we grow, -- we now have an agent first go-to-market motion from demand generation to prospecting to customer success, and it is working.

On how we operate. We are moving from individual productivity to team-level transformation to what we call institutional productivity, where the context and processes of the company are encoded and available to everyone when they need it. We are investing aggressively in AI innovation while expanding operating margins at the same time. We not only beat our Q1 operating margin target, but also expect to deliver 2 points of operating margin expansion in 2026. That is a meaningful step up, and it reflects the operating leverage we are building as an AI-first company.

In closing, our core growth drivers upmarket momentum, multi-hub adoption and pricing remains strong and durable. AI is adding 2 incremental levers, core seat and credit monetization. Together, they give us confidence in our ability to deliver durable growth while expanding profitability.

With that, I'll hand it over to our CFO, Kate Bueker, to walk you through our financial and operating results.

Thanks, Yamini. Let's turn to our first quarter 2026 financial results. Q1 revenue grew 23% year-over-year as reported and 18% in constant currency. Q1 subscription revenue grew 23% year-over-year, while services and other revenue increased by 22% and both on an as-reported basis. Domestic revenue grew 18% year-over-year in Q1. International revenue growth was 29% as reported and 18% in constant currency representing 49% of total revenue.

Yamini mentioned, Q1 marked a major milestone for HubSpot as our total customer count climbed to nearly 300,000, a 16% year-over-year increase. This was fueled by the nearly 10,800 net new customers we added during the quarter with a particular strength in starter customer additions. Average subscription revenue per customer was $11,700 in Q1, up 6 points year-over-year as reported and 2 points in constant currency. We continue to expect quarterly net additions in the $9,000 to $10,000 range, along with low to mid-single-digit ASRPC growth in constant currency, with growth ramping throughout 2026.

Customer dollar retention remained healthy in the high 80s, while net revenue retention was 103%, down sequentially as expected, but up over 0.5 point year-over-year. As a reminder, we typically see a seasonal step down in net revenue retention in Q1 following peak upgrade activity in Q4. For the full year 2026, we continue to expect net revenue retention to expand by 1, 2 points year-over-year, driven by a combination of seat expansion and increasing consumption of credits. Q1 calculated billings were $912 million, growing 19% year-over-year as reported and 17% in constant currency.

Non-GAAP operating margin was 18%, up 4 points compared to the year ago period. This expansion reflects our disciplined approach to hiring and the benefit from FX movements in our partner commissions program change partially offset by strategic investments in AI initiatives to drive both customer value and internal operating efficiencies. GAAP operating margin was 3% in Q1, compared to a negative operating margin of 4% in the year ago period. This 7 points of expansion reflects our non-GAAP operating income expansion and a 3-point reduction in stock-based compensation expense as a percentage of revenue.

Non-GAAP net income was $143 million, and non-GAAP net income per diluted share was $2.72, up 49% and 53% year-over-year, respectively. GAAP net income was $33 million in Q1 and GAAP net income per diluted share was $0.62. In the first quarter, the company generated $154 million of free cash flow or 17% of revenue. Our cash and marketable securities totaled $1.8 billion at the end of March. During the quarter, we bought back $211 million of stock under our current $1 billion share repurchase program. Our continued strong cash position provides us with the flexibility to return capital to shareholders while maintaining our focus on investing in organic innovation and opportunistic M&A, underscoring our conviction in our long-term opportunity.

Before turning to guidance, I want to share a bit more color on a couple of shifts we're seeing in our business. First, as we continue to move upmarket, we've seen a shift in linearity in our quarters to a more back-end loaded bookings cadence. We saw this dynamic again in Q1 and expect it will continue. Second, AI is transforming our selling motion. Customers want pricing more directly tied to outcomes, and they are increasingly looking for proof of value earlier in the sales process. In April, we made several pricing and packaging changes that are aligned with these customer expectations.

We believe these are the right actions to drive adoption and usage of our platform and ultimately, long-term growth. These include lowering the price of customer agents, moving to outcome-based pricing for customer and prospecting agents and introducing 28-day free trials for our agents and HubSpot AEO. In the near term, these changes may extend sales cycles as customers evaluate our agents and AEO as part of broader purchases. In addition, we made a deliberate investment in April to train our sales reps on the Spring Spotlight innovation and the shift to credit, which reduced sales capacity during the month. As a result, Q2 got off to a slow start and we've reflected these dynamics in our guidance.

We are confident that we have the right product and pricing strategy to drive durable growth and margin expansion over time as we transform as an AI-first company.

With that, let's dive into guidance for the second quarter and full year of 2026. For the second quarter, total as reported revenue is expected to be in the range of $897 million to $898 million, up 18% year-over-year on an as-reported basis and 16% in constant currency. Non-GAAP operating income is expected to be between $173 million and $174 million, representing a 19% margin. Non-GAAP diluted net income per share is expected to be between $3 and $3.02. This assumes [indiscernible] fully diluted shares outstanding.

And for the full year of 2026, total as reported revenue is now expected to be in the range of $3.7 billion to $3.708 billion, up 18% year-over-year on an as-reported basis and 17% in constant currency, up 40 basis points from our previous guide. Non-GAAP operating income is now expected to be in the range of $762 million to $766 million, representing a 21% margin. Non-GAAP diluted net income per share is now expected to be between $13.04 and $13.12. This assumes 51.8 million fully diluted shares outstanding.

Before we turn to some modeling notes, I'd like to provide context on our margin expansion trajectory. As Yamini shared, we are balancing growth and profitability as we transform as an AI-first company. We are transforming how we build, grow and operate. And this creates the opportunity for more meaningful margin expansion going forward. This is reflected in our updated 2026 guidance which now places us firmly within our 20% to 22% non-GAAP operating margin range, reaching our 2027 targets a year ahead of schedule.

This progress gives us even greater conviction in our ability to meet or exceed the targets we laid out at Analyst Day at an even faster pace. We'll have more to share on our margin expansion expectations at our Analyst Day this fall. We're also focused on driving GAAP operating margin expansion over time as we drive stock-based compensation as a percentage of revenue down. In 2026, we expect SBC as a percentage of revenue to decline approximately 3 points to 14%, and we see the opportunity to bring this down further over time.

As you adjust your models, please keep in mind the following: we continue to expect our legacy Clearbit business to be a 40 basis point headwind to full year 2026 revenue growth. And finally, we continue to expect CapEx as a percentage of revenue to be 5% to 6% for the full year of 2026 and now expect free cash flow to be about $750 million.

With that, I will turn the call back over to the operator for questions.

[Operator Instructions] First question today is from Samad Samana with Jefferies.

I wanted to pull on the thread around the pricing model change for AI credits that you guys did in April, completely makes sense, driving better ROI for customers I was wondering, I know at the Spring Spotlight, you hosted the webinar that gave some usage statistics. But if you tie it to the change, how does the pricing change impacted customer adoption and utilization?

And then maybe I'll incorporate a component to the question as well, where the customer feedback suggests there's some meaningful spend growth coming from those that are consumer credits already. Any color that you can share on what the NRR for that cohort of customers looks like as well, just as we think about how the model evolves over time.

Yes. Thanks so much for that question. You're absolutely right. Spring Spotlight, we launched a number of product innovations that showcase the agent capabilities as well as how growth context is driving the outcome for our customers. And look, the way we think about it is pricing is one of the clearest signals that we can send about how much we believe in our product. And with all of the announcements, agent quality improved growth context improved outcomes are clear and we have high confidence.

And so we did 2 things coming into the quarter in terms of driving agent adoption. The first thing is that customers want proof of value earlier in the process before turning on agents. And that's understandable because we're no longer just providing applications that can drive adoption that can then drive growth, we're actually delivering work outcomes. So we added a 28-day trial for key use cases like AEO, prospecting and Customer Agent.

And then second, as you mentioned, customers really want to see pricing that is clearly tied to the outcomes and they want predictability of that spend. So as we came to the quarter, we dropped the price of Customer Agent, and we moved it to per resolved conversation so that when customers say, it's actually based on what we have delivered as an outcome. And similarly, for Prospecting Agent, we really are tying it to the qualified leads that we are delivering. Now both of these are really in response to customer feedback, both in terms of proving value as well in terms of understanding how that is tied to the pricing.

They are the right decisions that we have intentionally made and will have a clear impact in terms of adoption. The feedback is very early days because it's only been 3 weeks, but it has been very clearly positive. And look, what we are doing is methodically removing every blocker in terms of AI adoption so that our customers have confidence in terms of adopting AI and driving outcomes.

Kate, maybe you want to answer the NRR question.

Yes. Sure thing. And, we're not going to talk about cohortized net revenue retention. But what I would share is that we continue to believe that we can expand net revenue retention 1 to 2 points in 2026. And if you think about the drivers of that expansion, they are very much tied to our emerging growth levers of core seats and credit. And so we are looking at the credit adoption as a key driver of net revenue retention, especially in the back half of 2026.

Next question comes from Mark Murphy with JPMorgan.

Yamini, I'm wondering how commonly are you seeing a scenario which a customer would elect to use the Customer Agent rather than having to go out and hire more people and where the credit consumption for that Customer Agent ends up meaningfully above what the Service Hub subscription would have cost, say, $1,000 or $2,000 type of level sub. I'm just trying to get at -- is it clear to you how often you're going to net out quite positively by selling an agent rather than that traditional subscription?

Yes. Mark, thank you so much for that question. Look, our thesis and what we are seeing in early adoption is clearly that that it's not only that we are delivering great software that humans can use to drive productivity within go-to-market, but agents can deliver work. And I'll take the question on Customer Agent we are seeing 2 or 3 common use cases. The first use case for Customer Agent is that customers use it for after-hours or weekend at augmenting to their support team. The second is they are using it for Tier 1 support ticket so that their teams can now spend it on much more complex customer resolution and leave the Tier 1 support to our customer agents.

And I gave a couple of examples at the investor webinar. In one case, the customers turned on Customer Agent, used up the included 5,000 credits pretty quickly in the first couple of days and then turned it on for more of the augmentation use case. And then are now in the path of going from 100,000 credits to 300,000 credits on a monthly basis. That is clearly what we get above and beyond what we would have gotten from a service hub seat. And those are obviously initial patterns.

We are now seeing it over and over again, where customers are going beyond included credit and using it to resolve tickets that then increases our TAM. And that is what we're leaning into. And that is exactly why we're making the set of changes in terms of the pricing because we're so confident in terms of the resolution of tickets that we're ready to put our product strategy to work right there and that increases our ability to drive adoption of these agents as customers get comfortable with it.

And then just one quick add to how many comments is around. This is one of those examples where as the frontier model companies make the models better and better, customer agents and other AI features within HubSpot get better and better as well. So we see -- we will see as the models get better going from just the kind of Tier 1 support to higher level of support, we'll see increased resolution rates as the models get better. So this is one of those examples where as the kind of tied list on what the frontier miles are capable of, HubSpot gets increased leverage and our customers get increased value for super-excited about that.

Next question is from Raimo Lenschow with Barclays.

Can you talk a little bit about the retraining for the sales organization, doing that in April seems a little bit off because usually, that's what you do kind of at January, February time frame and when you have the sales kickoff, et cetera. It does feel like the product kind of got ready later, but can you kind of speak to kind of the timing there and also the impact a little bit more? You mentioned it a little bit, but a bit more detail.

Yes, Raimo, absolutely. Look, I think that this was really in tying to our Spring Spotlight innovation. And at Spring Spotlight, we launched a number of agents, and we've also changed our pricing mechanism, as we just talked about. And so we had the planned time to get the sales team out and be able to get them trained on both the innovation as well as the pricing model change. Now the thing that I will point out is this. Typically, yes, we would do it in the kickoff and the kickoff happened earlier in Q1. But what we're really taking the time to get the whole organization behind is the new selling motion because we are leaning into helping our customers adopt and transform with AI.

So specifically, we got the entire sales organization out to drive proof of value earlier within the sales process because that is what customers need. They want the confidence that our AI capabilities and agents will work in their environment and that requires our sales team to be clear articulate with proof of value earlier in that process. And then second, we want them to establish agent use cases and set it up for expansion. This is a learning curve as we get our entire organization to land with the right value and set it up for expansion, and that is exactly what we took the time to do and that is associated with the set of changes that we are driving in being an agent first good market company.

Look, we're changing a lot. We have high confidence in our product strategy. It's showing up in early adoption of agents, and we are evolving the pricing and go-to-market model to reflect the feedback that we get from customers. And more importantly, we know that there is a huge opportunity to be a trusted AI partner for our customers, and that's what we're leaning into.

Next question is from Terry Tillman with Truist.

I wanted to talk about like the credit growth and how to think about that I think Kate talked about potentially it's the second half where it really kind of picks up. But it was 67% Q-over-Q growth in 1Q, that seems strong. But how do we think about the ramp of that growth into 2Q and beyond? And what do you all see as maybe the next big breakout agent beyond just the Customer Agent of 53% attach or adoption rate?

Yes. Thank you so much for the question. We are definitely starting to see real usage beyond included credit. And it is happening because customers are getting clear, measurable value and outcome. And we were pleased to see total credits consumed up 67% quarter-over-quarter. But more importantly, that consumption is becoming much more balanced across use cases, right? I talked about Customer Agent Prospecting Agent, data agent, they're all very balanced now. They're kind of really growing which we like.

And so in terms of the question of what do we expect to see. Now in the current set of agents that we already have, we're really focused on improving the quality of the outcomes that we deliver. So Customer Agent, Cobin use case here the focus is improve the quality of resolution as well as expand the number of channels. And as we probably noted here, we -- the resolution rate has gone up from 20% last year to 70% now. It's one of the highest resolution rates in the industry. And in some customers, we're seeing even higher.

So all of the work that we're doing now to unlock and even bigger opportunity is to expand the e-mail channel and to increase the volume over a period of time. Similarly, for prospect and data agents, it is the quality of what these agents deliver and the outcomes that they can drive. Now beyond this, of course, there are a handful of other agents that we'll continue to like work on, but we have high confidence in the set of agents that we are driving.

One word I will say about AEO because that is now also part of Spring Spotlight and will begin consuming credits. Look, AEO is a big opportunity for us and we're leaning very hard into that. If we look at the organic traffic that our customers are seeing, it's down 27% this year. So almost every B2B marketer out there is looking for additional sources of leads and AEO happens to be one of the more effective nascent but very fast-growing one. And we launched AEO at Spring Spotlight. We now have over 15,000 Pro+ customers who activated it in trials.

Now the trials for a month. So it will take a little bit of time for those trial volumes to convert, but really great activity, and we just like to see that type of innovation. So we're innovating at an accelerated pace with our first-party agents. We're clearly seeing adoption beyond the included credit, and we are delivering even more as an open platform. So pretty excited about what we're seeing there.

Next question is from Jack Ader with KeyBanc Capital.

The -- one I had was really about -- it sounds like a lease or maybe I'm perceiving this message tonight shifting more towards margin delivery and kind of away from top line growth. So I just wanted to focus on that net new. You've talked about net new growing above revenue, I think it was 6 quarters coming into this quarter. And so I'm curious where that metric fell this quarter.

And then if we still expect to see acceleration in the subscription revenue line, this year? Or are these like go-to-market and kind of pricing changes or some of these April disruptions may be going to shift some of the growth trajectories out this year?

Yes. Thanks, Jackson. I guess maybe I'll just start with the high-level comment, which is you should not note this as a shift away from a focus on growth to a focus on profitability. We have always been committed to balancing growth and profitability, and we remain committed to balancing growth and profitability. So I'll just start there.

The second thing that I would say, in response to your questions around net new ARR, what we shared last quarter was that we expected net new ARR growth to be above constant currency revenue growth for the full year of 2026. And we continue to believe that we have all the ingredients we need to deliver net new ARR in excess of constant currency revenue growth for 2026. I will say that Q1 net new ARR growth was a bit below constant currency revenue growth. Again, it was against a more difficult comp than what we saw in Q4. And the sales enablement and sales motion changes that I talked about in the script and that you heard from Yamini, do challenge net new ARR growth in the short term, but they are the right things to do to seed and grow those agent use cases.

All that said, we think that the combination of our core growth drivers, right upmarket momentum, multi-hub adoption and pricing in combination with the increasing contribution throughout the year, of course, seeds and credit are the ingredients that we need to deliver net new growth in excess of constant currency revenue growth this year.

Next question is from Alex Zukin with Wolfe Research.

Yamini, maybe for you at a high level, you're seeing some of your peers do things around headless and make motions around kind of becoming an agent first platform, plugging into third-party agents that want to get that rich context to accomplish tasks across front-office workflows. How are you kind of -- what is your evolved thinking there? How is the new pricing model? How does it touch on that type of dynamic? And then Kate, I've got a quick follow-up for you. .

Thanks for the question. This is Dharmesh. I'll take this one because excited about the platform initiatives. So we're big believers in the idea of head list. Not big believers in this notion of human list. We think the right platform going into go-to-market for our customer base is going to be a combination of serving humans with a very personalized, modern user experience. I think that's going to continue to be important. And then we supplement that with a really, really good agentic experience, opening up APIs, opening up MCP, opening up CLIs.

We were the first company to launch MCP last year. First one is to build connectors for ChatGPT and lot of the major AI apps. Now what we're seeing now is that as kind of usage shift, we see an increased adoption of these kind of agentic-based consumer use cases. And the platform will be open. And so we really -- I won't say ambivalent, but we see the shift from the human usage to agentic usage and doesn't really matter if it runs on our run time, agents that we've built or if it's third-party apps and agents that have been built.

We think all of those agents are going to need a common foundation and the growth context that we talk about on this common platform. So we think this is a massive opportunity for us in the agentic era because there's going to be a need for an agentic customer platform exactly like what is building.

Next question is from Arjun Bhatia with William Blair.

Perfect. Actually, if I can follow up on that question about head list and how sort of credit consumption evolves as HubSpot provides context to maybe third-party agents. I'm curious if you at HubSpot would have a preference of whether a dollar or credit consumption is being used for a third-party agent versus your own proprietary agents? And does that make a difference at all in terms of sort of the feedback loop back into HubSpot's data and the future improvements in the context that you can provide depending on which agent it's you're powering essentially? .

Arjun, I really like that follow-up question. And maybe I'll kind of double down on what I said in the prepared remarks, which is that our vision is really simple. Agents run on HubSpot and a run HubSpot. And for us, any agent, whether it is first-party, second-party, third-party agent can easily plug into HubSpot data and intelligence as a building block, and we welcome that, right?

That is our ecosystem strategy, and we are pretty excited about that. Now specifically, this week, we shared our complete API strategy and how we want to be open and think about what our APIs will deliver both to first-party agents as well as second and third-party agents. And we think about the API as 2 layers. The first is the data layer. This has always been there. It's basic, right? You can get contacts, companies, deals, activities and they are open and accessible, and it's already powering thousands of integration. And, as always, we have a very open ecosystem stance, which means that bringing data into HubSpot history. And more importantly, the customer should have full confidence and trust that their data is there, right?

What is exciting and where we are going with our API strategy is we are adding an intelligence layer in terms of bringing our growth context into that intelligence layer or what does that really mean? I'll give you a super practical example. Today, a sales manager or sales director, can go to an LLM and they can say pull pipeline information from HubSpot and a mountain stage and that type of data will go in, and they can then ask what is the risk?

But what the LLM will provide at the time has no sense of what is normal within the last 30 days. What is normal across that industry if something is changing with the champion and the conversations that the deal has involved. So that is what the intelligence is that from our growth context and to make it super tangible, you can now make a single API call that can return that precomputed risk score. And so over a period of time, of course, people can continue to get the data, but we think that more and more both second and third-party agents will pull on this intelligence layer.

And the way we monetize that intelligence layer will be commensurate with value that we deliver because it will be amplified value. So it's a 2-part API strategy, continue to take data, but at some point, you're going to not find enough intelligence there. Then continue to take the growth context, and that is really the vision. And we're pretty excited about what this means in terms of having a thriving ecosystem around us.

Next question is from Brian Peterson with Raymond James.

I appreciate all your comments on sales capacity margins. But just curious, as we think about the rest of the year, any help on unpacking some of the moving parts or assumptions that are underpinning the outlook.

Yes, I appreciate the question. I want to start by taking you through maybe the math and assumptions that underlie our guidance. Just if you look at Q1, we beat our Q1 guidance by $18 million, and we raised our full year guidance by $9 million. In addition, we anticipate there is about $4 million of lower benefit from FX versus when we guided the full year in February. So all this implies an organic raise of about $13 million. So we passed through roughly 2/3 of the beat to our full year revenue guidance. .

Overall, Q1 was a solid quarter. We had strong business results that were supported by our consistent core growth drivers, upmarket, multi-hub pricing and that's what gives us the confidence to actually raise the full year guide, right? And so you saw, as a result, raising our constant currency revenue growth by 40 basis points from 16.2 to 16.6. You also heard both Yamini and I talk about the fact that we're seeing early traction from agents and AEO when we made an intentional choice there to better align our pricing and packaging with customer expectations, and that's going to help us seed and grow those important agent use cases.

Those decisions are going to have a near-term impact to net new ARR, but it's going to drive durable growth in the future. And so what our updated guidance implies is actually a step down in constant currency revenue growth to 16% in Q2 and then a modest acceleration for the remainder of the year. This is a reflection of the momentum we're seeing across our core growth drivers, but it also takes into account the offset from the pricing and packaging dynamics and then the slow start to Q2.

Like all that said, I think you know by now that we approach guidance very consistently, and we want to put forward guidance that we feel good about across a variety of scenarios. And our guidance for 2026 is not mandate that we see a reacceleration in net new ARR in the back half of the year to hit this.

Next question is from Keith Bachman with BMO.

Good lead in, Kate, I wanted to come back to that because you are assuming that that the pricing and packaging does contribute in the second half of the year where it's more modest expectations. So you're assuming things get better, and yet you've only had a couple of weeks to synthesize data, I think, 3 weeks.

And so I'm just wondering what candidly, the risk profile is on not being able to meet the improvement in growth rate associated with the second half? And just a follow-up, you said you're not assuming net new increases in the second half to meet the targets, but that I just wondered if you could speak to your confidence interval because presumably, that would impact the following year would -- there would be consequences to that. If you can't meet the net new growing in the second half of the year. So really 2 questions related to confidence associated with some of the guidance comments.

Yes. Yes. I mean, thanks for the question. I can understand that there's a lot going on here. Maybe I'll start by reiterating that there is a set of core growth drivers that have been delivering consistently over the last 6 to 8 quarters, right? And we've been talking about them every quarter. It's the strong and consistent momentum that we're seeing on market. It is a consistent trend toward multi-hub adoption, and it is a pact that we've seen the benefit of the pricing change that we made in 2024. .

We also have an expectation that there will be an increasing impact of seed and credit over time, but that is just one piece of the overall growth equation. And so when you think about what we are assuming in terms of guidance, right, we want to put forward guidance, so we feel great about hitting across a variety of scenarios. And our guidance assumes that does not assume that we have to see net new ARR acceleration from where we are in the back half of the year in order to deliver that 16.6% full year constant currency revenue guidance.

Next question is from Tyler Radke with Citi.

Can you just give us an updated view of kind of the -- if the stack ranking of growth drivers has changed this year? And I guess one of the areas you called out that hasn't been asked as much about is the core seat, which I think grew over 90% this year in the quarter. If you could just kind of give some color on how you expect that growth to play out in the midst of a bit of a greater focus on agents as well.

Yes, absolutely. Let me kind of like walk through each of the drivers and how we think about this setting us up for durable growth. I'll start with the core drivers and the way I stack rank the core drivers is up market momentum and multi-hub are kind of at the top. We've seen this consistently the number of customers with 500 or more seats have grown over 50% year-over-year, and that has been a consistent trend that we have seen. And that's because product needs the needs of upmarket customers, brand awareness is great, ecosystem is tuned in, and that also means multi-hub adoption is really solid.

So those 2 are at the top of the stack rank have been performing consistently as I shared in terms of the prepared remarks. And then another core driver that we have now seen in operation for the last couple of years is pricing. We changed pricing -- we lowered the pricing. We then really remove the seat minimums, and we've now seen that dynamic play out, and we know how these trends. And so that remains a driver. So that's the second one, I would say.

Now in terms of the emerging growth drivers, as you're rightfully pointing, it's core seats and credit. That's how we think about AI monetization. And in core fees, we've consistently quarter-over-quarter added a lot of value into core seats. Brief assistant, which is now consistently rated really high in terms of customer satisfaction. And adding all of the company enriched data into the core seat and as you pointed out, we saw nearly 25-plus percent of proceed customers upgrade to more core seats. And you can ask why because that's the gateway for all of our AI features. That's almost the foundation that you get started. It has like included in our credits, and that's the gateway in which customers begin to then turn agents on.

From there, from that foundation, we build on agents. And specifically, just to kind of really bring this back up what we did is we're listening to customers and we're removing friction points. And we just started with agent adoption, and we're making sure that customers turn it on, get the proof of value, get a trial period for it and make sure that they can then consume it based on the outcomes it's delivering and those 2 are the emerging drivers. So the way you should think about this is the growth formula we've been talking about is intact and the stack rank starts upmarket and multihub followed by pricing, then followed by emerging growth levers.

And the combination of all of that plus what we are doing in terms of the products is important, right? The product quality as well as the pace of innovation and how quickly we are driving adoption is really, really story here. And look, we're just getting started with this very big transformative shift with our customers. Customers continue to talk to us about how we can be the data and AI platform for their transformation and the conversations we're having makes us lean into this moment so that we can be the partner of choice for our customers in their AI transformation.

Next question is from Matt VanVliet with Cantor.

I wanted to dive in a little bit deeper on some of the longer sales cycles that you're talking about. How much of that is a factor of giving customers this longer trial period with agents as sort of one driver. Second being just kind of understanding better the pricing and packaging and maybe what the total cost of ownership is?

And then the third being the sales folks that were out of the market, maybe partners that now also have to have a little bit more training and pitched on kind of what's changed. Just curious on how sort of short lived this might be as that training happens versus customers continuing to take longer to evaluate ultimately what the platform brings.

Yes. I think that's a really good question. It's a combination of 3 things. The first one is as customers kind of like really look at AI, they do want to see how AI and agents within their environment is going to drive outcomes. And that's what we mean by proof of value. And the best way for us to show proof of value is to turn it on and give them a period where they can trial it, and that's exactly what we have done with AEO as well as Customer Agent and prospecting agents.

And we're confident in our product strategy. And when they begin to see value, that timing is going to moderate, right? I don't see that as something that will be a consistent long-term factor. We are still in the beginning of this transformation period. I think the second thing, which is we obviously are -- we talked about the sales enablement, the folks are back in seat and they are now fully trained. And I think we're making this transition. We're moving really fast, and there's a slightly different sales motion and people will adapt to it. And the set of pricing changes that we have leaned into helps them do that because it's pretty easy to go and now up to customers and say, "Hey, we deliver outcomes and our pricing is now tied to that value."

So look, I think all of this is reflected in the guidance. We had a solid Q1, and we've obviously made a set of changes that lean into the feedback that we are receiving from customers, and this gives us the confidence that we have the right feed and expand motion for the agentic use cases. And this is really us leaning into the AI adoption motion.

Next question is from Billy Fitzsimmons with Piper Sandler.

Perfect. The net customer adds quarter-over-quarter were nicely above the directional range provided last quarter. And obviously, net adds is only one measure of success, ARPU and the test of customers you're adding matters. But it was the second best quarter for customer adds in 7 quarters. And there's a narrative out there generally in software, not specific HubSpot that we've entered a harder environment to add net new customers.

And there's a variety of reasons for that. So curious what you're kind of seeing and hearing in real time around kind of the ads? And is this just better execution from HubSpot or -- on that front? Or is it more kind of timing of the lands and customer ads?

Yes. I appreciate the question, and maybe there's -- two general comments that I would make, which is we're obviously pleased with the number of net adds that we added in Q1. Q1 tends to be our highest starter ad quarter. And so we saw that again in -- and the expectation is that we would see that moderate back to that $9,000 to $10,000 range in sort of Q2 and beyond.

That said, you're making a really interesting observation, which is lots of companies are finding it harder to add new customers. And I think that our ability to consistently add new customers and retain have funnel has been a result of the fact that we have been investing to diversify our top of funnel for a number of years now, right? You saw us starting in 2022 by a company called the Hussle. We bought a company called Mindstream that has an AI-focused newsletter that is driving lots of top of funnel demand for us where we have YouTube and other media outlets.

We bought 2 incremental acquisitions in Q1, starter story and future PDS. So we keep leaning into this motion of diversification of top of funnel that is helping us retain our customer acquisition motion. The other thing that I would say is, we were really early in experimenting with AEO internally. And the team continues to grow AEO as a contributor to our top of funnel demand, it's still a relatively smaller part of our overall demand equation, but it is a highly effective one. It converts about 3x higher than other leads for HubSpot. And so we continue to just focus on building a durable demand engine as part of the overall HubSpot equation.

Thank you. This concludes the HubSpot First Quarter 2026 Earnings Call. Thank you to everyone who was able to join us today. You may now disconnect your lines.

Good morning, and welcome to HubSpot's Spring 2026 Spotlight Investor Webinar. I'm Chuck MacGlashing, and I'm here with Yamini Rangan, our Chief Executive Officer; and Duncan Lennox, our Chief Product and Technology Officer.

Today, we'll walk through HubSpot's 2026 strategy and how the new products and innovations we released at Spring Spotlight yesterday are making AI work for growth companies. I'll also share an update on how HubSpot is transforming how it builds, grows and operates with AI.

Before we start, I'd like to draw your attention to our safe harbor statement. Statements made during this webinar that are not historical facts may be considered forward-looking within the meaning of the federal securities laws. These statements reflect our views only as of today involve risks and uncertainties, and we undertake no obligation to update them. Please refer to our most recent SEC filings for a discussion of the relevant risk factors.

Now it's my pleasure to turn it over to HubSpot's Chief Executive Officer, Yamini Rangan.

Thank you so much, Chuck. Welcome. Hello, everybody, and thanks so much for taking the time today to join us in this investor webinar. Look, the pace of innovation is just accelerating. There is a lot happening in the industry in just a matter of weeks, and there's a lot happening at HubSpot. We just had our spring spotlight yesterday and a lot of exciting updates. So we wanted to provide a product and strategy update.

There's just too much happening within the industry and HubSpot that we didn't want to wait until Analyst Day, which happens later in fall. Just as a reminder, we're still within our quiet period, so all of the financial updates will be part of our earnings call in early May.

With that, here is the agenda for today. I'm going to start by setting the stage with our 2026 strategy and the progress that we are making within that strategy. We are going to spend the bulk of time with Duncan Lennox. He's our Chief Product and Technology Officer, and he's going to walk through our Agentic customer platform and the agent updates from Spring spotlight. You're going to see a lot of the product demos, and you're going to see how context shows up as a distinct advantage in all of those demos. And then I'm going to close out by sharing how we are transforming how we build, grow and operate as an AI-first company.

Okay. With that, let's jump straight in. Our strategy is clear, and it is focused, make AI work for growth companies, reimagine marketing with a new playbook as well as products and accelerate upmarket growth with a platform that delivers the power and simplicity.

Let's start with AI. Look, there is no shortage of tools in the market for AI, but there's a huge gap between AI output and AI outcomes. And that is exactly the gap that we are focused on. We're focused on democratizing AI for companies with 200 to 2,000 employees in order to drive growth for them. Now small and mid-market companies, they don't have the time, and they don't have the expertise to keep up with the blistering pace of AI improvements that we see every week. We do it for them.

We are their growth partner in AI transformation and our AI strategy is really clear and focused. We want to deliver a platform that has all of the growth context, which will help both agents as well as the humans using our software to drive outcomes. And today, we're going to share the progress that we're making in both a set of first-party agents that we just updated as well as the growth context that drives that outcome.

Okay, let's talk about marketing. The pace here is accelerating really, really quickly. Now discovery of brands as well as products is shifting pretty dramatically to a diversified set of new channels, including LLMs and that is why I'm really excited to announce HubSpot AEO, HubSpot's answer engine optimization solution that we just launched yesterday.

Now this solution provides AI visibility so that you can see the share of voice. It provides prompt recommendations as well as content recommendations. And HubSpot customers can now buy this and use this in 2 ways. First of all, as a stand-alone solution. And that means there is a new front door for HubSpot's marketing solution, start just with AEO. And second, they can use all of these advanced capabilities with Marketing Hub Pro as well as enterprise.

I'm really excited about this launch and the value it's going to deliver for our customers. And then upmarket. Look, we continue to have strong product market fit in this segment of the market. Customers are looking to lower total cost of ownership, consolidate their stack and drive AI innovation. And with the updates that we are delivering this week, we are bringing more powerful agent capabilities as well as platform capabilities to help them grow and drive AI transformation. Very exciting updates and you're going to see us accelerate innovation throughout this year in these 3 key priorities.

At the same time, we're completely transforming the company. How we build, how we grow, how we operate as a company is changing and the pace of change there is accelerating internally. So I'm going to share more about the progress that we are making here in a few minutes. This is our 2026 strategy.

Now let's jump straight into the highlights from Spring Spotlight. And in order to do that, it is my absolute pleasure to welcome Duncan Lennox, our Chief Product and Technology Officer. Now Duncan is responsible for product, engineering and UX and he's setting this pace of innovation within the company.

Before HubSpot, Duncan spent time at Google to have their applied AI and prior to that at Amazon. So you are seeing scale and significant scale and he was also the co-founder and CEO of 2 companies in the sales and e-learning space for more than 2 decades. Duncan, welcome and take it away.

Thanks, Yamini. It's really great to be here with you all today. And Yamini just laid out our strategy for 2026. And at the heart of it is AI that doesn't just deliver output but actually drives outcomes for go-to-market teams. And I want to dig into what we believe is key to making that happen, which is context. And I'm going to walk you through how we think about context at HubSpot, why it's so important and demo products that we featured at our spring spotlight this week that uniquely use context to deliver real value and outcomes for customers.

At HubSpot, we're building the Agentic customer platform. And that means we capture all your customer context in one place, then make it available to both your team and AI, embedded AI and agents so they can work together to market, sell and service your customers. You might have seen this visual of the Agentic customer platform before and even heard others talk about the importance of context.

But we've thought deeply about the role that context will play, specifically for go-to-market teams. And I want to walk you through how that thinking has refined and shaped what we've built. First and foremost, not all context is created equal. Personal AI tools are building personal context, your preferences, your conversation history, your communication style. Then we've got enterprise tools that are building organizational context, so documents, wickies and institutional knowledge. But go-to-market teams need something different.

And that's why we are focused on building growth context, the specific dynamic understanding AI needs to drive outcomes across marketing, sales and customer success. And this isn't just a concept. We've built real infrastructure that will help customers capture and maintain their growth context. And we see growth context as having 5 dimensions.

Firstly, we've got business context, everything that makes your company yours, your unique positioning and voice, your pricing, your differentiation. Then we've got team context, how your best people actually work, the judgment, the methods, the instincts that live in calls and deal notes, not in your onboarding docs. Process context then is really the nuts and bolts of your workflows, what triggers a handoff, what makes a deal urgent, what your companies are actually built to do. Customer context, of course, is the full history of every relationship with every customer you have; what they bought, why they bought it, where friction happened and what comes next. And then finally, network context is the collective intelligence of HubSpot based on 18 years of working with hundreds of thousands of go-to-market teams, pattern recognition at a scale no single business could ever build on its own.

Okay. So let's walk through how all this comes together in the life of a customer. We're going to follow one company through the full journey, and that company is Sonder, an employee wellness platform. They work with businesses to give employees access to mental health support, well-being coaching and preventive care programs. And they're a real HubSpot customer and a great example of how shared context across the go-to-market team leads to growth.

So let's start where every business has to start - getting found. For Sonder, their target buyers are HR leaders and people and culture teams inside mid- to large enterprises. These are people who are actively looking for wellness solutions, but the way they're finding those solutions has changed a lot. The search bar is no longer the first stop. Buyers are going to ChatGPT. They're going to Perplexity. They're asking LLMs who to buy from before they ever click a link.

And if Sonder isn't showing up in these answers, they're invisible at the most important moment. And most marketing teams have no idea how their brand is represented in this new world of discovery. And the campaigns they're running are often built on assumptions rather than their actual customer data. And this is where HubSpot gives Sonder's marketing team an advantage with HubSpot AEO. It shows marketers how their brand and content appear in AI-generated answers, how they compare to competitors and what to do to fill that gap.

So let's use it to track Sonder's AI visibility. So to get started with AEO, the first thing Sonder will do is set up their brand profile. And HubSpot pulls from Sonder's brand kit that they've already configured, prefills their name, domain and any brand variations an LLM might use to refer to them, then Sonder reviews a list of key competitors generated by HubSpot based on their unique customer and business context. And Sonder can also add anything that might be missing. And lastly, we confirm a set of tracking prompts. These are the suggested questions buyers might use to search for businesses like Sonder that HubSpot generates based on customer context, things like what employee wellness platforms are recommended for midsized companies in Australia.

And if Sonder notices a prompt is missing, they can add their own like what should I budget for a global employee wellness platform. And with all the prompts set, they can do a quick review to make sure nothing is missing and start tracking.

Okay. Now that we've got that set up, let's take a look at Sonder's AEO dashboard. Here's where they can see things like their brand visibility, what percentage of those tracked prompts actually mention Sonder. They can also see their competitive share of voice, how they stack up against each competitor. Then we have the prompt dashboard where Sonder can manage the prompts that they're tracking. And if they want to go deep on any single prompt, they can click to see the actual responses that models are using. Then we get to the citations view. This tells Sonder which websites are driving the AI answers.

And finally, recommendations. This is key. Based on areas of low visibility, HubSpot AEO recommends actions Sonder can take to increase how often they appear for that search. That's exactly the kind of insight that informs their content strategy and lets them know where to invest. And here's the thing about these prompts. Get them right and you optimize how you show up in LLMs; get them wrong and you're spending resources trying to show up for questions no buyer is actually asking. Growth context makes the difference here. We pull from brand kit for your positioning, your value props, the foundations of your business. But as your customers engage through forms, e-mails and calls, their questions and areas of interest become critical customer context that informs the prompt suggestions. That's what makes HubSpot AEO more valuable over time. It's learning from your real customer conversations, not just your marketing copy.

Six months ago, Sonder had no idea whether they were showing up in AI answers at all, and now they do. And more importantly, they know exactly what to do about it. Now that Sonder has a better understanding of their AI visibility, let's use Breeze Assistant and Loop Marketing to build an awareness campaign to close that gap between them and their competitors.

Breeze Assistant is now a Loop marketing expert. It's built on the playbook we launched at INBOUND last year for marketing in the age of AI. As a quick refresher, Loop has 4 stages: Express, where you define your brand and ICP; Taylor, where you use AI to personalize content; Amplify, where you diversify your campaign across channels; and Evolve, where you learn and iterate with the help of AI. Sonder has already an established brand and ICP. It's mid- to large enterprise companies in Australia who've recently expanded.

Yamini talked about helping customers navigate marketing with the new playbook and now Breeze Assistant can help them do it. We know from HubSpot AEO that peer-reviewed sites and listicles are strong performing citations for Sonder. Breeze pulls those citations, and we ask it to draft a marketing brief so we can double down on the content that's working. Based on Sonder's brand and the customer data stored in HubSpot, Breeze Assistant builds a comprehensive brief, targeted at increasing the quality and volume of their citations in AI Answers.

Now, Sonder uses Marketing Studio to build out a full campaign. They pull in the content HubSpot AEO told them is working and use the remix functionality to create new assets like blog posts, social content and even a podcast. Now the marketing team collaborates live in the studio and within days, they have the new campaign in market. Now here's how HubSpot's context advantage makes Breeze Assistant different from other AI tools. Without context, the most AI can do is quote from a generic marketing playbook, but Breeze Assistant knows your business, your brand identity, your ICP, your product catalog, even customer feedback.

HubSpot doesn't just store thousands of contacts. It understands how you organize your market and the value proposition you're bringing to them. So when Breeze Assistant gives you loop marketing guidance, it's not generic best practices. It's grounded in your actual business. And it also knows where you are in HubSpot and what you're trying to do. So if you're looking at a contact record, it understands you need information about that specific contact.

If you're building a campaign, it suggests next steps based on your actual customer data. And it knows your role. So marketers get campaign strategy, sales reps get deal guidance. The recommendations aren't just helpful. They're specific to your job. New content is live and it's working. A Sydney tech company, about 400 people recently expanded, found Sonder through one of those peer review citations we just optimized for. The head of people visited the pricing page, spent time on the case studies and is now in the CRM as a high-intent lead. And this is exactly the moment where most sales organizations lose ground, not because the product isn't right, but because what happens next is too slow and too manual.

And it's not a rep problem, it's a context problem. The rep gets the lead notification, but they still need to figure out who else is in the buying committee, what the company is going through right now and what's going to land. And this research takes hours, if not days, and we all know time kills deals. And that's what the rebuild prospecting agent is designed to solve. Now instead of researching the companies that you enroll, the agent prospects for new leads based on your ICP and monitors those leads for new buying signals that tell the rep when it's a good time to reach out.

So when the rep opens the agent, they already see a prioritized list of companies showing intent right now. The rep can see each signal and exactly what triggered it and click through to the source. Every company also comes with a reason to reach out already written. The rep clicks in. One contact is already in the CRM who came through the campaign, but the agent also identified 2 others, a CPO and a CFO, so the rep adds them to the CRM. The agent has already drafted personalized outreach, pulling in the rep signature, referencing the buying signals and using Sonder's value proposition specifically for the role. It even knows the campaign content because that's all on HubSpot.

Context is what makes prospecting agent actually useful. It's pulling from your full CRM history every interaction, every conversion to understand which accounts close for your business, not just which ones look promising. The outreach isn't generic. It's grounded in your product positioning, a rep's role and priorities, what triggered the contact in the first place, past conversations and similar feedback from other customers. And it learns. The more your team uses it, the smarter it gets at finding accounts that actually convert.

And that's the true line here. The content Sonder created to improve their AEO citations brought this buyer in. The same customer context that informed the campaign is now informing the outreach. Okay, fantastic. The outreach worked. The Chief People Officer responded, sonder's rep had a discovery call and it went great. The budget is confirmed, requirements are clear and the next steps are agreed.

Now here's what normally happens. The rep tells themselves, they'll update the CRM after the next meeting. They're busy. But by the next morning, they've already forgotten some of what was said, the deal stalls, not because the customer wasn't interested, but because the rep ran out of bandwidth. And smart deal progression closes that gap. The moment the call ends, instead of reps patching notes together manually, HubSpot preps next steps for them. But it isn't just a to-do list. Smart deal progression knows more than just this call. It operates like a rep's second brain with the same deal and customer context. That means in the post-meeting recap, the rep can see a clean summary and action items, each one cited back to the transcript. So the rep isn't guessing. They can hear it for themselves and approve with confidence.

Action items are attributed to the right person, editable and can be saved as tasks in one click. The follow-up e-mail is also written. It summarizes the call, capture what was agreed and the rep can refine the tone right there with Breeze and after the tweaks, they hit Send. And this is where things get really interesting. Smart deal progression then makes suggestions for CRM updates like deal stage, amounts, next steps, all suggested, all accurate, all pulled from deal context.

The rep reviews and approves in seconds. The CRM is updated, nothing is left to memory. Look, I've said it already, I'll say it again. I genuinely think this capability is underappreciated. Smart deal progression is like giving every rep a second brain. It sees past e-mails, notes and deal activity, everything. So when it suggests next steps or follow-ups, it's reflecting the whole relationship, not what just happened on the last call. And it knows how your team actually sells. It understands your pipeline definitions, your deal stages, the way your team executes.

So when it suggests CRM updates or flags risks, those recommendations reflect where the opportunity actually stands, not some generic sales framework. Context turns a transcription tool into something that actually moves deals forward.

Okay. The deal Sonder was working just closed, fantastic. 400 employees at that Sidney tech company now have access to Sonder's platform, but this can often be where the relationship is made or lost. Employee well-being is a sensitive category. When an employee reaches out about accessing mental health support, they expect a response that knows them, their plan, their history, their situation. A generic automated reply in that moment doesn't just miss the mark, it does real damage to the relationship and trust.

But Sonder's support team can't personally handle every query at that level. That's the tension. And it's exactly why context here matters more, perhaps more than anywhere else. So let me show you how Sonder sets up customer agent for e-mail, their highest volume support channel. The first thing they do is set their expressions, the specific instructions that tell the agent how to behave, how it greets employees, closes conversations, when to apologize and which topics it should never answer autonomously, like anything involving clinical escalation, which Sonder always routes to a human. HubSpot provides out-of-the-box templates to make this fast, but Sonder can refine the instructions if needed.

Okay. Great. Now before going live, Sonder's team tests the agent with e-mail, simulating real customer queries without putting anything in front of actual customers. They can see exactly how it would respond and build confidence before they commit. Okay. Then deployment. Sonder starts with working hours only. The agent handles e-mail after hours and on weekends when the team isn't available. They also use a workflow to route by customer tier. So free plan users go to the agent, enterprise accounts go straight to a human. Billing and clinical questions are excluded entirely.

And here's what it looks like on the receiving end. An employee e-mails in asking about booking a wellness coaching session and whether her plan covers it. The agent reads the e-mail, pulls her record from the CRM, her plan tier, her session history, what she's accessed before and replies with exactly what she can book, how to do it on a direct link, formatted clearly, signed off professionally, thanks to our agent expressions and answered in seconds. She got an answer that felt like someone actually knew her because the agent did.

And that's what context in support looks like. Customer agent responds based on the complete context. It's pulling from your knowledge base, your product data and that individual customer's full history. So every response is tailored to the person and their situation, not just the ticket in front of them. And when a human needs to step in, the handoff actually makes sense because customer agent lives inside of HubSpot, so it sees the entire relationship. Your service rep isn't starting from scratch. They're starting with full context, what the customer tried, what the agent recommended and how to make the agent better based on your customer context with Smart QA.

And what you saw wasn't 5 separate product demos. It was one story. Sonder building awareness in a world where discovery has moved to AI answer engines. converting that awareness into pipeline by reaching the right people at the right moment using the right context and closing that deal because nothing slipped and then cementing the customer relationship through support that actually knows who it's talking to.

And we are already seeing real customer outcomes, thanks to HubSpot's context advantage. With Breeze Assistant, we're seeing 4x more leads created. Without business and market context, AI can only use generic marketing advice to create generic plans. With it, it gives you one that's actually targeted to your customers. That's the difference between campaigns that resonate and ones that don't.

With prospecting agents, we're seeing a 19% increase in e-mail send rate. And the increase in send rate is important, but the real measure here isn't value. HubSpot is focused on high-value outreach that reps want to sign their name to. Send rate went up, but we also went from reps rewriting 2/3 of the drafts that we produced to approving 2/3 of the draft without edits. That shift happened because the outreach is grounded in the growth context they needed, real buying signals and e-mails written in the rep's own voice. It feels authentic because it is.

Smart deal progression, we're seeing 10x improvements in CRM update accuracy. Anybody can build a CRM updating agent. What's hard is earning your sales team's trust. Reps hate filling out CRM data, but they're held accountable for it. So the bar is high. With growth context, not using just the current call, our suggestions are 10x more likely to be accepted, and that's what it means to actually save a rep time. And then with customer agent, we're seeing 70% of tickets on average resolved. We can achieve average rates this high because growth context means we surface the full customer history. And if the agent fails, we can tell you exactly why, whether it's a gap in your knowledge base or missing customer data. That's the flywheel here.

Every escalation makes the agent smarter for next time. And for HubSpot AEO, we're excited to have it in market, and we've seen incredible early results from customers who've been early testers. For example, in a matter of weeks, Sonder drove 8,000 new website visitors and moved their brand visibility score 2 points. ADO didn't just tell them the problem, it told them what to do about it. Docebo went from guessing at their AI visibility to leading their category with nearly 15% of leads now coming from AI sources, and that number is growing. And for Fresha, the question was simple, are we showing up? And now they know the answer and their traffic growth reflects that.

The thread running through all of these results is growth context, that rich set of information that go-to-market teams to actually deliver outcomes. And that is only possible on HubSpot. That's because growth context isn't one thing. It's business, team, process, customer and network context, all working together. Almost every meaningful AI action in the go-to-market process needs all of them. Point solutions might have one slice, but that's why they hit a ceiling.

Let's say you're preparing a prospecting e-mail. A point solution providing customer context might help you get their attention with a personal hook, but it won't match your voice or product positioning without business and team context. A support tool that's great at surfacing your knowledge base can answer some questions, but it won't know why the customer bought your product in the first place and be able to use that context to the team's advantage.

Even platforms that had been unified have found it incredibly challenging to solve this. Many may have the data, but they aren't and haven't been opinionated about the structure, so they can't understand how the pieces relate. HubSpot has always been deeply opinionated about what it takes to grow, opinions that were formed very early on about what a contact is, how a deal works, what a campaign does. These are the foundations that allow us to organize disparate data into rich complex infrastructure of growth context. That's what the Agentic customer platform is built on, and that's what HubSpot's context advantage makes possible.

I'm going to hand it back to Yamini to close this out by discussing how we transformed, how we build, grow and operate at HubSpot.

Well, thank you so much, Duncan, for that really comprehensive walk-through of agents as well as the context layer. I hope that was very informative in terms of what we launched and how growth context shows up. Look, I'm super excited about the outcomes that we can drive for customers with our agent customer platform. And that is exactly what everything is about, driving outcomes.

We've always taken sophisticated technology and democratized it for small and mid-market customers to drive outcomes, and that is exactly what we are doing with AI. So let me actually walk you through a few examples of what customers are adopting and how that is driving outcomes for those customers.

Okay. And let me start with Metrie. Now Metrie is a North American buildings material company. They have about 400 HubSpot users across multiple regions. Now they had a completely manual process to triage incoming e-mails. So they created a workflow and used customer agent to read every incoming e-mail, route that e-mail to the right queue and then respond to that request specifically with the growth context.

Now they used up their 5,000 included credits in 2 days. And then they bought 100,000 more to keep that agent running, and now they are well on the way to leverage nearly 300,000 credits. In Q1 2026 alone, customer agent handled over 16,000 customer conversations for Metrie. Quote turnaround time is down by 33% and order turnaround time is down by 35%. Clear example of a customer scaling with customer agent.

Then let's talk about ISSA. Now they are in Ed-Tech, and they run one of the largest fitness certification programs in the world, which means you can imagine that they get a constant stream of customer questions across chat, across e-mail as well as phone. And now they came to HubSpot to consolidate their entire fragmented support stack, and they went all in on AI from the start. They started leveraging customer agent as well as all of the AI features within Service Hub. And now customer agent handles 100% of their chat volume. And that means they have freed up all of their human agents to be able to handle more complex questions that comes in phone. And that means the phone abandoned rate has dropped by nearly 70%, clear example of leveraging platform capabilities as well as leveraging customer agent.

And then I'll finish up with an example on prospecting agent. And this is with Aventus. They are a fast-growing fintech company. They wanted to scale prospecting, but within their CRM, they had nearly 10,000 dormant contacts and inaccurate data, which means that every outreached e-mail they were sending, they were just getting a huge bounce rate back. So they wanted to fix that problem and drive growth.

So it started with adopting data agent. And Data agent helps enrich the company data, helps enrich the contact data and provides a very clear prospect score. From there, they started using prospecting agent to personalize the outreach across multiple channels, and that is working. Because of this, they've been able to replace point agents and other solutions and data that they were using.

In Q1, 50% of all of the engaged prospects interacted with prospecting agent, and that is driving more pipeline for Aventus. And you can see that they have continued to scale credit consumption based on this.

I hope you can see the clear momentum in both our product innovation as well as customer adoption and the kinds of use cases that we are seeing traction in. With that, I want to shift gears, and I want to talk about the internal transformation at HubSpot. We're transforming how we build, how we grow, how we operate and the pace and progress here is exciting.

We have fundamentally changed how we build products at HubSpot. Now we started with copilots like everybody did. We scaled very quickly from there with coding agents. And now we have a HubSpot-specific agentic execution platform that is compounding our advantage and driving the pace of innovation.

I'll walk through each of these phases. Now in Phase 1, it was all about copilots. We proved that AI could make every engineer go faster while maintaining the reliability of the product. That gave us organizational confidence as well as the data to move into agentic AI, and that was our Phase 2. So in Phase 1, if it was about making engineers go faster, Phase 2 was about using coding agents to do the work. So we moved from AI-assisted coding to fully autonomous agentic coding.

But here's the thing that we found. We found that off-the-shelf coding agents that we were using were okay. But at our scale, which is 1 million bills a day across tens of thousands of micro services was just not optimized for HubSpot. We needed an agent harness that was optimized specifically for our environment.

So we moved into Phase 3 pretty quickly, and we built that infrastructure ourselves, a Kubernetes-based agent execution platform where every single coding agent runs in an isolated container that replicates a real HubSpot developer environment, complete with internal build tools, the test suites and the service access that is needed. What does this mean? Well, this means that instead of building every agent from scratch, we have built a foundation once. How agents access data, what actions they can take, how they connect to the rest of the growth context within HubSpot, everything.

Now that decision has compounded really fast. Many of you know that we have always taken a platform-centric approach to development, and that has helped us maintain the pace of innovation and built that pace. You've seen that with reporting, automation, messaging, things that are platform primitives that helped us ship more capabilities across every hub.

That is exactly what we are doing with agentic execution. We now have the unified tools, the unified skills and all of our agents are interoperable. That means they speak the same language. They share the same tool sets, the same skills, and they draw from that same growth context. And that means our customers, they get a consistent experience regardless of which agent they are using because underneath it is a platform that is scaling. And that is a massive advantage. For our customers, it's an advantage. But for HubSpot, this is what enables the pace of innovation with AI. Really exciting.

Okay. Let's talk about how we grow. Now over the last 3 years, we've systematically rebuilt how we attract, engage and delight with AI agents as well as assistants. And the result is an agent-first go-to-market, a flywheel where agents are doing the work and humans are operating with higher leverage, but much deeper connections with our customers. And of course, the value is really clear in why we do this. When we are in the bleeding edge of go-to-market, we learn faster. We provide that feedback to product, and we also educate our customers in terms of best practices.

So what exactly is this agent-first go-to-market? Let me talk exactly about the agents that we are now using. In terms of how we are attracting the top of funnel is nothing as it looked like 3 years ago. We once had a bunch of content leads and inbound chat teams to be able to do it. Now top of funnel is an AI-powered demand generation engine. And it actually starts with the demand agent.

Demand agent builds up our TAM. It identifies our ideal customer profiles. It enriches the company and contact information through a variety of HubSpot as well as third-party sources, and it scores every single account, so we know what to prioritize. Then we use an inbound agent that handles roughly 82% of all of our website chats with no human involvement. It qualifies visitors, it handles competitive questions. It identifies real buying intent from who is on that, and it's now even beginning to start closing starter deals, pretty exciting.

And then we also have AEO agent. We've been proponents of this. We've been experimenting, iterating and working on AEO for the past couple of years. And that is why we are the #1 CRM with visibility across all of the LLMs. Our qualified leads are up significantly, and those leads convert 3x better. That's the transformation in the top of the funnel.

Then let us talk about how we engage our customers. We have built agents at every stage of the sales motion, and we have provided assistance to our reps using AI. Our prospecting agent does exactly that. It tracks the intent of our prospects. It then generates personalized messages that our BDR teams as well as our reps can use in terms of outreach. And that has improved the productivity.

At the same time, for active deals, reps have now a conversational assistant that sits on top of everything that they do. It provides risk scores. It provides similar one deals as they're having the conversation. And when reps and managers use it together to work a deal, the win rates go up. And finally, we've also built presales agent that handles technical questions and a demo agent that spins up a tailored demo environment on the spot for a specific prospect. So lots of innovation going on there.

And then finally, let's talk about the delight stage. Here is where we have seen 2 clear patterns emerge. The first is in support. We found product market fit here almost immediately. Customers got faster answers here. Our team got capacity back and obviously, AI has added to productivity. We have not hired a single Tier 1 support rep since 2024. So clear value that we are driving there.

In the case of customer success, the story is more interesting because that actually is providing an assistant for all customer success managers to understand the usage of a customer by specific use case, where they're using HubSpot, where they're not and how they can have deeper conversations with customers.

So really exciting to see this transformation within our go-to-market. And I'll kind of finish off with this. HubSpot is going through a process of massive AI transformation and metamorphosis. Over the last couple of years, we have inspired HubSpoters to leverage AI, provide access to tools. We have transformed the culture to be one of experimentation, and we have changed the organizational clock speed to go from annual planning to now operating in just 6 week sprints.

The result is an organization that is genuinely AI first with 95% of HubSpoters using AI weekly. We're just getting started. What we've also recognized is that employees becoming 10x more efficient with AI tools does not automatically mean the company is 10x more productive. There is a gap between AI driving individual productivity to AI driving institutional productivity, and that is what we are focused on.

We're investing in the infrastructure to provide context across all of the teams. We are reimagining workflows within specific teams and identifying pods that can actually drive this type of institutional productivity. That is the transformation that we are building towards.

So let me close this out. I hope you found this very useful. I hope you take away a few things. One is that we have a very clear and focused strategy. We want to make AI work for SMBs. We're reimagining marketing, and we are accelerating upmarket progress. We have a clear advantage - growth context. It shows up across the platform. It shows up in every single agentic use case that you saw in the product demos today. And our pace of product innovation is accelerating. We remain laser-focused on delivering great outcomes for our customers. We know that it is a massive opportunity for us.

Thank you so much for taking the time to join us for this update. I look forward to speaking to many of you in early May.

Good afternoon, and welcome to HubSpot's Q4 2025 Earnings Call. My name is Gigi, and I'll be your operator today. [Operator Instructions]

I would now like to hand the conference over to Head Director of Investor Relations, Chuck MacGlashing. Please go ahead.

Thanks, operator. Good afternoon, and welcome to HubSpot's Fourth Quarter and Full Year 2025 Earnings Conference Call. Today, we'll be discussing the results announced in the press release that was issued after the market closed. With me on the call this afternoon is Yamini Rangan, our Chief Executive Officer; Dharmesh Shah, our Co-Founder and CTO; and Kate Bueker, our Chief Financial Officer.

Before we start, I'd like to draw your attention to the safe harbor statement included in today's press release. During this call, we'll make statements related to our business that may be considered forward-looking within the meaning of Section 27A of the Securities Exchange Act of 1933 as amended. In Section 21E of the Securities Exchange Act of 1934 as amended, all statements other than statements of historical fact are forward-looking statements, including those regarding management's expectations of future financial and operational performance and operational expenditures, expected growth, FX movement, and business outlook, including our financial guidance for the first fiscal quarter and full year 2026.

Forward-looking statements reflect our views only as of today and is except as required by law, we undertake no obligation to update or revise these forward-looking statements. Please refer to the cautionary language in today's press release and our Form 10-K, which will be filed with the SEC this afternoon for a discussion of the risks and uncertainties that could cause actual results to differ materially from expectations.

During the course of today's call, we'll refer to certain non-GAAP financial measures as defined by Regulation G. The GAAP financial measure most directly comparable to each non-GAAP financial measure used or discussed and a reconciliation of the differences between such measures can be found within our fourth quarter and fiscal year 2025 earnings press release in the Investor Relations section of our website.

Now it's my pleasure to turn over the call to HubSpot's Chief Executive Officer, Yamini Rangan. Yamini?

Thank you, Chuck, and welcome, everyone, to the call. I'll start with our Q4 and Full Year 2025 results and the consistent themes driving those results. Then I will highlight our 2026 strategy, how we are positioned to lead and win with AI and the specific levers we are activating to drive growth. Let's dive in.

We had a solid finish to 2025. Q4 revenue grew 18.2% year-over-year in constant currency and full year 2025 revenue grew 18.2% in constant currency to $3.1 billion. We delivered another quarter of standout operating profit growth, with operating margin of 22.6% in Q4 and 18.6% for the full year. We now serve more than 288,000 customers globally. We added 9,800 net new customers in Q4 and more than 40,000 customers for the year. That growth at our scale says something important. Customers trust HubSpot during a time of transformative change in the industry driven by AI.

I'm also pleased to announce that our Board of Directors has authorized a share repurchase program of up to $1 billion, a clear signal of the confidence we have in our business and the growth opportunity ahead. Two teams drove our performance in 2025, strong core fundamentals and momentum with our Agentic customer platform.

First, our core fundamentals are solid. Upmarket momentum was consistent all year with a strong finish in December. Large companies are consolidating on HubSpot because we deliver the power they want with the ease of views they need. In 2025, deals over 5,000 in monthly recurring revenue grew 33%, and deals over 10,000 grew 41%. The number of customers with 500 or more seats grew fivefold, making it one of the strongest upmarket years. This is the direct result of years of focused product investment, the moat we have built with our partner ecosystem and our growing brand credibility with larger companies.

[ Rentokil ] Initial, a global leader in pest control, used Marketing Hub to increase leads by 76% and deliver a 671% ROI. That success led them to expand HubSpot. Now they use HubSpot's Enterprise product suite across more than 100 teams to scale their go-to-market strategy. [ Mercantile ] Bank, a financial institution with over 700 employees consolidated 6 separate solutions on to Marketing Hub and saw an immediate improvement in efficiency and personalization capabilities. This success prompted them to expand to Sales Hub, Service Hub and Content Hub and replace their legacy CRM, giving them a unified view of customer while lowering costs.

Multi-hub adoption accelerated again this year. In 2025, 62% of new Pro Plus customers landed with multiple hubs. We saw 2 common patterns with new customers. They landed with Marketing and Sales Hub or with marketing, sales, service, content and operations, 5 hubs operating as one go-to-market platform. Multi-Hub expansion is also showing up across our installed base with 40% of Pro Plus installed base by ARR, owning 4 or more hubs, up 6 points year-over-year. We are the genetic customer platform of choice for scaling companies, and Multi-Hub adoption is the new norm.

Our pricing changes also created meaningful tailwind in 2025. As a reminder, we lowered the price point to get started, removed seat minimums to make upgrades easy and added core seats that deliver platform value. That shift is largely complete. Approximately 90% of our legacy customers have moved into the new pricing model and almost 50% of our ARR has gone through their first renewal. The impact is showing up clearly in the data. We continue to see strength in net customer additions aided by the changes. And despite the questions we get on seed compression, we saw customers by more sales subseats, service [ subseats ] and core seats throughout the year. All of this reinforces our confidence that our core fundamentals are strong and built to drive durable growth.

Strong fundamentals matter, but what defined 2025 was our momentum in AI. We are clear about who we serve, growing companies with 200 to 2,000 employees. That's where we win, and that's what we build for. Our AI strategy is simple and focused on helping those companies grow. We embed AI across the platform. We deliver agents that do real work, and we give teams tools like Breeze Assistant and LLM connectors to turn their data into action. That strategy is resonating. AI native like [ Lovable, Pralserbase and Quint AI ] are choosing HubSpot as their platform to drive growth.

Now let's talk about AI adoption. Our agents gained real traction last year. Customer agent handle support tickets and answers questions across the full customer journey. More than 8,000 customers activated it last year and are seeing mid-60s resolution rates driven by product innovation. Prospecting agent help sales teams research accounts personal lines outreach and engage prospects. Over 10,000 customers have activated it, up 57% quarter-over-quarter. This is a clear use case with strong pull customers using it are booking nearly twice as many meetings compared to last year.

At INBOUND, we launched Data Agent, which automatically enriches customer data. More than 2,500 customers have already activated it, a clear signal that customers want AI to take on the manual work that flows teams down. And while it's still early, our usage-based credit model is starting to scale. In Q4, customer agent accounted for about 60% of credits consumed. Data Agent, prospecting agents and intent monitoring each contribute between 10% to 15% of credits consumed. All of this reinforces the clear point. AI is becoming a core driver of how our customers grow and therefore, how we grow.

Okay. Let's look ahead to 2026. Our strategy is clear and focused on 3 things: making AI work for growth companies, reimagining marketing with a new playbook and products, and accelerating up market growth with a platform that delivers both power and simplicity.

First, we are focused on making AI work for growth companies. While there is no shortage of AI solutions in the market, there is a real gap between generating AI output and driving growth outcomes. Closing that gap is what will unlock broad AI adoption, and that requires context, having the right information at the right time, with the judgment to know what to do with it. And that's where HubSpot has a clear advantage. Most AI tools ask customers to bring their own context, upload brand guidelines, teach the system who their customer is and how their business works then do it again for the next agent or LLM and again, that's backwards.

With HubSpot, context is shared and powers everything. Our AI vision is to lead with our agentic customer platform where unified customer data, business context, pure benchmarks across more than 288,000 customers and deep domain expertise, powers, workloads and agents.

To do this, we are bringing together 3 interconnected layers, context layer where customer understanding lives, action layer with our hubs and agents where they help do work, and a coordination layer to connect everything. You will see us accelerate this vision throughout this year. it will show up in powerful use cases where AI does real work for teams and drive measurable growth that is our AI strategy.

At the same time, marketing is going through the biggest shift we have seen in decades. Search traffic is declining as AI-generated answers becomes the starting point for product and brand discovery. Customers are spending time across more channels, and AI is creating new ways for companies to be found. We saw these changes coming and have deliberately diversified our marketing channels. Last year, YouTube bleeds grew 68%. [ New Letter ] leads grew 53%, and HubSpot became the most visible CRM in LLM generated answers. And now we're turning what we have learned into a playbook and products for our customers. We launched the loop, a new growth playbook for the AI era, along with AI-powered solutions to help teams put it into action. Data Hub gives customers clean, unified data foundation, essential for marketing in the AI era. And Marketing Studio provides an AI-powered workspace to plan and create campaigns faster. And our AEO tools give marketers a real opportunity to offset declines in traditional search. Customers are already seeing results.

[indiscernible], an AI-powered learning platform with over 1,000 employees shifted towards AEO as organic traffic declined. Using some spot, they improved their visibility in LLM and saw 13% of their leads come from new AI-driven sources. And [ Crunch ] Fitness, a global brand with over 1,200 employees and 500 franchise locations use HubSpot to deliver personalized on brand messages at scale, sending more than 15 million targeted e-mails a month and generating 2 million leads in a year. HubSpot helped define the inbound marketing era, and we are uniquely positioned to lead what comes next.

The third pillar of our strategy is to keep winning up market. Last year was one of our strongest upmarket years. That was driven by product innovation that delivered real results for larger customers. Within their first year on HubSpot, upmarket customers generated more leads closed more deals and improved ticket close rates. In 2026, we're doubling down on that momentum. We are aligning dedicated product resources to the needs of this segment and increasing sales capacity to drive growth. This is a large market where we have clear product market fit, a strong and energized partner ecosystem and significant room to grow. Our strategy shows up clearly in our growth levers. Our core drivers remain strong and durable, upmarket momentum, multi-hub adoption and pricing tailwinds. These are working consistently, and we expect them to continue to scale.

As we enter a more transformational phase with AI, we see 2 emerging growth levers, core seat adoption and credit. We have significantly expanded the value of the core seat by including brief assistant, enriched company data with buying intense signals and platform features that brings teams together. As a result, adoption of enrich data jumped from 51% to nearly 70% in Q4, a clear signal of the value customers are seeing with core seat. Our goal is to make the core seat the foundation for every go-to-market employee and to drive broad adoption across teams. And as customers rely on agents to do more work on their behalf we will scale credit consumption. Together, these levers expand how customers get value from HubSpot and how we accelerate growth.

With that, I'll hand it over to our CFO, Kate Bueker, to walk you through our financial and operating results.

Thanks, Yamini. Let's turn to our fourth quarter and full year 2025 financial results. Q4 revenue grew 18% year-over-year in constant currency and 20% on an as-reported basis. Q4 subscription revenue grew 21% year-over-year, while services and other revenue increased 12% on an as-reported basis. Q4 domestic revenue grew 17% year-over-year and international revenue growth was 19% in constant currency and 24% as reported, representing 49% of total revenue.

For the full year of 2025, revenue grew 18% year-over-year in constant currency and 19% as reported. Full year subscription revenue grew 18% year-over-year in constant currency and 19% as reported. Services and other revenue grew 16% on an as-reported basis. We added 9,800 net new customers in Q4, bringing our total customer count to over 288,000, growing 16% year-over-year with particular strength in Pro Plus customer additions in the quarter. Average subscription revenue per customer was $11,700 in Q4, up 1 point year-over-year in constant currency and up 3 points on an as-reported basis.

Moving into 2026. We continue to expect net additions in the 9,000 to 10,000 range with low to mid-single-digit ASRPC growth in constant currency. Customer dollar retention remained in the high 80s in Q4, while net revenue retention increased sequentially as expected to 105%. For the full year of 2025, net revenue retention was 103.5%, up from 101.8% in 2024, reflecting continued momentum in seat expansion and the benefit from our pricing change.

Looking ahead to 2026, we expect to grow net revenue retention by another 1 to 2 points year-over-year, driven by a combination of continued seat expansion and ramping credit adoption. As a reminder, we tend to see a seasonal downtick in net revenue retention in Q1, followed by expansion as we move through the year.

Q4 calculated billings were $971 million, growing 20% year-over-year in constant currency and 27% on an as-reported basis. Q4 billings benefited from strong upmarket performance, resulting in a modest increase in overall billings duration in the quarter. For the full year of 2025, calculated billings were $3.4 billion, growing 19% year-over-year in constant currency and 23% on an as-reported basis.

The remainder of my comments will refer to non-GAAP measures. Q4 operating margin was 23%, up 4 points compared to the year ago period and 3 points sequentially. Full year 2025 operating margin was 18.6%, up 1 point year-over-year. We delivered operating leverage as a result of disciplined hiring as well as the ongoing benefits of our partner commission changes. Net income was $163 million or $3.09 in Q4 and $516 million or $9.70 per fully diluted share for the full year of 2025. Free cash flow was $209 million or 25% of revenue in Q4 and $595 million or 19% of revenue for the full year of 2025. Our cash and marketable securities totaled $1.8 billion at the end of December.

As Yamini shared, our Board has authorized a new $1 billion share repurchase program. While we remain focused on investing in organic product innovation and selective M&A, we also see the opportunity to return capital to shareholders through an additional share repurchase program. This reflects our continued confidence in the long-term opportunity ahead.

Before turning to guidance, I want to highlight several factors shaping our 2026 growth outlook and the underlying strength of our business performance. In 2025, net new ARR growth exceeded constant currency revenue growth in every quarter, with full year net new ARR growing 24% year-over-year, 6 points above constant currency revenue growth. Looking ahead, we expect full year 2026 net new ARR growth to outpace constant currency revenue growth, supported by the underlying trends Yamini outlined, continued upmarket momentum, multi-hub adoption and pricing benefits and the emerging impact of core seed adoption and credits. Going forward, we plan to share net new ARR growth on an annual basis as part of our Q4 earnings.

With that, let's dive into our guidance for the first quarter and full year of 2026. For the first quarter, total as reported revenue is expected to be in the range of $862 million to $863 million, up 16% year-over-year in constant currency and 21% on an as-reported basis. Non-GAAP operating profit is expected to be between $144 million and $145 million, representing a 17% operating profit margin. Non-GAAP diluted net income per share is expected to be between $2.46 and $2.48. This assumes 52.5 million fully diluted shares outstanding.

And for the full year of 2026, total as reported revenue is expected to be in the range of $3.69 billion to $3.7 billion, up 16% year-over-year in constant currency, and 18% on an as-reported basis, modestly above our guidance for Q1 constant currency revenue growth. Non-GAAP operating profit is expected to be in the range of $736 million to $740 million, representing a 20% operating profit margin. Non-GAAP diluted net income per share is expected to be between $12.38 and $12.46. This assumes 51.8 million fully diluted shares outstanding.

As you adjust your models, please keep in mind the following. We expect our legacy Clearbit business to be a 40 basis point headwind to full year 2026 revenue growth moderating slightly from the 60 basis point headwind for 2025. Our EPS guidance for Q1 and full year 2026 includes the expected impact from our share repurchase program. And we expect CapEx as a percentage of revenue to be 5% to 6% for the full year of 2026 and free cash flow to be about $740 million.

With that, I will turn the call back over to Yamini for her closing remarks.

Okay. In closing, I'm energized by our position heading into 2026. We are moving with urgency and focus. Innovating quickly to help our customers grow with AI and evolving how we operate to support that pace. We have transformed how we build products and how we serve customers with AI and we are turning those learnings into real value for our customers.

We enter 2026 with clear momentum. Our core growth drivers remain strong, and our emerging AI levers are gaining traction. Together, they give us confidence in our ability to deliver durable growth. I want to thank our customers, partners and investors for your continued trust and support and a heartfelt thank you to HubSpotters around the world for staying focused on solving for our customers every single day.

With that, operator, please open up the call for questions.

[Operator Instructions] Our first question comes from the line of Samad Samana from Jefferies.

Yamini, I thought I'd kick off with just ripping off the Band-Aid that everybody is focused on. The software complex is under a ton of pressure on AI destruction fears. It's asked on every earnings call of every company. And you spoke a lot in your prepared remarks about contacts and moats, but can you dig deeper into how defensible HubSpot's role is as a system of record and maybe what your fear on LLM disruption is.

And related to that, how would you respond to investors that fear that more data will be sucked into and be retained in third-party agents and that this could threaten the role of a system of record itself. I know there's a lot to unpack there, but I think that's what we get asked the most every day now.

All right, Samad. Let's start with ripping off band-aid. I'll probably start with the disruption threat and specifically our moat to address that. Then I'll take the second part of your question, which is about value getting captured outside of SaaS as data gets sucked out of solutions. All right.

First off, in terms of disruption, there's a big difference between point [indiscernible] for solutions and platform. And that difference matters even more in the AI era. Look, in the last decade, HubSpot won as a platform because we were the source for customer data. With AI, we will win because we are the source of customer context and that matters. As I talk to customers right now, the biggest challenge we see with AI adoption, particularly in mid-market companies is not access to more AI tools, more LLM, more agents. There are plenty of those. The biggest challenge that I see is that there is a huge gap between AI output and AI outcomes.

And when I say outcomes, I mean more leads, more deals, more growth. And by the way, that's all our customers want to talk to us about. Mid-market companies don't care about AI for the sake of AI. They don't want to just adopt it. They care about driving growth and if AI can help with that, they will adopt it. And in order for AI and agents to drive outcomes, you need customer context. This is the context it wasn't the heads of people, but now you need the patterns of what works, what doesn't work in your business, in your industry, in your particular function, and then you need to be able to take an action on it and that is what a platform like HubSpot delivers.

I'll give you a practical example. You can ask an LLM to generate outreach for under prospects and then do the same thing in the platform with a history of interactions with the prioritization of what sales care is about with how your best reps handle competitive objections within your industry and then ask it to generate outreach. The difference is, one will be AI output and the other will be AI outcomes. One produces words, the other wins deals. One knows a lot about the external world, the platform knows specifically about the customers' world and what is happening today. There's this whole idea that AI is like a magic wand and you can attract away all of this problem and expect agents to work. It just will not. Context has to come from somewhere. It has to be trusted, it needs to be real-time and it needs to be actionable, and that is what a platform like HubSpot delivers.

Our strategy, as I just articulated, is to be that intelligent system of customer context and we have the data, but more importantly, we have the business context, the industry context and the domain context to deliver it, and that's why customers come to us. They rely on us for that context. They want to use more of our APIs and partners want to customize and build on top of us. And as AI adoption accelerates the value of our Agentic platform increases.

Okay. Now going to your second part of the question, which is, can all the data just be completely sucked out so that there is no value captured by SaaS. Well, first of all, that assumes that we will not build agents rapidly ourselves. We are building agents on top of that customer context, and it is working, and we're seeing that adoption. Second, it assumes that SaaS platforms are data. SaaS platforms are more than data. It is the logic, right? You can certainly get a nondeterministic output for a sales female, but try taking a nondeterministic output for your sales forecast. That is not possible. It workflows, like forecasting, routing approvals, permission, that is logic. It's not data to be sucked away.

And ownership, accountability and governance, all of those lives inside applications, and it's much easier to bring AI into these applications rather than try to extract all of this away as like as if it's just data, it is not.

And then the last thing I'll say is that we serve companies that have 200 to 2,000 employees. That mid-market segment is what we care about. They're focused on growing business. They're not managing model complexity. They're not looking at the latest LLM version. They're not stitching together AI infrastructure. They want AI that just works and drive measurable growth, and that's our focus, making AI simple, practical and actionable and driving outcomes for scaling companies. That's our strategy, and we think it's very defensible for the segment of customers that we are delivering value for.

One moment for our next question. Our next question comes from the line of Jackson Ader from KeyBanc Capital Markets.

Great. The question I had was on acceleration. The guidance for 16% constant currency growth this year. It's certainly not an acceleration, but all the bread crumbs that you're giving us on kind of net new ARR would mathematically just leads to acceleration. So I'm curious what the disconnect is there between guidance of slowing growth and net new ARR, which would hit that accelerating growth?

Yes, Jackson, thanks for the question. I'll just start by saying that we remain confident that we can reaccelerate and hit our 20% revenue growth goal. You're right, all of the leading indicators are pointing in the right direction. Net new ARR is like an important forward indicator of growth for us, and we've delivered net new ARR growth in excess of revenue growth now over the last 6 quarters. Our revenue guidance implies a modest acceleration throughout 2026. As I shared in my prepared remarks, net revenue retention is expected to expand by another 1 to 2 points this year. And we're also expecting that 2026, net new ARR growth is also going to outpace constant currency revenue growth. And that's supported by all of those underlying trends that Yamini talked about.

Continued upmarket momentum, continued multi-hub adoption, continued benefit from our ongoing pricing changes and then those emerging growth levers of core seat adoption and credits. The momentum that we're seeing with net new ARR is what gives us the confidence that we will be able to deliver a 20% revenue growth in the future.

One moment for our next question. Our next question comes from the line of Keith Bachman from BMO.

It's a good follow-on question to the previous one. Kate, I was hoping to dig a little more into the context of guidance. In particular, a few things I wanted to flush out. You talked about pricing and I just wanted to try to understand, a, how much pricing help was in '25 versus '26? And b, if you could comment a little bit about any guidance, so to speak, on how much core seat growth may add and/or credits as we think about what is embedded in guidance as you articulated?

And then the final one, I know a multipart question here, sorry, is could you give any color on what like-for-like seat growth was in '25 and how you think that may be different or the same in '26. So sort of 3 engines within the context of guidance of CY '26?

Yes. Maybe I'm going to just start by giving a high-level view of how we set guidance for 2026. And the short answer there is that we set guidance in a way that is very consistent with how we have always set guidance. And that is that we -- it's early in the year, we want to set guidance that we feel comfortable that we can meet or exceed throughout the year under a set of outcomes across economic and execution outcomes that are that capture a range. Again, it's early in the year, and we want to establish a set of guidance that we're comfortable that we can deliver against.

That said, like our guidance -- our starting point guidance for 2026, is higher than our starting point guidance for 2025. It's up about 0.5 point from last year. Our full year guidance this year is higher than our Q1 guidance. And so that would indicate that Q1 is going to be the low point for growth this year and that we're going to accelerate growth throughout the year. So it's early we wanted to put forward guidance that is consistent in terms of methodology that we're comfortable we can deliver against and that shows that we can reaccelerate revenue over time.

One moment for our next question. Our next question comes from the line of Gabriela Borges from Goldman Sachs.

Yamini, I remembered your [ Dario ] Keynote from INBOUND. And Dharmesh, I know you spent a lot of time in the quality ecosystem. Maybe talk to us about how you see your leading-edge customers using a tool like [ Cord Cork ] alongside HubSpot to get to a better and complementary outcome with [indiscernible] working side by side.

Thanks for the question. So the topic [indiscernible] is still very early, and it's solving for a consumer-oriented use cases. So it's still very early to kind of see our customers using it. What we do see customers using is our cloud connector that connects an individual's account on cloud to HubSpot via our connector, and that's going getting increased usage. And what gives us a lot of kind of optimism is around what that's really doing is extending the customer platform that Yamini has been talking about and providing it via a new channel, which is these kind of AI applications like cloud and ChatGPT. So we're not seeing Claude Core yet, but we are seeing the kind of classic cloud connector and the ChatGPT connector being used.

One moment for our next question. Our next question comes from the line of Alex Zukin from Wolfe Research.

Maybe following on the previous question. To the extent that the modality develops around third-party agents and agent networks that continuously have to access both to read and to action information within HubSpot. Can you just give us a framework for how you're thinking about monetization in that type of environment? And then to some extent, how should we think about the to [indiscernible] for your question, kind of the potential for consumption tailwinds on ARR growth, net new ARR growth on the credit side for '26 and maybe beyond?

Alex, thanks a lot for the question. So I'll start with the first one and then talk about the credit consumption. So in the first part, the way we think about ourselves is we are an open platform. That's how we won in the last decade, that's how we will win in the next decade. And we want customers to bring in and use as much data as possible. So we're very open about that. We don't charge for customers, bringing in the data, the more complete the customer context, the better outcomes that we can deliver. So we don't try to restrict that and we try to enable that.

The second thing I would say is we also won partners building on top of HubSpot. And this could be workflows, this could be agents, this could be custom apps, and we want that to happen. And we've always been partner-friendly. We will continue to operate that way. The part that we will say we're very disciplined is around platform access at scale. If other agents or other platforms that are emerging are relying on our customer context, that access, we will monitor it, we will meter it and we will monetize it. And specifically, if it is high frequency, extraction of scale, if it's like bulk export of data or content, or context, we will govern it and monetize that accordingly.

So the way to think about our platform is we are open by design, but we're not a free data pipeline for everybody to take that information out. We're an intelligent customer platform. Access to that context is valuable and we'll price it in a way that is fair scalable and aligned to the value that we create for customers, which means we will also share the value. So that's in terms of our culture.

The second question you asked is around monetization, specifically of credits and how we think about that as tailwind now and maybe in the future. And I'll start by saying that we're beginning to see real usage beyond included credits and that's, again, happening because customers are getting clear measurable value. And the biggest driver today that we see is customer add. I talked about this, this has got clear product market fit and clear value and customers are using it to resolve support tickets, but they're also using it to answer tickets in terms of marketing as well as sales and it represents nearly 60% of the credits that are getting consumed right now.

We're also seeing very strong adoption in 3 other use cases. Prospecting agent has found really clear fit. And think about this agent can do account research, it can monitor your contacts, and then it can drive out reach based on the signal that you get about a company or a particular contact. And given how much disruption is happening in terms of the lead, this has got real pull. I talk to customers day in and day out of our prospecting agent.

Now what is interesting is how customers are buying credits. They're not just like thinking of this as an experiment. They're beginning to scale it. They're starting with what's included, then they replace real work and then they allocate more budget and that is a real positive green shoot. One of our customers [ Skytrack ] and [ Boltec ], they piloted this customer agent and started with 10,000 support chats a month, that agent was so effective that it used up all of the included credits in 4 hours. Then they allocated another $50,000 of their budget for credit and treated it more like a work replacement. So where we are is we have this cap holding in place. Agents are clearly adding value. They are being adopted. The credit mechanisms are in place. We're beginning to deliver value, and we see this as a tailwind and an emerging lever both in 2026 as well as many years to come.

One moment for our next question. Our next question comes from the line of Parker Lane from Stifel.

Yes. This is [ Jack McShane ] on for Parker. Yamini, would the Agentic ecosystem build-out seemingly accelerating throughout the quarter, whether it be through Frontier, [ Clad code ], [ OPIS ], et cetera. How did deal velocity trend in the quarter relative to prior periods? Are the weekly announcements of the next use case specific agent or a new model that specializes in coding, are those types of announcements causing any sort of confusion or slowdown in the pipeline? Maybe a better way of asking it is how are customers and prospects digesting to these announcements in real time?

I actually really like this question. I'll tell you there's a huge disconnect between what's happening in terms of announcements and how Investors are processing it and the actual conversations that we're having with customers in terms of AI adoption and use case. And again, I'll remind that the segments that we serve, there are 2 to 2,000 employee companies. They have a business to run, and they're not chasing every announcement that is happening out there. And if you look at the pipeline and what we are talking about, the first conversation that we have is how can they drive innovation and growth. It is not how can I adopt name the announcement of this week. It's really how can I drive adoption and growth and for ourselves, and we deliver that.

We deliver our platform. We deliver solutions that can -- that are easy to use, that have past time to implement, and they look to us to drive it. So we talked about Q4, in Q4, specifically upmarket was very strong. And if you look at the nature of the conversations we had, it was consolidating multiple applications is driving growth through innovation and then making sure that there is clear data and clean context to be able to get them to grow. That is the kind of conversation that we have. Again, huge disconnect and we had a very robust Q4.

One moment for our next question. Our next question comes from the line of Taylor McGinnis from UBS.

Kate, maybe just on the acceleration that we could see throughout this year. You mentioned a lot of growth drivers earlier, but could you just break out how much price is contributing to that versus some of the other growth drivers around seats and credit adoption, like could we be getting to the point that you mentioned earlier that credits are scaling, could that start to add a point to growth this year? Would just love to hear a little bit more about the breakout of those.

Yes. Thanks so much for the question. I actually think, Taylor, the easiest way to talk about it is through the trends that we're seeing in net revenue retention. As you know, net revenue retention was up this year, 105 in Q4, it was up to 103 for the full year, and that's about 1.7 points up from last year. The components of that, if you think about what was actually driving the expansion. It was very much all of the benefits of the seats pricing model change.

Now that is not pricing. The biggest impact actually is that we saw higher upgrade rates for seats across sales seats, service seats and core seats. As you remember, one of the things that happened with the pricing model change is that we lowered the barrier to get the barrier to entry to get started at HubSpot. So people bought the seats that they needed, and there was a much more natural upgrade path from there.

The other contributor was what you're talking about, which is as customers migrated and came up for a renewal we would -- they would see up to a 5% pricing increase, and that did help support net revenue retention in 2025. It will support revenue retention in 2026 again to a similar amount. But the bigger driver of the expansion this year and into next year were the other factors associated with the pricing change.

One moment for our next question. Our next question comes from the line of [ Kirk Materne ] from Evercore ISI.

I was wondering, Yamini, if you could just talk about the benefits you're seeing from AI internally, just in terms of your own R&D and maybe sales and marketing efficiency, where you're seeing some real levers there? Just any anecdotal comments would be great.

Yes. Thanks a lot, Kirk. Look, we have been transforming HubSpot completely through AI, and I'll start with coding. The way we build products has transformed completely, 97% of the code that was committed last year was done with AI assist. And if you look at our top engineers, they are living and breathing in agenetic coding with Claude Code, and that's how we build. And that has certainly changed the pace of innovation, but also the types of innovation that we are able to bring to the customers. So that's like number one, it has changed dramatically.

And then when you think about our -- how we serve -- we have been on this path of transforming with AI. Support completely done, our support is the first year support and nearly 60% of our support is handled by AI, which means our teams are spending on much more complex cases. We've been using AI to transform marketing as well as prospecting. Our prospecting approach internally has changed the level of meetings and the level of pipeline that we bring and in a given quarter, 10,000 to 15,000 meetings are being set up internally through prospecting. And almost everything that we do from sales in terms of node capture, in terms of deal progression, in terms of smart guidance for getting deals. All of that has grown, which means at the end of the day, our efficiency in terms of support our efficiency in terms of sales and our efficiency in terms of building pipeline has increased.

And overall, as a company, we are leaning very hard into AI. We set ourselves a target last year to say we want every HubSpotter to be inspired and work in this new AI-first manner. And we put out targets, then we blew through it. At the end of the year, almost every HubSpotter is using AI every day of their week. And so we're transformed and that helps us, of course, to move faster and operate at speed. But more importantly, everything that we learn of what works and what doesn't, we're building it into the product, and we are sharing it as best practices for the customer. And so a really good story that we're happy with that. And we're going to continue to do that. Now it's like we are on to the second phase of AI transformation internally to scale up our efforts even more.

Thank you. One moment for our next question. Our next question comes from the line of Rishi Jaluria from RBC.

Wonderful. This is Rishi Jaluria. Maybe I want to drill into one thing. So Yamini, I was struck by you mentioning [ Lovable ] as a customer during the prepared remarks. Talking specifically about a single customer, definitely it's striking that the market clearly is worried that by coding is going to replace incumbent platforms, but one of the leaders in [ Viecoting ] is using the intelligence to power that.

So my question is really this. As you think about your own adoption amongst AI natives, especially the cutting-edge ones that have the common household names over the past year in many cases over the past 6 months. Can you talk about why you might feel HubSpot is uniquely positioned for those companies? And why they're choosing to use you instead of leveraging all the [ Vicoating ] tools and [ Cloudco ] and [ CodeAx ], et cetera, to try to build their own.

Coding has gotten easier, but domain expertise and platform value has not just gone away, right? So people just equate coding to your ability to build everything. Look, I've just said that internally in terms of how we build products is completely transformed and we're building products completely. And of course, everybody within the company known as [ Vibe coding ] or how managers do it or U.S. managers do it. Our marketers do [ Vibe coding] , but it doesn't mean that we are turning around and replacing our core platform, a core HR platform, ERP platform. We're not building any of that.

And so I think there's a lot that is getting lost in terms of ability to code versus value of a platform. And that's what I would come back to, which is -- what we deliver is a platform, and it used to be that, that platform has unified data. That's why customers came to us. We were -- we built something organically. People came to us because there was value in unifying marketing, sales and service. But what has fundamentally changed now is that it's not just about the data, it's the context because context is what you need to make decisions, whether you're an agent or your team member that is making decisions on behalf of that agent, and that is what we deliver. And that is why all AI native companies, including the ones that you mentioned are starting with HubSpot as their platform of choice in terms of go-to-market.

Dharmesh, go ahead.

Yes. As someone that spends hours a day using Agentic coding tools. Both AI native companies and non-AI companies, the best companies spend the most amount of calories in adding value to their customers. They don't spend their engineering calories, going off [indiscernible] coding CRM or an ERP or an HR system, whatever, that just doesn't make sense. So just because it's possible. So we have, as Yamini mentioned, a large engineering team knows what they're doing, spending 97% of their kind of calorie using Agentic coding tools, they're not doing it to replace internal platforms.

So we think the best companies, both AI and non-AI we'll not be using [indiscernible] to replace core systems. They'll be doing it to add value to their customers. That's what [ Loveable ] is doing. And I think that's what's the best company in the world will continue to do.

One moment for our next question. Our next question comes from the line of Raimo Lenschow from Barclays.

This is [ Dave Cougar ] on for Raimo. Great to hear that YouTube and Newsletter leads are driving to differentiation with LLM generated Answers. Given the challenges of the SEO channel faced throughout 2025, should we think about that as a tailwind to top line throughout 2026?

I'll maybe talk about the Tofu trends, the top of the funnel trends. And look, yes, there's been overall decline in terms of content generated traffic, traffic that comes into the website, but that's something that we have seen coming. We have diversified the channels. We've talked about our playbook, and that playbook is working, right? And specifically, you mentioned a couple of like channels for us YouTube has grown newsletters, leads from Newsletters have increased more than 50% and AEO is increasing.

And I think like part of the way you should think about it is that the diversification strategy as well as our ability to lead with AI is going to help us continue to drive top of the funnel which is why you're seeing us double down on our guidance of net customer additions of 9 to 10,000 every quarter going forward. Last year, we added 40,000 customers, and this year, we'll continue to do it at that pace. And a lot of what we have done in terms of the playbook is what helps us drive customer additions even in a very challenging environment and marketing is completely changing.

One moment for our next question. Our next question comes from the line of Siti Panigrahi from Mizuho.

Great. Yamini, I just want to ask about the early adopters of AI agents, whoever using it. What kind of trends are you seeing in terms of them talking about their user expansion or seat expansion. Where is -- actually, that's getting funded, all the AI investment they are doing right now. Also, are you seeing any trend differently from your segment versus scaling mid-market companies.

Okay. Great question, Siti. So I'll start with like what are we seeing and then is there any difference between segments. In terms of the AI use cases, use cases that are getting resonance are the ones that show clear, measurable value and clear outcomes. So it's really about delivering value and showing clear outcomes that is driving the usage. I would say the strongest traction is customer agent. And I would also say prospecting agents, we talked about both of these agents data agent that we launched at INBOUND is another one. So all 3 agents are seeing really good traction and adoption.

And in terms of what customers look for? Is it easy to implement and get started and is it clear enough to see value. And can they get confidence that the credit consumption is somewhat predictable. And I think it checks the box in each of those areas.

The one thing I will talk about is core seat, right? Because when we think about AI monetization, it is both core seat as well as credit. And we've talked a lot about the credit. On the core seat side, as you remember, Siti, last year, we included Breeze Assistant into the core seat and now 50% of Core seat users have tried and are using Breeze Assistant. So we know that AI is adding value there. Similarly, we added all of the company enrichment data into cores. Again, the level of adoption for that core seat has really increased in the last couple of quarters. So our strategy to add a lot of AI and data value into the core seat is working. And the combination of core seat plus credit is what we think of as durable emerging levers in terms of AI tailwinds.

And in terms of what you mentioned in terms of upmarket and on market, look, broadly speaking, very similar trends. I think upmarket customers care a little bit more about data security. In fact, a lot of them talked to me about which prompts are encrypted, which prompts are retained and so on. So there's a little bit more sensitivity towards data security and prompt usage versus the down market segment, but the use cases are about same.

One moment for our next question. Our next question comes from the line of Brian Peterson from Raymond James.

Yamini, I wanted to follow up on the unified data model. I'm curious where you think that near-term cross-sell opportunity is most significant? Historically, I think we've heard a lot about building on Marketing Hub with Sales Hub. But as you've kind of broadened out the portfolio, where do you see like the incremental product adoption really ramping up in 2026?

Maybe clarify the question a little bit when you said unified data model. Are you just talking about Multi-hub adoption? Or are you talking about something else?

Yes. Multi-hub adoption more from a cross-sell perspective. So as you're looking to kind of go back into your base, where do you see the biggest opportunity for cross-sell in '26?

Yes, that's a great question. Look, I think in terms of where our customers land they mostly land with marketing plus Sales Hub. That is a common man pattern or they land with all 5 hubs, right? Those are the 2 common patterns. If they land with Marketing Hub and Sales Hub, then what happens is that in a few months, they begin to see the need for Data Hub because in almost everything that you do with loop or in almost everything that you're doing with sales automation, you need better quality data, ability to ingest more data ability to real-time bring data through AI promts and that's what Data Hub provides. And so the next combination we see is Data Hub.

Service Hub is another one where there is a ton of like cross-sell opportunity, especially with the advancements that we have made with customer agents, but also across the full platform, embedding summarization of ticket, sentiment analysis as well as being able to respond quickly, we're beginning to see Service Hub adoption. So the patterns are land with Marketing Hub and Sales Hub expand to Service Hub, Data Hub and content hub. And we are continuing to invest across the platform, and that is the motion that we want to continue to build.

One moment for our next question. Our next question will be from the line Arjun Bhatia from William Blair.

Perfect. Thank you so much. I just talk to touch on maybe the other side of the net retention rate dynamics. I'm curious where sort of upsell and upgrade basically the non-seat-based expansion levers, how those are playing out and whether we've seen sort of an uptick there at all? And maybe as a part of that, I would just love to hear Yamini from you on how the kind of SMB broader SMB macro environment is evolving given we've heard a little bit of noise there.

Yes. Thanks, Arjun. We've been talking about the dynamics with net revenue retention for a number of quarters now. So I think that you're familiar. But we always start the conversation on NRR with customer dollar retention and customer dollar retention has remained really strong and consistent for us in the high 80s. It's like ticked up a little bit this year, and we expect the same in next year. Where we've seen really strong upgrade motion is in the seats upgrade motion adding Service Hub seat, adding Sales Hub seat, adding core seats, and we're starting to see a building trend around credit adoption.

The other upgrade motions that we've been talking about contact tier upgrades, some of the cross-sell motions. They've sort of been in this holding pattern for a while. That's the conversation we've been having over time. And people are adding contacts. They're just not doing it at a rate and pace that is increasing and our expectation is that those trends are going to continue here for some time. That said, as I shared, we do expect net revenue retention to be up 1 to 2 points next year, and that's going to be driven by the continued success in seat. Upgrade rates as well as building momentum around core seats and credits.

Thank you. This concludes the HubSpot Q4 2025 Earnings Call. Thank you to everyone who was able to join us today. You may now disconnect your lines.

Guys, welcome to our next session. Really happy to have Yamini from HubSpot here with us.

Good to be here, Raimo.

And it's nice. It's -- sorry, I've been working with [indiscernible] for like 20 years, and it's the first time I met Yamini in person, which is kind of for me, very special actually.

Thank you so much.

The -- let's start -- bigger picture. So 2025 was a year of like a lot of AI-supported product announcements, and that was across the market. How do you think the industry is going to change? Like it's more bigger picture question now, but it feels like we're in the middle of a big storm.

It is massive. You talk about 20 years. I mean, every 20 years, there is a fairly big technology shift. And I think the difference is that AI has an even bigger transformative impact, and it's just moving faster in terms of the market. And so I think big implications for almost every layer of the tech stack. But for us, we look at the -- just the potential of AI and our focus is simple. How do we take that and how do we apply AI to help our small and medium businesses grow. That really is the focus because you can imagine the market that we serve, 2 to 2,000-employee company, they are not really focused on, is it Sonnet 3.7, Gemini 3. They can't keep up with every model change that is happening. But they clearly understand that the playbook for growth, whether it is marketing or sales or customer success is changing. And so for us, as the landscape transform pretty significantly, we look at the opportunity to apply this technology, democratize it for the segment that we serve and help them grow.

And do you think we will see like -- that's the opportunity and the fear that we have in the market at the moment, do you think it will change the competitive landscape and there are these big tectonic shifts where we had the mainframe guys and they're no longer there and now we have the new guys. Do you think this is going to play out here?

Absolutely. Any new technology wave is going to change the competitive landscape. The value that LLMs provide, let's just assume that it is going to continue to increase, which means the value that we provide on top of LLM has to continue to increase. It will attract new entrants. And when I look at like HubSpot and how we plan to compete from the product perspective, our strategy is to ensure that we are delivering value on top of what AI can do. We really look at AI and LLMs being complementary to what the capabilities of HubSpot are. And so our strategy is to provide value on top of what LLMs provide and be exceptionally complementary.

We do have distribution capacity, which means when we build a new AI feature, the next day, we can get it into the hands of thousands of users, get feedback immediately, use that as reinforced learning and improve the ability of our models to deliver value. And so we have distribution capacity as well as the partner ecosystem.

I think when an early-stage technology like this comes in, I do think customers need a bit more handholding to where do they get started? How do you get going? What are the use cases you need to look at? And we have the benefit of a very large ecosystem of both application and partners that can drive this. So yes, the landscape is shifting. And for us, the strategy is adding value to our customers beyond what we did before.

And can you put some more concrete stuff on there is like, there's a lot of AI stuff that went GA, sorry, for you guys. Like where are you most excited about?

Exactly. In terms of our AI strategy. So let me start with our AI strategy. It is threefold. The first one is that we decided to embed AI into every part of our hub as well as the platform. The reason is we want to lower the friction. If someone is already using our product today, they should be able to take an AI feature tomorrow that is released and be able to use it in the flow of their work. And so strategy number one is embed AI across all of the hubs and platform. I'll come back to how that's going.

The second part of the strategy is deliver agents. We know that one of the biggest capability is the ability of agents to do work on behalf of humans -- with humans in the loop. And so we've launched 3 that have now in the process of going to GA. The first one is customer agent that resolves tickets. We have over 6,200 customers on that customer agent, resolving more than 60% of tickets through that. We have a prospecting agent that can do account research and can reach out to customers on behalf of the end customer. And we have 6,400 customers on prospecting agent and growing. And we have a data agent that we just launched. So there's a set of agents that we've launched. And we also deliver capabilities to help our customers create and customize their own agents.

So that's the second part of the strategy. And the third is to deliver a Breeze assistant, something that acts like a copilot that helps every go-to-market employee get better insights from the data in a very conversational manner. That is our AI strategy. And a lot of this has gone into GA over the past year, and we're seeing traction. In the embedded AI, which people are beginning to use, we can see direct improvements in terms of the leads generated deals closed, which is a pretty big area to move as well as service tickets that are being resolved and agents I kind of mentioned. So it's early stages of a fairly massive change in terms of our product architecture and how we deliver value, and we are very, very encouraged by both the adoption as well as the beginning of traction.

And how do you -- like since we the financial guys, like we kind of care about the money all the time, how do you think -- how do you -- how does it play out from a monetization perspective? And there's kind of what you can do now, but there's a question about the industry overall in terms of is it going to be table stakes, you need to have it and it's part of the product? Or can you kind of do higher value-add SKUs? Like how do you think about that?

Yes. So multiple parts of that question. How do we plan to monetize AI? How is that going? And broadly in the industry, as the industry is trying to figure out value-based, outcome-based kind of pricing metrics, how do we think about it? Great question.

So I think I'll start from our monetization philosophy, which is that we always start with delivering value before we monetize anything. Why? Because there's something called churn. If you do not deliver value, if you do not make sure that customers are using it and getting repeated value out of something that you deliver, then you're going to see growth. And then in the next 12 months, you're going to see churn. And it's really important for us from a monetization philosophy to deliver value consistently and then drive monetization. That is the philosophy, and we are strategically very patient in terms of how we approach monetization.

Then in terms of our pricing model, our pricing model is hybrid. We believe that the future will have some seat-based monetization and some usage or outcome-based monetization. We don't think that the industry is going to turn all the way to just credits and tokens and that's -- and especially when you think about the segment that we serve, which is 2 to 2,000 employees, small and mid-market customers, they want a level of predictability in terms of the budget and just having consumption or outcome-based is not going to work. And so we are very thoughtful in terms of having a hybrid monetization model with both seats as well as what we call credits or usage-based pricing.

So how is that going? And how is the monetization working? I think I'll go back to the strategy. The strategy is threefold: embedded AI, agents as well as assistant, Breeze Assistant. So the embedded AI also has monetization. What happens is that when customers use features, they are much more sticky and they also see value and they continue to upgrade. We've seen that this year in terms of Sales Hub seat upgrades. We've seen that this year in terms of Service Hub seat upgrades. They're using it more, they're getting value and they continue to buy more seats.

Now this is interesting. There's a whole conversation about seat compression. This year, we've seen seat upgrades in terms of Sales Hub seats and Service Hub seats. In terms of -- I'll finish the monetization thought. In terms of agents, agents are what we will monetize through credits. which means if we are resolving tickets, if we are doing outreach, if we are writing a blog or if we are managing data for our customers, that will consume credits. Credits are included within the tiers and then they can buy more than that. And what we have seen, it's been pretty early days in terms of credit consumption, but half of our credit consumption is coming through our customer agent and the other half is coming from prospecting agent, data agent, data syncs and so on. So that is the way we're doing.

And then finally, in terms of the value of our Breeze Assistant, we've embedded that into core seats, and we'll talk a little bit more about this. And so we are also seeing the level of uptick and usage in terms of core seats expand. So our monetization philosophy is hybrid, and we do it through our core hubs as well as credits as well as the core seats. And again, this is one of those where I would say that we're pretty early days in terms of this massive shift that's happening within our customer base, and we're very thoughtful in terms of how we monetize and create a tailwind for ourselves and our business over the many years to come.

Yes. And then will you -- will be -- should we see that in the NRR number in theory, if you upsell, if you have credit, et cetera, then...

In theory and in practice. You have to -- you should see that. I mean any -- like if you break down NRR, it is how do we retain customers, how do we upgrade customers. That's really the basis of that. And the more we retain customers by delivering value and the more customers upgrade either in terms of seats or additions, then it will show up in NRR. And that is the tailwind that we have.

Yes. Okay. Perfect. And then the other part of the story, and it's not AI actually is multi-hub, like -- where are we on that journey?

Correct. Exciting journey. I think if we step back all the way, Raimo, like we went from a single product marketing automation company to a multiproduct company to a whole platform. And as we have gone on that journey, what we have recognized is that customers really want visibility across multiple go-to-market functions. The biggest drain for go-to-market functions is that if you have a great marketing solution, but you are not connected to sales or if you have a great sales solution, but you don't know what support is asking for, then you cannot grow. And so our customers within the segment really focus on wanting better visibility across marketing, sales, service, so they look to us for multi-hub.

In addition, I would say that through the pandemic, post the pandemic, there were a lot of point solutions and it was easy to kind of deploy these point solutions, but it was exceptionally complex, both from a cost as well as the ability to maintain. And so when I talk to customers and as I look at like our pipeline, most of the conversations are how can I drive better visibility across my go-to-market functions? How can I lower my total cost of ownership and how can I drive growth? And that, in combination to how we've built our product over the past few years have really driven a multi-hub adoption to the point that I think the number of customers that buy single hubs is now about 10% to 11%. That means more than the majority are basically using multi-hubs within HubSpot.

Yes, yes, yes. And then as part of that -- it's actually not part of it. It's like another side story is also like you slightly moving upmarket or you're moving upmarket. Talk about the journey there a little bit in terms of -- and also like you worked in an organization that we're selling really enterprise. Like when you say upmarket, like how do you define it?

Exactly. That's a great question, Raimo. So I will say that I've been talking about the segment that we serve, which is customers with 2 to 2,000 employees. And we typically call 500 to 2,000 as really upmarket customers. So think about the difference between a 20-person company that we serve and maybe a 2,000-person company that we serve. So I would say like 500 to 2,000 is really where we consider as upmarket.

Now you said that the journey that we've been on, maybe taking it like maybe 3 to 5 years ago, we set on this journey of focusing on the segment of 500 to 2,000-person companies and really driving market share. So sometimes people say, you're going upmarket. It's not like we're going to go to enterprise. I've certainly worked in companies that serve 100,000 employees or 200,000 employees. That's not what we mean. We really mean that 500 to 2,000-person company, and we really want to drive market share acceleration within that segment.

And we've done three things in order to do that. The first is really building a product that is very powerful, but super easy to use. You think about a typical 500 or 1,000-person company, they do two things. They either have a whole bunch of point solutions that they're trying to kind of stitch together, which is super complex or they take an enterprise-class solution and try to make it work for their team, and they mostly find it's not usable or it's super hard to make changes and it doesn't serve the needs of the business. And so what we focused on is really making our product easy to use, super intuitive, at the same time, meeting the level of sophistication that is required by a 1,000-person company. And our product market fit has gotten really better over the last 5 years. It's not one feature, it's not one product. We've just consistently focused on making the product better for that segment.

Second thing that we did is really we took the partner ecosystem. We have about 7,000 partners that work within the solution partner ecosystem, and we pointed them upmarket. And we said, this is the segment that you should target. Here is where we can drive joint customer wins. And those partners who way back used to be marketing agencies have really transformed now into CRM implementers, full customer platform implementers, and they've done a really good job of helping our customers see the value of HubSpot.

And then third is brand and driving brand awareness with an upmarket customer segment, and we have consistently invested. And so the combination of those three, but also the consistency with which we have focused on that segment has helped us drive value into the upmarket customers. And I think we have a lot more room to increase market share within that segment.

And what do you -- talk a little bit about go-to-market. So If I can give you feedback, when I talk to partners, they are like, okay, HubSpot as a product up there is actually very well suited. But if I think about sales maturity, there's still a long way to go compared to like the guy that sits above you. Like what are you doing there?

Yes. I think it's there. I think there will always be like one side that kind of catches up. I think the product is absolutely there. From a go-to-market perspective, a couple of things. One is that we recognize the profile of a salesperson that is targeting a much larger company needs to be different. And so for the past few years, we have hired people. We have spent a lot of like investment in terms of enablement of how do you do committee selling, right? Like when most of the time, the buyer is no longer a single decision maker, it is committee buying. And typically, they have to present to their Board or a PE firm or a VC firm's investment, how do you actually do that? And so there's just been a lot of emphasis on the profile of people that we have brought there, but also the level of enablement. And when do you bring partners, how do you communicate with the customer as a team of partner, HubSpot and the teams that we bring together.

So I think we have invested a lot there. And I also think that one of the things that we have done this year is leverage AI internally in go-to-market to help with all of this. It's one thing to enable and drive material for our sales team. The other thing is like within the product to use AI to say what's the next best action or how do you kind of like have the right message for that customer. We are leveraging AI internally. But this is not a one-and-done thing. I think we have to constantly kind of like up-level the people that we bring in and constantly up-level the skills of the people that we currently have.

Yes, yes. Okay. Perfect. On that kind of more top line featured kind of part of our discussion, like if you bring it together, now I had like a good few e-mails from the audience then saying like, okay, well, you need to ask them at the end, like, okay, how do I have to think about your growth profile? If I'm listening to you now, it all sounds like there's a lot of things coming together. It makes a lot of sense. How does that translate into growth for you?

Yes. I think it's a great question. I think you've laid it out really well. If I look at the growth formula for HubSpot, the current set of levers that are working really well are the ones that you touched on, which is our multi-hub momentum as well as upmarket momentum. Both of these have been at play for multiple quarters in a row, and we feel confident that we can continue to execute with those current levers, and they have been able to drive growth. So those are the existing levers.

The thing that we haven't talked about is that in 2024, we made a set of pricing changes. And we lowered the seat price. We removed seat minimums, and we said that we are going to add a core seat, and those were the changes that we made. It first went through for our new customers. And then this year, in 2025, it's actually rolling through our installed base and will continue to roll through our installed base next year.

That is a tailwind. That is a tailwind for multiple reasons. One, because we decreased the seat minimums last year, customers bought exactly what they needed. And this year, they're coming in and upgrading and buying more seats. And so it creates a very healthy customer base. And so that impact has been good. The second is, as it rolls through our installed base, we first migrate them to this new seat pricing. And once they do that, the next renewal is when they see a price increment, and that has been a tailwind. It was a tailwind this year, and it's going to continue to be a tailwind going into next year. So those are the levers.

Now the two additional levers that we feel are emerging and will play out over the next many quarters is AI monetization. We talked a little bit about credits. When we have agents, agents consume credits. And when we have data, data things consume credits and the combination of those 2 today is super nascent, but we will see that flow through, and that will be an emerging lever for our growth. And so is core seat. We mentioned it briefly, but that is the platform seat. We've also embedded AI and data value into that core seat, and that becomes another lever.

So if I put this all together, we have clear current levers that are working, which is multi-hub upmarket and seat pricing, and we have emerging levers in terms of credits as well as core seat, which will play through for multiple quarters. And that gives us confidence that we can drive durable growth.

Yes, yes, yes. I mean if I look net new ARR is getting better, but the overall growth hasn't kind of moved yet. So is this just the timing because like there's something that needs to build on top of each other?

Yes. I love that you're talking about net new ARR. We shared a slide, and we have gotten tons of questions about it. So maybe I'll take a minute to clarify it. At Analyst Day, we shared our trend in terms of net new ARR. The rationale for sharing that is to say the underlying foundational levers have been consistent and have improved over the past few quarters. The lowest point of our net new ARR was Q1 2023. And since then, we have seen net new ARR improve, and it went over our revenue in second half of 2024. Now that is good news. You should take that as like the trend line for net new ARR is improving.

Having said that, revenue base is much larger than net new ARR. So it will take a bit of time for that net new ARR acceleration to drive up revenue growth. But our focus was to kind of show what changed in terms of the underlying levers. And it's a question of time in terms of where that impacts revenue because the base is like much broader.

Yes, yes. I mean it's funny if I talk to some of your peers, larger and smaller, that discussion comes up. They might not call it net new ARR, et cetera. But that kind of -- and maybe the question is actually then 2020, 2021, did you have like this crazy cohort as well that kind of was a headwind and now you kind of -- everyone in the industry had to work themselves out.

Absolutely. Absolutely. I mean if you zoom out and look at like what has happened within the software and application space for the last few years, 2020, massive pandemic, everybody didn't know what to do. Second half of 2020 and 2021, everybody bought digital tools. And we had like massive acceleration within our installed base in terms of as well as new, and we all saw massive growth.

Now you can look back in hindsight and say that, that growth was like pull-forward growth, but we saw massive growth. And then starting second half of 2022, we saw customers do a lot of budget optimization and consolidation. And we talked about this during those years and those quarters, and we saw a massive decrease in terms of net new ARR because of that level of consolidation after that acceleration that happened in the market. And then since 2023, Q1, we have seen net new ARR continue to improve.

And so I think we've been through a roller coaster. It certainly feels like that, but you can look back and see what happened during the massive acceleration followed by the budget optimization to now kind of like more sustainable growth. And that's the dynamic that we're all kind of seeing through. And I'm sure you're hearing it from other companies as well.

Yes. So some of that is mechanical in a way.

It shows up as mechanics. Sure. Yes.

Yes. Yes. Okay. Last couple of minutes, I wanted to talk a little bit about profitability, margins, et cetera. Like there's so much new innovation coming out. There's so much you could do like especially for you that has so many growth vectors that you potentially could go against it. Like how do you think about that framework growth versus margins?

Yes. We've always looked at it as balancing growth as well as profitability. Again, if you zoom out over the last 5 years, we've gone from 10% operating margin to this year where we are close to 18-plus percent in terms of operating margin. And that has been a very intentional strategy. And part of that strategy is that we have continued to invest in R&D. We have continued to improve gross margins, and we have continued to find efficiencies in terms of sales and marketing, and we put a short-term target as well as a medium-term target. The medium-term target is to get to 25% operating margin, and we are committed to driving balanced growth as well as profitability.

And then you mentioned gross margin. There is -- today, I got quite a few that talked about, oh, there were some industry guys that talked about gross margin for the SaaS world, et cetera. How do you -- from your perspective, like how do you think gross margins will evolve for you?

I mean, look, we're not going to talk about 2026, but at least this year, the dynamic for us in gross margin has been that we invested pretty heavily in terms of our upmarket customers, and we actually rolled out 3 new data centers, and that obviously consumes a little bit of capital, and that has been a little bit of a headwind, but the right kind of headwind in terms of gross margin as we have continued to invest for upmarket customers. And I think more broadly, we'll see. I think there is a lot that is changing in terms of AI, both in terms of cost compression and new innovation that is coming up, and there's just a lot of ability to switch across LLMs that provide the best most efficient kind of inference costs. And so I think we'll be exploring that as we go. But it's a very dynamic time within the industry.

Yes. No, perfect. Last question I wanted to ask is capital allocation. How do we have to think about M&A, share buybacks, et cetera?

Yes. We've been thinking about all of those. And as you can see. And over the last few quarters, we have done a number of tuck-in acquisitions. And our M&A philosophy has remained very consistent, which is we want to keep the customer experience very pristine. The value proposition of HubSpot is that we are easy to use. We are pretty fast time to value, and we have a unified customer way of providing information back to customers, and we want to keep that, maintain that integrity of that.

So we've always looked at tuck-in acquisitions where we can acquire teams or talent to accelerate the road map. This year, we acquired this company called Frame that actually improves unstructured data processing. We acquired this company called Dashworks that brings in information across multiple sources into HubSpot. We -- last year, we acquired this company called Cacheflow that really accelerated our CPQ and commerce road map. And so I think we'll continue to do those. And as you know, Raimo, we also did a $500 million buyback, which we completed faster than we originally set out. And so we will use the capital responsibly across internal expansion, M&A as well as buybacks.

Perfect. And look, perfect timing.

Perfect timing. Thank you so much. Such a pleasure.

Thank you. Yes.

Thanks a lot.

Awesome. Hello, everyone. I hope you're all enjoying the day, and thank you so much for attending this session. For those in the audience that don't know me, my name is Taylor McGinnis, and I head up the SMID-cap application, SaaS space here at UBS. And with me today, we have HubSpot's CEO, Yamini Rangan. So thanks so much for joining us, Yamini.

Thank you so much for having me.

Perfect. So before we get started, just a reminder, if you have a question, you can ask it in the app and then I'll try to save a few minutes at the end to address any of them. So with that, Yamini, should we kick it off?

Let's go.

Okay. Perfect. So Yamini, maybe to just start at a very high level, when we think about the horizontal application seat-based SaaS space, I think there's 2 big investor debates. So the first would be around potential AI disruption, and then the second would be related to where are we on the market maturity curve. So I'd love to get your thoughts first on those debates. Do you think any concerns related to that are genuine or overstated? And we'll go from there.

Okay. That's a really broad question. And look, I think AI is absolutely transformative. And it's going to be disruptive if you do nothing about it, but it is going to be a tailwind if you know how to bring AI to your customer segment in the way that they will take adoption, right? And so I think like my perspective is that I don't know if it is overblown or overhyped at this point, but I will tell you, like, minus the hype, we do think that it is transformational to bring AI to SMB customers within our segment that we focus on to help them grow. And I think the strategy for HubSpot has been how do we change and evolve our product strategy in order to be the best at applying AI for our customer segment. That has been the focus for us. And we have evolved our overall customer platform and architecture pretty significantly in the last 2 years, Taylor.

First of all, like if you think about the data, one of the fundamental transformations with AI is that you can now process unstructured data in real time to deliver value, right? That -- we could never do this. I started my career in sales, and there was no way for someone to listen to my call and to summarize that notes and write an email and send it to the customer. It was just not possible. That is unstructured data. So one of the first things that we have done is gone from just having really robust structured data to being able to capture all of the unstructured data that is in calls, that is in video transcripts that you'll have, to be able to capture that in real time, that's the data layer, and we have made a lot of the changes there.

The second thing with AI that you can do is you can build a context layer as an application that delivers a lot more value. So again, think about HubSpot as we deliver marketing sales and service solutions to our customers, the ability to understand that business deeply and the ability to connect their tone, their voice, their position, their value proposition across almost any agentic work that we can do, that happens in the context layer. And we're building that context layer where you can -- as an end user, you can say, "Write me an email," it immediately knows that your brand tone, your brand voice, your value proposition, it pulls from that instead of just kind of writing a generic email. So that context layer is what we have built within the agentic platform.

And then the third thing that is happening at the user interface layer is that we used to have point-and-click applications. You can go to any of the SaaS applications, point somewhere, click and create reports for yourself. Now you have conversational interfaces as well as agents doing that action. And that's a pretty big change where it's not just helping you do work, but it is actually doing work for you. And an example is a customer agent that resolves support tickets. And so we've built those agents that sit on top of the context layer that is built on top of structured and unstructured data.

And if you step back and think about that, that is a fairly big evolution in terms of the architecture, but it allows us to do what we care about doing, which is we want to help small, medium businesses grow, and we want to bring great AI solutions to do that. And in order to do that, we have really evolved our product strategy.

Yes. That's great context for how HubSpot is evolving with the introduction of AI. Focusing on the opportunity and the runway left, a common question that we get from investors is still with Marketing Hub, let's call it, roughly 50% of ARR, how much runway remains in that business? So maybe you could talk a little bit about -- you've introduced the new Loop Marketing, right, solution. How is that playing into how you're thinking about opportunity longer term? And what gives you comfort that there's still ample runway ahead?

I mean, look, marketing in 2022 and 2023, the channels were pretty saturated. People were trying to do what they were doing before in the playbooks, and there was just not a lot of like improvements in the playbook. But what has happened in the last couple of years with AI is that marketing has fundamentally changed. And it has changed in a couple of ways.

The first one, obvious one, everybody knows about this, is that AI Overviews are providing answers, which means people are not clicking blue links and coming to websites. And that means traffic to websites, content leads -- that's what we would call it, right, content leads have gone down. And that is a fairly big shift that has happened in the last couple of years. It's going to accelerate from here on out because as AI Overviews become global as well as AI Mode becomes global and pervasive, you're going to see content leads go down. So that's like #1 change.

At the same time, there is a new channel called AEO because now instead of customers going to Google and searching for information, they start in LLMs with a deeper question. And when they do that, that actually becomes a source for leads. So there's a completely new channel that's coming up. And in the industry, we call it AEO or Answer Engine Optimization, and that's super nascent. But what is interesting about AEO and Answer Engine Optimization is that because people ask very specific questions, then they convert faster. In fact, within HubSpot, we've seen leads from AEO sources, from LLMs convert 3x faster. Again, something that is unheard of within the marketing industry.

And so there is -- it's such a transformational time that is happening within marketing. And there was a lot of questions at the beginning of this year is, like, "How is HubSpot going to deal? Your website traffic has come down." Of course, but we've been watching this. We knew this was coming. And so specifically within HubSpot's top of funnel, we diversified. We went to YouTube. We opened up like 10 YouTube channels. We actually bought podcast networks, and we have expanded podcast. We actually acquired email newsletters. And so we diversified our sources of leads, and we've now been able to really go from content and education sources to people-led channels. And that was number one. And then we've experimented pretty heavily with AEO. We're the #1 in CRM in terms of AEO visibility as well as share of voice.

And so what we did, you mentioned Loop Marketing, at our conference INBOUND, which is a conference that we had in September this year, we shared a new playbook for how customers and companies can grow in the age of AI. And it starts with the playbook of diversifying channels, of leveraging AEO and really coming up with new ways to show up within LLMs as well as use AI to drive better personalization and conversion. And the reception from our customer base and partner base has been just exciting. They know that they need a new playbook. And when we launched this, it makes sense to them, and now they're in the process of adopting it.

And so to answer your question, there's a lot that is changing. It's an exciting time in marketing. Our customers within the segment that we serve are looking to us for answers of how to drive growth when content leads and inbound leads are going down. And we now have the playbook. We have products that actually help with the playbook operationalization and an ecosystem that is ready to help our customers. And we think that there is a big opportunity ahead for us to grow marketing as well as just help our customers through the multi-hub journey there.

Yes. You raised a lot of interesting points. Because I think when you -- when investors heard about the potential SEO disruption, it created a lot of uncertainty on what happens with the HubSpot type of funnel. You just spoke about some of the things that you've done internally, right, to mitigate any impact and then also, too, that the shift to AEO and GEO actually creates new opportunity. So I'm curious, like, today, in your conversations, knowing we're still very early days, right, in this transition, is that resonating with customers? How are -- how is that playing out into contract negotiations, deal negotiations and what you're hearing about future traction with Marketing Cloud?

Yes. The first thing that I would say is that HubSpot's Marketing Hub was never an SEO product. SEO is a top-of-funnel tactic. And our marketing solutions have always been a full funnel platform and a solution. We've always had exceptionally strong email marketing. We've always had customer journey analysis across multiple sources and multiple channels that we support. And so I think, like, one maybe misconception is that SEO is the only thing that we did. We couldn't be farther from the truth. We are a full funnel marketing solution for our customers.

In terms of the conversations, I do think that we saw the inbound shift early because the volume of inbound leads that we're generating over the last decade was just really high. And so we were one of the first companies to see the inbound shift early and therefore, diversified pretty significantly. And so what resonates with our customers is now they are beginning to see it. Now they're beginning to see a lot of the website traffic go down. And so they are looking to us. The conversations that we have with our customers is how can you drive lead volume when one of your sources of lead volume is going down. Well, we talk to them about the playbook, and that resonates.

And so it is early days in terms of Loop. It took us quite a bit to kind of like establish inbound as the methodology, and we're very clear that, that is a similar opportunity for us to help our customers. So we have a lot of partner readiness that's going on. We have Academy, which is like we have an internal university where we train marketeers on the new strategies for how to grow with AI. That is now in full force, and the conversations are resonating.

Perfect. And in talking about a lot of these emerging growth opportunities, I think that's a good segue to the momentum that you guys are seeing on the bookings side and also the strength in net new ARR growth, which now has surpassed revenue growth. So first question there. I think part of what you guys have said in the past is the reason you disclosed that slide at the Analyst Day was because you saw that trend consistent over the last couple of quarters. So one, what's driving, right, that sustainable growth that you guys have seen today in net new ARR growth?

Oh my God, that one slide. I will tell you that just to step back, the reason we shared that trend is that we've -- as a business for multiple quarters in a row, we have seen consistent trends that have reaccelerated net new ARR from what was the low point in Q1 2023. And there are a handful of things that have driven and sustained that acceleration.

First one is upmarket momentum. For multiple years now, we have looked at our upmarket segment as a place where we have improved the product market fit. We've pointed our entire partner ecosystem to delivering solutions for our customers there, and our brand awareness has increased. And because of that, we see momentum upmarket with larger customers. And that has been multiple quarters in a row, and it has been consistent in terms of how we have executed there.

I think the other thing that we have seen over the past few quarters is platform consolidation as well as multi-hub momentum. One of the things that we find with customers is that when they use point solutions or now point agents, it becomes really hard to get insights about their customers and how they can drive growth. So one of the most common conversations I have with customers is how can I bring marketing, sales together so that I can get customer information in one place and therefore, insights to drive growth. Simply, that's what we do. That is the bread, butter, jam for us in terms of the conversations. And that has led to multi-hub momentum over the past few quarters. And so we wanted to share that there are consistent trends that are driving that acceleration of net new ARR, and we feel that those will continue to operate as we go forward into the next year.

And in addition to that, we think that there are 3 emerging levers for our growth. One is the seats pricing model change that we did last year. In 2024, we changed our seat-based model pricing. We lowered the seat minimums as well as the ASP. And our thesis was that, that will allow more customers to start with HubSpot and buy as they need and continue to grow, and that's exactly what we are seeing. Taylor, this year, we have shared that we've seen seat upgrades from the initial cohort of customers as well as the installed base rolling through the seat pricing model. That is a tailwind for our business and will continue to be so next year.

The other 2 emerging, and I'm sure you'll ask me more detailed questions there, is core seats, which we also launched last year. We have seen the adoption of core seats continue. And we also launched credits monetization this year. So if you look at the current trends that have been at play for multiple quarters in a row, we feel confident about that. And we have a set of new emerging levers for pricing as well as core seat and credits that will -- that gives us confidence that we can continue to execute well.

Yes. Perfect. That's all great color. So I appreciate all the insights. The next question that you got on this slide, which I'm sure you've gotten a lot of these questions today, is, does that potentially mean that you could see faster revenue growth on the back of net new ARR growth reaccelerating? So one, any comments you could give there? And two, is getting back up to that 20% plus still the target and goal?

Look, we want to grow faster than where we are today. Absolutely. And that is why we've tried to be very, very clear about the growth formula and -- which is a set of consistent levers that we currently have as well as a set of emerging levers. And I do think that AI is a multiple year tailwind, and some of the pricing changes that we have made also aids that tailwind. We're not ready to give 2026 guidance. I have Chuck somewhere here, and he will literally stare me down. So I'm not here to give any guidance in terms of 2026, but our aspiration is to grow faster than where we are today.

Perfect. Don't worry, Chuck. We'll leave it there. Maybe moving on to Data Hub. So Data Hub at INBOUND, it seemed like there was a greater emphasis on that, which resonates and makes a lot of sense because the common refrain that we hear is that in order to unlock a lot of the value with AI and the application layer, you need to get your data state in order. So is that the exact opportunity that HubSpot is targeting? And maybe you could just talk about some potential emerging growth opportunities out of Data Hub.

Yes. I think you're exactly right, Taylor. In order for you to unlock any value from AI, you need data in one place. And you don't just need data, you need high-quality data in one place. If you take a typical 500-person company, a lot of times, data is disparate in multiple places. They need to bring it together. And data is incomplete or just duplicate data. I mean I used to live in this RevOps world for many years. And the problem that you have is that even if you have customer data, it is inaccurate because it was not updated in real time, or it is duplicate because you will have the same Taylor McGinnis with multiple emails. Which email am I going to send to you based on what I know about you, that is a huge problem.

And if you don't have that sorted out, then you're not going to be able to put agents on top or a use case with AI on top to get the real value from it. And so Data Hub does exactly that. It does 3 things. The first thing it does is it helps bring data from across the go-to-market stack and across the enterprise stack into HubSpot. We have something called data sync that allows you to pull all of that information in one place.

The second thing it does is improve the data quality. We now have the ability to use LLMs to say, give me the exact name, email as well as the role of this person and fill that information out. That is something that you can now use LLMs to do and make sure that, that data that comes back is high quality as well as validated data. That's the second thing it does. And then -- we -- the third thing from a Data Hub perspective is we created a workspace for data analysts and RevOps analysts. It's called the Data Studio, where you can now make joins and make changes to that data and drive automations from there.

And that is the foundation to then be able to use a data agent or a customer agent on top of it. And so it is almost foundational to getting value out of AI. And we see that as a multi-hub opportunity. If someone wants to actually drive the Loop Marketing, they need clear data for segmentation, for personalization. So they need to start with Data Hub and get that high-quality data, then they'll be able to use Marketing Hub to drive better personalization. So it's a multi-hub opportunity to drive Loop Marketing for our customers as well as improvements on the sales side.

Yes. So moving up the stack into the AI agent layer, HubSpot introduced a number of AI agents. On the last earnings call, there were some pretty impressive statistics just in terms of the quarter-over-quarter customer growth that you're seeing with those offerings. So maybe you could just talk about the pace of adoption today, how that's trended relative to your expectations? And when do you think we're going to reach -- I know this is a hard question to answer, but when do you think we're going to reach that tipping point where we get more widespread production use of AI agents?

Yes. I would say we have widespread production use. I would say there's a lot more of adoption to go to. But if I step back, we have customer agent, prospecting agent and data agents. Those are the 3 featured agents that we have launched, and they are now in general availability. Customer agent resolves tickets, whether it's support tickets or sometimes sales and marketing questions, it resolves that. We have over 6,200 customers on the customer agent and with an average resolution rate over 60%, and that was in a fairly short amount of time since we got to general availability. Of course, the opportunity for us is to get it to multiple times that in terms of customer usage, and I'll talk about what will help us get there.

Prospecting agent is another one. It does something very, very important within sales, which is research accounts and helps you figure out which accounts that you need to prioritize and who within that account you need to contact. And that's one of the foundational, kind of, like, jobs within a BDR team or sales team that used to be exceptionally manual. There's almost -- like, nowhere -- when I started my career in sales, I used to, like, literally spend hours trying to get to which accounts do I need to do? Prospecting agent does that. And we are seeing really good traction in terms of that particular use case. We have over 6,400 customers that are using that particular use case.

I think you asked a question of how does it get, like, widespread. I do think that it starts with some executive within the customer account that wants to drive AI and wants to get value as well as growth out of AI. A lot of times, people have asked me this question of like, "Do you see more usage in upmarket customers or down-market customers, or what does it take?" I actually think it takes a leader within the organization to say, let's just not -- let's jump in and start adopting AI. That's the #1 thing that we look at. Then you need high-quality data. We talked about the data that is needed for AI. Then you need high-quality data, and then you basically need a road map for driving adoption. Where do you start? What are the set of use cases that you start and drive.

It's not dissimilar from every other technology cycle that we have seen. If you step back and think about the other technology cycles that we've gone through, you always have customers in 4 broad buckets. You have bleeding-edge customers that are running towards a new technology. If Gemini Model 3 comes out, then next day, they are actually beginning to use that. And that's like the first group. The second are early adopters. They wait to see that there's a little bit of momentum in certain use cases. That's the second group. And then there is a majority of customers that are waiting for early adopters to get really good benefits so that they can start. And then there are laggards that we're still talking to people who are -- for the first time, are adopting SaaS, right? And so we're seeing exactly that. When we talk to customers, we categorize them into 1 of these 4 buckets. And based on that, we help them come up with a road map of how they should be adopting AI.

And so I think I continue to believe it's like a huge tailwind, and it's all about delivering great customer value that is repeated, visible for them so that the adoption begins to kind of like increase in pace.

Yes. And another interesting move that HubSpot made in the AI space, it was the first B2B SaaS company to create a number of connectors LLM providers. So I'd love to talk about those partnerships, right? So on one hand, it seems like there's a lot of value that can be unlocked by partnering with the LLM providers. But then you have, as an example, OpenAI, where they released a number of videos showing what they're doing internally with AI agents, especially as it relates to front office use cases. So I think that, that sparked, amongst investors, a question of, well, could these LLMs start to get into the AI agent space, be potential competitors in the future to the SaaS incumbent.

So first question for you on this topic is, can you provide a bit more color on these partnerships? Are they mutually beneficial on both sides? And because on its DevDay, OpenAI actually showcased HubSpot using AgentKit, maybe you could just talk about what that is and what that means.

I mean there's a lot going on there.

A lot going on there.

Look, I mean, I was just really surprised with the reaction for the demos because almost every large company internally uses their own products. It's not been -- it's not a new thing for you to use your product internally and to -- like, Google has done this for years. They have an internal CRM. And Meta has done this for years. They have an internal CRM. And so I think, like, it doesn't mean that you're immediately going to productize it and build an ecosystem, build an army of sales reps and start working with customers and drive deployments. I think there's a lot between those 2.

But having said that, maybe taking a step back, we do believe that LLMs and HubSpot are complementary. There is tremendous complementary value that we add on top of each other, which is why we were one of the first CRMs to build connectors, like you said, with Claude, with OpenAI, with Gemini. This is a new surface right? If you think about LLMs, there are a new surface and a new operating system where people are asking questions. And we want to be the one that is bringing insights -- business insights into that surface.

If you go to an LLM and you say, 'Write me a sales email," it's going to write you a very generic sales email. But if you can bring the HubSpot context and say, "Based on what's in my pipeline, based on the last 20 conversations, write an email," that is a much better email that converts better. So I think our strategy has been let's take the value that LLMs provide, which is insights, and add the context of the business application in order to deliver exceptional value to our customers. And that's why we built these connectors.

And what we are seeing is that a lot of the high-level questions of, "Give me the trends between the last couple of weeks," happens within an LLM, and the actions that people take come back to HubSpot. "Let's build a campaign, let's create a set of sequences to reach out to 1 million-plus customers in our contacts." Those types of actions happen within HubSpot, and we're pretty comfortable with insights generating from LLMs and actions being taken within HubSpot.

The other thing is that as applications, we all need to be present within LLMs. We talked about AEO. When you think about AEO as a new source of leads, you have to be recognized within the LLM. And so partly, what we are also seeing is that when customers or prospects use an LLM to start with asking a question to generate insights, it also -- it's a pretty easy citation process to get them back into HubSpot, and there is tremendous value. We want to be the best AI go-to-market customer platform on top of LLMs that add value to our segment of customers, and that's literally the strategy we are executing.

Perfect. And then before I take one from the audience. You introduced this new pricing model, and you're one of the few SaaS companies that's actually talking about seeing a pickup in activity as it relates to seat expansion. So could you just elaborate in terms of what inning are we in terms of this transition? How you expect that to evolve going forward? And not to throw too many questions at you, but there's been the introduction of this credit space model as well too. So how are you thinking about for the customers that have adopted agents? Like what does that mix look like going forward? And how do you think about the balance of those 2?

Yes. So maybe I'll step back and talk about what is our pricing strategy and how do we think about the opportunity of core seats as well as credits. And if you step back, our pricing philosophy has always been very consistent, which is add value before we monetize. And we've been very disciplined about that approach because when we do that, we know how that delivers. Once we deliver the value, then we don't see the churn associated with it in the back end. So the repeat value and visible value to customers has been the North Star for us.

Having said that, our pricing is hybrid. The way we monetize is through persona seats like Sales Hub seat or Service Hub seat that is for a specific role. We also monetize through core seat, which we'll talk about in a minute, and credits for certain aspects of agentic actions. Those are the 3 mechanisms through which we monetize our product. The Sales Hub, Service Hub seats, you know it, it's been consistent. We have embedded a lot of AI value. And as our customers use AI features within that, we begin to see seat upgrades. We're seeing that within Service Hub seats. We're seeing that within Sales Hub seats. And as they engage with our platform and use embedded AI features, they continue to upgrade.

Core seats was something that we launched in 2024. And when we launched it, we actually added platform value. Now think about a user that wants to edit a CRM record. They want to create a new custom object or custom property. They needed the core seat. And when we did that, we saw pretty good traction, and we've built a $100 million business in a fairly short period of time with the core seats. What we did at INBOUND this year is we added AI and data value into the core seat. Typically, you'll see a lot of SaaS companies have a separate copilot, and they'll charge x dollars per month per copilot seat.

What we did is we took that, Breeze Assistant, which is like our copilot. We added it to the core seat. In addition, we took a lot of the data that we had from Clearbit, which is the company enrichment data and contact enrichment data, and we put that as part of the core seat. So now the core seat is what you will need for every go-to-market employee that is not using a Sales Hub or a Service Hub seat. And we think that as we add more AI and data value, we can drive a lot of adoption, and we are in the early innings of core seats growing within our customer base.

And then the last part, which you already mentioned, is credit. So agents use credits as well as Data Hub syncs use credits, and we're in the early stages of rolling that out. We talked about some of the early traction that we are seeing with customer agent and prospecting agent. And customers will get included credits, and once they are done using those credits, they can upgrade to $100 packs or $1,000 packs of additional credits. And so early days, but again, the combination of these 3 is how we monetize AI across the platform.

Perfect. And to wrap it up, just one from the audience. So the question is Loop seems more of a playbook than a product. It seems like this could evolve into an actual product and be a potential revenue opportunity for you going forward. So doesn't HubSpot have the right to win here given the breadth of the customer base for whom you're already solving demand gen problems for?

Yes. Loop is a playbook. Just like INBOUND was a playbook. INBOUND was not a product, and Loop is not -- it's very similar to that. And we think about giving -- when so much is changing within marketing, we want to provide a step-by-step approach customers can take in order to grow their top of funnel. One of the things that -- primary things they come to us for is, "We want to have digital presence, and we want to grow our top of funnel," and that is, "The playbook is Loop." Now what we have done is that for every step of the playbook, there's a combination of features within Marketing Hub, Content Hub and Data Hub that powers that playbook. So we talked about diversification of sources. Well, we have within Marketing Hub and Content Hub, the ability to drive content across multiple sources, and that is an example of how we deliver it.

So the way you should think about it is Loop is the playbook. We have Marketing Content and Data Hub that enables the steps within the Loop playbook. And we are now training our internal teams across customer success and sales as well as our partner ecosystem to help our customers drive that methodology and adopt it. And we do think it is a pretty big opportunity as customers look to us to help them navigate where AI is taking them.

Perfect. Well, we'll wrap it there. Thank you, everyone, for joining, and let's give Yamini a round of applause.

Thank you so much. Thanks a lot.

I was tempted to leave the doors open because we always talk about having a fireside chats, but this is like having an oceanside chat. So like that's not too bad, especially on Day 2 of a conference. But for those who don't know me, I'm Ryan MacWilliams, mid-cap software analyst here at Fargo here for the 9th Annual Wells Fargo TMT Conference. With me today from HubSpot is CEO, Yamini Rangan.

Thank you, Ryan. Thanks for having us. It is a great location.

Like people are complaining on Day 1, and they're like, "Oh, it rained today." I was like, this is -- like I'm used to New York City in the winter right now. This is amazing.

Right. We'll take it in California. .

I'm jealous. Well, it's been an interesting time in software coming back after my break in launching coverage. And it does seem like it's kind of a rainy day in software right now, but that's a lot of opportunity. And I'm excited to hear about what HubSpot and what you're doing with AI. So for investors in the room, we're going to be speaking mostly around the product to start and then maybe some of the earnings at the end. So if you have questions, let me know, but we won't be taking questions directly from the room. So e-mail me at ryan.macwilliams@wellsfargo to get those in.

But Yamini, just to start, with your Investor Day, there's been a lot of new products from HubSpot at this point. What would you say like differentiates HubSpot's AI offerings?

Yes. We just had our annual conference a couple of months ago in September and we did launch a lot in terms of AI. It's an exciting time to be in the industry. Even though it feels rainy, it is actually an exciting time to be in the industry right now. And just to give context, our AI strategy is to take something that is super powerful and apply it to the segment that we serve, which is we serve small, medium businesses. And we want to take AI and apply it in a way that can help them grow. That is really the strategy.

And in order to do that, we are embedding AI into all of our products. So you saw a number of feature releases there. We're building agents that can help do work. We have featured agents that we launched at our conference, which is customer agent prospecting agent and data agent. And we have a world-class Breeze Assistant that forms like the Copilot that every go-to-market employee can use. So that has been strategy and we kind of launched a lot of products to drive that. And the reaction has been very, very positive. I mean, talk about AI adoption broadly and where we are seeing green shoots in terms of consistent usage.

If we step back, you asked a question what differentiates our AI strategy. The first thing is we know SMBs and we've taken an approach of taking super sophisticated technology and making it very accessible to SMBs. That's been the business that HubSpot has been in, and we're doing exactly the same thing with AI right now. And that is the #1 differentiator because we know SMBs, we know what they need and their day-to-day job and their day-to-day challenges, and that's like #1.

The second thing I would say is context, and I'm sure we'll talk a lot more about it. The way to think about HubSpot is we bring the context of every sales conversation, every campaign that was sent out, every e-mail that was generated, every deal that was closed and turns out while AI is really good at generating insight it needs context. Ryan, if you go to an LLM and say write me an e-mail, it's going to write some e-mail that might not actually convert the right outreach for you. But if you have the context of all of these conversations over a period of time, then it generates much better responses.

And so our differentiator is that we have 280,000 customers using us for marketing, for sales, for service across the customer journey. We've become a platform that small, medium businesses rely on to drive growth, and that context helps make AI much, much better. And the combination of the domain expertise plus the context that we bring in helps us drive AI adoption and give value back to our customers.

And a lot to touch on there. And on the domain expertise side, it's always so interesting to me when I hear like the bare cases on what AI can do versus application software and people are like, oh, what if you can recreate this exactly, but it's like, okay, what happens the next day, right? Like who is going to think about like what's the best ways for customers to make more money off their own customers, right, or new use cases from a customer service standpoint? And I was like, that's what HubSpot does. You have hundreds of people and like decades of experience doing this. So like it's not static in terms of like, okay, this is what a lot of can do with, okay, how do we continue to advance on the platform for what customers need.

Exactly. I mean, look, coding has become easier, but expertise is still important. And I do think that the domain expertise and being able to apply something to a certain segment and make it easier for them is still where there is value that is getting created. And we think that HubSpot today is adding way more value for our customers than we did 3 years ago because of AI. And so it doesn't just go away with AI.

And I think that's a great point in terms of like there's a difference between like the coding advances that we've seen from large language models because that's a publicly available data set, right, that has stack overflow and has a more deterministic outcome, right? Like code is either right or wrong. You can debate like a better way to do it, right? That's like chess. It's a much easier type of problem to solve. We're like, how does the B2B organization correctly address this one use case for their customer. That seems a little more challenging.

That's exactly right. .

So when it comes to HubSpot, you have a large customer set you've been working with for a long time, like they're going to be asking you for certain AI solutions that you can help with. But how does your data advantage also help build like a more holistic workflow?

Yes. So I think before we go into the workflow, maybe fundamentally, what is different in an agentic architecture that enables all of these workflows, right? So if you take it down to the foundation of this, you can do much more with unstructured data, like CRM and customer platforms have always had structured data in roles, in records, that's what we are good at. So an example is like a customer record. You have the name of the customer and the address of the customer, the revenue, that's a customer record. That is what we've always been good at.

Now with agentic -- becoming an agentic solution, you need to be able to handle unstructured data. the conversation that we just had, the transcript of a Zoom call, something that you said that is out on social that we can now grab, that is all the unstructured data. And what AI made it possible is to process all of that unstructured data and add it to the same context that you have. So that's the one big change at the data layer that we need to. And so our solution, it was easier for us to go from having all that structured data to now capturing e-mails, like Zoom, transcripts, video calls, audio calls, all of that and add that unstructured data layer.

The second thing that you need within an agentic platform is really orchestration. It's not just about dumping raw data but having the context across all of the data, where do you get feedback, was that answer good or not, which is evaluation and where do you have the memory of the questions that you've asked in the past. So the orchestration layer becomes really, really important with evaluation, feedback and memory, and that is what we have built. And then what is changing in terms of how applications work is it used to be that you would go and point, click and navigate to something. Now you can have conversational ways of asking software to do something for you and you have agents that actually do it.

So just to be like really clear, what has changed is the amount of data that you can process, the level of orchestration that you bring and how you can have a conversational way of interacting with software, that is literally what we have built over the last couple of years in terms of the foundation. And so then it becomes like how do you enable workflows? Well, because we have much better unstructured data, now the workflows become much better.

So instead of using a CRM where someone had to go and contact and create a contact and say, I met Ryan for the first time today, and this is a contact and this is the conversation. Instead, you can process all of that through the unstructured data that you're able to capture. So workflows become much more dynamic and workflows become with much more context of unstructured conversations that you're having, and that enables much better output for our customers.

Yes. We were just talking about that in regards to what we do every day. And how like before you log a call, and you're like, I'll put a couple of notes and it doesn't matter. But now that you know it's going to help you down the line. okay, maybe I'll add more detail there.

Sure. Yes. Exactly.

Your data second is even stronger. One of the funny things I keep running into is like we talk about like Oh, I'm going to do this, and it's going to write all my research for me. But sometimes it's just like summarizing an earnings call is like the most helpful thing right? So it's like some of the unstructured use cases are actually getting adopted faster or customers are more interested in. So far when it comes to your customer base, is there any use cases that might have surprised you or are things that your customers are more interested at this point?

Well, I mean, a ton. So I'll tell you, I got started in sales like multiple decades ago. And back then, the hardest part of the job was you'd get a set of accounts. let's say, at the beginning of the year, you got like 500 accounts. You just never had the time to look at 500 companies look at 5 people per company of who was the right contact? And are they going to buy? Are they ready to talk about your product? Like that was all manual work, Ryan.

What has been fascinating with AI is that the prospecting use case, specifically one where you can now use AI to go and grab the right information about each of your 500 prospects that you have, did they get funding? Are they adding marketing reps or sales reps or service reps? Are they mentioning initiatives that you can help? This is all kind of unstructured information and structured information that you can now get. And based on those intent signals, you can say, next Monday morning, these are the 10 that you need to talk to based on the intent. That is so much more value than we have ever been able to deliver. And so I think the prospecting use case.

The other ones are pretty obvious, like support is done, right? Like everybody knows that AI can be used to resolve support inquiries, and it's getting better and better across the different channels that we're supporting, marketing in terms of content creation. But to me, like sales as a function is fundamentally changing of where you spend time and where you get leverage out of AI, and it's just been fascinating to see our customers adopt that and get value.

Have you heard any pushback from salespeople who are saying, no, I'd rather dig around through all this information?

Oh god, no. Actually, salespeople, the things that they don't want to do is dig around for information put in notes of the calls that they have had show the work and activities that they have done to the managers so that they can contribute, like those are the kinds of things that salespeople do not want to do, which AI is actually really good at doing. What salespeople enjoy doing is being in front of customers and having deeper conversations. And when you take a lot of the extra work that you used to do and you make AI really good at that, then the time in front of customer and the relevance of the conversation that you're having with customers goes up, which means your close rates need to go up. And I think that's the exciting part of what AI can do for sales.

Yes. You can spend more time on actually selling with all the back-end work.

Exactly.

And as a former customer support agent, I can tell you a lot of those use cases, I'm okay with.

Oh yes. If you start in support, that's awesome.

Yes. It was, for student loans. Buy siders are tough, but they're not as tough as people calling in about their student loans. But when it comes to like your new consumption and credit usage model, For me, like I'm still really interested in apps here because I've used Cursor and Claude Code. And when I'm using those services, I'm clicking, AI do it for me constantly, right? So something like Claude Code, developers use like $6 a day worth of token usage. So that's something around like 40 or 50 times, you're clicking the AI do it for me button, right?

So when I think about that, it's like, oh, well, what people live in all day and the platforms they use all day, right, they'll start to like do the AI do it for me button or whatever it is Atlassian or HubSpot or what they're used to. So in terms of like that credit motion that's newer for HubSpot today, can you just discuss what you're seeing like the early customer trends and use cases where those comes?

Yes. And just to maybe step back. We talked about the AI strategy, which is embedded AI agents and Copilot or Breeze Assistant, and our monetization strategy is hybrid. We monetize AI both through seats as well as credits. And so for the example that you just gave, within HubSpot, if you are a Sales Hub user and you're clicking multiple times today to summarize the e-mail and get me the next follow-up e-mail, which you can do today. You can say, summarize that call and give me the follow-up e-mail, that is part of seats and that does not consume credits. So I just want to make sure that people understand that part of the way we monetize is through seats.

Now the credit specifically is for agent work that we do. our customer agents resolve support tickets and consumes credits. Our processing agent does the account research that we just talked about, and it consumes credits. Our data agent brings in data and cleans up data that consumes credits. And so there are a handful of agents as well as Data Hub that consumes credit. And that we launched in June for all of our customer agent customers, and it moved into installed base in August. So it's early days but in terms of credit consumption, Customer Agent is #1, leading, because it's been in general available mode. And our customers, we have over 6,000 customers resolving over 60% of their tickets using the Customer Agent and they're consuming credits.

And the second area is Prospecting Agent. This is where I do see a lot of promise because this is a known age-old problem for sales that we are now able to solve, and that's the second area. And the third is intense signals within data. So those are the areas that we are beginning to see. But look, it's early days. We believe that AI monetization for us is hybrid monetization, both through seats as well as credits, and we're seeing kind of all the right signals.

I actually appreciate that distinction as a part of your pricing strategy. So it's like if you're doing work in tandem with like HubSpot, then that's a part of the platform, right? But when it's doing work for you, that's where you can start to monetize those credits?

Exactly. And for example, our within Marketing Hub, you can be at content, you can remix content. That's all just part of Marketing Hub. But if you take agents that do actions for you, that's where it consumes credits. And so I'll probably keep repeating this, that our AI strategy is hybrid across seats and credits.

That makes sense to me. When it comes to software today, like if I was a power user of HubSpot, I use it all day every day and there's some one who use HubSpot like once or twice a day, you might be paying this amount on a per seat basis, right? And I don't know if that is going to be the same way in the next couple of years given some of the usage dynamics here. But in terms of who is adopting AI first that you've seen so far in your platform, is it power users? Is it SMBs versus like larger businesses? Or does it run the gamut?

Yes. It's a good question. I'll tell you, it is not based on the number of employees. In fact, the really distinguishing factor is, is there a C-suite leader that's pushing AI priority within the company. Like I've looked at it by industry, and I've looked at it by segments, and we've done a lot of analysis. It's really not based on the size of the industry. It is like, is there someone there that is top-down pushing AI because I would say that I know there's a very different narrative with investors, but when we talk to customers, there's still a level of fear and uncertainty and lack of trust with where is the data going and how do I make sure that my company's data is not used for some LLM training somewhere. So there's just a lot of like mistrust associated with that.

So the #1 factor is top-down kind of initiatives on AI. And once you get past like there's someone within the company that is looking for an AI road map, then I do see that there is a strong ops role. And in order for AI to really drive value, you need kind of what we used to call RevOps back then, and now we call them AIOps. And there's someone within the company, the power user is like an AIOps user that trains that gets the right quality of the data, trains the data, uses the AI features and then makes it available for everybody else. That role is kind of like the power role.

And when we see customers that have these AIOps roles and someone like a go-to-market engineer, that is leveraging AI, then the AI adoption actually accelerates within the company. And for a lot of our customers, they're still at the stage of, we want clean data that we can trust that we know is not being used outside of our company, and then we want to have a road map of reasonable set of use cases that we can experiment, scale and then grow with.

And it's amazing how like just one champion can really move the needle and you can activate a lot of others. Like we did a Wells Fargo off-site, and I had ChatGPT answer a question then I had everyone build the agent on their ChatGPT. And I think actually got commissioned about the number of ChatGPTs. They paid licenses we sold after. But once you get started to like, oh, I can do this, I can do this, then that makes sense to me. And that's actually a good segue into, I think the Data Hub strategy is one that kind of needs more airtime with investors here where your customers are already trained to like put everything they can in HubSpot and then activate off that. So you've rebranded that at the most recent Investor Day. Can you just talk about how Data Hub helps your broader AI road map?

Yes. And look, already today, we have talked about the criticality of data and context for AI to work and that is known. And if you step back, Data Hub does a few things. One is it pulls data outside of HubSpot into HubSpot. So we have things called data things that are kind of like integrations that pull data from across. And typically, we'll see HubSpot customers have anywhere between 8 to 14 integrations, but sometimes they're bringing data from more sources to bring into HubSpot. So that's like Data Hub and helps you do that.

The second thing that we are finding is that data quality is exceptionally important for AI to be accurate. And Data Hub actually helps you improve the quality. It can run a set of prompts to LLM. And so for example, if there's like a column of like funding data that you need, Data Hub will pull the right problems and get the funding data for every contact that you have within your database. So it improves the data quality. And then the third thing is it provides a workspace, we call it the Data Studio to now manipulate the data. You've brought in the data. The data has higher quality. Now can you build workflows and sequences and automation with that data so that your AI works better.

And so it's almost like a foundational workspace for AIOps and for RevOps to do much more with higher quality data. And that is kind of one of the reasons why we rebranded it from Ops Hub into Data Hub because you're really working on the foundational in a need for AI. And as we look into the future, we haven't talked about marketing. But in order for marketing playbook loop to work, you need higher quality data and Data Hub provides that. So that's the vision. And it's now one of those areas where if you're a Marketing Hub customer and you want to do AI, then you need to get like Data Hub as a foundation for it. And it's the same thing with Sales Hub. It's part of the multi-hub play that we have.

And we've kind of touched a lot about the data advantages and the differentiators of HubSpot here. But it's interesting when people was like oh, well, I use these AI cases. I'm like of what or how, right? So I mean, I think like the larger question that's been on top of mind for investors is like, does HubSpot add AI features first or do I end up doing a lot more of what HubSpot does. So we touched on a lot of these things today, but we'll have kind of just like you probably had this question over and over again over the last month, but we just kind of love to hear like all the things we talked about today like why you guys are better positioned in that world?

Yes. I like the framing, is it easier for a SaaS company to add AI? Or is it easier for an AI-native company to add like CRM. It's a very -- I like the question but foundationally, I'd go back to the conversation that we just had, Ryan. We've already become an agentic platform by adding unstructured data, by adding a context layer that ties together all of the data by building agents that can then take the context across that. We've kind of done a lot of the actual plumbing and the architectural changes that are needed to support becoming an agent platform.

If I were starting out as an AI native from scratch, I need to build still a CRM record and I need to build the structured data. Many of them are starting as point solutions. So let's say, you start as a support agent and dealing with support tickets. The minute you get a question on sales pricing, then you don't have that context. So you now have to extend and start building what are products that you sell, what is the pricing associated with it and what are the common questions and objections. So you have to go from structure to unstructured. You have to go from point solution to full platform, then you have to build the full context associated on top of it.

And then I would say a couple more things. We're still finding that AI features get adoption with feedback. So we have the advantage of building an AI feature and making it available for thousands of customers to use that we can get the feedback and improve AI development cycles are very iterative in nature based on customer feedback. But if you're an AI and native company and you're starting with 10 customers, where you're going to get that feedback. So there's an inherent advantage in agentic world that benefits from the scale of the number of customers.

And then the final thing I would say is you still need a partner ecosystem. AI is still one where you require someone to look at your road map to help you with what are the use cases to start and drive, and we have partners within the ecosystem. They're all driving AI readiness. So if you are a new company that is getting started as an AI native, you also need -- that's why you see a lot of forward deployed engineers, right? The model is like you not only build a product, but you also invest in forward-deployed engineers that go and sit in customer sites. And we have that, which is an ecosystem that we have. And so I think like there is platform advantages, there is scale advantages because of the distribution and then there is an ecosystem advantage that we have.

During the break between the jobs, I was sitting around thinking like I could probably try to build one of these. You know what I mean? And then as I did some research on it, it's like...

Did you?

I tried to build a project management tool and it looked a lot better in my head than it ended up being on paper. And it turns out like having the initial draft is a lot harder than having actual working SaaS, right? But as I talked to other developers, it was like would probably be one of the most difficult things to build given like the high data density, mission-critical workloads and unstructured data but it's such a big market opportunity that people are going to try.

Yes, absolutely.

And so when it comes to like you have the data already with your customers, and you're already training your models on that data that also extends like the time that a challenger would have to take in order to create the same, like use case based off your new training model.

Yes. I mean, look, I think we've always been in a very, very competitive or has never been a winner-take-all market or a noncompetitive market. It's always been the case. And I'd go back to like why do platforms win over point solutions. One of the most common use cases for HubSpot is that we'll go to customers and they'll say, our data is fragmented across 15 different solutions that are point solutions, and we've lost visibility of our growth. So that's like -- that continues to be the case in the agentic world.

When I talk to customers who have even attempted like different agents. They're like, well, we now cannot manage this across all of these different agents. And we want to look at your road map, if you have it, then we're just going to continue adopting with you. And so I think like it comes back to why platforms win over point solutions. It's the same reason why an agentic platform wins over point agents that are kind of sprawling across go-to-market.

I would love to touch on that point. Just like that complexity that you're speaking to. For me, like it's a very interesting dynamic of like, oh, I can custom build my and use case now. But like that's investors kind of thinking that's the case? Where like are you seeing customers being more willing to like know whether buy AI to start and build on my own?

Yes. I mean, custom building has gotten easier. But think about our segment true, right, which is a 500-person company and let's say, a manufacturing company in the middle of the country that's trying to grow their business. Now they have to figure out Sonnet 3.7 and ChatGPT 5.1, the latest Gemini that dropped today for 10 different use cases, and they're building a custom application on profit, that is not what an SMB wants to do. And while there is a narrative that yes, custom applications and custom agents are easier, average mid-market company still focuses on growing their business versus let me run and adopt AI just for the sake of it and build a customer application.

And you said this earlier, there's a lot in terms of building an agent, which is there's a front-end piece to it, there's a back-end piece to it, there's getting the right data into it and there's this constant iterate process of getting feedback and improving it. And in order for a company to do that, they really have to change their strategy and invest pretty significantly in either AI engineering talent or AI pods that just do that. And that is not the conversation that we hear with our customers, they, in fact, want us to make it like easier for them to adopt and they just trust us to do that because of the level of innovation that we have brought to them.

Absolutely. And the thing that worked yesterday is not going to work today. And you guys have definitely been on the forefront of speaking about how SEO is changing as a part of AI and you guys had the acquisition XFunnel in regards to SEO, and I love all this about it. We've been following pretty closely for what we cover. But can you just talk about your own efforts to solve some of those top of funnel changes? And then like how you're helping your customers with the new SEO?.

Yes. I mean it's a topic that we can spend quite a bit of time on. I mean if you step back we, as an industry used to put content and get people to click on the blue links and have people come to our website. Then we captured their e-mails and then we nurtured and then that became part of the marketing funnel. That is completely disrupted, right? Because AI overviews are providing the answers, and that is if you search half of them are not even searching, they're just going to an LLM to ask questions. But because of that, there is fairly massive drop in terms of a very specific type of lead call content leads, right? Content leads are the ones that have really gone down.

Now in terms -- you asked a question about how have we navigated it, I mean, I'd say that in 2022, even before ChatGPT came on and AI overviews were part of our language, we saw that customers were spending a lot of time on other channels, social channels, podcast, we're all listening to podcasts and we saw that everybody is listening to like hundreds of podcasts now. And so e-mail newsletters. And so starting 2022, we went through a process of diversifying our marketing channels. And that strategy has worked. We have 10 YouTube channels, and the leads from YouTube channels are growing between 80% and 90% year-over-year. We acquired a podcast network and we now have like over 100 podcasts within that network, and that generates leads.

We also acquired e-mail newsletters, which we did, people were scratching their heads saying, "Why are they doing this? But that has also increased the leads. And so for us, the story has been how to diversify out of content leads into all of these different sources. Now one of the channels happens to be AEO, and that is Answer Engine Optimization where you show up within an LLM. But that is still low -- it's nascent, right? It's pretty early days in terms of AEO and how you show up in answers. And the lead volume is still very low. It's single digits, but the conversion rates of the leads from LLMs is much higher. It is 3x because you're doing a much thorough, deeper questioning and you're ready to convert if you get the right answer.

And so that's kind of how we have navigated it. And the thing that we are really good at is once we figure something out, creating a playbook and getting our customers to be able to adopt and get the benefit of the playbook. So at our conference this year, we launched Loop, which is kind of the playbook of how to diversify your lead sources and drive a level of personalization. And so we launched that, we launched a set of products across Marketing Hub, Data Hub and content hub features that support that playbook, and we're early days, but kind of getting the new playbook out.

And I've now spoken to a lot of our customers Polestar inbound and they've started doing some of this, and it clicked. It's like, oh, I now see that here's how I need to diversify. Here's how I need to use AI for personalization and this is what is happening within the AEO channel. And so early days, but we feel like this opens up a big opportunity for us.

I mean there's always something, right? But it's like there's more complexity now is to reach our customers where they are, more personalization, like these are all things that they need some of the help with.

I think we're actually excited about it because if you look at the last 2 or 3 years in marketing, channels were saturated ROI was really, really hard to get, and conversion was slowing. And the playbook was like super difficult. And if you're in marketing within a small and medium business, you couldn't get even 1% to 2% improvement in any of these key metrics of lead conversion. But now there's a completely different way to do it. And the level of return that you get from figuring 1 or 2 of channels is just much better.

And so there's just a lot more excitement in terms of what you can do with AI within marketing. So while everybody talks about the disruption of SEO, what maybe is less understood is that A is actually creating a much bigger opportunity to optimize your marketing channels and strategies, and that's an exciting opportunity for us.

I have a few minutes left and this might be a bigger question. But for me, investors often ask like, oh, why don't they have all these like amazing products now this for every sulfur company. I'd like look, reasoning large language models came out at the end of last year. It takes time when you have hundreds of thousands of customers to put like really intricate products in. So besides like there'd be a little more seasoning across like all of AI within solutions, like what do you think really picks up the adoption curve for your customers?

Yes, I think that I would still say the technology is ahead of customers' ability to adopt the technology. And so I think you're absolutely right. LLMs came on board and a lot of us, we understand the transformative power of AI, and we've been building, but it's an iterative process and you got to get customer feedback to be able to make an AI feature better, and that's one part of it. The other part of it is helping customers through the do I have good quality data. Can I trust where my data is used can I make sure that my problems and all of the interactions are within my company's walls are not being used to train something else. And there's a level of just comfort around that, which is the adoption cycle.

And again, it's not dissimilar to any other technology cycle that we have seen in the past. And I've been in this industry when -- I joined the industry in '96 when that was like before we went into cloud, and it was very, very similar. Everybody saw the value of it, but it took a little while for people to begin adopting and it's the same thing that is happening. There is just transformative value in AI, but adoption comes from building trust, better quality data and then making it frictionless for customers to try something and then scale it. And that's the process that we're in.

I'm really sad to see what comes next, and I'm certainly not going to miss searching through our CRM for all the clients I talked to over the last 2 weeks.

That's exactly right.

And to those that comes through next. But guys, thank you for your time, and thank you, Yamini for coming.

Thank you. I really appreciate it.

Thank you.

Good afternoon, and welcome to HubSpot's Q3 2025 Earnings Call. My name is Gigi, and I'll be your operator today. [Operator Instructions]

I would now like to hand the conference over to Head of Investor Relations at HubSpot, Chuck MacGlashing. Please go ahead.

Thanks, operator. Good afternoon, and welcome to HubSpot's third quarter 2025 earnings conference Call. Today, we'll be discussing the results announced in the press release that was issued after the market closed.

With me on the call this afternoon is Yamini Rangan, our Chief Executive Officer; Dharmesh Shah, our Co-Founder and CTO; and Kate Bueker, our Chief Financial Officer.

Before we start, I'd like to draw your attention to the safe harbor statement included in today's press release. During this call, we'll make statements related to our business that may be considered forward-looking within the meaning of Section 27A of the Securities Exchange Act of 1933 as amended and Section 21A of the Securities Exchange Act of 1934 as amended.

All statements other than statements of historical fact are forward-looking statements, including those regarding management's expectations of future financial and operational performance and operational expenditures, expected growth, FX movement, and business outlook, including our financial guidance for the fourth fiscal quarter and full year 2025.

Forward-looking statements reflect our views only as of today and Except as required by law, we undertake no obligation to update or revise these forward-looking statements. Please refer to the cautionary language in today's press release and our Form 10-Q, which will be filed with the SEC this afternoon for a discussion of the risks and uncertainties that could cause actual results to differ materially from expectations.

During the course of today's call, we refer to certain non-GAAP financial measures as defined by Regulation G. The GAAP financial measure most directly comparable to each non-GAAP financial measure used or discussed, and a reconciliation of the differences between such measures can be found within our third quarter 2025 earnings press release in the Investor Relations section of our website.

Now it's my pleasure to turn over the call to Spot's Chief Executive Officer, Yamini Rangan. Yamini?

Thank you, Chuck, and welcome, everyone. Today, I'll share our Q3 2025 results and the key trends driving our performance. I'll then dive into what we launched at inbound, where we are seeing AI momentum and how we are reimagining marketing for the AI era. I'll wrap with our growth formula and how both our current and emerging levers create a durable path to growth.

Let's dive in. Q3 was another strong quarter for HubSpot. Revenue grew 18.4% year-over-year in constant currency, reaching $810 million. We delivered solid operating leverage with an operating margin of 20%, reflecting our ability to balance growth and profitability. Total customers increased by 10,900 in the quarter, bringing our global customer base to nearly $279,000. Our results were powered by 3 drivers that continue to show up quarter after quarter platform consolidation, multi-hub addiction and upmarket momentum. These themes are consistent compounding and reinforced the strength of HubSpot as we scale.

HubSpot is winning as a truly unified customer platform. Companies are consolidating their go-to-market stack on HubSpot to reduce total cost of ownership, gain a unified view of their customers and accelerate AI innovation. Multi-hub adoption has become the norm across both new and existing customers. 43% of Pro+ installed base by ARR now subscribed to our 3 core hubs, up 4 points year-over-year and 39% owned 4 or more hubs, up 6 points. This expansion shows the value customers see in growing with HubSpot, and it is clear proof that our platform first strategy is working.

Our upmarket segment is humming. Larger companies are choosing HubSpot cords power sophistication and speed to value. Deals over 5,000 monthly recurring revenue grew 35% year-over-year, reflecting the payoff from years of product investment, strong partner alignment and rising brand awareness amongst upmarket physician makers. A great example is QS, a global education services company with over 1,000 employees. They replaced a 20-year-old legacy CRM and chose HubSpot to power their AI-first transformation, citing our AI strategy, approach to agents and pace of product innovation as key reasons for signing a multiyear multi-hub agreement.

AI [ innovation ] took center stage this quarter. The highlight was, of course, our annual INBOUND conference. It was great to bring together 13,000 people in person, another 550,000 online from across our ecosystem. We launched more than 200 new updates and products that were well received by our customers and partners. The end and feedback from inbound reinforce that our AI strategy is resonating and customers see HubSpot as the platform to help them grow and win with AI. Our strategy is simple: Embed AI into hubs, our customers use every day, build agents that do work and create brief assistant and connectors that turn data into insight.

At INBOUND, we launched new features in every hub from AEO strategy tools in Content Hub to AI-powered e-mail in Marketing Hub and AI meeting assistance in Sales Hub. We introduced Data Hub, which helps customers bring their data together in one place to get more value from AI. We enhanced our featured agents, customer agent and prospecting agents launched a new data agent and opened up Brief Studio, so customers can build and customize their own agents. And we became the first CRM to connect directly with the 3 leading LLM, ChatGPT, Claude and Gemini. These innovations are delivering real results for customers.

Customers who use our embedded AI features in marketing come get better results, higher click-through rates and over 50% higher lead conversion. Similarly, customers who use AI features in Sales Hub are winning almost 10% more deals. Our agents are also gaining strong adoption. Customer agent now has over 6,200 customers, up 48% from last quarter, with an average resolution rate in the 60s prospecting agents has been activated by 6,400 customers, up 94% from last quarter, and customers have used it to engage over 1 million prospects. Data agent is new, but already has 1,700 customers who have activated it. Breeze Assistant is the digital assistant for every market employee, and we have seen weekly active usage increased by 56% in the past 6 months as customers use it to summarize records, and uncover insights to drive performance.

A key part of our AI strategy is our LLM connector approach, and the momentum we're seeing here is impressive. Our ChatGPT Connector has been activated by more than 47,000 customers with 55% of them being Pros customers. and our Cloud Connector is already being used by over 6,000 customers. We believe that LLM and HubSpot are powerful together and complement each other. Why? Well, there are 3 reasons. First, LLMs create insights. HubSpot provides the context that makes insights possible for go-to-market teams. LLMs are great at generating ideas from public data or a user prompt, but HubSpot is where the full go-to-market context lives, every interaction, sales conversation, support ticket, marketing campaign. That context is necessary to turn generic AI output into insights that are accurate, relevant and actionable.

Second, LLM generate ideas. HubSpot turns them into action within a business context. On their own, elements can tell you what to do with HubSpot, you can actually do it. HubSpot is where companies build sophisticated workflows launched multichannel campaigns and take actions to drive growth. And third, LLM are great at single-player task, and HubSpot is built for multiplayer teams. HotSpot remembers each user, their role, preferences, team, what they have permissions to access and where they can take action. Now stepping back, HubSpot houses AI and is the customer platform where intelligence and context are applied, acted on and shared team. platforms were sticky pre-AI, they will be even stickier in the AI era.

Okay. Let's talk about how we are reimagining marketing for the AI era and the opportunity it creates for HubSpot. Marketing landscape is changing fast. Search traffic has declined globally as AI overviews provide answers. Customers are spreading their attention across channels and visiting fewer websites. At the same time, AI is creating entirely new opportunities via LLM like Answer Engine Optimization or AEO. At HubSpot, we saw these shifts coming early. We've been diversifying marketing channels and experiment with AO, and that strategy is working.

At INBOUND, we introduced the Loop, our new playbook for growth in the AI era. It gives customers clear step-by-step guidance on how to drive growth by combining human creativity with AI efficiency. And the response has been incredibly strong. with 270 million impressions on Loop content and over 100,000 views of the Loop playbook experience. We also launched new products to help customers put the loop into action, including Data Hub which makes it easy to build ideal customer profiles and marketing studio, which helps marketers personalized content based on buyer intent. A key part of the loop is helping customers show up in AI-generated answers. Our AEO greater and AEO strategy tools launched at INBOUND, make it easy for businesses to come up with a strategy and improve their visibility in LLM.

And last week, we announced an agreement to acquire XFunnel, one of the first and most complete platforms for tracking and improving how brands appear across LLMs. XFunnel shows when and how often your brand is mentioned in AI-generated answers and provides clear guidance on how to strengthen that presence. We'll natively build XFunnel into HubSpot, giving our customers even more ways to understand, improve and grow their brand visibility in the AI era.

Now let's talk about our growth formula and how we are unlocking new levers for HubSpot. Our core growth levers continue to perform, platform consolidation, multi-hub adoption and upmarket traction. At the same time, emerging levers are gaining momentum, including seats, pricing change, core seats and credits. We introduced the core seat last year to give customers edit access to the Smart CRM, the unified record that powers our platform and that strategy is working. At INBOUND, we made the core seat even more valuable by adding AI and data capabilities like Breeze Assistant part starts, projects and enrichment data.

And by unbundling the smart CRM, so customers can start right there. Our vision is to make the core seat essential with AI and data value for every go-to-market employee. Credits are another powerful emerging lever. They are our universal usage-based pricing system, covering AI agent actions and data hub pints and soon will extend across the entire platform. Credit tie our growth directly to customer value. And as customers use more data, use more AI and automation inside HubSpot, they'll grow with us. Together, core seats and credits expand how HubSpot captures value, building on our durable foundation and creating a long runway for growth.

As I wrap up, I want to share our conviction that HubSpot is positioned to lead in the AI era and drive durable long-term growth. We are innovating badly transforming into an agentic customer platform and operating efficiently at AI speed. We have durable differentiators and growth levers and we deeply understand our segment and what small and medium businesses need to grow with AI. We are uncovering new ways to drive efficiency and finding signals to show our customers what's possible with AI.

I'm more confident than ever in our strategy and our ability to deliver value for customers in this new era. Thank you to all our customers, partners and shareholders for your continued support and a huge thank you to all HubSpotters around the world for staying focused on solving for our customers every single day.

With that, I'll turn the call over to Kate to take you through our Q3 financial results in more detail. Kate?

Thanks, Yamini. Let's turn to our third quarter 2025 financial results. Q3 revenue grew 18% year-over-year in constant currency and 21% on an as-reported basis. Subscription revenue grew 21% year-over-year, while services and other revenue increased 19% on an as-reported basis. Q3 domestic revenue grew 17% year-over-year. International revenue growth was 20% in constant currency and 25% as reported, representing 49% of total revenue.

We added 10,900 net new customers in Q3, bringing our total customer count to 279,000, growing 17% year-over-year. Average subscription revenue per customer was $11,600 in Q3, up 1 point year-over-year in constant currency and up 3 points on an as-reported basis. While we're happy with the strong net adds in Q3, we continue to expect net additions to be in the range of 9,000 to 10,000 in Q4 and for ASRPC growth in constant currency to be up roughly 1 point.

Customer dollar retention remained in the high 80s in Q3, and net revenue retention was flat sequentially at 103% as expected. As I shared last quarter, we expect to see a step up in net revenue retention in Q4 and resulting in a couple point improvement in net revenue retention for the full year of 2025. Calculated billings were $804 million in Q3 and growing 19% year-over-year in constant currency and 18% on an as-reported basis.

The remainder of my comments will refer to non-GAAP measures. Q3 operating margin was 20%, up 1 point compared to the year ago period and 3 points sequentially. Net income was $140 million in Q3 or $2.66 per fully diluted share. Free cash flow was $147 million or 18% of revenue in Q3. Our cash and marketable securities totaled $1.7 billion at the end of September. In Q3, we repurchased 780,000 shares of common stock under our share repurchase program, representing $375 million.

With that, let's dive into our guidance for the fourth quarter and full year of 2025. For the fourth quarter, Total as reported revenue is expected to be in the range of $82 million to $830 million, up 16% year-over-year in constant currency and 18% on an as-reported basis. Non-GAAP operating profit is expected to be between $183 million and $184 million, representing a 22% operating profit margin. Non-GAAP diluted net income per share is expected to be between $2.97 and $2.99. This assumes 52.7 million fully diluted shares outstanding.

And for the full year of 2025, total as reported revenue is now expected to be in the range of 3.113 billion to $3.115 billion up 18% year-over-year in constant currency and 19% on an as-reported basis. Non-GAAP operating profit is now expected to be in the range of $574 million to $575 million, representing an 18% operating profit margin. Non-GAAP diluted net income per share is now expected to be between $9.60 and $9.62. This assumes 53.2 million fully diluted shares outstanding.

As you adjust your models, please keep in mind the following: we now expect CapEx as a percentage of revenue to be 6% for the full year of 2025, driven by higher capitalized software expenses. And we still expect free cash flow to be about $580 million for the full year of 2025.

With that, I will turn the call back over to the operator for questions.

[Operator Instructions] First question comes from the line of Samad Samana from Jefferies.

So Yamini, my question is for you. I think the core focus of investors is HubSpot getting back to 20% growth and I think investors were pretty excited and myself included seeing that accelerating ARR slide at the Analyst Day. We might have gotten ahead of ourselves, but how do you think about the path to get back to 20% growth? Can the current focus on that 200 and below segment support that level of growth? Or do you think that hitting the gaps on enterprise is necessary. Maybe just help us think through that.

Yes. Thanks a lot, Samad. I appreciate it. Look, we believe we can grow faster than where we are today, and we are focused on doing it in a durable disciplined way. The thing I would point out is that net new ARR is the leading indicator that we shared with you, and revenue is the lagging indicator with flow through. Now if I step back and look at the foundation, our core growth drivers are strong, and they are proven. We have a playbook that works, which is platform consolidation, moving upmarket and multi-hub momentum. And you can see that in our customer retention numbers, the seat upgrades that are consistent and the large deal momentum that are compounding quarter after quarter. And we're going to continue to expand our sales capacity as well as the productivity across all of our segments to capture that opportunity.

Then we have a set of emerging growth drivers that strengthened that outlook further and put us on a path of durable growth. And we shared that at Analyst Day, but I'll kind of walk through it. the pricing changes that we drove last year is a tailwind. We are seeing seat upgrades pick up. And as the change rolls through our installed base this year and next year, we'll continue to see growth from that. And AI is a multiyear tailwind. We are in the very early stages of this whole innovation cycle playing out, and we are acting with urgency to cement a leadership position and set ourselves up for long-term growth. And specifically, with AI, we see the opportunity to monetize both through seats as well as credit.

Now core seats, which we talked about at Analyst Day, it's becoming more valuable and it is embedded with AI data platform value, and that opens up a large opportunity credits gives us a new way to monetize usage for customers as they consume more AI and agent-driven actions -- so look, we are very excited about the emerging drivers, the strength that we see within the emerging drivers. And we're really excited about the upmarket momentum. We think we have plenty of TAM to be able to drive and grow faster than where we are. Most importantly, we have a track record of consistent execution and helping our customers grow, and that gives us confidence that HubSpot can be a durable growth business for years to come.

Our next question comes from the line of Mark Murphy from JPMorgan.

Yamini, several of your partners have said that they're pretty encouraged about their own growth potential moving into 2026. I'm curious if you see any signs that Google's AI overviews are actually driving an extra wave of interest for HubSpot to try to use your lube concept and your answer engine optimization tools so that they can gain their own kind of visibility in AI-generated answers. Is there anything tangible there that you can speak to?

Mark, thanks a lot for the question, and thanks all this for talking to our partners to uncover the underlying trends. Look, I would say marketing and the trends that we are seeing within marketing is a big opportunity for our customers and for HubSpot to grow. And specifically, you talked about what is happening with the marketing, but really we see it as a couple of things. One, AI overviews are providing answers, which means website visits are declining. And that means there is a need for a new playbook to diversify the channels that you are present and including AEO. And that is exactly what we launched at INBOUND.

At INBOUND, we launched the playbook, which you referenced, which is the loop. And I would say that the response from customers and partners have exceeded our expectations across every metric in terms of impressions, we had 270 million impressions, playbook compressions, number of conversations that's generating. And specifically, there are 3 parts of the playbook that are resonating with customers as well as partners. The first one is diversifying channels, and the #1 thing that you can do to counter SEO volume decline is to diversify your channels into YouTube and Insta and podcast and new flatters and that is resonating.

The second is, how do you actually use AI for deeper segmentation and personalization Look, there's just a lot of talk about AI driving disruption. But the bigger story is how AI can be helpful in using intent data to drive better conversion, and that is resonating. And then, of course, building visibility in LLMs through answer engine optimization that you mentioned. And all of that is resonating, and we're seeing lots of conversations within AEO. AEO is early. We got in earlier, and we have an AEO-radar out in the market that has been used by 70,000 customers already.

Our AEO strategy tools are getting used in order to look at this nascent channel. And last week, we announced XFunnel, which completes our platform of providing visibility to customers on which LLM they're showing up and how to improve their presence within that. And so look, if I step back, SEO is a big disruption, but this is also one of the biggest opportunities for our customers to figure out how to grow, and it is one of the biggest opportunities for HubSpot, which is why we have the playbook, we have products and we have the whole partner ecosystem activated to deliver on that, and we're super excited about the opportunity.

Our next question comes from the line of Parker Lane from Stifel.

Yamini, you continue to point to platform consolidation is 1 of the key drivers of the business. Traditionally, cost savings were one of the things that drove people towards that consolidation. How often are you seeing with the launch of Breeze and these new agency brought to the platform that a desire to really embrace agents and have a unified data source is the reason for consolidation versus just traditional cost savings?

Yes. That's a fantastic question. I think equal parts, I would say 3 reasons why customers talk to us about consolidating on a platform. The first one, as you said, is total cost of ownership. And remember, we've come from a period of people buying a lot of point solutions and tools and TCO kind of like bloating up, and that continues to be the #1 reason.

The second reason is actually getting all of the customer data and context into a unified platform. whether they're leveraging AI or they're just getting their whole digitization journey going, you need data and you need customer context across the whole journey. And if you have a lot of point solutions and a lot of point agents, you just do not have the visibility to drive insight.

And then I would say the third reason is really AI and wanting to adopt AI. And while AI is not always the primary driver. It is a clear pool in terms of the conversations. And what we hear from prospects from customers is that they want one platform that has all of the data and they want to be able to have a clear road map so that they can future proof their investments and they like what we are doing with our AI strategy, which is embedding AI across all of the hubs and making it easier for our customers to build on that road map. So I see all 3 reasons probably kind of split equally.

Our next question comes from the line of Alex Zukin from Wolfe Research.

I guess, Yamini, I'll be a little bit direct. If I look at the magnitude of the beat this quarter, it was a little bit lower than I think some people were thinking about. But at the same time, the raise for Q4 is I think the strongest raise on a constant currency basis that you guys have ever done. Is there any moving pieces that you can comment on in billings, I think, was a little bit of a decel. But then net new ARR, as you showed us that slide at the Analyst Day was really, really strong in the first half and seemingly in Q2. So any commentary on a forward-looking metric basis around how net new ARR performed in 3Q, maybe to disentangle some of the in-quarter versus guide confidence dimensions?

Yes, Alex, it's Kate. Maybe I'll take this and if Yamini has more to add. She's welcome to do at the end. We did outperform in Q3, we executed really well. And the outperformance was driven by all the things you heard Yamini talk about, continued strength of market, continued strength with multi-hub and continued strong seat expansion. We also saw some incremental FX tailwind versus what we expected when we guided last quarter, and we fully flowed that through our revised 2025 guidance.

I think you heard from Yamini, and I've talked about it in the past as well as at Analyst Day. It will take some time for our strong net new ARR growth to translate into an inflection in revenue growth. just given the scale and size of our installed base, but we've seen stability here over the last few quarters. All that said, our approach to guidance was consistent this quarter.

Our next question comes from the line of Keith Bachman from BMO.

Yes. Actually, it's a good segue from Alex. I wanted to ask a similar question, but I'll phrase it differently in that billings growth is one metric but the constant currency billings growth is generally in the last 6 quarters, been in a range of the highest 21%, the lowest 19%. So in that range for 6 straight quarters. And so, a, you've talked about a lot of goodness that's coming through the model. But what's the offset? What's been constraining? And particularly this quarter with 19% which was a little bit lower beat than you've had in the past quarters. What's not going as well that's constraining it?

And then also, Kate, per your last comment, you've mentioned that net new ARR has been strong, but it takes a while, but maybe you could put a little more context around that in that the stock is down pretty materially here in the after hours. How should investors think about when will the net new be fulfilling enough, if you will, to then translate or show up into the revenue growth. What is the -- any timing expectations that we should put in consideration?

Yes. Maybe I'll start with the billings question because I want to make sure that we are sort of aligned with the metric and our expectations for billings. My baseline expectation is that constant currency revenue growth and constant currency billings growth are going to track each other really closely. And that's because more than 90% of our billings come from revenue in period. Billings, as you know, is a bit noisier. It shows impacts of FX that shows impacts of the mix of net new ARR, and it shows duration pretty acutely.

And when you kind of look at what's happening in these factors this quarter versus last quarter, like we had a bit of a mix shift towards installed base selling, which has lower months upfront. And we saw less of a benefit of expanding duration versus what we saw in Q2. And so as a result, what you're seeing in billings growth is something that is still above revenue growth in constant currency, but just a little bit closer than what we saw in Q2. And for Q4, there's always going to be some variability here. But I would expect that constant currency billings and revenue are going to track each other.

If you want to address the net new ARR question as well.

Yes. I mean I think that what we have said, and you said it yourself, is like net new ARR is the leading metric Revenue is the lagging metric. And it will take repeating quarters of net new ARR growth above revenue for revenue to inflect. Net new ARR hit its low point in the first half of 2023. We saw a steady increase throughout 2024. And since the back half of 2024 through the first half of 2025 as we shared at Analyst Day, it has been above revenue growth. And so what we have seen is that our installed base ARR has started to inflect and then revenue will follow the inflection of the core installed base. It just will take time to get there.

Our next question comes from the line of Elizabeth Porter from Morgan Stanley.

I wanted to follow up on some of the emerging drivers. You called out the customer adoption of HubSpot agents has been strong and credits are going to be a lever for growth in the future. So although some of the subscription plans currently include some usage credits, Among your early adopters, what are -- are customers using up those credits? And kind of where are we on the path for customers to purchase additional ones? Just overall, some of the trends you're seeing in credit consumption or usage intensity that can give confidence to that lever become a larger impact in the near to medium term.

Elizabeth, thank you. Thank you for that question. We launched HubSpot credits. And as everybody knows, that is our universal usage-based pricing system. Today, it includes agent actions for any agent that is out in general availability and it also includes data hub things. Those are the 2 things. And as we go into the future, we'll add more and more usage-based features into that system. Now we have a clear framework for how we monetize credit. We focus on product and feature activation, then we drive repeat usage then consistent customer value. And from then on, we monetize. And having this disciplined structured approach ensures that when customers start paying for usage with credit, it's because they are getting clear value and repeatable value.

Now in terms of the patterns that we're seeing for credits monetization, it's been just about a quarter but customer agent is the strong growth driver of credits. It's delivering value. It's highly retentive. So when customers adopt customer agents, they continue to use it. we're seeing positive signals on credit growth, which is they start with the included credits and they continue to grow beyond that included credits with customer agent. The second one is data hub sync. This is where when you bring in data and when you use that data then it consumes credit, that's the second area where we're beginning to see credit consumption.

And then the third is prospecting agent. This is one where our customers use it to research accounts and scale their outreach campaign, and we're beginning to see clear trends. And I shared some of the adoption trends and metrics, which is also driving credit adoption. Look, it's early days, but we are very pleased with the progress so far in terms of credit consumption. The one thing that I will point out, and it's an important point is that credits are just 1 way for us to monetize AI.

Broadly, if you step back and think about it, credit monetization does not equal AI monetization at spot, and that's because we have embedded AI. And when that embedded AI drives value, then we see it in attach rates as well as seat upgrades, which we have been seeing now for a few quarters. We're also driving AI value within core seats. And we will see the monetization there as we shared in Analyst Day, and we'll continue to monetize usage-based components with credit. So overall, we feel very good about our AI strategy and the monetization strategy, and this is a solid foundation for credits to emerge as a lever for growth.

Our next question comes from the line of Tyler Radke from Citi.

Yes. One clarification and then sort of a product question. So just on the clarification, I know there's been a lot of questions just around the net new ARR trends. And I guess the clarification is, did the growth rate in net new ARR that you observed in the first half, did that continue or perhaps accelerate or decelerate into 3Q? And then, Yamini, you talked about a lot of customers, I think 47,000 customers using the chat connector. Just curious if you're seeing any interesting usage or expansion trends for customers that are using that versus customers that are not.

Yes, Tyler, I'll start with a short answer to your question on net new ARR growth. In Q3, net new ARR growth did remain above constant currency revenue growth.

Yes. And then on the second part of the question in terms of the LLM connectors and what kinds of use cases and what types of trends we are seeing there. Look, I think we'll start with why we build these connectors. It's because our customers and prospects are spending a ton of time in LLMs, and we want to bring the insights from HubSpot regarding their business and growth opportunities there. And the more deeper reason is this, LLM are basically the new AI operating system. And just like you had browser wars and mobile wars, there will be a clear winner in the AI operating system.

Now that has just a lot of implications across the industry, but there's a very specific implication for our customers and our ecosystem, which is that will be a way in which companies will be found. And that means it will become an AI referral source for companies. We see this in our AEO efforts. We are seeing this in our customers' AEO efforts, and that is why we want to be the best platform that enables that growth. And so that's the reason.

In terms of the traction, I shared some numbers. ChatGPT Connector is our fastest growing app in 5 years, 47,000 installations. And if I look at the patterns, I see a lot of directors and about using it for meeting prep. So they're using it to understand pipeline trends before they go into a weekly meeting or a board meeting. They are using it to get insights on what changed week-to-week or month-to-month. The second pattern that we see is doing deeper research and then being able to take actions within HubSpot.

For example, we were talking to customer base group they used to prompt from our library to identify the highest converting persona and then they built a nurture campaign in HubSpot, and that campaign open rates actually jumped from 8% to 40%. So huge improvement in terms of conversion rates. So we're seeing customers ask questions, get insight and then begin to you take the action within HubSpot. And that's the pattern that we want to see, get insights and then drive actions to drive growth within HubSpot and very encouraged by the patterns there.

Our next question comes from the line of Brad Sills from Bank of America.

I wanted to ask a question about ASP growth. It's been kind of bumping along at this kind of flattish level for several quarters. And historically, before we entered into some of these macro pressures, it was more in the 12% to 15% range. So my question is, what would it take to get ASP growth back to -- back in the green and moving up north? What are some of the unlocks there? I think in the past, Kate, you've talked about how you're still seeing some pressure on those upgrades. So maybe an update on that trend as well.

Yes. Thanks, Brad, for the question. I think you know that there are a number of factors that drive ASRPC and it is also a lagging indicator because revenue is the numerator for ASRPC. It's impacted by the volume and mix of our customers. It's impacted by new ASPs and as you rightly point out, upgrades. We continue to see a number of headwinds and a number of tailwinds around ASRPC growth. They're all going to sound very familiar to you. The headwinds are robust starter additions and the lower ASPs associated with new customers post our pricing change in 2024.

In terms of tailwinds, we continue to see momentum in large deals, multi-hub adoption and then increasingly seat upgrades and credits. We did see a bit of inflection in the third quarter with constant currency growth of ASRPC up 1%. We expect to see that continue into Q4.

One moment for our next question. Our next question comes from the line of Rishi Jaluria from RBC.

Wonderful. It's Rishi Jaluria from RBC here. I wanted to dive a little bit deeper into kind of the existing traction that you're seeing with your current AI products and agents, right, including the most recent announcements out of inbound. Obviously, you've seen some really good traction there. As we kind of think about new products that you're going to be working on and getting out into the market, totally understand it's going to take a while before this turns into direct monetization, whether through credits or any other way.

But how are you internally gauging the success of newer AI offerings and working directly with your customers to not only make sure that it's delivering kind of their value that I think they expect out of you, but also that the way they're using it is aligned with future contemplated pricing mechanisms that you have in place?

Rishi, that's a great question. And I think I'll kind of start with our strategy, the momentum and then how we are driving customer adoption because that is the right question to be begin in due. If we step back, our strategy for AI has been consistent and clear, which is we want to embed AI into all of our hubs and platform. we want to build agents that deliver work for our customers, and we want to deliver a brief assistant and connectors that convert data into insight. And the strategy has just been consistent across the board.

So when we look at momentum as well as traction in terms of the strategy, we look at all factors there. So the first thing is, is the embedded strategy is working. And the answer for us is very clear because embedded features are being used across Marketing Hub, Sales Club Service Hub they are improving the outcomes for our customers, things like conversion rate that I mentioned before, win rate and improvement in 10% win rate in sales. I mean, previously, before AI, I don't think that types of outcome would have been possible with Sales Hub, I think that's a huge improvement for our customers. And similarly, Service Hub customers who are using AI see much better ticket closure as well as much better customer sentiment.

So all of that translates into much better attach rates for us, which we have seen with Content Hub, which we've seen with Service Hub as well as adoption of sales seats as well as service subsea. The second part of the strategy, which is probably earlier in the journey is the agent journey, right? All of us within the industry, we know we're building agents that do work. And the adoption is slightly less than the embedded products. And for us, the 3 featured agents that we launched which is customer agent, prospecting agent and data agent, we look at what are the signs in terms of adoption as well as repeat usage. And customer agent is the most mature there. It has been in GA the longest. 6,200 customers with 62-plus percent resolution rate and credit consumptions that are really in the pattern that we expect. So really clear trajectory in terms of the agents.

And you ask broadly about customer adoption. Part of what is happening is that when you look at AI, people have road maps, but they are on very different portions of the adoption journey. So we work with our partners. We work with our customer success managers and we work with our customers to help them build a road map, and they like our strategy. They like that they're getting a platform that is future-proofed and all of that leads to traction. I continue to believe that AI is early, and this is going to play out for the next 5 years, 10 years, and we are setting ourselves up to be leaders within this and doing it in a way where we focus on small, medium businesses and helping them grow with this new technology. So very confident there.

Our next question comes from the line of Gabriela Borges from Goldman Sachs.

For the Yamini or Dharmesh, I would love to hear you talk a little bit about what are you seeing in terms of customer data states and the quality of those data estates. Yamini, you mentioned a handful of times this idea of converting data into insights. How would you describe customer readiness from a data hygiene standpoint? Is there a period of time where they have to work to clean up their data and/or work to standardize a HubSpot. I think you have a really unique position because of how your hubs are organically connected together. So maybe just a little more on what you're seeing in the environment with respect to that.

Yes. I'll start there, Gabriela, and I'm sure Dharmesh can add to it. That's a very broad question in terms of the state of data. And I would probably say that it's multiple the stage of data maturity for our customer depends on how mature they are within their stack. And I would maybe talk about -- the first is, if they already have data, is it high quality? And within our customer base, if they have adopted all of our hubs, then it tends to be in higher quality versus having point solutions that they're trying to bring together. But partly, we also have Data Hub that we launched at INBOUND that helps with improving data quality. That is exactly what the purpose is for Data Hub.

The second is it's not just about data quality. Can you get data across the customer journey -- and can you build a context layer on top of that data that enable you to do more with AI and this is where I have a very strong point of view that platforms that bring together data that the -- and helps build a customer context, they're going to be able to get much more done with AI. And then the third, which you didn't answer the question, but comes up in all our customer conversations is unstructured data. you can do a lot more with sales conversations with service conversations, pulling unstructured data together, and that is exactly where HubSpot shines.

Our ability to bring better quality data to bring data across the entire customer journey and to add both structured and unstructured data. Those 3 things are the foundation to get anything done with AI. And that's 1 of the reasons why we brought Data Hub to the forefront at inbound.

Our next question comes from the line of Taylor McGinnis from UBS.

Kate, maybe for you. I think if I heard you correctly, you reiterated the outlook for NRR to be a couple of points higher this year than last year. And if I look at what that potentially implies for 4Q, it looks like it could be closer to $106 million. So one, just wanted to check if I'm doing that math right. And if that is right, that would be probably the biggest uptick in NRR that you guys have seen over the last several quarters. So is that an indicator of some of what you're talking about of like seat expansion activity and the new pricing model throw through like evidence that you're starting to see that materialize into numbers and that alone potentially is a leading indicator? Or maybe you can just talk about like what are the puts and takes in that number and how to think about it going into next year?

Yes. I appreciate the question. Thank you. And you are right, I did reiterate that we expect net -- we continue to expect net revenue retention to be up a couple of points this year. And it does imply a nice step-up in NRR in Q4. The drivers of net revenue retention are the same as they've been all year. We continue to see healthy customer dollar retention we've seen very stable downgrades, which is a nice indicator that customers have rightsized. And then we've seen strong seat upgrades across both sales and service seats as well as core seats. And we have also seen a benefit and will continue into Q4 to see the benefit of the pricing increase for customers post their migration onto the new model.

Our next question comes from the line of Raimo Lenschow from Barclays.

This is [ Damon Cogen ] on for Raimo Lenschow. I guess piggybacking off the last question. I know it might be a little early for 2026. Would you be able to tell us a little bit about how you're thinking about net retention with stronger pricing, healthy renewal rates on new seeds model, multiproduct adoption, is there any reason to believe that this metric does not improve?

Yes, I appreciate the question. I'm sure that you're not surprised why, and I'll start by saying that I'm not going to make any specific comments around 2026. That's that we do think that there is a path for further improvement on net revenue retention over the longer term. We talked about some of the drivers, but I'll reiterate them for the sake of completeness here, like we are very confident that we can continue to support really healthy customer dollar retention. We've talked about the fact that we've seen downgrades stabilized nicely over the last year or so and actually cancellation in downgrades together were a benefit to net revenue retention in 2024.

The pricing model change is resulting in both stronger seat upgrade motions, but also we will expect to see a continued tailwind to net revenue retention from the pricing changes on the remaining 50% of our installed base that will go through the renewal post the end of 2025. And then finally, Yamini talked a lot about the core seats and credits as emerging drivers and they will begin to contribute in a more meaningful way in 2026. So again, nothing specific on 2026, but I do believe that this is a business that's capable of delivering higher net revenue retention.

Our next question comes from the line of Steve Koenig from Macquarie Capital.

Great. Some of your peers have also talked about expectation of net new ARR growth higher than revenue, but it's more of an anticipation than historically seeing that. and predicated on sales capacity expansion as being an important driver. I'm wondering, you touched upon many of the drivers that you see for potential acceleration but I'm also curious, to what extent is your sales capacity or execution tactics aside from the pricing model change. To what extent are you lined up to for further acceleration or changes to that strategy might be helping you?

I really like that question. I think that it's really good to ask us about the sales capacity and how we are teed up. And look, we have been investing in 2 fronts: one, in headcount, and we have consistently hired sales capacity in regions, in segments where we see clear opportunity and that has continued, and that will likely continue into 2026. We see further opportunities for expanding sales capacity. The second thing, which I'm really excited about is all the investments that we have made in terms of AI to transform our own go-to-market has had a clear impact in terms of sales productivity, how we are using intent data to drive our prospecting, how we are using data during the deal process, we call it guided selling.

So internally, every sales rep knows the insights that they need, they prepare better for meetings. They follow up faster for meetings. They have much better competitive insights during the call and post call. And all of that has had measurable impact in terms of sales productivity. So both sales capacity and sales productivity are really humming, and we see that continue going into 2026. So we feel pretty good about that.

Our next question comes from the line of Brian Peterson from Raymond James.

So I'd love to understand from your perspective, what you're seeing in terms of demand trends, maybe a little bit more by geography. We saw international was a little bit stronger this quarter. We picked it up in some of our partner checks. But as we think about the growth opportunity going forward, anything you can call out in terms of international versus North America in terms of growth expectations?

Yes, I appreciate the question. Look, we have not seen any material changes in terms of segments or industries or geographies. It continues to be very consistent with prior quarters. Now if I look at our pipeline, we see a handful of trends that is not geo-based but generally based on the products that we have launched and the momentum we are seeing. We saw a very positive response from inbound. Specifically, customers are excited about the loop they are excited about Data Hub, and I explained why Data Hub is almost and critical for value from AI. And I see a lot of our customers and prospects talking to us about customer agent prospecting agent.

All of that has landed really well. I continue to see multi-hub adoption as well as upmarket momentum. The large deal momentum this year has been consistent and compounding and we see that both in Q3 as well as entering into the Q4 in terms of the pipeline, and that has been a consistent driver. So not as much as segment or geo change, but really consistency in what is driving the demand across all of these regions.

And Brian, just one small point to make on the domestic versus international growth. You will just remember that the legacy Clearbit business is about 0.5 point of headwind to our growth in 2025. All of that business is domestic business. And so it's about a 1 point headwind to domestic growth this year.

Our next question comes from the line of Jackson Ader from KeyBanc Capital Markets.

I was just curious to ask on 2 things as far as the fourth quarter is concerned. Number one is, is there anything kind of seasonally here in the fourth quarter? I know you guys aren't giant deal dependent. But is there anything in terms of the fourth quarter bookings that could impact the timing, Kate, of when you expect that net new piece to be kind of large enough to accelerate growth as we head into '26? And then also, I think we're getting into the heart of the pricing renewals for some of your larger customers. And so just curious whether the pricing -- how those larger customers are handling the pricing increases, whether it's firmer or softer than you expected?

Yes. Thanks for the question. Look, I think that Q4 is always our most important quarter of the year in terms of being the largest net new ARR quarter with November and obviously, December being pretty critical. We do see a lot of the migrated customers coming up for renewal at the end of this year, and so that will impact Q4 a bit. And so far, they are performing as reported over the last couple of quarters.

Thank you. This concludes the HubSpot Q3 2025 Earnings Call. Thank you to everyone who was able to join us today. You may now disconnect your lines.

All right. Fantastic. We will go ahead and kick it off. It's a real pleasure to welcome Yamini Rangan back on stage with me, CEO of HubSpot, particularly off of the massive success of INBOUND last week. Thank you so much for joining us.

Thank you, Gabriela. It's always a pleasure to come back and thanks for having us.

So I know how much energy you and your leadership team put into the messaging at INBOUND and the product and the functionality that you're announcing. Now that you've had a whole weekend to digestion process, would love to hear what stood out to you? What was the one takeaway from INBOUND that perhaps investors should be aware of?

Yes. For those of you who don't know, we have a conference called INBOUND, it's a customer conference. We had about 13,000 people. And for the first time, we did it here in San Francisco. For the last 15 years, we have done it in Boston. So this time, it was in Moscone. And I'd say that across the Board, the pace of innovation at HubSpot shown. We had 200-plus updates and features and product releases across the Board, across the entire platform.

But if I were to pick a couple of themes, I would say the first one is data. And we all know this is the foundation for anything to do with AI is data. And so we actually transformed what we had originally called an operations hub into a data hub -- and this now brings the data together. It uses AI to make it very smart, you can enrich any data by running LLM queries -- and it allows for customers to take that data, bring it into reports and workflows.

So I think almost every single customer I spoke to since INBOUND have mentioned data and the value of having the data in a simple streamlined manner in order to enable AI. And then the second big thing, which we will talk a lot about is marketing. There's just such a big change that's happening in marketing now, and our customers want us to guide them through the process with playbooks, with products as well as the entire ecosystem. So that was a big theme.

And then third, all the stuff that innovation that we have done in terms of AI, specifically the AI agents and the marketplace and the custom agent builder that we launched last week. I'm sure we'll talk about each of those, but those were the top 3 themes.

Yes, absolutely. Let's start on the data piece of it. One of the things that I've appreciated about HubSpot is you're the CRM and the front office suite for growing and scaling companies. So talk to us a little bit, when you look at your customer base, how far along do you think they are in getting that data strategy organized such that they're then ready to take the next steps and adopt AI agents?

Yes. And I think that's a broader question. One of the reasons why HubSpot consistently wins and more folks are consolidating on HubSpot is the data story. And so that has been part of the reason why we now have over 270,000 customers, and that's because of one simple thing. Our core platform brings together data for marketing sales, service ops teams. And if we ask our customers that's a single reason that they buy, it will be one of the top three reasons. And so I think that's number one.

Specifically, what's happening with AI is that you can now do much more in terms of looking at intense signals even before someone shows up on your website. That's a huge change in terms of what you can do with data and having the ability to capture intent signals on multiple channels, and then being able to take actions and then being able to drive agents is such a critical part of the strategy for scaling companies.

And you put these two together, you need to have a clear data story. To your question, where are our customers in the journey? Like any of those, they are in multiple parts of the journey. There are some that have been using HubSpot for years. They already have their customer data together. Now they want to get better intent signals and do much more sophisticated things with AI.

They are more advanced. And then there are some that are just starting their AI journey and realize that they need clean, not siloed data to begin the journey and they are starting. So I would say kind of across the spectrum.

Yes, absolutely. And the other thing you mentioned there is marketing. And I thought the way that you introduced the group really takes INBOUND and brings it into 2025. Tell us a little bit more about how you purchase this concept of the loop and how it takes into account the way that the interactions are changing between the brands and their customers.

Yes. So for those of you that were not there at our conference, we launched a new marketing playbook called the loop -- and the context is this. We all know that search is getting disrupted. And the easiest way to think about why search is getting disrupted is AI overviews are providing answers at the top of every search. And the data point that you should all remember is that 60% of Google searches today end up with 0 clicks, which means less traffic to website and less content leads.

So that is the disruption that is happening. The untold part of the story, the other part of the story is that what AI is taking away in terms of content leads it is doing something very different. It is allowing for you to capture intense signals, match your customers' intent with the information that they need to see and driving much better conversion in the marketing funnel. So in marketing language, if you want to say the top of the funnel is getting disrupted because of AI overviews.

The bottom of the funnel is actually transforming with better conversions from AI. So there are two parts to the story. And if you put these two together, marketing playbook needs to change. It needs to fundamentally look very different. And therefore, we launched a new playbook. We've been iterating on this playbook for the past couple of years. It's powered HubSpot's growth and now we launched it for our customers.

And the way to think about it is threefold. First of all, you should start with human voice and authenticity. Otherwise, AI is just going to give out [ SAM ]. Then you got to like use the data, all the intent signals that you can now gather about your customers to drive much better personalization of messages at scale, that's kind of the second step within the loop.

And then you've got to be in every channel. The diversification of channels in marketing is such an important part of the story. You cannot wait for customers to come to your website. You have to be in social channels. You have to be in podcast, you have to be newsletters and there is a new channel now called LLM and therefore, there's a whole approach in marketing called AEO, which is AI Engine Optimization.

So to put this in a nutshell, the marketing playbook is changing. What we launched is how you use human authenticity with AI's efficiency to diversify your content and channels and grow with AI. This is a big deal for our customers. This is a big massive opportunity for HubSpot, because we're not only providing the playbook that we launched, but we are also supporting it with our products across Content Hub, Data Hub and Marketing Hub. And we are helping them reimagining how they can drive growth with AI and was a pretty exciting moment for us to launch it.

And all week long and continuing into this week, we've been talking to customers about how to help them adopt it.

So you made a really interesting comment there on, this has been incubating at HubSpot for a couple of years as you've transformed your own internal marketing facility. So tell us a little more about that. What has been the iteration process internally? How do you feel about your own internal strategy to get new customers on HubSpot.

Yes. I think that's a great question, Gabriela. So a lot of the articles this year has been about search disruption. However, for us, the journey began in 2022, because in 2022, second half of 2022, we noticed that our customers were spending less time on blogs and much more time on social, on podcast and newsletters. And this was before AI overviews even came into the picture.

So we did a few things. We actually made a huge set of bets back then to diversify our marketing channels. Some of you may remember that back in 2022, we acquired a media company called the Hustle. And at the time, it was like what are we doing? But the reason we did that is we wanted a business podcast network. And that network strategy has diversified, and we now get 90% growth in terms of leads from that podcast network channel.

Same thing back in 2022, we also acquired a couple of newsletters, because we saw that e-mail newsletters is also a new way to reach customers, and that was pretty big. So the changes that we made in terms of our marketing channels and strategy started in 2022, and it was launching YouTube, launching Instagram, launching podcast network, launching newsletters and all of that has paid off.

And if you put it all together, we've seen a pretty dramatic shift in terms of HubSpot demand mix shifting over the past three years, leads obviously from blogs and web apps have gone down over the past three years. But at the same time, we've seen leads from social, from e-mail, newsletters and podcast more than double in that same three-year period.

So diversification is really important. One of the other things that we've started experimenting and this is early, it's new is AEO that I just mentioned. It's a pretty early approach to kind of being part of an answer rather than being part of the search result. SCO and the playbook of the prior generation was all about being in the top 5 blue links that you get.

Now there's a new approach and a methodology of showing up in the results, the one single answer that an LLM provides. And it's super nascent, but we've been experimenting with it. And what we are finding in terms of AEO is that the strategy, the content is the same. But the strategies that you need to use in order to come up in the answer is quite different than what you did to show up in the links and we've been experimenting with it.

Our internal leads from AI and LLM have grown tremendously over the past couple of years, small base. But the more interesting thing is that leads from LLMs are converting 3x better. And that is because when someone asks the question of an LLM, it is -- they are much deeper in research. They have much higher intent and they are ready to act. And this, again, by the way, is a big breakthrough for marketeers.

In marketing, you don't get 3x conversion improvement in any channel. You mostly get 5% to 10% improvement. So this is actually a pretty big breakthrough. And so now we're helping our customers begin to think about AEO strategy. And this is going to be multiple years in the making in terms of how to show up in an answer and how to figure out how LLMs work in terms of marketing messages.

But the bottom line is this. We diversified our channels. We've been ahead and looking at all of the changing dynamics within the marketing landscape and the things that HubSpot does really well is this. We iterate -- and then once we know the playbook, we educate our customers, and then we activate our entire ecosystem of solution partners to go out and help our customers win. That is what we did with INBOUND. It was playbook. It was a set of products, and it was an entire ecosystem behind it, and that's exactly the approach and strategy that we are taking with loop, and we think the opportunity is pretty massive for HubSpot.

This idea of AEO is a really interesting one. I know it's early days. Any initial observations as to what that strategy could look like? How do you figure out? Is it primarily through iterations. How do you figure out what works well and how to get that lead conversion.

Yes. I think -- to answer the question, there's -- you have to understand the difference between how to show up in a blue link and how to show up in an answer. The average LLM question is 23 words. And the average search was 5 words. And that begins to tell the difference between AEO and SCO. That means customers and prospects are asking very specific questions of LLM, -- it's not, "hey, what CRM should I use"?

It is I am -- would manufacturer in the Midwest serving population that looks like this ideal customer profile, what are the ways in which I can grow my lead. Now in order for you to show up in that answer, you got to have very specific staff, you have to have quotes. You have to repeat your content across multiple sources. And so there's a whole strategy and marketing customers are super excited to do it.

And the best way I would describe it is a ton of iteration and experimentation with your content. The way our teams are figuring it out is not just have one case study but have like very specific case studies for every industry with very high repetition of the content in multiple channels. And when you begin to do that, you're also going to need the visibility because what's happening within AEO is that you got to know, are you first of all showing up in the kinds of questions that you want to show up. You need marketing tools to be able to do that.

Then what is your share of voice within LLMs compared to all of your competitors? You need marketing tools to be able to do that? And then how do you drive better conversion of your content through LLMs and AI referrals, you need marketing tools to do that. And so I think that's where we get excited about the opportunity in front of us.

Yes, absolutely. And you're describing a strategy that has required a lot of heavy lifting from an R&D, from a product innovation standpoint. The question I want to ask you is, -- the beauty of HubSpot's tech stack with the primary color is you're able to do this kind of evolution or transformation. I wouldn't use the word seamlessly, but you're able to execute it in a way that's incredibly fast based. So my question is, are you seeing HubSpot separate out more from the existing SaaS competition because of the technical decisions that Dharmesh and Brian made very early on and because of how you've organized your R&D team?

Yes. I love this point about the primary colors that you're making. And we've said this for many years. A couple of choices that we made strategically really early on. One is that we're going to build our platform and organically build it, and that drives value for our customers, because when customers have to take a bunch of cobble together acquisitions and put it, they take the owners on like integrating it and unifying the data.

We want to take that paying for customers and that really has a huge impact in terms of a smaller company trying to grow. That was choice #1, which is like we'll build it. And then as soon as we made the decision, we also made a decision to be platform first -- and what that means is that we have a set of primitives within the platform. Think of it as automation, reporting, data and those primitives are across multiple applications and hubs, which means if we improve a platform level work in automation, that's going to show up in Marketing Hub. That's going to show up in Sales Hub and Service Hub.

And to your point, we've taken an exactly similar approach for AI. We have a set of AI primitives, AI skills being one within the platform, which allows us to build agents faster and build products and features within the hubs much better. And so -- what I think you're getting to is that is speed a moat. And I do think being platform first and having a set of core capabilities within the platform allows us to move with speed.

And one of the things that this translates into for our customers is just the pace of innovation. I talk to customers all the time, and I tell them when you buy HubSpot, you're future-proofing, your technology investment because we are driving a pace of innovation, and that deeply resonates with our customers.

Yes, absolutely. Well, let me flip the question around from a competition standpoint. You're iterating on an existing tech stack that was built 10-plus years ago. You set it up to succeed. But there are companies that have been born in the last 1, 2, 3 years that will say, we're starting with a clean sheet of paper. The AI native tech stack is fundamentally different from the SaaS tech stack. Therefore, we can build something clean and more beautiful, more dynamic, more disruptive. How would you respond to that claim that the AI native companies can be more successful relative to companies that are starting with an existing tech stack?

Is it fundamentally different in terms of the tech stack.

I wouldn't love to hear you. Yes.

I think that there are changes that AI is enabling. And I would characterize the changes that AI are enabling into 3 things. The first one is that context that AI can process is much broader. So if you think about an application like HubSpot, we always had structured data. That's what we are known for.

CRM is structured data of companies, contacts, deals, tickets. That's what we are known for. With AI, we can now process unstructured data and external intent signals at a much better pace than before. And so our contact layer has now transformed into bringing together structured data, unstructured data and external data that we got from the Clearbit acquisition.

And I would say that is huge differentiator for HubSpot and our architecture, because if you just start with unstructured data, you still have to go back and build. So if you're an AI-native startup starting with just e-mail or Gmail or calendar, for instance, you have to go back and build the structured data. So that's like number one.

The second change that is happening is where you take actions. It used to be that we can take actions or we can -- our customers would take actions on hubs, go to marketing hub, create a campaign, go to a sales hub and close your deals. That was the way in which our customers were using it. Now with AI, we're also doing work for them. And the agents that we have launched over the last 1.5 years, we now have 15 plus agents that we have built or the ecosystem is building, those agents do work, and that is the transformation that we've already enabled within our platform, and that gives the flexibility of our customers for go-to-market employees working in hubs to get work done or using agents to do more work for them.

So that's the second layer in terms of the transformation. And then we bring together all of this so that hybrid teams of humans and agents can work together. And that is kind of the orchestration layer. So what I would maybe suggest is that AI has enabled us to add more value by bringing in more data that we can help our customers with, more places to drive action and ability to orchestrate across hybrid teams.

And I don't think this is one where there is fresh sheet of paper actually has an advantage. I would say the context that you have from, in our case, 19 years, 270,000 customers worth of context is much deeper and that provides the right kind of foundation for AI to drive effort at work.