Thank you for joining our 2024 TIMT Conference. Just want a special thank you to the 100-plus companies that are attending over the next two days, as well as the 700-plus investor clients that are here today and tomorrow. Before I introduce our guests, I also want to thank our partners. It takes a village to put these events on. I want to thank our partners in investment banking, sales, as well as marketing and corporate access. We'll host close to 2,200 one-on-ones and small group meetings over the next two days. Without that partnership, that's not possible. Congratulations, and thank you for being here today. If we could have our first panel come up to the stage, I want to introduce Jay Kreps, the co-founder and CEO of Confluent, Matt Hedberg, who is our global head of technology research, and Anurag Sehgal, who runs client banking data analytics for our tech organization. Without further ado, I'll hand it off to you. Thank you. Thanks, Marc. All right, let's do this, right? We're doing it. Thanks, everybody, for coming. This is. I've been here. This is probably my 18th TIMT Conference at RBC, and this is going to be the best. It's going to be the best yet. We're excited to kick it off with Jay this morning, and Anurag, Anurag and I have done. We've co-teamed a lot of these calls in the past, so hopefully, an RBC IT element brings another interesting angle to this conversation, so Jay and Anurag, thanks for joining us. Marc, Vinny, everybody else, thanks for coming here, so all right, Jay, I think everybody should know Confluent, but it's still, you know, occasionally I'll talk to investors and they're like, "I don't know what they do. I don't know what, you know, I don't know the difference between Confluent and Kafka." Maybe just a quick overview of the company, a bit of your background, and then maybe I thought it would be on your Q3 earnings call, you kind of talked about different phases of growth. Yeah, where are we in that trajectory of your company? Yeah, yeah. So you know, probably the easiest way to understand Confluent is really by reference to other pieces of data infrastructure. So mostly when we think about data, we've thought about databases, you know, kind of these big storage systems where you can put your data. It's the back end for some application. You know, that's been probably the dominant force in data for a long time, biggest chunk of the spend. Confluent's a little bit different. You know, if you think about that as data at rest, kind of where does data go to sit? Confluent's all about data in motion. And the observation is, as software has evolved, you don't just have, you know, one big database and one big application that kind of does everything. You have, you know, hundreds or, you know, in a larger organization, thousands of different data systems, data sets, pieces of application software, SaaS systems, data platforms, analytics tools, and it all has to interconnect. It has to all act as one company, and increasingly, the use of software has kind of moved from the edges, you know, little productivity apps to something that's really driving big parts of the customer experience, that's driving big parts of how the goods and services are produced, you know, kind of right in the real-time flow of the business, and you know, so what Confluent does is, you know, kind of connect all these things up, allow the real-time flow of data, and then allow applications to kind of react or respond, you know, continuously in real-time to whatever's happening. You know, this idea of data in motion or data streaming, you know, that's become a sizable component of the company's architecture over time. You know, that's very much what we helped to kind of, you know, create and, you know, take out to the world. The original technology was actually a piece of open-source software called Kafka. This is something I helped create prior to Confluent when I was working at LinkedIn. And it was originally an internal piece of infrastructure there, went out and was released as open source, was adopted by a lot of the big tech companies of the time, you know, the Ubers and Netflixes of the world. And you know, they were really moving to this architecture where instead of, you know, shipping big files around at the end of the day, you would take data as it was generated as a continuous stream. And so, you know, what does that mean? You can imagine in an Uber, you know, wherever a car is driving, there's some stream of data of where is it, what's happening, whenever a ride is requested. There's a very kind of real-time logistics component to that that has to be continuously tracked and modeled so you can do, you know, supply and demand and pricing and dispatch, everything kind of keys off this real-time model of the world. And you know, you can imagine for them being able to operate off of that. It's a very different type of data problem. It turns out that, you know, although it might seem quite different, there's some similarity between that and the internals of a social network where you're bringing together all the data of what's happening for relevance or something like Netflix, you know, where it's, again, a lot of personalization and recommendations and user behavior. As this technology has been adopted, you know, we found it doesn't stop there, right? It's useful in financial services, you know, some of the largest banks in the world, you know, many of their systems, very diverse, come together and are ultimately working off of, you know, hey, a transaction occurred, you know, we interacted with our customer. That triggers a lot of activity downstream that occurs. You know, so Kafka and Confluent have become a kind of key component really across virtually every industry you can imagine. So that's the 30-second story of both, you know, the technology and a little bit of Confluent as well. So then, you know, how did we take that from an open-source project to a company? Yeah, it's happened, you know, in a couple of phases. So the first phase of the company was, you know, creating a commercial software licensed software offering around this. And we started with that because there's a ton of people using the open source. This is the easiest way to get it out in the market. Yeah, I would think of that as kind of act one. That was the initial growth was built off that. But even from early on in the company, we knew and had planned to build, you know, a fully managed cloud service. And that was kind of the, you know, second wave of growth. So as we were going public in 2021, you know, we had a pretty significant investment in this cloud offering, but it was still relatively nascent. Maybe it was about 17% of revenue, something like that. But we had a lot of confidence that that was going to be, you know, one of the drivers of the next kind of leg of growth as this came out and allowed us to capture, you know, a broader set of the open-source usage. And indeed that, you know, that was the case. And you know, now a lot of our focus has been on kind of broadening this platform. So if Kafka was this core stream of data that connects things, you know, our vision is really to broaden that into a full platform for working with real-time data. How do I process data continuously in real-time? How do I capture it in real-time? How can I govern it across a company? We would call that a data streaming platform, so you know, really taking everything people want to do with these real-time data streams and making it available in a coherent package, and you know, that's received, I think, fantastic reception from our customers. A lot of the work is bringing all that functionality to life. Some of these processing capabilities, you know, the governance capabilities, and that, you know, that to me is very much our kind of third act, as it were, is, you know, really going to a general-purpose platform for real-time data, helping customers realize that across the thousands of use cases it's applicable for, and you know, that's a significant focus for us now. Excellent. Thank you for that. Anurag, maybe introduce yourself quickly and then maybe just describe how RBC uses Confluent today. Sure, Matt. So I've been with RBC for about a little over two years now. I used to work at Credit Suisse before this, where I started using Confluent back in the day. And so if you think about, you know, Confluent is the enterprise-grade enterprise standard for, you know, event streaming. What does that mean to us? You know, 200-plus applications streaming real-time events, ingesting events, distributing events, and consuming events. There's about 3 billion-plus events on a daily basis that are being streamed over Confluent Kafka. It is essentially growing exponentially as more and more real-time sources of data are consumed by various teams across RBC, but also distributed to clients. Use cases, I mean, you think about any and all use cases, you know, in the current day and age leveraging real-time data. I mean, fraud detection is leveraging a significant portion of Kafka. You look at payments, real-time payment events in our retail bank, that's leveraging Kafka. You know, within capital markets, if you look at the recently launched U.S. cash management business, that is leveraging Confluent in both consuming, sharing data across regulatory events, payment events, and as the sort of, you know, all of the cash balances events that are coming up with clients. We're also leveraging Confluent in being able to provide real-time customer alerts on certain events that happen. So again, like, you know, enterprise-grade, well-supported, right? I mean, we could easily use open-source Kafka, but the reason why we choose to use Confluent is because it is enterprise-supported. And so, if you think about the day in the life of a developer, if they're using open-source, the amount of time we end up spending in maintaining the number of risks and vulnerabilities we see with open-source in managing that process ourselves versus leveraging a partner that is investing in the product, but yet having the flexibility that it is open-source and that it can be deployed across multiple clouds, on-prem or cloud, that makes our lives really, you know, much more simpler and allows us to scale much faster and allows us to, you know, achieve better time-to-market solutions, but being sustainable at the same time. Excellent. I'm going to get into where we might be going with, or maybe Jay, maybe you might be interested in where we're going with Confluent in the future, but maybe just this is a, you know, it's an opening keynote and, you know, a lot of us are, you know, interested in other things than software. I guess, Jay, from your perspective, you've been around tech for a long time. We've seen a lot of trends from the mainframe to the PCs to the internet, generative AI and SaaS. What excites you the most about, like, what is it about today that excites you the most about the future with all this, you know, incredible innovation out there from a tech perspective? Yeah, I think it's a pretty exciting time. You know, I mean, the, you know, the set of things that are coming together, I think, is very interesting. And a lot of these waves kind of build on each other. You know, the rise of AI, I think, is really interesting. And this is an area I followed for a long time. I got into computer science by way of, you know, studying AI and have been involved. Before it was GenAI. Yeah, yeah. And that was actually what brought me to LinkedIn was to, you know, work on some of the more analytical data-driven machine learning applications. And that was some of the early use cases for Kafka was how do we actually get access to all this and be able to apply it back in some kind of customer experience. And, you know, so to see some of the harder use cases start to come to life, you know, really a major leap forward, I think it's amazing. You know, I think we're starting to see beyond just the language models, you know, some of the things in the real world, some of the robotics applications are kind of on the verge of being something. I mean, that takes time, but, you know, I think that's very exciting. So when you put all that together, it's certainly an exciting time for just technology. You know, if you put the pieces together over the last whatever decade, you've gotten this, you know, incredible cloud and data systems that allow you to kind of harness some of the stuff. You're getting these kind of AI capabilities that really allow you to start to close the loop in different ways. You've gotten, you know, kind of ubiquitous kind of internet or IoT or the ability to get stuff out in the real world that can connect back into that system. And so, you know, you kind of put those pieces together, the parts of a company or organization that can be kind of modeled or improved in software, you know, in the digital realm is just like orders of magnitude what it was. Yeah. And so I think that's really interesting. I mean, I think it's fascinating. I think we're going to see a lot of change come out of it in the next few years. A lot of disruption too, right? I mean. Yeah, yeah. You know, it's interesting. People have always very bold predictions, but actually when you have a lot changing like this, the distribution is flatter. You know, you actually, you know, there's more uncertainty in how it all turns out. But I think it's certainly just purely as a technologist, it's an exciting time. Yeah, I'm excited to see where it goes. Maybe because we'll kind of get the AI piece out of the way here early. I mean, Anurag, we've been leveraging AI for years, and all of a sudden now we're starting to build our own generative AI applications. How do you think about, you know, sort of where we're at and what we're doing internally as a bank from an AI or a GenAI perspective? Yeah, I think, I mean, we're really leading the way at the moment. If I look at the work we're doing in generative AI with the launch of a product called Aiden Assist and, you know, really think about Aiden Assist as like in two swim lanes, one being a generalist swim lane, which is essentially how do we enable, you know, the entire bank to leverage ChatGPT-like capability in a way that's compliant, that's safe, that allows us to really experiment fast. But also behind it is a RAG architecture that allows individuals to upload their own information in a safe and secure manner. And the second swim lane is where we really start to see much more automation, which is the specialist swim lane, where we're starting to look at the day in the life of a user persona and sort of how can we start to answer very sort of simple questions or provide straight-through automation. A great example of that is, Matt, you know, within the research team where we've just launched Quick Takes, which really looks at real-time press releases, real-time company filings, SEC filings, real-time earnings call transcripts, and allows us to generate out-of-the-box quick takes that analysts can take and augment and publish to clients much faster time to market. And again, that's a really great use case where real-time streaming of data plays a really important role in that GenAI world. I think that's where I think the sort of Confluent Kafka intersects with the GenAI world in a really nice way. Interesting. I guess the question that I always get is, well, it all sounds great, but like are customers actually going to pay for this? Like how, you know, to what amount is it table stakes? What is it? Where is it a monetizable feature? I mean, how do you think about, like from a, I mean, it could be from a consumer perspective, whether you're buying stuff for RBC or, you know, whether you, what you guys are consuming externally from an AI perspective, how do you think about the, you know, customers' willingness to pay for this kind of technology? Yeah, yeah, yeah. I have a few thoughts on that. I mean, you know, I've kind of followed that debate of like, okay, you know, this much venture capital has been sunk into AI companies and only this much money has come out and one number is a lot bigger than the other number. And yeah, it's true, actually, right? Like the investment is either ahead of or much larger than the return we've seen so far. But if you look at what we've seen so far, it is kind of, you know, these very early things. You know, it's a bunch of SaaS companies sticking a chatbot in whatever little tools you use. You know, I think that that's not the end state here. You know, I think it's unlikely to be the end state that a company like Confluent is consuming AI as, you know, 115 little chatbots embedded in different SaaS tools. Yeah, that's not the end state. That's not the end state. Like even these chatbots are kind of a step, right? But a lot of what we would want is actually something more directly integrated, you know, into the work that we do, right? And I think that's true for a lot of our customers. I mean, even what we've seen in the usage of our platform, you know, I think these chatbots are great. We show up in a lot of these different architectures trying to get the data for RAG. But the kind of more interesting stuff is I think, as you're saying, you know, okay, what is the core business? How can we apply this? Something happens in the world and we want the AI to do something. And so, you know, I think there's a great example Anurag gave, but there's similar examples. You know, you look at like a customer that's an insurance company, you know, a claim is filed. There's a whole set of work that's done around processing, a fair amount of which is actually like not digital. It's humans, right, and so, you know, the ability to actually have the AI take a shot at some of that and give the human something that's like a pretty solid first draft, then they can edit it and you can kind of start to iterate and get that loop going of maybe it doesn't have to be a first draft. Maybe it could be a final draft. You know, I think that's the type of use case that I think is where you're going to start to see the real payoff. That takes time because if you think about that company's kind of now really changing their business process in a meaningful way versus, you know, buying a module in a SaaS tool. Yeah, but if you think about the payoff from that, I think it's much more substantial, and so I think, you know, I think that's not unusual for any technology that that time to trickle out and actually have the impact. You know, it's years, not months. Yeah, and you know, maybe that's surprising. Some people, I think it should not be, you know, so I do think there is a question of like, okay, how rapidly can companies realize this, but I think the actual impact is pretty substantial. I mean, the models continue to make progress. I mean, rumors to the contrary, right? Like they continue to progress. Already just in what's there today, there's a fair amount of unrealized value. You know, if companies took what was on the shelf today and fully put that to work, that would take some years to do, but you would already have a fair amount of value unlocked in that. So I'm pretty optimistic about where that's going. You know, nothing's overnight, but I do think that there's a wave of use cases and applications kind of building around that. You know, and I think that's, you know, just as, again, as a technologist, exciting to see. I think a positive thing for Confluent and then that becomes a consumer of data and in many cases something that actually directly operates on these streams. You know, something happens in the business, that's an event that they want to react to and process. We've built, you know, functionality that helps with that. You know, even more broadly, I just think this is a, you know, really closes the loop in software. Our paradigm in software has been very much like, let's give humans a UI that they can kind of click on in different ways. That's kind of the way software does work, right? That's not, that's not really what we want, right? In the end, we want something that can, you know, take on more of that, do more of it. You know, I think you're starting to see little bits of that shine through here and there. Anurag, one of the questions that I get all the time is, it seemed like AI really disrupted kind of run rate IT spending this year because I think a lot of people came into this year, you know, uncertain of how they're going to address AI and GenAI, and I think it did cause a lot of disruption. You know, now that we're sort of a year into it, or how many years are we into the hype cycle? I don't know, two years, three years? Do you think there's some more predictability in how like a bank like RBC thinks about deploying assets for dollars for AI and that it, like we're almost at like a new normal now and it's not as much of like, oh, we got to cut some spending and divert it to AI? What's sort of, you know, your perspective on that? I think somewhat there, but not quite, I'd say. You know, when you look at generative AI, I mean, you know, still at the very early stages of thinking about how disruptive this can be. You know, I think of it as a once-in-a-lifetime moment, honestly, because there's so much disruption that this can drive. The interface in how we operate and how we sort of, you know, work with clients and how we work across teams can really change with generative AI. And so I think, you know, it's the prior question you asked, like when you think about any of the, you know, disruptive technologies of the past, it takes about 10 years before you start to realize, you know, like an iPhone gets launched. Well, you didn't know how many more in the app ecosystem on day one. No one could have realized that. Or when the internet came out, like you wouldn't realize like in the next 20 years how disruptive that could be, right? So I think it's the same with generative AI. We will in the next 10 to 15 years realize how disruptive this is going to be. So I think as you think about that, like, you know, think about the spend on AI and generative AI. I mean, you know, we're just setting up a GPU farm on-prem just to be able to reserve capacity on GPUs. That's not a problem we would have like in the past. We could easily get GPU capacity on cloud. But at this moment, like everyone's going after GPUs. And so, you know, we're setting up our own sort of farm on-premise to be able to support that. I mean, I think our cloud spend in many of these cases will continue to go up. I mean, we weren't paying for, you know, OpenAI services like ChatGPT models and, you know, or Cohere or, you know, any of these models. The compute that goes behind this on cloud will continue to grow as well. So, you know, a lot of that growth will depend on how many use cases we're tackling and how big these use cases end up being. I mean, we know these models take a large amount of compute, both on embedding or inference. So yeah, I think I don't quite think that it's as predictive today. I think we'll get there in the next two to three years. It's a good perspective. I think we all want like instant gratification from AI, but it's a good perspective that these things do take time, decades. But the excitement is certainly there. Jay, the other topical question that I'm getting, and we talked a little bit about it beforehand is, you know, now that we're post the election, at least there's less confusion around that. You know, we've seen one Trump administration. I think there's a lot of questions about the second one. One of the questions I get all the time is like, you know, with, you know, the Department of Government Efficiency, you know, with things like, you know, potential tariffs or things like lower corporate tax rates, there's a lot to think about from a technology perspective. Do you have any perspective on what the next four years might look like from a tech perspective? Yeah, you know, I mean, I think this is again one where there's a lot of uncertainty. You know, certainly we sell to the government. Anybody who sells to the government, you do feel that there are some inefficiencies that can be improved. You know, like how this manifests, I don't know. Probably everybody in the room has a smarter opinion on, you know, what the impact of tariffs will be or, you know, whatever. I think that stuff's fascinating, but, you know, I don't have any kind of concrete prediction. You know, it certainly is the case that there is an opportunity in the government to apply technology and make things more efficient, right? Like, so, you know, whenever I hear that, okay, there's 100,000 people working in the IRS, you know, as a former computer programmer, I do feel like, you know, a fair amount of computing your tax return, it should be something that like computers could do. Yeah. Yeah, I don't want to make it sound easy, and obviously there's a lot of interfacing with people, but 100,000 employees is a lot of employees, and so I do feel like, okay, there must be some opportunity for the, you know, application of software in these areas to, you know, kind of drive efficiency. To benefit, right? Yeah. You know, just catching up to where the rest of the world has been for the last 20 years or so. And, you know, I think that would be a great thing to see happen. I don't know that that's going to be what does happen, but, you know, if that's the case, I think that would be positive. You know, certainly one of the interesting things, you know, as you travel around and you can see how different countries have approached digital services, I think it is an opportunity for relatively low investment to drive a lot of efficiency. I think some of the stuff India has done is amazing. You know, like we talk, we're involved in a lot of different payment systems. And, you know, when you look at what these payment systems do, you know, they process some number of transactions and, you know, they're very important systems. You go to India, the number of transactions is like, you know, whatever, 100 times, 1,000 times larger than the next thing. And it's because, you know, there's a system that's universally used that you can buy anything that has kind of very low, I mean, effectively no fee, no merchant fee. So you can go buy, you know, some fruit at a stand and pay, you know, with this. And this is something that they helped standardize. It's kind of a private-public partnership. There's a whole thing, set of things like that around, you know, banking, identity, et cetera. They actually just make it really easy to get services out of the government. If you think about what the U.S. Government does, you know, a shocking amount of it always comes back to figuring out who you are and what you have permission to do. You know, it's kind of identity authorization, but all done with like, you know, some card and some PG&E bill or some way. I mean, it's the most ridiculous way of proving who you are. And so, you know, it does seem like there's some like fundamental missing stuff there that would actually just make everything more efficient. I don't know if we'll get that. You know, maybe it's just a matter of, you know, whatever people have said, firing, you know, people whose Social Security number ends in odd numbers or something. You know, so I don't know if that gets you, you know, somehow a better system or just maybe at least a smaller system. So I think at this point, nobody has any kind of deep insight, but I would certainly love to see it happen that we get kind of a more efficient government. I think a lot of what the government does is really important. And, you know, there's certainly some opportunity there that you see if you, you know, if you work with the government closely as a customer. It seems like software technology should be more of an enabler for increased efficiency if you've ever gotten a job. Yeah, that would certainly be the direction I would hope for. Yeah. You know, I think it's obviously at this point, nobody knows. Yeah. That's great. I wanted to talk about open source. It's been one of the most disruptive, you know, trends that we've seen and you guys were actively involved in that. I guess the high-level question is, when you think about the future, what are areas that you think in the tech stack that are potentially at risk from open source disrupting, you know, sort of paid, you know, tracks? Yeah. Yeah. Yeah. Yeah. I mean, there's risk and reward. Certainly, you know, the areas that I think are most interesting and what I think open source does well is help create these kind of de facto standards that people want to use and build around. And so where I think that there's opportunities is where there's some missing standard that could actually change how some part of the industry works. One of the ones we've been most interested in and are, you know, kind of investing around ourselves is something called Apache Iceberg. Yeah. And, you know, there's some investors who follow the data space may be aware of this, but, you know, what's happening is the kind of raw storage in the cloud is obviously something that's very open. It's a cloud service, right? And yet if you store data in most analytics tools, it's kind of locked up in that tool. And in many ways, that's been kind of the foundation of how data warehousing, you know, even in practice, a lot of these data lakes lakehouses have worked. There's some silo that has the data. And anything you want to do with that data, you would in practice kind of have to do through their tool. So there's some kind of toll that gets paid for every usage of data. And you end up having many copies of the data in each different system. And you end up paying that toll many times, even if that tool is not the best thing to use your data. You know, what this standard Apache Iceberg, you know, it's an open source project, but I mean, it's not even the world's most complicated piece of technology. It just makes tables, you know, like database tables of data available in object storage in a way that is across any of these different systems. So you can imagine you're kind of going from a model where there's really only a single company that can sell you the, you know, the analytics processing of your data to a model where effectively, you know, it's an open market, right? You know, capitalism comes in and everybody can compete to be, you know, faster or more efficient or more cost-effective or have better functionality for a particular use case. So instead of having kind of one, you know, global warehouse or lakehouse that, you know, solves all problems, you kind of end up with more of an ecosystem that, you know, allows you to harness data in different ways. And I think that's a very powerful thing when you get that kind of standardization and unlock. And so I think we're very excited about that. You know, at Confluent, we've done something called TableFlow. So any of these streams that come in, you know, can just be opened up as iceberg tables, then exposed out to this wider world of different analytics technologies. So you can query it in Snowflake or in, you know, the cloud provider tools, things like Glue and Athena and AWS or, you know, other cloud providers, something that's very portable across. And, you know, this is really appealing to customers. You feel like, hey, in some of these cases, we've been a little beholden, you know, to some of these vendors. We don't feel like we totally have control over the bill that we're getting in the end. And then, you know, beyond that, we often end up with tools that are not really the thing we want because that was what worked with them. And so, you know, I think that kind of openness is going to drive a lot of innovation, both by, you know, kind of goosing the existing players to do better, as well as by, you know, inviting new players to come in and compete for different workloads. So we're excited about this idea of, you know, taking all these real-time streams, landing them in Iceberg and, you know, in an up-to-date way and opening that up to the wider world. I think that's going to be a pretty exciting trend in that analytics space, you know, over the coming years, and we're starting to see adoption of that in a lot of the larger, more sophisticated customers already. Do you know, Iceberg comes up on just about every, you know, sort of infrastructure type call, whether it's analytics or even observability. Are we leveraging Iceberg? Absolutely. I mean, look, I think, you know, when we look at our principles, guiding principles on cloud, a lot of what we try to do is provide for open data formats and portability at its core, right? So, you know, today, if you're, you know, deep in one provider and you're sort of, you know, dealing with their formats, it becomes extremely hard. It's so sticky, and, you know, we've seen that time and time again, both on-prem and on cloud, where we've had providers, you know, increase pricing on us, and it is so hard to get away from them, and so open data formats is at the core of what we are trying to do. Iceberg and Delta are the two leading formats for us. Certainly, Databricks, you know, talks open data all the time. I mean, you know, when I started working with Databricks eight years back, I mean, open data was their driver, and that really resonated with us back in the day and still does. They've since then acquired Tabular, which is, you know, really, again, big on Iceberg, and again, Iceberg and Delta, I mean, you could pick one, but Iceberg has more flex across more providers and more cloud providers and more analytics providers. So I think that there's no doubt there's value. There is still a journey to be had on how we move everyone else that lives in legacy worlds over to open formats, and that's going to be a hard path forward for many of us. No, yeah, so what I'm hearing from you, Jay, is because I think it could be disruptive to a lot of companies, you see it as additive to the Confluent story at this point. Yeah, yeah, you know, we're a little different. You know, obviously some, you know, kind of data platforms have been about sort of locking up your data. Yeah. We, you know, our business is actually to unlock it. We are trying to get it to many places. So one of the exciting things for us is there hasn't really been a great way of landing data, you know, in real time for analytics. And so you have to do a lot of different kind of workarounds, but it's a fair amount of work for customers to hook that up. And so this is something that basically takes all our, you know, the customer use cases we have today and kind of opens up the analytics world to those. So it's, yeah, very much additive and very much something customers are excited about and, you know, excited about pursuing. Yeah. We have a couple of minutes left. I wanted to touch on just one thing really quickly because it strikes me interesting that there's been so many, I'm thinking across tech, changes in go-to-market approaches. You guys had one. Others have, I think, honestly, I think struggled with how to sell in a post-COVID world. It feels like today it's very much an ROI-driven sale, which feels like you guys are at the core of as well. Can you just reflect on like why we're seeing so much disruption in sales forces, technology sales forces these days? Yeah, I mean, I think the root thing for tech is a, you know, there's pressure on spend, which requires everybody to up their game, right? And so I don't, you know, I feel like that's one force that's happening. You know, the other thing is I think company, you know, as you up your game, what are you trying to do? I think companies are trying to get smarter about helping their customers, you know, people to realize value. That sounds like a euphemism, but it's actually not, right? Like what are you trying to do? You have some widget, gadget, data thingy, whatever the thing that you've built is, it's not inherently valuable on its own. You know, customers have to somehow put this in practice in some way. You know, I do think companies are getting much more sophisticated about how they help customers realize that value. In many cases, the business models are aligning to that. A company like us, it's kind of oriented around consumption for our cloud. You know, it's very much the case that as customer uses more of our product, they're spending more. The driver for that is going to be, you know, kind of successful production use cases, which is usually the thing valuable to the customer. So, you know, that's certainly a better model, I think, than kind of unused seats or, you know, whatever pre-allocated clusters or, you know, whatever the precursor might have been for different companies. So the closer you can get to that actual unit of usage, and then that allows, you know, the providers to be smarter about ways that they can help. You know, what are the things we can offer to customers that help them find use cases that are actually worth doing, that are exciting, they're going to, you know, have high ROI. And of course, the motivation for that is, yeah, you know, an environment where IT spend is a little tighter. You want to be smart, you know, you want to be smart about it. And I think you're right that, you know, I don't think you switch from, you know, purely innovation to purely value selling or whatever else. I think these things are ultimately a mixture, right? You know, customers are looking for products which unlock something genuinely new that's worth doing and save them money and, you know, have a really achievable path from point A to point B, you know, and the things that get bought in tighter times are the ones that kind of check all the boxes. Yeah. Two minutes left, and you can't say generically GenAI. I want to know a big bold prediction. Do you have a bold prediction or something that we should be watching from a tech perspective that maybe you're most excited about that maybe is not, you know, top of mind for folks? Yeah. Both of you. Yeah. Yeah, you know, I think there's a number of interesting things happening. You know, I won't say Gen AI, you know, broadly, but I do think that watching these models get applied in different domains, I think is one of the more interesting unlocks. So like, you know, I'm involved with this company, Anthropic. They just did this computer usage, right? Which is like literally the model using the computer. That's one example of taking something where, you know, we tend to, in our imaginations, think based on what we've seen, right? So if we saw a chatbot, we think more chatbots, right? And, you know, I think that's an example of how this applies more broadly. You know, I think you're starting to see some of these end-to-end models work with kind of vision and robotics, as I was saying. The self-driving cars, which went through their own hype cycle, you know, they're actually kind of working in San Francisco. You know, you can get anywhere in a relatively complicated city. And, you know, I think that's one which is weirdly underrated in what a change it is. So I think the application, like this expansion of AI into these different kind of domains, you know, I think it's actually a really big deal as you plug that in. More vertical-based use cases though. Yeah, yeah. You know, think of it as the I/O, right? Like to some extent, computers haven't changed, but having a phone that knows where you are and has these other capabilities makes it very different, right? I think it's the same thing for these AI models where going from chat to something else that you can interact with the world in different ways actually really changes what's possible, and so I think we're in the early parts of kind of seeing that realized. Anurag, what about you? I mean, I think, you know, again, I'm not going to say generative AI, but the application of it in various verticals, as was mentioned. I mean, I look at entertainment and like, you know, how is this going to change, you know, making of movies or music or art and like the unexplored. I think you can see the beginnings of that. That's, you know, again, really interesting. Or in healthcare, where you're starting to see more and more of the sort of patents that are coming out and medicines that, you know, for different diseases, different, you know, applications that are leveraging AI at its core. Or even when I think about, you know, our own data centers and intelligent operations, you know, that's a more lower-hanging use case in my mind. Like that's probably in the next three years, like how we look at managing our data centers and capacity and everything else, you know, when things go wrong, like predicting where things are going to go wrong and how to address them in an automated manner. I think bots are going to be doing a lot of that. Just, no, I appreciate that answer. As we wrap up here, one of just a final last question. Is there any tool, a Gen AI tool that you use in your personal life that you find particularly helpful? I'll start and give you a second to think. If people have not used NotebookLM, it's a great tool. It's just a fun little tool on Google. So I would suggest checking out that. Are there any other, you know, fun little personal tools that you guys have used from a Gen AI perspective? I mean, I don't do a ton of programming anymore, but these programming interfaces, things like Cursor, I mean, it's kind of amazing, you know, the quality of the models for coding and just what this enables. So, you know, even if you've been away from it for a while and you're kind of rusty, you can actually suddenly do. You're a coder again. Yeah, fairly meaningful things, and so, you know, I think we're early at actually seeing the impact of that. Like we, you know, we kind of rolled some of this stuff out with our team and like, yeah, it's helpful, but it's not like the team is, you know, 50% more effective yet, but I think if you look at the progress of those models in the last year, just on like benchmarks, problem things, there's still work to do to get it fully integrated, but it's like amazing improvements, so I do think that's one that, you know, is going to have interesting implications where suddenly a much broader set of people are going to be able to do this. Yeah. Anurag, any last? I've used NotebookLM. It's really good. Obviously, ChatGPT is very regular use and Meta AI for, you know, sort of transforming your pictures. What is it? Which one? Meta. Meta. Oh, yeah. It uses a lot of models in the backend, but, you know, how you see like your own picture visualized in a different way. Like it's pretty interesting. All right. Helpful. Well, Jay, we're out of time. Really appreciate the conversation. From all of us at RBC, thanks. Thanks Thanks for your time. Yeah, thank you. You guys look there.