Speaker 1:
From the New York Stock Exchange at the corner of Wall and Broad Streets in New York City, welcome Inside the ICE House. Our podcast from Intercontinental Exchange is your go-to for the latest on markets, leadership, vision, and business. For over 230 years, the NYSE has been the beating heart of global growth. Each week, we bring you inspiring stories of innovators, job creators, and the movers and shakers of capitalism, here at the NYSE and ICE's exchanges around the world. Now, let's go Inside the ICE House. Here's your host, Kristin Scholer.
Kristen Scholer:
Artificial intelligence has rapidly transformed the business landscape, revolutionizing industries by enhancing efficiency, automating complex processes, and unlocking new opportunities for growth. From personalized customer experiences, to advanced data analytics, AI has become a driving force behind decision-making and innovation. Its ability to learn from vast amounts of data and predict trends has empowered businesses to make smarter, faster decisions, while reshaping everything from product development to supply chain management.
Snowflake, with the NYSE ticker symbol, SNOW, is a leader in AI, transforming how businesses leverage data for smarter decision-making. Its powerful data cloud platform integrates AI and machine learning to streamline data workflows, enabling real-time insights and innovation across industries. By simplifying processes, Snowflake is setting the standard for AI-driven transformation, helping companies maximize the value of their data.
Joining us now is Snowflake's CEO, Sridhar Ramaswamy. After first joining Snowflake in May of 2023 in connection with its acquisition of Neeva, Sridhar has been at the helm of the company since February of 2024. Sridhar previously spent 15 years at Google, leading all the company's advertising products, including search, display and video advertising. He's also held positions at Bell Labs, Lucent Technologies, Telcordia Technologies, and currently sits on the board of trustees at Brown University.
Sridhar, thanks so much for joining us Inside the ICE House.
Sridhar Ramaswamy:
Kristen, excited to be here. Looking forward to our conversation.
Kristen Scholer:
Likewise, very much so. Let's get started here. In 2012, Snowflake's founders, Benoît and Thierry, set out to reimagine the data warehouse by building it entirely from the ground up for the cloud. 12 years later, Snowflake has evolved into a global leader in mobilizing data, serving over 10,000 customers worldwide. And since February of 2024, you've taken on the challenge of advancing their vision and building on more than a decade of innovation. How would you define Snowflake's mission and describe its transformation from its inception in 2012, to where it stands heading into 2025?
Sridhar Ramaswamy:
Our founders, and this, it's amazing, that it was over a decade ago, had a singular vision that was based on a really cool insight that out in the world of cloud computing, you could scale storage orthogonally from compute. In other words, if you made the compute engine flexible enough, that you could replicate it, then you got some amazing characteristics. What does it mean? Like when you buy software in a box, if you want to do lots of things in that box, you're no longer able to do it, you have to buy a new box, a bigger box. Snowflake was infinitely scalable because of its unique architecture, but over the years we've built on it. We are the premier collaboration platform for enterprises wishing to exchange data with one another. And over the past few years, we've also been adding on additional machine learning and AI capabilities.
I described Snowflake as an AI data cloud. We are the platform that enterprises, small and large, use to store their data, get insights about how their users are using their product, and then make predictions about which parts of their product their users will want to use. Leading to an end-to-end platform that tightly integrates with the products that companies ship. And AI is of course, is going to massively accelerate the power and value that companies can get from data, and that's the reason why Snowflake has been growing and thriving so much, and why we and our customers and partners are so excited about what's up ahead.
Kristen Scholer:
And since Snowflake's IPO in 2020, the company's partnership with NYSE has grown and flourished, with the collaboration prominently showcased at the New York Stock Exchange's annual tree lighting in December, where Snowflake's logo crowned the tree and CMO, Denise Persson, joined the podium to ring the closing bell. Additionally, the NYSE president, Lynn Martin, does have a role as a judge for the Snowflake Startup Challenge, underscoring this strong connection between the organizations. How has this partnership propelled Snowflake's growth and deepened its ties to the broader financial and technology communities?
Sridhar Ramaswamy:
Well, you know this, the New York Stock Exchange is the most studied stock exchange ever on the planet, and we are thrilled and honored to be on it, but there's also a very deep collaboration between the firms. It's at the level of us being the data platform for ICE and for the New York Stock Exchange, and being an important distribution mechanism for all of the amazing data that NYSE wants to share with its customers and partners. We get a lot of great feedback about where Snowflake should evolve from Lynn and her team. And things like the tree lighting and the judging are the icing on the cake that demonstrate how closely the companies work with each other to partner, but just as importantly, create success for all of our joint customers. It's a deep partnership that I'm very, very grateful for.
Kristen Scholer:
And with previous roles at Bell Labs, Lucent Technologies, Google, among others, you eventually founded Neeva, a search company that used generative artificial intelligence to redefine how users discover information. In 2023, Neva was acquired by Snowflake, and looking back on that acquisition, what factors influenced your decision to join forces with a larger platform? And what excited you the most about the opportunity to collaborate with Snowflake?
Sridhar Ramaswamy:
Yeah, Neeva was born off an idea that the world of search needed to be changed in a dramatic way. Back in 2019 when we started it, we did not know that GPT-3 was in the offing or that language models would become the force that they are widely acknowledged to be today. But we knew that there was a better search engine to be created. But four years into the journey, honestly, we had built an amazing product, but we were struggling to achieve commercial success at scale, which is when we started talking to Frank Slootman and Christian Kleinerman, Benoît and Thierry, the Founders of Snowflake. And we realized that this was going to be a great opportunity to take the technology, the search, the AI that we had built at Neeva, but apply it in an enterprise context for it to have massive impact in the enterprise.
I tell people that AI is already magical because these language models can do things like understand fluid language, like the language you and I are speaking, and make sense of it, extract context from it, and do that effortlessly. We knew that how people are going to consume information was going to be changed forever. Just like back when the first smartphones came, most people in the world kind of simultaneously realized that there was a before and an after, that everybody was going to have these amazing devices with them. I think we're going to think about AI the same way. There is a before and there's an after. The before is clunky little interfaces that you and I had to type information into very, very carefully. And the after is fluid language that we write or speak, and every application, every phone is able to understand exactly what we mean.
We thought that this was a match made in heaven, and both the folks at Snowflake and the senior engineers and my co-founder at Neeva, felt that these were people that we could work with, that we'd have a lot of fun doing it, and have big impact. And I'm pretty happy with how the past 18 months have turned out, not just for me but for the entire company. My co-founder is the head of engineering at Snowflake now, he's having a broad impact, but most of the key engineers at Neeva are influencing the roadmap and product of Snowflake in a very big way. And in as much as I said, there's a before and after with AI and data, we feel very proud to be a part of charting where enterprise data, where this AI data cloud is going to go.
Kristen Scholer:
Sridhar, I know you spent 15 years at Google, a decade of which were in various leadership roles. How did these experiences shape your leadership style and philosophy as you now oversee Snowflake?
Sridhar Ramaswamy:
Google is an amazing place to work at, and I was lucky enough to be part of possibly the best ever business on the planet ever, which was the search ads business at Google. But there are many characteristics that made my time at Google and my learning very, very special. I'll just touch on a few of them.
First and foremost is, we always knew that success could be fleeting. And one thing that I, because you learn this is the land where the most iconic of founders went around saying things like, "Only the paranoid survive," or you could see what happened to companies like Silicon Graphics, and so the lower of companies that succeeded and didn't succeed was deeply imbued in us. And so we had this relentless drive to excel, to exceed, and that was part of the mentality that created what was eventually a $100-plus billion business. There are not many of those on the planet, but I think that is an important quality that I bring to the table at Snowflake, which is yes, it is like achieving success is important, but achieving excellence, which is a way of living on an ongoing basis is just as important to create iconic companies, to drive lasting success. I would say that that was an important characteristic.
At Google, I went from an individual contributor, an engineer that wrote code sitting in a corner talking to no one, and to running a team of over 10,000 people. I got to work with many iconic people. People like Eric Schmidt, who was the early CEO, the founders of Google, whose drive and intellectual curiosity was just amazing. But also, people like Bill Campbell, famously the coach of Silicon Valley, who taught me what it was to be a human leader. Bill always knew what was happening with my children. He cared about what I did at work, but he cared just as much about whether I was doing well in my life.
These were very humbling people to learn from. I try to bring that humility, that care, that warmth to the relationships that I have with the people that I work with. And balancing those two, how to be a human, how to be a good human being, how to make sure that we take care of the people around us, while simultaneously creating a culture of excellence and creating lasting success. I'd say those are big, big lessons that one learns quite often just by watching and imbuing, and I'm very grateful for the time and opportunity that I got at Google.
Kristen Scholer:
And you assumed the role of CEO at Snowflake in February of 2024. What were your immediate priorities upon stepping into the position, and how did you approach tackling new opportunities and challenges in the rapidly evolving data cloud space?
Sridhar Ramaswamy:
Yeah, it's a great question and initially, I set myself three goals. We knew that we were in a rapidly changing world, and so making sure that we had very good product velocity was something really important, was a huge priority for me. I worked very closely with the product and engineering teams to make sure that we were iterating, that we were rapidly shipping, that we were getting feedback from our customers, that we evolved different go-to-market motions than the ones that we had used to make the cloud warehouse successful. So that was a huge, that, getting new products right and getting them out to the market quickly was a huge priority.
I also spent a lot of time on the road. I was out on the road probably every other week this year. This is to learn from customers and getting direct feedback from them about what we were doing well and what we needed to do better at. And by the way, being on the road is also a great way to meet your field team, whether it's sales executives or sales engineers or sales leadership. So you learn traveling the world, traveling through the cities in the country, meeting customers, and I would say that has stood us in good stead. So we laid the foundation for faster product delivery, we laid the foundation for being able to take new products to market, to market efficiently, and then also learning from our customers.
Things take a little while to manifest themselves, but I'm exceptionally happy with how things came together for our Q3 earnings for example. And also honestly, the confidence that it's given the entire company that they can step up and deliver at even greater scales than what they're doing right now.
Kristen Scholer:
Sridhar, you've been characterized as an AI CEO, with Snowflake positioned as a leader in this very dynamic field. What are some of the key challenges in steering a company so deeply rooted in AI, especially given the technology's constant evolution and the need to stay at the forefront of its rapid advancements?
Sridhar Ramaswamy:
Yeah, I would say yes, AI is changing very quickly, but there are also a set of fundamental observations that you can make. I talked earlier about how AI is going to take the language that we speak and the language that we write, and be able to make sense of it, be able to extract structure from it. It spans the gamut, by the way. If there is a table in an analyst report that somebody puts out, the language model can extract that quantitative data from that, from the table, and put it for analysis into a Snowflake table. It can also fish out interesting tidbits about feedback that an analyst has about a company, which is text. In addition, one of the most interesting things about AI is that it can do these data transformations with much more ease than before.
I'll give you a simple example. Like five years ago, if you had access to a bunch of clinician notes and wanted to know, what are the symptoms that patients had? That would be a custom project. You would need to go get software engineers, they would need to know machine learning and AI, and they would work for many months before they built you a system. Right now, somebody that uses Snowflake can write a single SQL statement and be able to do exactly this. And so we went about it very much from the viewpoint of, how do we put a foundation of AI that is accessible from every part of Snowflake, whether people are ingesting data or transforming data or building reports with data? And then on top of that, we've started building out the key primitives, the chatbots as it were, that can then be stitched together This way it becomes almost like assembling a Lego set, where the important pieces are there. Yes, you might get new pieces, but you're able to flexibly combine them into systems that create value for you as a customer.
I'm very pleased with how far we have come, and we announced this agentic platform called Snowflake Intelligence, that's going to push the boundaries of what is possible even more. It's going to let people compose chatbots on unstructured text, like on unstructured documents, like text documents or PDF documents, but also unstructured data that they store within Snowflake, and then also be able to write updates to other systems like a Workday, like a Salesforce. It's this gluing ability of AI models that we are currently exploring. But hopefully, this gives you an idea of how you start from the bottom, the world of data, then let customers use the power of AI to transform this data, and then get value from it.
And part of what is really exciting is that business users, like my CFO, can get answers to questions without needing to know SQL, without needing to know complex technology in very, very flexible ways, and these are some of the things that we are creating. But you're right, we have to keep pushing the boundary of what is possible with AI models and data.
Kristen Scholer:
To that end, while AI growth does present exciting opportunities and capabilities, it does also bring new risks and concerns for companies and their clients. What risks do you foresee arising from the rapid pace, Sridhar, of AI innovation? And how is Snowflake ensuring that its AI tools are developed and deployed responsibly?
Sridhar Ramaswamy:
Oh, it's a great question. I mean, and we went through a lot of these questions ourselves when it came to Snowflake using AI products. And so we have a set of guidelines, we have a set of principles for how we think about AI and putting it into Snowflake's products.
The first and ironclad guarantee that we offer our customers is, their data is theirs. Your data as a Snowflake customer is yours. We are never going to use it to train anything that is shared across customers. That sanctity of data is the thing that gives our customers comfort, that their data is safe. And obviously, customers both care about the data from a business perspective, but also from a regulatory and doing the right thing perspective. So that's the bedrock of how we think about data and AI.
On top of that, we provide a set of tools to make sure that AI products that our customers build cannot be misused to generate harmful output, for example. You have something called Cortex Guard, that can be used to filter away malicious input that bad actors put into these applications to try and trick the models into doing bad things. So we build protection like that right into the core product.
And then how AI is going to be combined with other technologies like machine learning, is a further topic that we are actively exploring. As you know, in the world of machine learning, there are fairness laws. We need to make sure that these models are not biased, and so we are providing a set of tools to let our customers be able to easily judge these themselves so that they can both stay compliant with laws and regulations, but also just as importantly, do the right thing from a larger perspective. It is a very active dialogue, as you pointed out, in a pretty fast moving space.
Kristen Scholer:
At BUILD 2024, Snowflake's annual developer conference, your company unveiled new advancements to its platform designed to help enterprises navigate the complexities of data and AI. It highlights the need to simplify these technologies. Why is simplifying such a complex field so crucial for customers, and how does it help them maximize the value of their data and AI initiatives?
Sridhar Ramaswamy:
Look, simplicity is a topic that's near and dear to our heart. It goes back to the founding of Snowflake. Our founders had previously worked in a company where product complexity was rampant, where you needed armies of administrators to make sure that things were well-tuned. At Snowflake, they took the opposite approach. They said, "We want to make this product easy to use and easy to get value from," and this is where we really shine through compared to our competitors. Snowflake is easy to use, you can load petabytes of data incredibly quickly into Snowflake, but magically, you can start running queries on them interactively right after you load them. And it is that simplicity that drives broad adoption.
AI is great, but if you need to be deeply experienced in AI to get any value from it, that's going to limit its application. Our dream is more that business users, for example, that know business, that know what makes revenue for them, that know what creates profit for them, are able to get up the data they need without complex business intelligence tools. Without the need for an army of analysts that need to interpret every single thing that they said. And I said, that's what we love doing. We love taking complicated technologies, but making it easy and safe for people to use.
One worry that a lot of people have about using AI is whether things like access control will be preserved. I mean, think about it. All of us have documents that are stored in either Google Drive or in SharePoint, and putting these into an AI system needs to guarantee that only the right people can still see it, it cannot be the case that you stop obeying access controls. Things like that, we build right into Snowflake so that our users don't have to worry about whether access controls will be followed. They absolutely will be.
I think these are some of the things that people need to keep in mind as they're thinking about how they get value from data. And at Snowflake, we try very hard and to make sure that things work as expected, things work quickly, things are easy to try out. I tell people that you can build a chatbot on Snowflake in 10 minutes flat. And then you can see, is that something that you want to deploy at scale to your whole company or not? Simplicity and ease of use drives value creation.
Kristen Scholer:
I know in November, Sridhar, Snowflake and Anthropic, an AI safety and research company, announced a multi-year partnership aimed at helping global enterprises develop and scale AI products, applications, and workflows with greater ease and efficiency. What inspired you and Snowflake's leadership to collaborate with Anthropic on this venture, and how does this partnership align with Snowflake's long-term vision for AI and data cloud services?
Sridhar Ramaswamy:
A great question. The world of AI models is evolving incredibly rapidly. Even just now, I was reading yet another big announcement from one of these companies.
Anthropic has been a world leader. I've used Anthropic a lot personally. Even the language that it writes in English has this amazing smooth, silky feel to it. It just is a delight to read. They're among the most capable technical teams on the planet with world-renowned researchers. They're at the cutting edge of this incredibly hot but exciting field. And this is where we felt like combining our expertise with data, with over 10,000 customers, with the whole world putting its data on Snowflake, we thought it would be a great partnership to work with Anthropic to then be able to combine the power of Anthropic's models with the data in Snowflake.
And we have a unique arrangement with them, it's not just a vanilla partnership. We actually run Anthropic's models right within Snowflake's security parameter, which means that our customers do not have to worry that their data is getting sent or they're wired to some other data center in some other country. We guarantee that these models are running as part of Snowflake. And it's absolutely one of these, one plus one equals five kind of equations, because our customers get access to the best model on the planet, but also one that's running right next to the data. We are getting ready to launch it pretty quickly and we're super excited for how it's going to help us develop the next generation of AI applications on top of Snowflake.
Kristen Scholer:
I want to talk more about the models. Sridhar, the partnership is going to make Anthropic's newest Claude 3.5 models available for users to securely leverage within Snowflake Cortex AI and Snowflake's agentic AI products, which will leverage Claude as one of the key large language models powering these experiences. How does leveraging Claude enhance the value for customers and benefit their AI driven workflows?
Sridhar Ramaswamy:
Yeah, yeah, another great question. The fact of the matter is that the best models on the planet, Claude 3.5 is definitely one of them, have gotten so far ahead of the field and academic research around what makes good models. They're very good at generating code. They are very good at coming up with plans for how to solve problems. And as I said, the language that they generate is also, is just it's amazing. And being able to combine them within Snowflake Intelligence, our agentic platform means that it'll help decide which tool to use.
An Agentic platform, by the way, it sounds complex, is very simple. It has a set of chatbots with different capabilities. In the context of a company for example, you might have an HR chatbot that has access to all HR policies, but you might also have access to a Snowflake table that has information about your Salesforce, what they have been up to, how much revenue they have been driving. As a user, of course, you need to make sure that you have access to the data. But then what an agentic platform can do is when you ask a question, it can decide, do I want to consult the Salesforce data or do I want to go consult the HR data? Or, if people say they want to update some data, the platform is able to decide which tool to use. It is this kind of flexible tool, like tool picking, tool disambiguation, that Claude is very, very good at.
Claude is also amazing at generating user experiences on the fly. It's a little surreal until you have actually seen it, but you can ask Claude to generate a UI that is then a delight to navigate with. We are pretty excited about bringing all of these capabilities and then having Claude be the orchestrator for Snowflake intelligence, the Agentic platform, and to help it decide, which sub-components should it be using? How is it going to put these components together, and so on? So it is a perfect match for us.
Kristen Scholer:
Well, in November, Snowflake expanded its existing partnership, Sridhar, with Microsoft introducing a new Snowflake power platform connector for Microsoft Power Platform. How does this collaboration help customers simplify their data processes and leverage AI to address their business needs?
Sridhar Ramaswamy:
So we have a long and thriving partnership with Microsoft Azure and the Fabric team. We announced a couple of months ago actually, that we are going to make data more interoperable between Fabric, which is Microsoft's data platform, and Snowflake, so that customers can read data from each other's platform without having to do anything special. We also collaborate very closely with the Power BI team. Power BI is easily among the best business intelligence tools, probably the best business intelligence tool that is out there. And having it have tighter integration with our family of offerings from Snowflake is an ongoing effort for the two companies. And whenever we do things in AI, like Cortex Analyst and Cortex Search, we work closely with them so that they can be surfaced in any of the immediate surfaces that Microsoft has for example, in Office in addition to Power BI. So it's an active and thriving collaboration between the companies.
Kristen Scholer:
As Snowflake recently announced an agreement to acquire Datavolo, a company focused on accelerating the creation, management, and observability of data pipelines for enterprise AI. How does leveraging Datavolo into Snowflake enhance the company's existing suite of products and services?
Sridhar Ramaswamy:
Especially in the world of AI, the more data that you have in one place, especially a place like Snowflake, the more power you are going to get from it. Remember, all of us use applications at work, but it's very hard to move data from one application into another. It's sad, but even in 2024, a lot of us are literally cutting and pasting information from one app that we are using into another app in a different tab. Certainly, I do this every single day. Part of what we've been thinking about is that more and more, is we've been hearing from more and more customers that they want their data accessible in one place so that they get a global view, so they're able to look at things and juxtapose them, and really be able to run predictive analytics on them and really understand what that is about.
We decided to acquire Datavolo because the company has over 110 connectors to different kinds of sources, it's everything from databases. But just as importantly, two products like Google Drive or SharePoint, which is where a lot of unstructured data sits, and unstructured data is a key ingredient for AI products because remember, you are constantly looking through your SharePoint or your Google Drive to get answers to questions that perhaps someone else from your company has already answered or has already dealt with that topic. What Datavolo brings is a rich set of connectors that can pull from all of these sources and put them into Snowflake. It is going to enhance the value that our customers are going to get from this data.
We're super excited about the acquisition, that the acquisition has gone through and the teams are working together and racing to release the first version of Datavolo right within Snowflake. It should happen in a matter of weeks.
Kristen Scholer:
Well, Sridhar, in 2024, Snowflake introduced Snowflake Intelligence, a new platform that allows enterprises to ask business questions across their data to unlock data-driven insights. How will this technology transform the way the businesses interact with their data? And what distinguishes it from other AI-driven data solutions?
Sridhar Ramaswamy:
Well, I mean, first of all, Snowflake is known as the place where customers bring different kinds of data, whether it is data about their HR system, or supply chain, or sales, or anything else really about their business, into one place. As I said, that one pane view. What is unique about Snowflake is even if your data is sitting in different databases, you can join them, you can stitch them together, you can get that kind of a global view.
And with AI, as I said, we first announced Cortex AI, which is a way to do AI transformations from SQL or from Python, and then we introduced Cortex Search and Cortex Analyst, which you can think of as primitives to create chatbots for going against unstructured data and structured data. With Cortex Search, for example, you can search through a set of documents but be able to just answer, ask questions, and we'll give you sighted answers. So, not only do you get an answer right at your fingertips, but you know where the answer is coming from. Similarly, Cortex Analyst is for accessing structured data. As I was saying earlier, we made a chatbot for my CFO and me to be able to ask questions about sales performance within Snowflake.
What Snowflake Intelligence, the agentic platform does, is have a capability to bring different kinds of data sources together so that people can get this view. It doubles up as also a BI tool, so if you want to look at some data and visualize it, absolutely it does it. But what distinguishes it also is the ability to then take actions based on your data. You can actually go make updates to Salesforce data or to some other data using APIs that you already have access to as a company. And we are also envisioning that people will be able to write slick new user interactions, UIs as it were, in order to be able to update data, to look at structured data, and so on.
It's our ability to bring all of these together into a single product, but also make it super, super easy to set up and use, that I would say distinguishes Snowflake Intelligence from everything else. Part of what we pride ourselves in being able to do is to make complex technology simple and Snowflake Intelligence is no exception. We want to make sure that it's a delight to use both our administrators that are configuring it, but also for end users that just want to get their job done.
Kristen Scholer:
Sridhar, you recently wrote an article on LinkedIn titled, Predictions for 2025: How AI's Real World Value Will Come to Life. And in IT you wrote, "Those who integrate AI thoughtfully will drive efficiencies that free up resources for critical tasks, making 2025 an adapt or die year for organizations." For companies that resonate with your message for 2025, what strategies can they adopt to successfully implement AI technology without rushing or poorly planning its deployment?
Sridhar Ramaswamy:
I mean, first and foremost, I tell people that this is such a rapidly changing technology, that first and foremost, they should make sure that they're familiar with what is possible. A lot of people are intimidated by this technology, they're even afraid to try it.
I'll give you a simple example. I consider myself pretty proficient with using AI tools, and yesterday my son told me that if he wanted to convert a table, for example, in a paper that he's reading, he would just take a picture of it, upload it to a model, and have it pull out the structured information. And sure enough, somebody sent me a presentation today. I just took a screenshot of that, used an AI model, extracted the structured information, transformed it, and made a new view of the data, and it took me more time to describe what I did than to actually do it. It's like less than 30 seconds, and that's the magic. At one level, people, executives, but also mid-level managers, employees of companies, have just got to internalize what is possible and honestly, have fun doing it. I generate a lot of cartoons sometimes to my PR team's dismay, about various things that are going on in the world. It's just having fun. So I would say that level of baseline knowledge is important.
But the thing that we tell our customers is, AI needs to be a real enabler. Just spending money on AI for the sake of AI is just not useful. We always start with utility. I tell people, any place where you have a search engine, for example, an internal search engine, a chatbot is likely a better answer because it makes it easier to get at the data. Any place where you have a fixed and unwieldy dashboard, a chatbot like Cortex Analyst that can help you look at the data, slice it and dice it however you want and get different perspectives of it, is a clean, new application.
And then come the composite applications where you say, I'm going to take data from one place, put it into a different application. Those are almost second-order things. That's sort of what I tell our customers, which is, yes, some of this technology looks a little bit magical, but you don't need to know the internals of the magic to be able to get value from it. Familiarize yourself, try simple examples. You don't need to waste money building AI products, rapidly iterate on and build more useful things that your teams, your company gets value from, and the value will accumulate soon enough.
Kristen Scholer:
Sridhar, you also touched on the varying approaches to data privacy and AI regulation across different parts of the world. While we've discussed some of the risks of course that AI can bring with regulatory frameworks differing by region, what role should companies like Snowflake play in shaping global standards for the ethical use of AI?
Sridhar Ramaswamy:
Yeah, this is an important topic. We work with a lot of nonprofits of regulatory boards across the world to make sure that there are safeguards for the technology, that they're used in responsible ways. There's a lot of worry in Europe, for example, about the climate effects of using AI models. So it is important to use the smallest model that can deliver value for you, so that we are not wasteful. And absolutely having clear guarantees, for example, that customer data is not going to be fed into models or data is not going to get mixed up, which can cost both privacy problems, as well as business problems. So these are things that we deeply believe in that we make part of the product that we are building, and that we espouse as standards that all other companies should also be following so that the use of AI is not set back by heartless use of data.
Kristen Scholer:
Sridhar, as we wrap up our conversation and look ahead into 2025, how are you and your team mapping out the next 365 days for Snowflake so that the company continues to grow, innovate, and of course, remain successful?
Sridhar Ramaswamy:
So at one level, we think that data is even more important in the world of AI, so we have a number of initiatives that are going to be expanding the scope of what we do. For example, in last quarter's earnings, in Q3 earnings, we announced that our data engineering products, which is a new category that we have gotten into, hit $200 million in rendering. So we absolutely are working on broadening the base of Snowflake, the data that Snowflake is brought to act upon. And as I said, there are a ton of migrations happening from on-prem systems to cloud systems, because they are seen as more flexible and more cost-efficient, and so that drives a lot of strength that Snowflake has in its core.
With AI technology, Kristen, there are things that we know that we are working on. There are many components, for example, that are going into Snowflake intelligence, but an important part that you have to accept is that there will be new things three months from now, and so you have to be flexible about how you're going to adapt it. Certainly, integrating unstructured data sources, structured data sources within and outside Snowflake, APIs, and being able to create new user experiences or new background agents. These are all things that we have concrete plans for, but I'm pretty positive that we are going to add one or two or three more initiatives over the coming months.
Kristen Scholer:
Sridhar, I enjoyed our conversation. Thank you so much for joining us Inside the ICE House.
Sridhar Ramaswamy:
Thank you, this was a great conversation and I'm sure our listeners are going to have a lot of fun listening to it.
Kristen Scholer:
Absolutely.
Speaker 1:
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