Snowflake Inc. (SNOW) Earnings Call Transcript & Summary

Please welcome Head of Investor Relations at Snowflake, Jimmy Sexton.

Good afternoon, everyone. Thank you for coming out to Summit this year. I hope all of you had a chance to see Sridhar and Sam yesterday afternoon and the incredible platform keynote this morning with Christian. It's been a really exciting last year for the company. You've seen us really hit our stride in terms of product innovation. And that has led to a number of new markets that we've entered, and we hope today you walk away from this event with a better understanding of kind of where we think we play and how we're well set up to go execute against this next phase of growth. So before I dive into the agenda, the safe harbor is here, we will be making forward-looking statements today. And so now the agenda. So you have Sridhar, Christian and Mike all talking about this next phase of growth for us and where we fit inside the data management life cycle, our right to win within this new world and how AI benefits us. I also think it's important for you to understand how we're organized, both from a go-to-market and a product standpoint to help you better keep track of our progress and get comfortable with how we're speaking about the business going forward. And then lastly, we're going to have the 3 of them join us for Q&A as well as Mike Scarpelli, who will be dialing in and so when we do enter the Q&A session, I'll come back up, but we have mic runners, so please just raise your hand, and we will find you and then you can introduce yourself and ask your question. So with that, I'll hand it over to our CEO, Sridhar Ramaswamy.

Thank you, Jimmy. Hey, folks, it's really great to see many, many familiar faces here. And it's been even more amazing to have gone through the past day at Summit. And Sarah Guo, who was here, Sarah Guo, my friend who interviewed Sam and I. Her feedback to me last night was thank you for inviting us to your rock concert. I thought I was running a data company, but it speaks to the interest and excitement that data and AI have in the enterprise world. I mean I'll let you know this. For much of humanity's history, data has been a little bit of an afterthought. It's something that you used to report the news. That's why early writing was invented to keep track of access or land ownership. I was very fortunate to be part of a company, Google, which thought about data fundamentally differently. There are other examples like that. But in the world of search ads knowing how you did was essential to improving what you did. Let me think about it, the company itself. Google was founded on the premise that the best answer, the best web page, for a given query was one that the rest of the world said was the best answer for a given query and how you break that feedback loop. In search ads, we had a similar mentality. It's really the combination of the advertiser intent, combined with what did users find to be the best answer that really drove how the team talked about what the product should do. And in many ways, my data team there was just as large as the product team. Accidented, but it influenced everything that we did. We built machine learning systems at planet scale, 20 years like '05 before -- like from now a decade before they became widely popular elsewhere. I think we are in a moment, where the rest of the world, thanks to AI, is waking up to the fact that data can transform how businesses can and should operate. And it is that movement from data as an afterthought to data is really important for you to get ongoing visibility and start predicting what you should be doing in the future in your business as many of our customers like Disney have done to do things like predict next best action to now thinking about, wait, AI can take this one step further. It can drive business transformation in a fairly fundamental way is what drives momentum for Snowflake and I know I speak for many of us at Snowflake that we feel very lucky to have this opportunity. And as you saw today, we are doing everything that we can to seize this opportunity with all our heads. There is a little bit of a schematic. It's a caricature of how we think about what we call the end-to-end data life cycle. When is data conceived, typically in things like transactional systems. When you open your app, that app sends a little message that says, "Aha! Sridhar opened this app." It gets recorded somewhere. And so we see our like technical mission as being there at all key aspects of this end-to-end data life cycle when data is born, when it is ingested, transformed and cleaned and then when analytics is done on it and then the predictive analytics, where the history is used to predict the future then gets done on it. And what is also unique about this moment is this is the big unlock from unstructured data. You saw a ton of announcements today, whether it is Openflow or Cortex Search. That's because unstructured data has always been a little bit of an afterthought in how people thought about data. It's just too unwieldy. You couldn't do that much with it. I joke to people were much in my life, Command F is my favorite PDF search engine. All of you write very nice reports, but God help me until recently, if I had to figure out, what did you actually say about Snowflake or a competitor. And -- but it is that unlocking where everything that we talk about, whether it's data ingestion or cleaning or processing or analytic can now act on structured and unstructured data. And in a world where metadata and governance is going to be even more important. You have heard the phrase, AI-ready data, both in my keynote as well as Christian's keynote. It's not enough to have data. You need to have additional information about what does it mean? And any of you that has done -- written SQL queries for a living or try to analyze data, knows that is super messy. I've spent thousands of hours analyzing data. And we always give like terrible names to all the columns. It's really hard to figure out what the metrics actually are. It's just like that's sort of how it works out. You have a column called revenue, you don't quite like it, you will come up with another column called Rev2 on the poor person that shows up 2 years later, it's like who are these people and what did they do. And -- but having that layer of intelligence about that data becomes very important. And of course, finally, the world of consumption. This is roughly how we think about it. And when we talk about wanting to play a critical role for our customers into this end-to-end data life cycle. We talk about playing meaningfully in these different layers. So the product strategy very much over the past year has been, how do we release the key components that will let us play this meaningful role. Look, we understand that it is easy to say things like all users, all data. That's an aspiration. That's not a plan. And part of what Christian and Benoit and I and the rest of the team have been doing is be very methodical and deliberate about how do we attack this big opportunity? What are the critical first steps that we should take? And how do we always lead with strength? It's easy to aspire to be something else. It's very hard to get there. If a set of companies have gone at some area for 10-plus years, it is not all that easy for any other company to catch up. And so I think it is really important that we have the big vision, but we are -- but that we are very deliberate in how we go about seizing that vision. And we'll get into some detail later. But for example, in the space of databases and transactional stores, this is everything from recording a session that you're having with a chatbot. Two, how do you have a user store that remembers preferences that a user has. We have taken steps. We've been working on something called Unistore for 5-plus years now. That's meant to combine the best of a transactional store with analytics, clearly has turned out to be a harder project than we thought, but it's up. It's doing well and the acquisition of the, finally named Crunchy Data. It's an amazing company, by the way. And when I first heard them out, like really you call Crunchy Data? But it's a really good company, full of world-class Postgres developers. It gives us an important component in how we go after this transactional market. And another component is how do we make it much easier for people to ingest data into Snowflake into cloud storage. That's the data Volo acquisition that happened 6 months ago. We have brought it at record speed to public preview. That's another thing that I want to stress, which is we are leaning much more into being effective with acquisitions, getting them out to market, getting that feedback, getting that happy loop of delivering customer value and iterating from it. We think Openflow is going to make a meaningful dent in how people look at us when it comes to getting more data in from existing systems, sometimes legacy systems. And Openflow is also much more even between unstructured and structured data. As I said, unstructured data is, I think, it's a massive unlock in many different areas. And then we continue to invest very heavily in the bronze and the silver layers. These are the earlier stages of data processing. Previously in many of my conversations, Christian and I have talked to you about how paradoxically Iceberg was the Aha! moment for us a couple of years ago about what Snowflake could do in earlier stages of the data pipeline. We continue to invest very heavily in things like Snowpark, how do we make sure that we are seen as best-in-class when it comes to early-stage computation. And the partnership with dbt, bringing -- bringing dbt right into Snowflake. So it feels very much like a first-party product and the ongoing collaboration with them, I think, positions us really well in the earlier stages of the data life cycle. And we've always been best-in-class in analytics. We intend to keep that lead. Again, finally, I've worked on databases for a very long time and have started working more with the team, and it's been fun both to see what they have done and also to lean into what we can do in the future. And then as I said, getting data AI-ready is going to be a key differentiator. We will have partnerships, but we also think that it is an area where we can drive the ecosystem in terms of having customers ask for their data to be data ready. Right now, we live in a world where, yes, you can buy a catalog on the site, but that information is largely isolated from what BI tools do or if you have some information stored in a particular report. I tell people that every meaningful report is actually an encapsulation of meaning because somebody has taken the trouble to go in and say, "Hey, these are the different pieces of data that need to sit in this report, and it's meant for a particular persona. That's the semantic context of the underlying data. A lot of our work around semantic views and making that commonplace is really based on this belief that data and metadata need to be close to each other. And the closer they are to each other, the more value our customers are going to get with it. And we also think that this turns into things like data just being ready for agents, data, both unstructured and structured data being much more usable and composable. We lead in governance and you will continue to see us invest and keep that lead. And then finally, we see the world of consumption changing. And so we are investing into it with the things that make sense. BI as a category in our humble opinion, it's an established category, and we have great partners that we work with. Us trying to do on other BI tool doesn't make a lot of sense. But on the other hand, you saw what Snowflake Intelligence could do today. And again, it's not that far to then be able to say, well, I want to remember all of the questions that I asked today in my conversation and have it turn into a report with these configurable parameters. So we see these as democratizing data access and redefining BI as a category. Remember, BI today works in a pretty straight jacketed way. You have underlying data. You have some genius that knows about the semantics of that data. They have to create the views and then they have to create the dashboard. And if you as a business user want something different, sometimes the BI tool will let you do it, most of the time you get to wait. To us, the flexibility of AI unlocks those things and lets us think about the category very differently. You're not the only ones to be doing this, but we feel like we can do this with strength. We continue to invest in the notebook experience in things like machine learning, the predictive part of AI. And that is a product where we clearly came from behind, but positioned as a natural add-on to what we do at Snowflake. It is gaining strength. And we also see a world in which things like native applications, native applications that are Snowflake specialty, it is a way to encapsulate procedural logic close to data. It's not -- it was never meant to be a full-fledged app development platform. But what it does is it lets a partner like S&P Global, take their data, attach logic to it and then ship it to a customer so that they can use that logic. But now they're very excited about the fact that this can be an agentic component that can respond in natural language and it lets them create value at a whole different level than what they have done before. And finally, when it comes to sharing, you saw Christian demonstrate today already. Things like sharing now joined with AI and semantic models makes a lot of interesting things possible that were not really possible before. Cortex knowledge extensions, it's a super simple concept. It's a data set plus a search index plus a model on top. This is a product that literally did not take much time to build because all of the components are there. It gets turned into you have all of the data repository of a gamut available as 1 single component that you can embed into anything that you want to create, any agent that you want to create. So we feel very good about how we are set up and how we are executing on it. And it plays a lot of emphasis with ourselves, with the product teams on practicing what we preach about AI. It has already gone through pretty major transformations where the initial versions of foundation models are very good if you could figure out how to stitch things together and send it a particular bit of context and then they would give you a useful answer. It went from there to these. These models are now very good at figuring out which data set that they should be talking to. And with things like Snowflake Intelligence, we are not that far away from they can begin to replace pieces of workflows that are really important functions, whether it is loan underwriting or like some -- like what our sales team does, for example, they should be able to automate pieces of it. So we do a lot of practicing what we preach and there's an internal tool called Raven, which is an agent that is meant to surface everything that matters to a salesperson in 1 interface. And we spend a lot of time thinking about how do we make our engineers more productive or folks in sales more productive. And a lot of that kind of reflecting has led to 1 thing that I forgot to mention at the very beginning, which is we made a big push about roughly 6, 7 months ago to accelerate migrations, moving data over from legacy systems to Snowflake, again using AI. You saw Christian's announcement about SnowConvert, the idea is how can agentic clues makes the very painful process of moving data from legacy systems over to Snowflake. But that's a little bit, as I said, using AI ourselves, using AI in the product is a little bit of a theme that we are constantly pushing ourselves. And it's a world that's evolving rapidly. Cursor was barely a company a year ago, but not only is the UX team that developed all those cool UIs using Cursor as their primary IDE, they're being inspired by and creating experiences like Cursor right within Snowflake. So it's an incredibly rapid pace of evolution. One question that you folks will ask yourself that we ask ourselves is, gosh, this is a really broad surface. How do we make sure that we actually get things done? And how are we organizing ourselves? And Christian alluded to it in some of our slides. We have done a natural organizations, both in the product and engineering team, but also in Mike's go-to-market team where we have pillars of teams organized everything around analytics, including things like AI-driven migrations. And we have another team that's focused entirely on data processing, everything that you heard around Openflow, the Spark implementations, dbt and so on is housed within that team. And then we have a team that's focused on AI products. How should we be thinking about the future of business intelligence or data science. And then the final pillar is what are the foundational things that we need for people to be writing applications and how do we make sure that we turbocharge things like sharing and semantic context that can be attached to data. And this lends itself to a very clean organization and part of the change that we have made over the past year is driving accountability. There are leaders in the product organization that are singularly focused on making these areas successful. And they have partners in marketing, in Mike's sales team that know when certain products are specialized and bring along the requisite expertise. And Mike will talk about it a little bit. But as you folks know, the specialist motion is one that should be used carefully. So we understand the power of it, but we also think a lot about how do we distribute to the entirety of the sales team. An area is like analytics are very much that joint responsibility. And we have put in place mechanisms by which the levels of our account executives, the sales engineers, that's the dominant workforce in the sales team is getting better with respect to the newer areas. With this larger lense, we feel that we are able to take on a much larger market. This is obviously a thesis is based on 2 things. One is the on-prem to cloud migration that all of you folks are very knowledgeable about. But it is also our belief that a platform that's centered on data and AI is going to play a larger and larger role in the world of cloud computing as we know it because we feel that, that data centricity is really important in helping customers realize SaaS value. On the number of customers that I have met even in this conference or barely 1.5 days, that I've bet big on Snowflake and I've had amazing results, sometimes in 6 months, sometimes in 9 months to show for it is proof that we are well equipped to take on such a large TAM and realize a meaningful part of it. You folks asked a lot of questions in all of our earnings calls about what's the thesis for why we believe we are going to be successful. And our consistent feedback to you has been that we drive our strength as a company in continuing to be world class at analytics. We'll get into a lot of detail about this, but we are at the very least, many years ahead when it comes to the core technology. Others talk about serverless, we've literally been doing serverless for a dozen years. And the same goes for what we announced today, which is the adaptive warehouse, it represents a step change in how people should be thinking about analytic computation as a whole. And we have a world-class team when it comes to how good we are with analytics, lots of unsung stuff, including obscure, but really hugely important things like pipelined execution for user-defined functions. Most people don't know, don't care what that is. But if you are doing a Teradata migration, it turns out to be really, really critical. And there are a dozen things like that, that has taken multiple dozens, sometimes of person years to get into the core engine. And we feel very confident about leading with strength in this area and things like being able to drive migrations a whole lot faster. I've met customers who roughly tell me I have 50 on-prem data warehouses sitting in this thing, I really do not want to pay any money for them. All you need to do is move that data safely over to Snowflake. I can't have you miss any of that all. That money is yours. And so far, we've been hamstrung in our ability to scale that quickly because those projects could only go so fast because they were always human skill. Part of what excites us about AI is that ability to make that go a whole lot faster. And by the way, that means we have to transform ourselves in terms of how do we think about running migrations. We have a professional services team. And so we are busy thinking about what is the future of that kind of systems integration. And we'll talk about partners. And those folks, the GSIs are having the very same conversation. When it comes to innovation, I think last year was definitive proof for us that when we put our mind to it, we can really step up on the gas. We went from being, let's face it, nobody when it comes to AI last year to every customer wanting to know how they can use Cortex AI to not only get things done with AI, but to be able to transform their business. And we do it in a position of strength. I've talked yesterday, Christian has talked about it, where we price qualities like simplicity and tightly integrated products. You do something with Snowflake Intelligence and create an agent, everything that you put in with permissions, with data governance works out of the box. And that is not something that people that essentially think of technology as just adding on services is going to be able to easily catch up. So we have a long lead in many areas, but we also bring along a product philosophy that is going to strengthen our position in the market. Mike will talk about this, but we actually think that while we have a healthy partner ecosystem, we can be driving this so much more. I've personally involved in many conversations with many GSIs and many partners. They can sense change in front of them. They can sense that system integration 5 years from now is going to be nothing like what it was 5 years ago and they're excited to be partnering with us through this change. I think it's a point of real leverage for us. We have an amazing team. Mike Gannon, who you will hear from, comes with a wealth of experience in driving sales teams at scale, but I think will stand us in good stead. And Vivek became Head of Engineering, some 7, 8 months ago. And the transformation that he is right on the team in terms of driving accountability bottom to top in terms of how we think about talent, how we think about getting products done, the sense of urgency that he brings to the table. Like me, he's 24/7, all days of the year. I think that intensity shows through in what we do. I'm also very pleased that Mike Blandina joined us. So Mike ran the Payments Group at JPMC. If I remember correctly, some 8,000, 9,000 people processing a mere $10 trillion of transactions everyday. This is -- it's an amazing team. And before you ask a question about it, Mike continues to be super active. He is actually not here physically because his daughter is graduating this week. But we have many promising candidates for CFO and hope to be updating you folks relatively soon. And then finally, you see our event marketing. I'm blown away by the scale and just sheer quality of this event. And what often astounds me even more is Denise and her team do 20 more copies of this throughout the world. The energy in Sydney that I've been at or in Tokyo is like the energy in this building. And I got to tell you, again, sort of super funny for the world of data to be this excited. I'll just spend a couple of minutes on sort of values, which I think are important. We've always been customer first. I heard this from Frank. I did -- I think I told you folks this -- I basically did a tour with Frank last year, actually was here before last, where we spent 5 solid days together. It was conversations during those that first product the idea that perhaps I could succeed him. And part of what he drilled into me during the time that we spent was how much people were betting and they were betting on Snowflake. It's like Sridhar, people get fired if their migration doesn't work. They're bidding their careers on Snowflake. And that's why you need to take each and every customer seriously. And when something doesn't go right, we need to swarm and make things right. And you can sense that, again, in the conversations that we have with folks. We -- even more than before, price accountability, it has to come with the right structure, but we have key leaders in the right positions that know what success means for them and how that contributes to the team's overall success. We have mechanisms, we use OKRs, at least at the top level for driving down accountability and alignment. But these 2 go hand-in-hand. We structure ourselves so that there is a very clear alignment of how the parts add up to something larger than the sum, and we hold ourselves accountable. And the quality that I've stressed with the team is ongoing excellence. My take is that excellence is a way of living. It's not a milestone that you get to and that we are exceptionally lucky to be in the position that we are in, and it's really going to take us constantly pushing ourselves to be better to achieve the kind of company that we want Snowflake to be. And in terms of what this translates into things that folks in this room care about, we're super disciplined. Our big areas of investment are in R&D and sales, you will have a question about how many folks we added to the sales and marketing teams. But we hold ourselves accountable in every function. In R&D, for example, we realized at some point that we were too top heavy, and we are very intentionally focusing on people that are earlier in their career because in a paradoxical way, they come less encumbered by like all the assembly language stuff that's sitting in my head, for example, 25 years ago, it's just not that relevant anymore. And so we are very thoughtful in how we invest in our R&D teams and in our sales teams and we hold ourselves accountable, not just with folks like the account execs and the sale -- and the solution engineers for whom accountability is very direct and in your face, but in every function. We spend a lot of time thinking about how do we make sure that these prized teams that directly influence the future of Snowflake as a company or as productive as they can be. I've talked previously about things like stock-based compensation. Some of it was driven by previous policies of not being as frugal as we should be. But both in how we approach R&D and sales where the majority of our SPC goals, we have a very solid plan to have that be much more manageable. And the final point that I will make is that we continue to be very active with M&A, but on our terms. I don't want to pay $1 billion or $2 billion for companies making single-digit millions, it's just like the math just doesn't work. We don't live in that world. But thanks to acquisitions like Datavolo or even Neeva, which is a very modest acquisition, but laid the foundation for what we did in AI and search. We feel more and more confident about our ability to acquire companies, to turn them into meaningful businesses right from the get-go. So we are super active in this space. We are open to all kinds of acquisitions, but they need to make sense and they need to create accretive value, all of this, the product opportunity, the market opportunity, our shown ability to execute over the past year and more and the excellence of the team that we are building up to both at the leadership level, but also in the rank and file at every level of Snowflake, combined with what we think are internal values, cultural values and how we should operate with an eye towards being sensible in business make us feel really, really good about where we are as a company. And with that, I am going to hand over to Christian to talk to you a little bit more about our product road map, what we have done and what we are aspiring to do. Thank you.

Thank you, Sridhar, and hello, everyone. Awesome to see many of familiar faces, many instances. We're joking with someone that many of our interactions are on Zoom. So it's also awesome to be able to see you face to face. Let's get started. Maybe the most important takeaway from what I want to share today is aligned with what Sridhar shared, which is we feel really, really good about where we stand. Not only in the industry and relative to competitors, but most importantly in the relationships and the impact that we're having with organizations across industries of alternative sizes. The slide that I'm starting with, this is a third-party study. It's a vendor. He was grilling us, and you know who the managed Spark vendor is and he was holding us accountable at heart. Like he was saying, okay, how do you do this? And we'll give an answer and he would say, this part of it is true, but it doesn't work in this scenario. So he's a very technical vendor that went deep and published. There's a blood post on this, we think we can get you a source. This is the simplified version of that. But it highlighted something that we've been very proud of since the early days of Snowflake, it speaks to what Sridhar said yesterday, just retreated here, what Benoit said, which is we believe that we will win in the long run relative to the alternatives by leaning in on simplicity and the ease of use for our customers. And why is that such a compelling value prop? Complexity conspires against what customers are trying to accomplish, right? We'll talk about compute and I know there are some questions from you, but it is truly dizzying to try to figure out what is the compute instance I need to do -- used to achieve any 1 task. That's on 1 cloud, not do it times 3. We focus on that abstraction of complexity and simplifying things. And that is how we believe is the long-term differentiator and advantage for us. Sridhar mentioned something that is super dear to all of us in the product and engineering organization, which is you don't build an analytic system, a data processing engine to the quality of what we have overnight. Yes, you can get the synthetic benchmark to go and show results in some short amount of time, but there's nothing like the real world. You say, okay, the simple test is it get's going. Once you try to do a migration from an actual system, then you realize that it is hard. So sometimes even math doesn't add up. I've shared with some of you an instance that happened maybe a year or so ago, we were migrating from Teradata. Results from the 2 systems were different. Three months into big investigation, turned out that it was a bug in Teradata. And this was core financials of a company but it took us a while to go and validate those sorts of things. And this is the type of advantage that Snowflake has. It's been battle tested, used by many of your organizations and thousands of customers around the world and we feel quite good about what our position currently is. And of course, you saw this morning, and we'll talk more about it, we're not standing still in any way. What this study here ends up concluding is from a pure total cost of ownership, we come out ahead. And I think that where you start informs your choices like I have no problem talking to customers about Databricks' audience. They come from a tinker mentality, a very developer mentality and removing knobs to make things simpler, it's hard. For us, it's easier to judiciously decide where we have more control because going the other direction is hard because there's backward compatibility, there's unhappy developers, et cetera. It's not just total cost of ownership. I think pure performance also matters. I said on the main stage this morning, I'll reiterate it. All benchmarks need to be framed with the appropriate context. I think it will always be possible to find the 1 use case where pick whatever system shows better than another system, that I granted upfront. But what we're showing here, and we showed in the main stage is we took a set of representative benchmarks. We think that they are correlated with what our customers do in the platform. And what you see here are the results of the Gen2 warehouse, I know some of you have questions on this. Gen2 is more updated, faster instances from the cloud providers, but also a very comprehensive suite of optimizations that lead to these type of results compared to both M&S park implementation as well as a cloud-native database. But probably who were the battleground will truly evolve is in the open data. For the last couple of years, there was, oh, Snowflake is closed data. They're not truly committed to open platforms or went table formats, and that's where you're going to get killed. These are real numbers, similar set of real customer workload inspired numbers for benchmarks on Iceberg data. We are leading in many, many types of operations, many, many type of use cases. And our relationship with customers, I was meeting with a large bank last night. And it's like we want the level playing field, what open file formats, what open data formats, what Iceberg does is it levels the playing field and makes it easy for customers to switch back and forth between us and Macquarie and Microsoft and Databricks. Yes, that's what it does. So truly, it removes the lock-in. And where is the value-based locking going to come from, who is giving the best experience and who's giving the best price performance and we're ready to go and take on that battle all day long. What I was told this executives of the bank last time was, we welcome that battle. We want to engage because we believe that we're going to get an unfair share of that type of workload. This is what you see here. Then I want to preempt some of the questions on compute. Super clear we have always been and we continue to be committed to be the leading platform in terms of price performance. There was a lot of questions on do we announce both adaptive and Gen2 and why are you announcing 2 things that are similar, but different. And then how do we think about it? So I'm going to preempt some of your potential questions. In the same way that I said in the main stage, Sridhar just reiterated serverless, which is the notion where customers don't need to think about computer instances, was what we pioneered and what everyone else has followed up on or copied. We wanted to make sure that the thought leadership that we're applying into adaptive is out there because it's not just an idea. We have customers that are running it. We are running our internal instance of Snowflake. We call it Snowhouse. That's an Adaptive and the thing is phenomenal. At the same time, tuning a system that is Adaptive in nature that is learning from behavior is going to take some time. So before I don't know, a year from now, we will have a conversation. Well, but Adaptive is not GA yet. I don't know exactly what's going to be the time frame, but it's going to take some time. But we will continue onboarding customers and delivering value for those customers. That is already -- the train is in motion. But what happened with Gen2 was, again, there were new instances which were materially more expensive than what we've done in the past for as long as Snowflake has been in market, 10 years in June, very soon 10 years since the general availability of Snowflake, we've always transparently upgraded customers to newer instances. We all collectively went through the journey of migration from older instances, C2 to C3, than we did the R migration. And we always did is prices are stable, system gets faster, and we have this conviction that if we improve performance and improve the economics for customers over time that leads to longer-term commitment and additional workloads. The philosophy is still there, but what happened with Gen2 is the instances were so much more expensive that if we did what we've always done, then it would not work, you would all not be happy, we would not be happy. So what we did is we had to introduce a new type of warehouse. It is priced at a higher level than what you see in the traditional Gen1 warehouses. But it's so much faster that the price performance advantage for our customers is still there. And that's why I have no problem in the main stage telling customers, go try it. I've got a bunch of e-mails today already on the, oh, we're trying this thing, it looks super fast. And yes, nominally, it's more expensive. But if you're spending less time, customers come out ahead. So the notion is we had to do this just to lay out a ton of innovation, leverage new hardware instances. I don't know if, at some point, there's a Gen3 or something in between. The future is Adaptive and Adaptive is taking all of the benefits of the warehouses that we not to this date, but you don't have to know if the size is small or large. Over time, it gets unified with Snowpark Container Services, which is -- sometimes we'd lose over details. There's warehouses, Snowpark warehouses, Snowpark Container Services. And we've done each one of those steps for a reason, but we want to go back to our strength, which is if you have too many options, you're making it too hard, go and remove options, and that is what Adaptive does. Hopefully, that helps clarify how we think about it. And maybe something important for all of you. I think in many conversations I have with some of you in the room, there's always been a question on, well, but why are you passing all the benefits of performance and headwinds to customers? Both Gen2 and Adaptive have now given us the ability to decouple performance enhancements from revenue impact. And I'll say we are -- even if you go do the math and then look at the benchmarks on Gen2, we are keeping some amount of the value. But I want us to also collectively understand that if we become a company that is always keeping the dollars fixed. And just, yes, the performance is better, but the dollars are fixed. I can tell you that, that's how we become the next Teradata. And we are very clear and very determined that's not going to happen. But what this does enable us is to decide when and how much of those savings get passed to our customers, which I think is something that all of you have given us feedback over the years and it's live on Gen2 and continues to be true on Adaptive. Sridhar talked about product velocity. There is no 2 ways to go around it. Like if it's not evident by not only Summit, and in Summit we usually line up a lot of things. But Summit right now is a milestone. When we do build in the fall, there's also tons of innovation. And there has been so many other things that are happening in between our large events that we're still doing launches. Cortex agents, we did it like a month or 2 months ago. There's other capability that we're just launching on a regular basis. And where does this come from? Sridhar already alluded to it, which is we've organized product and engineering teams by what we call product category, the 5 areas that you saw and recapping in a short a little bit. And each one of those has a clear swim lane and a clear mandate and go as fast as you can and go and win in this category. It is also true that there is AI-driven productivity. We had this fascinating story like Sridhar mentioned our PE team, our product experience teams and many others are using cursor and other tools, but was this funny thing that Benoit himself, he's still coding. And he is man plus machine these days. It's Benoit plus AI cursor. And he's been using it so much that he got throttles last week, this a true story. And he was up in arms. He's like, how do we give them more dollars so that my productivity gets back to what it was and he was on fire drill. We gave him more money. Benoit got the latest thing, and he's back to being way more productive than he's ever been. That story is true, and it's not just Benoit, but his engineers along the way. So the ability to just go faster is becoming a key asset of how we think about this. AI thought leadership. Sridhar also mentioned, we've gone from 0 to something in a very short amount of time. What we have done is the original team that we had assembled that really no modeling an AI inference and true AI research. They continue to build models. They were the ones that build Arctic and Arctic Embed. They continue to build models for certain small areas where needed like Document AI now has a new Arctic extract model, and that's small, efficient, better than anything else out there, so we can do that. But we're also looking at where the state of AI can be improved and we have an AI research team blog. You can go and look at the details on all of these. And they're producing world-class results and sharing with the community for us to go and be part of that AI leadership. The examples here are a little bit in the weeds, but how do we make inference faster, which is what many of the slow chat bots go through it or it's too expensive, or we did some research on SQL generation. The moment that we published it, we were #1 on the Spyder SQL query benchmark. It's standard benchmark everyone is playing. I was asking Catherine, hey, can you confirm that we're still #1 because the world of AI is moving so fast. But yes, as of this morning, we're still not only #1, but we also are #2. So we are far ahead on the spots of this and we'll continue to invest in AI core technology, share with the world out there because we want to be part of steering where AI is going. Same is true of Iceberg. This one warms my heart in a very significant way because we are shaping where Iceberg is going. We have hired a number of PMC members, the program management committee folks, the ones that vote on the proposals. We co-chaired the Iceberg Summit that happened here in San Francisco a few months ago. And if anyone gets a recording of the presentation from Ryan Blue, who is the Chairman of the Apache Iceberg Committee. He was very upfront. These 2 proposals came from Snowflake, these 2 got favored from Snowflake. These are shaped by Snowflake. So -- and with you all as the audience, I just enjoyed to know and be able to say, we are steering Iceberg and we didn't pay the $2 billion that Databricks paid. Like it's true, like that is the reality. We're steering it, and we're part of it, and we're partnering with those guys at Databricks, but we're making good use of our shareholder money. The round up -- this is the similar slide what I had in the main stage this morning, the round up on types of data is very important. Analytical has been a story for a long time, of course, the beginning of Snowflake hybrid, the age stuff Unistore. Yes, we underestimated it by a lot. Yes, I came and told you is the Holy Grail of database, and it still haunts me that it is the Holy Grail of database, but it's working. It's going better than we thought since it hit GA. So GA took a long time. Someone stopped me in the hallway an hour ago and say, "Can we please get Unistore on Azure. I have the use cases." So I think we have the capability, and now we're rounding it up with the Snowflake Postgres that comes from the Crunchy acquisition. If I line up, the different enhancements from this morning, this goes now framed into the 5 categories that we mentioned. Sridhar has already reinforced the importance of some of these enhancements. We think Openflow is a major capability for us, just in be able to make more data available to Snowflake. Also, it gets us more upstream into the data life cycle. This is very important. If you think that data is being born in devices and IoT and LTV databases and somehow they land and then they go get processed by Spark and Databricks and then show up in Snowflake for analytics. This helps us be way more upstream in that life cycle. So we're early. We help customers with data as it's born. We help the customers with data when it's consumed. It might as well go do the middle thing with Snowflake. So there's a lot of strategic value in how we do this. AIC Go, we spent time in the keynote this morning, we spent there was a demo. But hopefully, all of you got to appreciate the piece, which is we're bringing AI to our core audience. The SQL savvy persona, the analyst that just now has this very strong tool set with multimodality with -- it's not just text, but it's images and audio, and we'll do video that's coming soon. Snowflake Intelligence. We talked about it. You saw it, I'm not going to spend time here. And Sridhar also mentioned the AI-ready data that matters a lot to us. Last couple of slides. A year ago, I stood here in front of many of you, I gave you a taxonomy of the different efforts we had. I said, "Well, we think that these are going to be material for FY '25 and chatting with Jimmy and the company is like, "I'm not going to stand in front of you and not hold myself accountable to what we said." At least the 3 that we say would be material for FY '25. They've largely turned out to be the case. We have stopped looking at unstructured data as its own category because at the end of the day, unstructured data is generating value for us through 1 of the 2, either Snowpark or Cortex AI, Cortex AI search in particular. And if you look at what we shared with you in earnings and calls and call backs, yes, the first 2, Snowpark and Cortex are doing well, but also Dynamic Tables played out quite strongly. And Iceberg, which was a conversation that we spent maybe way too much time has materialized the way we originally would have wanted it, which is the tailwinds and the opportunity of Iceberg far exceed and outpace the potential headwinds of Iceberg. Jimmy framed at the beginning of this conversation of all the product enhancements that we are working on and future innovations, we do want to think of them in these 5 product categories, that engineer analytics, AI applications, collaboration and platform, and you'll see us talk in these terms and frame all of our efforts in the same taxonomy. At the end of the day, I'll end in the same state where we -- I ended this morning, which is the AI data cloud. I want to invite Mike Gannon on to stage. Thank you. Great to see you all.

Good afternoon. Welcome. Hopefully, you're feeling the energy in the conference this week. I'm curious because this is my first Summit. Show hands first Summit for everyone else in the room. Okay, about 30%. Well, by way of introduction, Mike Gannon, 80 days on the job as Chief Revenue Officer for Snowflake and feel absolutely thrilled to be here. The energy and excitement that I'm getting from this conference is revitalizing why this decision, and this is only the third career decision I've made my life is probably one of the most exciting chapters of my career. But I want to share with you a little bit of background. First chapter of my career started in 1997. I drove from SUNY Oswego, New York to Hopkinton, Massachusetts for an interview with this company called EMC Corporation. It's probably the more unpopular route to take. Most of my friends have gone into finance and insurance. I went a different route and took a run in a technology company and had the good fortune of landing a job there as an inside sales associate. Number one job was setting up 15 qualified sales calls a week. And in 1997, EMC was not quite a household name. We were trying to get customers to decouple the store's decision from buying a computer, which is traditionally was a peripheral. But learned a lot during that time. One was, first and foremost, we had to define a market. EMC was really defining an element of what was considered the enterprise storage market. And over time, we had great success. I certainly learned the role humility plays and being hung up on 50 times a day, but continuing to pick up the phone with a smile and getting the next person on the phone. And eventually, we start hitting stride. But the tectonic shift that really occurred in the market that really prominent role for enterprise stores was the Internet, right? It really started taking shape there. And one of the lessons I bring to the discussion today that we're having with customers is let's make sure we've got a monetization plan in place before we go invest millions and millions of dollars into a data architecture because you can tell and you remember the dot-com boom, there was people spending hundreds of millions of dollars on a business model that never had a revenue-generating outcome. So we bring those learning lessons through time to have conversations with customers. But the other thing I'd learned during that time and again, this is the first chapter of what was the first 15 years of my career was maintaining relevance with customers. And at the time, we had a single product family. We had some software that help customers with replication from a disaster recovery perspective, but we weren't capturing the low end of mid-market, and we made an acquisition with Data General. So again, our relevance was important because customers had to have by criteria, not only the high end of the market, but the low end of the market. Relevance had to expand beyond that, right? We made an acquisition, that's where we first met Frank and data domain because customers want it a different way in a more transformational way of doing backup and recovery, right? And then we bought this little company called VMware, which is an interesting one, head scratching back then. But today, boy, what an acquisition that was. So one of my learning lessons coming out of that first chapter of my career was really what it took to define a market but then what it took to maintain relevance as a leader in that sector. And at the time, Pat Gelsinger was the President of the company. He had been moved over to run VMware. I started to get an appreciation for how software was eating the world. So I took a run, I had the good fortune of being mentored by Bill Teuber, our Vice Chairman at the time, I said, "Hey, I'd really like to go take a run at getting some software experience." And he got me the right introductions. I landed at a role in strategy and operations. And at the time, VMware was still an incredibly successful product company, but they were also faced with staying relevant, right? So my first task at VMware was to build a specialist sales force that was going to help define what was a software-defined data center. Ultimately, the elements of a virtual cloud, right? If you look at any of the hyperscalers today, right? There's some pretty common architectural decisions, which is x86 is the atomic unit which all software functions run on top of. And we virtualized and we abstracted both compute storage and network. That was my job at VMware is to build a specialized sales force that can go and help customers build out a private cloud function that was as real and as priced and as performing as they would get in the public cloud. And public cloud was obviously just starting to take shape and customers start to figure out what was the role that public cloud was going to play because they've got all these assets obviously running and fueling their existing business models, but they saw this great opportunity from a time-to-market perspective where they can swipe a credit card and have a developer up and running in the cloud. So during that time, again, it was a learning lesson of defining a market staying relevant, right, and making sure that we give our customers the greatest hybrid cloud experience. And after 11 years, and we had just finished taking the organization through a big integration through an acquisition, I started to recognize the data was starting to take over the world. And that to me was when I started to really start looking at the landscape of what was the next chapter of my career going to be? And I typically don't move around often. If you look at the LinkedIn, you'll see it's really been 3 jobs in 3 decades. So I started to look at the market. I've had some conversations with some senior executives that I maintained close contact with. It became really apparent that Snowflake was an exciting company. There was an exciting tectonic shift happening with AI that was going to obviously put a huge dependency on having a good data governance and data foundation that was going to fuel the next generation of companies. Very much like if you think back in 1997, no one would have thought of a business concept like DoorDash and Uber, right? But that foundational technology afforded new business models to emerge. And that's the same opportunity we have today is sitting with customers and talking about how we can unlock the value of the data that they have, the assets they have to open up and exploit new business opportunities, monetization and commercialization of data. So that to me is one of the most exciting conversations I continue to have with customers because it's very easy, as I come into this role days in to try and wrap my brain around all the concepts and the data and the products. It could just be an overwhelming amount of information. Our customers are feeling this as well. There's a lot of noise in the market. There's a lot of competing strategy. So my first role is really just asking the foundational questions when I've met with 35 customers and 30 partners, why? Why did you make this decision? And depending on what industry vertical you're talking to, for the most part, and as you've seen time and time again, it's easy, it's connected, it's trusted. But I had an opportunity to sit with a customer about a month ago. And I asked why this -- why the decision was made to go with Snowflake. I'm still in that kind of learning mode, asking the 5-year-old questions, why? Why? Why? And this is when it really dawned on me the impact we're having. So we had one of the largest contracting suppliers in the country, basically walk me through what a bid process look like. So a blueprint gets submitted into a portal. A bid analyst takes -- downloads the blueprint and they start looking at putting a bill of materials together for the amount of lumber, the trusses, the doors, the windows, the thresholds, everything that goes into specking out and building that house. They submit their bid, they get a 20% hit rate. It's okay. So where is the opportunity here? How can AI -- how could we unlock more value for this customer? Well, we had industry vertical specialists sitting next to this customer and basically start prototyping what the value and opportunity could be. So we took that unstructured data source, which was not in the platform, brought it into the platform, married it up with the contextual data of basically what they already had in their structured data set and basically started prototyping an AI model that would go in front of that bid manager and we can get through 100 quotes in 1 day, which typically would take 3 days for a human to scrape through that architectural plan and put a bid in place. So is the power of unlocking, right, the opportunity to say, basically, let's look at making sure all of your data sets come into this to make an informed decision, but increased productivity. Then we take it a step further and we say, okay, once you win that bid, now let's have the next discussion around supply chain. Now you want to set the expectation with your customer and how long is it going to take for these supplies to arrive on site because ultimately, a builder wants to get this house built as quickly as possible so we can sell it. And we started introducing partners like Blue Yonder into the conversation, who are going to help customers with that supply chain component. So now all of a sudden, we've unlocked a business outcome that had never been conceived before. And that to me is one of the most exciting elements we have in front of our market today is to help customers think differently about how they unlock value from their data. And again, these are business discussions. This is a discussion I can easily have with a customer. But obviously, it takes a different way of thinking about their business models. And I think it's most certainly true now, and you've heard this before, it will be the fast that eat the slow. It is most definitely not the big that eat the small. And we're excited by helping customers unlock that value and giving them velocity in their business plans. So easy. You've probably heard this multiple times today. We can get a Snowflake instance up and running with incredible ease. It doesn't take an army of engineers to get this instance up and running. We can help you get your data into the platform very quickly. Structured, unstructured. We marry it. We'll analyze it. We'll warehouse it. We'll build AI prototyping models and machine learning prototyping models against it, and we'll start unlocking value. Connected. So we have instances where one of the largest investment banks down on Wall Street had a conversation about how they get their data for market feeds. And it became obvious to me when they said, "Hey, we basically have this connection with Moody's and Standard & Poor's where we can get instantaneous access to data to start running our models against. But we've got 1 of the rating agencies that still continues to send us FTP data 7 hours every night. It's just -- that's where the slow is going to obviously have a disadvantage. So you start thinking about the connected tissue of connecting a buy side and a sell side customer together and say, "Hey, how could we share data to accelerate the business value so that we can be more informed." So we start to see this kind of network effect happen. And that's 1 of the most exciting things. I'm also starting to unlock as the value of the Snowflake network that we can provide to customers. And trusted, critically, the most important element of our value prop is the governance, the security the data protection, everything that we put into making sure that the data once it's in the Snowflake platform is a vault, right? That is the, I'd say, largest apprehension most executives have when they think about AI is security. It's data privacy, it's governance. We spend a lot, a lot, a lot of engineering time, making sure this is the most trusted platform in the market. But this is the theme that we get from customers. When I ask those questions on why they're making a Snowflake decision, the most common patterns we see is it's easy, it's connected, it's trusted. So I have the good fortune of running the go-to-market organization. It's not just sales. That is not the only people in the organization, obviously, that we -- I'll show you the organization a bit. We spend a lot of time with the sales organization. One of the things that I look at when I was interviewing for the job, and I talked about with Sridhar and some of the Board members about what I believe Snowflake needed to get to the next chapter of growth, it was scale and velocity. Scale in the sense of, we could double and triple the sales force, of course, to drive more revenue streams, but we've got to act smarter about how we scale an organization, which is why I'm going to talk a little bit about our partner community and our alliances in a little bit. But velocity is where we're starting to eat some of our own dog food. You saw today there's some announcements around Snowflake Intelligence. It was referred to earlier as Raven. I hired over 500 sales and marketing folks in Q1. Getting them as productive as possible, as quickly as possible is truly important in a hyperscale market that we're operating in today. What typically would take a year to get a rep fully productive, I need in 6 months. How do I onboard a rep and put them in front of a platform such as Snowflake Intelligence and allow them to run an instant large language query about a customer that they're going to go visit. And I'll use Caterpillar as an example. I've got a new rep on Caterpillar and he goes into Snowflake Intelligence and he says, "Hey, tell me about the profile of my customer." It will go and dip into our database and say, here's the consumption they have on your platform, compute and storage, right? Here's the use cases we're using with this customer. Here are the use cases that have been identified in pipeline, right, because we're looking at sales force data. You've got a couple of open ticket, support tickets you need to be aware of, right? And it starts to build a complete picture of what's going on with that customer. But now an exciting opportunity exists where you incorporate a technology like Canva into the equation. And say, build me a curated executive summary. So when I show up to that customer, I can show them a good profile of what we do and the business outcomes that we're delivering as well as what we're doing with some other companies in the manufacturing sector. That's a productivity boost we could never have done before if we weren't eating our own dog food. So I'm excited by what we're deploying. My goal is to make sure our sales force is the most productive people that are leveraging AI and machine learning to make sure that they show up and they ramp up quickly. Our partner community is critically important to scale. I talked about scale and velocity. The 2 are the most important elements of I believe of my job. We get good scale from partners. We have our hyperscaler partners that are clearly the Amazon, the Microsoft, Google, great partnerships with each of those organizations. We get some scale there. Our global systems integrators are giving us some scale. We've got 3 of the 5 that have committed to in practice $1 billion organization to basically build a practice around Snowflake because customers are drowning in data. They're looking to unlock value. They're trying to deliver business outcomes, but they can't lean enough intelligence about the data. So I'm excited to see the kind of lift we're getting from our global systems integrators. We've also got a number of customers that have built a business on top of Snowflake that gives us great scale. So companies like Fidelity that started as a great customer, have unlocked and monetize their data and using that as an opportunity to drive a new revenue stream. So helping a technology office now become a revenue stream through data and with Snowflake is an incredibly exciting opportunity. So I've built an organization that basically supports these what we call data cloud providers, and we've got hundreds of these data cloud providers. But the one area of the business I'm looking to unlock and activate is a true distribution network. Where I get what was those true value-added resellers. These are connections I've made over the years with VMware, I learned how you scale an organization through the channel. That is one of the areas I'm spending a lot of time is focusing on how I activate a distribution network through value-add resellers. It's something we really frankly, haven't done a good job at, but I know that, that will be the future on how we scale this organization. And around specialization, I've had a lot of experience not only building specialist organizations, but also understanding when you sense sunset a specialist organization. If I go back to my time at EMC, we had brought in Data General, and we had midrange storage specialists that were going in front of customers and talking about the differentiation of that particular technology. At some point in time, that became a core element to what our core sellers did. We didn't need that specialization anymore. Instead, right, we had just acquired Data Domain. We want to invest in specialization around backup and recovery. So at times, one of my roles here is going to make sure that we've got the right volume of specialization supporting the business. So I'm spending a lot of time right now on investing in both technology specialization so we can help customers unlock and build prototypes around AI and ML because they need help understanding the value and the vision around what we can do, but also our industry specialization. Having someone who's been in the corporate capital market who has been through a manufacturing supply chain discussion, bringing those business-minded specialists into a conversation so we can unlock the value and vision of what we can do for them with data is hugely important. So I'm getting really excited about we have an existing team but putting a little bit more wood behind the arrow to make sure that specialization isn't just product, right? It's also the industry and the outcomes, right? As I've told my sales teams many times, right, good people sell product, great people sell outcomes. So we're intent on making sure we're delivering outcomes. I've been confronted with a lot of complex issues throughout my career. And I was taught this very early. There's a really very simple decision support matrix I will go through. So whenever someone comes to me with an alliance question, a partner question, a compensation question, you name it. It's pretty simple. What's the right thing to do by the customer? What's the right thing to do by Snowflake? What's the right thing to do by the individual contributor? It's very simple. 99.999% of the time, you're going to get the right outcome. So I bring to this role a very customer-obsessed focus, right? Everything that we do is built around making sure our customers are successful. Are we bringing in the right level of resources to deliver the right outcome so that we earn our seat at the table for the next workload. In a subscription model, unlike anything I've sold before, hardware product sits on the floor for 5 years, software perpetual license contracted for 3 to 5 years. They own it in perpetuity and then you're just selling maintenance and possibly upselling them more capacity in a subscription world that we live in. You're only as good as your last use case. We have to show up every single day, making sure the customer is getting value of this platform. That's where we're spending a lot of our time. Sridhar talked a little bit about this earlier as well, accountability. Everyone in my organization -- and by the way, when I got here, there was only half the team that was on a variable compensation plan. Compensation always drives behavior. The behavior we are driving is a customer-obsessed mindset. We're moving the entire organization with the exception of customer support to a variable comp plan because accountability has to be in their compensation. And we have key metrics that are in place to make sure that the sales in the SE world is, quite frankly, very simple. It's a very binary outcome, right? Are you driving bookings? Are you driving consumption? But our partner community, our alliance community, they all have to have metrics. Our inside sales teams need to have metrics. Where we're looking at productivity on a weekly basis to make sure we're getting yield out of those assets because when I go to Sridhar, in a couple of months, and I ask for potentially another couple of 100 to 1,000 heads, I need to have a strong ROI statement against that. So I'm bringing a very strong operational and disciplined mindset to the business. Every person we bring into this company has to have a strong ROI. And we can have people sitting around being complacent in their role. This market is moving way too fast for us to be complacent in any way. So I'm spending a lot of time right now talking to the teams about accountability. We're putting strong metrics in place. We're looking at this on a weekly basis so that people know how they're being measured. It's the most important thing you can do is making sure people know what to expect and how they're being measured. Absent of that, you get complacency. So we talked about the organization, we made a decision about a couple of months ago to bring the customer support organization into go to market. To me, this was critically important, not just to make sure the customer feels smothered with support, which is truly important a subscription business, as I stated a little bit earlier. We got to make sure that there's a strong alignment with our customer support engineers so that after that use case goes live or after we onboard a new customer, they immediately know who to call if something goes wrong because something is going to go wrong. It's technology, we know that it breaks. So we got to make sure that, that team is completely integrated and ingrained in the customer fabric and the customer experience. So we made a purposeful decision to do that. It's also critically important that those support engineers give Christian and Vivek, the feedback loop on what we want this product to do, right? So that is an important element to making sure we stay relevant is that we're continuing to deliver value to customers and we're giving them the innovation they need. We've got a large organization built around marketing, everything you're seeing here this week, which is, to me, an inspiring collection of partners and customers coming together, I'm overwhelmed to see 20,000 people here. In my last role, I think at the peak at VMware, we had about 18,000. And this is our sixth Summit, and we're already at 20,000, just tells you how exciting of a marketplace we're in, but the draw we're getting for customers that are trying to figure out how they can put a strong data platform together and also leverage AI to unlock business value. We've got a strong professional services organization that helped get customers up and running once a contract gets signed. I'm being very clear with Ted, who runs this organization, we are absolutely not competing with our channel, right? Professional services job is to help customers drive consumption. If you've got a competent partner that's involved there, my first preference, let the partner drive the consumption. There's going to be certain instances where it has to be my organization that does it in a highly regulated environment, maybe on the first use case, we've got to prove it out. We're going to go in and do it. But my job and what I'd say Ted's job is to make sure our channel is completely enabled and competent, make sure they're certified, they're confident. They know how to get this up and running. And by the way, we've got hundreds of professional services. We call them TrueBlue's out there driving the platform and the consumption. But to me, my first preference is get those partners activated, get them enabled. This is not a profit center for us, right? In my previous lives, professional services is a profit center, but it's not the job of this organization. The sole purpose of this organization is to drive consumption. We've got a strong partner community. This is an area I'm going to continue to invest in is making sure our partners feel like first-party citizens, if you will, as important as my customers, and I have to treat them that way. And then we have sales development, which is really a part of the initial interaction of we got 450,000 impressions a week. That leads to 13,000 interactions, phone calls, which leads to 4,000 in-person meetings, which inevitably leads to a new logo. So we've got a good supply chain. We're looking at areas in which we can leverage AI to increase those hit rates in terms of 450,000 impressions leading to 13,000 meetings. I want to get that number up because that's going to lead to new logos. So we're doing a great job leveraging our technology to increase that count and that productivity. So one of the things, as I've come to learn is it's truly important. You've got a balanced business, balanced in the sense that we're driving net new bookings through new customer acquisition and balanced in the sense that we also have continued strong consumption. We actually have got to a place where we feel really good about our operating model. We've got certain skill sets, and I'll call muscle mechanics built around how to acquire a new customer, it takes a certain mindset of an individual and a certain amount of resources and talent to get that new customer acquired and onboarded. And then as we get through that transformation and we start looking for how we're going to continue to maintain relevance, it's truly important we surround the customer with even more resources to drive expansion. So we've got a good model set up where we've got people that are just focused on purely driving acquisition as well as purely driving expansion. And we've got a specialist organization that, again, goes through not only product specialization because we store, always going to have to compete for the next workload and we can differentiate our value proposition from a technology and product perspective. But then bringing the industry organization people to sit with a customer and talk about in a manufacturing case, hey, have you guys considered, right, a supply chain outcome? Or if we're sitting with a financial institution, hey, here's what we're doing with another bank to look for fraud detection, right, or anti-money -- or money laundering, right? Those are real use cases and real outcomes that customers want to hear from each other. The technology, I'd say, is kind of the easy part. As overwhelming as it is, that's kind of the easy part. It's really unlocking the use case that customers want to learn from each other on. So we're excited about the organization and how we're set up. It's yielding great results. And I think that's a wrap for me. So again, I thank you for giving us the opportunity today to talk to you. It's my honor and privilege to be in front of you as the new CRO, 80 days in. I've got a certain accountability on my shoulders, which is to continue to give you great earnings on a quarterly basis, which I'm dead set on doing, and we feel really, really good about the trajectory we're on. And we also feel really great about the vibe and the community of customers and partners coming out of this conference. And hopefully, you see and feel that too firsthand. So thank you.

So I'll invite Sridhar and Christian up to take a seat. And then as I mentioned, just raise your hand and the mic runners will find you. And again, we have Mr. Scarpelli on the phone.

It's John DiFucci from Guggenheim. And question is sort of for Sridhar and Christian. So you both talked a lot about unstructured data in AI and you've also been clear on the conference calls that the strength in the business is really from the core data warehouse and analytics business. So I'm just trying to understand like all the work you're doing, and Sridhar, you come -- like Christian, like at least as long as I've known you been here, and that's what I think of you. And Sridhar, know you come from a different world. I'm just curious if you feel like you're there now, where you can sort of play on a level playing field against what's at least perceived as you being in the data warehouse in the cloud vendor and there's a lot left there. And your competitor, Databricks, I'd say it as Christian did, is really viewed as the data science company. Is it more just perception now? Or is there still work you need to do on the product side?

Gosh, there's a lot to unpack in your question. What we have consistently said is that we have incredible strength in the analytics side of the business, it's the foundation as evidenced by our continued growth, things like our net retention rates and that our newer product efforts, whether it's in the earlier phases of the data cycle when it comes to things like ingestion, early data engineering workloads that they're gaining strength as well as in areas like AI. And this idea that AI is the purview of a data scientist is very 20-20, because AI is now the purview of the CEO because they want real transformation to happen. And similarly, when like if you're the Chief Data Officer at a company, you want to use everything possible in order to drive the kind of outcomes that you're looking for. And just as a matter of like practicality, unstructured data was harder to deal with before. But no one in their right mind says, "I want to write a lot of code in a notebook on files that are sitting in S3 for me to understand what a piece of text meant." And what we are able to do instead is tell them you can using Openflow connect Snowflake to a SharePoint repository, have data flow in, be able to create an index on it. These are as one configuration screen, one command. And all of a sudden, you have an interactive chatbot in the data that you can drop into a data agent, which now knows how to intelligently choose from, does it go against a quant data set? Or does it look at the unstructured data. And so with respect to capability, I feel very confident about where we are. And if anything, I would say, with things like our marketplace or the knowledge extension, the stuff that you saw today, we are leapfrogging because, again, no one wants complexity. They want outcomes.

Nothing to add.

Not simplicity. Ease of use is there always been important and the strength of yours. So that seems to play into everything here, too.

Absolutely.

Brad Zelnick, Deutsche Bank. Really a great event and the innovation and customer success is palpable. Christian, I tuned into something specific that you said when you were talking about Gen2 and Adaptive, answering the age old question or speaking to the age old question of separating performance from revenue impact where Snowflake gets to keep some of the value and avoids becoming the next Teradata. And I'm trying to reconcile that with open data formats and AI helping to reduce switching costs for customers. And I may have missed the point, but why isn't competition in lower switching costs going to dictate price?

To a degree, it's not competition. How I framed it was we want to be the leader in price performance, which is why my statement is if any of you goes and update models and say, "Oh, we're not going to go get the performance headwinds that would be the wrong conclusion. That's why I was very clear on that. No, no. We will still continue to pass on benefits to our customers. But what we did with both Gen2 and Adaptive is now we're in control of when and how much. And what's really going to guide it is our position on the price performance leaderboard. That's how we think about it. If we need to go pass it all overnight the way we've always done, we can do it. But if we don't have to, we don't have to. Does that help?

It helps.

Right next to Brad, Alex Zukin with Wolfe Research. I want to maybe ask from a little bit of a different lens, we've had 2 tech questions. It does feel like the vibes are different at this conference this year. It feels like the spend intensity, it feels like the demand environment, it feels like something has changed. And I'm curious, is it as simple as, hey, these AI budgets are having downstream significant impacts. Customers are realizing that they build AI on data, not on models. And Snowflake is, and your peers, are both kind of rising tide benefiting? Or is there -- is it a product intensity, innovation velocity that folks have like -- is reinvigorated by that you can contribute to? And then maybe just explain kind of the Postgres moves and why this is a really important part of the dynamic this year.

Lots to unpack. I'll start on the first part, and Christian will speak -- add on and speak to Postgres. There's always feedback loops in life, where if your team, you yourself, feel like work that you're doing is having a positive impact. That's first. More work because you can see that positive impact. And I'm -- I've been open about the fact that we went through a tough phase last year. But I'm pretty proud of the fact that like we stood up to it and produce better outcomes. With the things that we knew best, which is the hard work in sales to get things landed to deliver value. And then things on the product side to create world-class products, and then do the hard sweaty work of telling every customer about it. To this day, whenever I meet a customer, I tell them, you should let us do a half-day workshop. We'll cost you nothing. It will show you what's possible with Snowflake and AI. And then we'll talk about where we can create value. But thanks to things like that. I think there is a real momentum for Snowflake, both inside and outside the company, where the team believes they're headed to a better future. And all of our customers have the same belief as well. Remember what I told you folks earlier, every person that's betting on Snowflake is kind of betting their carrier on Snowflake. And the more they see, "Oh, wow, here are all the other things that I can get done as a result of my bet on Snowflake. And in fact, it's going to help me navigate the AI wave as opposed to me having to sit stuff and answer awkward questions about what I'm doing with AI." Snowflake is very much a part of it. I think that's the message that you see resonating now with every constituent. And you see this outside. You see this in the announcements. And we get customer feedback from tons of people, including new folks that helpfully compile feedback and send it to us. And so we see it in that as well.

Briefly, I think the journey customers have been on is also informing how some of the items that we say resonate. A year ago, we said there's no AI strategy with our data strategy. And so people say, "Oh, that's because you have nothing in AI. We also said, "oh, you want your AI to respect your governance and governance matters and permissions matter." And I can put my ChatGPT on the data. When you realize all these things are true. And that's where the post-risk conversation comes in. I want to be able to store state. I want to be able to personalize my agent where do you do that? That's with all this. So I think there's been -- I don't know if it's -- maybe it's the hype cycle, but a point where people now have tried to roll out AI and they realize that many of our core thesis is true, governance matters not taking data out matters not making copy matters. And I think that's what you see reflected now.

And being world class is essential. You folks know this. We have now gone through what appears to be what the generations of vector databases. And these are all companies that purported to be the next Snowflake. I mean, I wish them well, but a vector database is much better as an addition to Snowflake where it's like one command to create an index. So we have to walk the walk, and that's where we're able to lead with then the strengths of what we as a data platform offer.

And I think you also -- you look at the customer stories that we're starting to generate. And I talk about that supplier organization where we unlocked tremendous value for them where their bid officer can put together 100 bids a day versus 1 every 3 days. Those stories are getting out there. Customers talk to each other. And that's the most powerful sales force I have as my customers. When they talk about the success of what we've unlocked for them, right, the ease of use, the connected tissue, the trust, right? Those stories are probably my greatest sales assets out there, but it's also how my team shows up, right? And we show up talking about the technology we've already -- I won't say we've lost it. That's the rocket science. That's kind of the easy part, that's how we help customers about, right, their processing of the data, the people, right, the business ideas that are going to inspire the next revenue stream, that's a conversation we have to start with. And I feel like we tend to end there. So we -- I want to change the way we show up for our customer and have outcome discussions, so we can unlock value. We can show them great reference use cases, and then we get deep in the tech, and we have to validate it.

Kirk Materne with Evercore ISI. If you came here a few years ago, I think it used to be -- we talked about data warehousing and there's always importance of data. But it seems today that through the simplicity of the platform, you all are able to talk about solutions and business outcomes. And the question is really, are the people you're talking to today versus 3 or 4 years ago, different in terms of their ability to make a higher level decisions, work -- move faster with you, meaning it used to be, sort of, well, we'll move 1 data set at a time. Are they looking to move at a faster pace because they now understand the business relevance of all this? And I guess for you, Mike, how much farther do you have to go in terms of specialization, whether it's verticalization? I'd also just love you to just chime in really quickly on Europe and what you're doing internationally where you've still obviously a lot to go there.

So I can't speak for last year in terms of the buying profile and meetings we've been on. But I can tell you within the first 35 customers I met with, I think only one of the meetings had a chief technology offer in the discussion. It was lines of business. It was the CEO of a large publishing company in New York who wanted to have a discussion around tell me how I can monetize and commercialize my data. So again, how we show up and the people we're talking to are people that are trying to drive new revenue streams and optimize the organization. In terms of the investment specialization, we have foundational people in place today. We are definitely going to be investing more in those specialized business analysts or specialists as well as data specialists, so we can show up and not only talk about the business outcome but actually have people prototyping within a couple of days how we can unlock value and showcase that outcome and how we can actually let the data unlock value. Europe and APJ are 2 growth markets for us, right? Those are my fastest-growing markets. Again, a large portion of our revenue still comes from U.S. We're investing heavily in Europe and APJ. Europe, we've got a European leader that just onboarded. We've got 23 new managers. So we're expanding. It's obviously 138 countries we have to cover. We've got a great base of individuals there. Again, my ramp-up in productivity is my primary interest in those markets. How quickly are we bringing on people to those markets and how quickly are they being productive and having a business outcome discussion and bring in the technology discussion to marry it up. Same goes for APJ. Those are unbelievable growth markets for me.

Brent Bracelin with Piper Sandler. I wanted to go back to the pricing conversation around Gen2. What's the right value capture for 2x higher performance? Can you capture 20% higher price for 2x higher performance. Is that the right way to think about it, one? And then two, maybe more importantly, what portion of the installed base is performance, the #1 priority? Is this something that maybe half of the installed base as you contemplate might move to Gen2 versus sticking with the Gen1? Or is it more of the installed base that potentially would move towards kind of Gen2 and eventually these adaptive tiers?

Yes. So two thoughts, Brent. One is the price incremental from Gen1 to Gen2, that's a fixed ratio. That's the easy part of the equation. The benefit is not the same for everyone. And that's where it gets difficult. And that's why I said, hey, the benchmark on the number I'm sharing it's representative for some workload. But for example, if someone has update heavy workload, they'll see numbers way better than what's in here. If it's a workload that is more read-only, they'll see slightly lower than that. So that's the piece that makes it hard. We've calibrated in a way that customers will, for sure, see better price performance and ideally see an economic benefit relative to where they are because it's always easier for us to lower it later if we want to deliver more back to us being in control on when and how much. Going up is always complicated. So I don't know that there's an easy yes, this is the split because it's a customer-by-customer conversation. We're starting at a point where we think is conservative and roughly revenue neutral or a little bit lower, and then we can go on and adjust based on what we see, but the performance is going to be materially, materially better for pretty much all of our customers. My expectation is that everyone is on Gen2 or everyone like 90%, 95%, a large percentage of our customers are there within months.

The only thing I'll add on to this is that there are second order effects from performance improvements because people are willing to do a lot more. Things finish faster, they're able to get more work done for the same unit amount of either time or the dollar that they put in. And it's our ability to roll these out, that is more important than exactly how much performance because as Christian said, that varies depending on the workload.

I think that's a great point, which also talk about your second part -- second part of your question on sensitivity to performance. I think the willingness to open or bring new use cases to Snowflake are influenced by that. I just met with someone after the keynote. Large companies said, given some parameters, I'm going to use my internal customer support agents like internal traffic on Snowflake. If the latency is lower than some other things, I'm going to put it customer facing.

Kash Rangan here with Goldman Sachs. Zelnick and Zukin are typing the research reports and you'll have to use AI to interpret what they said. First of all, congrats on Summit. Christian, great to see you present today. And Mike, welcome. Scarpelli, hi.

Where is Scarpelli?

Hello.

There you go. I got you Mike to say something well posting me the question on the mic. Scarpelli, I will spare you of the question and Mark Murphy challenged me to keep my question to 1 question. So let me see if I can win the bet. It's a glass of wine, Mark. Consumer tech hides all the complexity and you've seen OpenAI, you had Sam Altman. It has had so much massive adoption captured the words imagination in such a short span of time. And Sridhar, you've been in consumer tech before at Google. Christian, you too. Why has the magic of generative AI or maybe there is a magic formula that Snowflake has. This is a question I've been asking other CEOs as well. Why has it taken so long in enterprise with respect to adoption of AI? And what do you think is the key to unlocking the magic that ChatGPT and other consumer apps have unleashed in the consumer world?

Yes. It's a great question. First of all, I would say that when it comes to things like meeting preparation. When it comes to a lot of things that we do commonly I think generative AI has basically penetrated most companies, most of our consciousness. We all get the utility asset. But until we launched Anthropic inside Snowflake, for example, all -- obviously, you can't take something that's confidential and put it into chat, even if you are paying for subscription. I think it is easy to use products like Snowflake Intelligence that don't require you to make compromises that I think are truly going to unlock large-scale use. OpenAI is doing a little bit of it. They have a set of enterprise deals for their enterprise products that are driving broad adoption. But to me, the magic of AI in the enterprise is going to come from all of the data sets that matter to you, whether it's sales information, consumption information, HR information or stock that's sitting on Drive or Box or Dropbox. When all of that becomes accessible, I think that's when the magic truly unfolds. And that's what we are excited to be driving. I don't think it's that far away, Kash.

I would add one quick thing. I also agree, it's not far away, but I also think the tolerance for incorrect results is lower in a consumer world than in an enterprise world. Like we all remember the original ChatGPT, it was hallucination left and right and it was okay. In the enterprise, that is less okay, and a lot of what we're sharing here at the conference and our overall strategy is, how do you do AI, but with the trusted piece that Mike Gannon talked about. That is the piece, and I think they're all finally coming together. Now you have the experience in Snowflake Intelligence as Sridhar said, so the time is near.

I think there's a great -- most executives worry that they don't have the prerequisite skills they need to drive an AI strategy. And that's one of the things that I'm most excited about is kind of simplifying, right? We can help solve this talk about business concepts first. We'll make the tech the easy part. But I think there's also generally a skills discussion that most executives are worried about. And secondly, to what Christian said, right, security, data privacy. Let's make sure we have all those controls in place. That's truly important because we all know who've been around tech long enough when it comes to warehousing garbaging garbage out, what data is going to be the most critically important data, but accurate data we're going to build our models on. We're spending a lot of time talking to customers around the data and the governance of the data that goes into the platform. And then obviously, spending a lot of time talking about simple large language models can make unlocking data or unlocking value from that data. It was simple and easy to understand. So I think it's skills, and I think it's privacy security.

Karl Keirstead at UBS. Sridhar, what do you think of the SaaS firms moving into the data layer. Obviously, Salesforce drives $800 million-ish from Data Cloud now just offered to buy Informatica for $8 billion. Bill McDermott at ServiceNow talks about workflow data fabric, SAP is pushing their business data cloud. So what's the signal there in your judgment that we should all recognize? And in particular, are there data use cases that, in your judgment, the SaaS vendors have a legit shot at winning and conversely, are there use cases that will never truly go to the SaaS vendors that need to go to data specialists like Snowflake.

It's a great question. And we spend a lot of time debating this question. I think a lot of their behavior is driven by AI. I think in the case of SaaS vendors that make software that assist humans in getting things done. Customer service is a classic example. I think there is an existential risk to that model of making that kind of software, which is the software that is based on things like agent can be a wholesale replacement for not only like the software that they make, but also the people that are doing this. And so I think for a number of SaaS companies getting into agent is not an add-on to their business model, it's a threat to their business model. I think that, in my mind, exemplifies the interest that they have in the area and why data becomes so important. Look, we collaborate actively with all of these folks. We have bidirectional data sharing with Salesforce, with ServiceNow and we are working on a close partnership with SAP. And what we offer is a place where all data can be brought into one place. That's what we do. We are very good as a data platform. It's a hard skill just like making a model that is world class is a hard skill that is -- that only some companies can truly be world-class at. I think building a general-purpose data platform is very different from building a SaaS application, and that's something that we are very good at. I think the kind of use cases that you will see SaaS companies capture will be along the lines of what I described, which is agentic versions, like insight into data about processes that they drive. Generally speaking, Obviously, they have bigger aspirations and part of the reason why we make this bidirectional is we want -- we need these partnerships to be win-win so that they feel like they're getting what they want out of it as well. And so you can move data from Snowflake onto one of these data clouds. But we feel confident about the value add that we bring to the table as that central place that can offer the 360 view, be able to act on it. And I see interoperability happen at multiple levels with fabric, for example, we collaborate at a bottom level where we are able to read a table that is sitting in OneLake or somebody that is in Snowflake and say, "Hey, I want to store this table in OneLake." We also work with them at the top level of agent components that are created with Snowflake Intelligence will interoperate at that level. So you're going to find this kind of cooperation and competition at both -- at both of these levels.

I'll add one thing, Karl. Most organizations have 2 or more of these SaaS applications, which creates a problem because Benoit says, "Oh, you want to combine our data or the data you have with Salesforce with rest of your data, give me the rest of your data." And then Mike McDermott, an hour later, goes and says the exact same thing. And you have CIOs, CTOs and CDOs saying, "You want me to copy all of my data into these different platforms. That's a massive no-no from efficiency, cost, governance, et cetera." I talked to the CTO of a bank recently and said there's no way I'm going to go for my data multiple times in one of each of these platforms. I think that is the single biggest gap in that thinking.

Mark Murphy with JPMorgan right here in front. Mike Gannon, I wanted to ask you a -- great presentation, by the way. You mentioned it, but the number of sales and marketing hires in Q1 was rather stunning. When you look at the history and the sequencing, can you clarify how much of that reflects just there's been a step-up in the pipeline, right? So we need feet on the street to go get it versus maybe you coming in and saying, our ratios have been off for a while. And then can you speak to that type of hire, should we look at that and say, well, these were the specialist hires, coming in where they would take a while to ramp. Should we look at it and say, these are people -- you're bringing in people that sell further up the -- excuse me, the totempole and would take longer to ramp.

I'll just provide 1 bit of historical context, and Mike will answer the question. I want to make sure that all of you juxtapose the hiring numbers that you saw in Q3 and Q4 with the numbers in Q1. We went through a period of just cleaning house. We called it a GetFit initiative, where we really focused on accountability and performance that was an ongoing thing. And then it was combined with the numbers that came out in Q1. That context is important for you to remember.

Yes. It's a great question, though. 70% of those new hires were additional capacity, 30% was really what I'd say, performance management. People are just plenty sitting back waiting for business to show up on their lab. So that was a large part of the activities that happened in Q3 and Q4 of last year. In terms of the question about the hiring profile, we're actually leveraging some modeling right now. We're looking at our most productive reps, the reps that are consistently overachieving. And we're actually scraping through LinkedIn to figure out the hiring profile. What's the most common, most successful backgrounds that these individuals are showcasing because that then becomes the new informed profile of the rep that's going to carry us into this data and AI space. We're spending a lot of time. We're a data company. We do this really well, where we actually can do a lot of intelligence gathering on the most productive reps and frankly, even the reps that are maybe struggling. There's a consistent profile we see there. So we're spending a lot of time looking at this. I mean productivity to me in this space that is moving at a blistering pace, we can't afford a miss hire. So we're spending a lot of time on not only just getting the right people hired critically important step 1. And then step 2 is leveraging the productivity tools that I talked about around Snowflake intelligence to get them productive within 6 months as opposed to what historically may have taken a year. But in terms of the growth projections we've put out onto the market, that's all been factored in based on the capacity I shared with you just now, that has been factored into the results that you're expecting to see the rest of this year.

Gregg Moskowitz from Mizuho. Christian, just getting back to the notion of keeping some of the value for adaptive compute specifically, some of our conversations with partners and customers have indicated an expectation of a significant cost savings just because they can purchase smaller clusters and flex them. I completely appreciate that this should drive more consumption on the platform, which is terrific, right? But might we see an initial revenue reset once we get to the point of Adaptive being GA, how are you sort of thinking about this?

We have not finalized the pricing level of Adaptive. One of the reasons why I caveated that we are going to go slow and see how this works. Part of it is a technical reason but the other one is we want to make sure that we understand that consolidation effect. Right now, all the guidance and numbers that we provided are factoring all the information that we have, and we do not expect to go and see the top line change dramatically because of this. We believe that a lot of the value of Adaptive comes from the ease of management, the TCO, et cetera, not necessarily because we need to go and effectively lower the price.

I think Jimmy says last question.

Yes, one more.

Last question.

Patrick over here from Scotia. I will make it a good one, Jimmy. So 2 years ago, at Summit, I mean, you coined the AI and data cloud message. I mean, back then, it was like right at the beginning, this kind of AI adoption. I mean, we're a bit further along now, and we're starting to get a sense of these kind of reference architectures in AI. Like you can't walk 1 block in San Francisco without talking about MCP. You guys and your closest peer bought a Postgres database, OpenAI was on stage yesterday. Sridhar, you're very deep in this ecosystem. I guess where do you see the AI reference architecture as of today? And where will it evolve to in 12 months? And where can Snowflake kind of move towards the puck where it's going in 12 months.

That's a great question. I think foundation models continue to evolve their capabilities rapidly. And I think what they are able to achieve with post-training achieve with some amount of time, like what they can do with deep research is I think truly, truly remarkable. I continue to be blown away by how that kind of tech is able to do so much, just acting on the open web. But from an enterprise context I genuinely think that having these world-class models then paired up with the right sources of enterprise data with the right reference architectures for interactive applications. which are fundamentally just incredibly different from here, do some research for me and it's fine if you take 10 minutes. Those are just not the same things. I think that's the stuff that will get settled over the next few -- over the next few quarters. So at one level, yes, we want AI SQL to happen because that is data processing at scale where you're able to crunch through 1 million rows and whether you take 3 minutes or 4 minutes doesn't quite matter. On the other hand, if you are building an interactive chat bot that you want to power customer support, then every decision, how long that search index takes, what's the right-sized model? You don't want to go pick the biggest model for that 1 because it's no longer quite so interactive or what's the underlying store for analytic queries but now being served out in real time because you want that data fast. I think those are the knobs that are going to get tuned over the next few quarters. And that's also how we internally think about this. Part of what -- like even with Cortex analysts, it takes a few seconds to give an answer, but it cannot be that way if I truly want all of you to be able to use it freely and fluidly or the kinds of things that you want to do. So I think there will be a set of these foundational components. There will be things like tool calling. I think web search continues to be a really powerful repository for anything that is recent, anything that is not part of your enterprise goal. I see very much composite tooling coming in, which knows what to talk to where. So where we need to partner for these cases, we will absolutely partner. We will continue to partner with the Foundation model labs that continue to outpace others in terms of what they're able to bring. But when it comes to all of the data that is proprietary enterprises that matters to enterprises, we feel very good about being able to figure out what's the kind of product and interaction model that is needed for them to be effective in creating products for enterprise use and that's what we are driving towards. Jimmy, Come on up. I think we're done.

Thanks so much, everyone. Catherine and I will be hanging out after to answer some questions, but they have to go to a customer event, unfortunately.

Thank you all. It was great to see you.