Hello, Data Nation. Welcome to Snowflake Summit. The energy in here is electric. And I have to start by simply saying thank you. Thank you for being here with us.
We have people from all over the world representing every industry imaginable coming together to do more with their data. And doing more with data is not just about the technology. It's about learning and being inspired. It's about pushing one another forward. It's about working together to build a culture around data and AI within your organizations. This is our biggest summit yet, and I'm confident it's going to be the best.
This week we have the privilege of hearing from many of you, our customers. You're the heart of this community. Of the 522 sessions this week, 482 of them will feature your stories, your learnings, and your successes. We are also joined by over 3,000 incredible partners. Our partners play a critical role in expanding what's possible with Snowflake. They help our customers get things done. They help us all go further faster with Snowflake. And of course to our superheroes, big thanks to our developers, our builders. Thank you for pushing boundaries and for pioneering us all forward. We can't wait to learn from you this week.
It's amazing for me to be here with you today. I'm 15 months into my time as CEO at Snowflake and already it's been the most amazing experience. When I first joined this company, one thing stood out to me immediately: Snowflake's unwavering obsession with its customers. It's part of what made me want to be a part of this company's journey. Over the past year, I've been hyper-focused on two things. The first is listening and learning from all of you. I've spent a lot of time on the road getting to know many of you, hearing about your challenges, your successes, all to understand how Snowflake can better serve you.
And the second thing that I've been focused on is innovating. Putting your feedback into action, making AI come alive. We are innovating at a pace faster than any other company on the planet. And I couldn't be more proud of our progress. You're going to be blown away by our new capabilities and the road map. I won't spoil it for you, but you do not want to miss tomorrow's platform keynote right here.
At Snowflake, our mission is to empower every enterprise to achieve its potential with data and AI. AI is fundamentally changing the way we work, the way we operate in our daily lives. And it's early. It's going to keep changing. The world's hardest and most ambitious ideas, from personalized medicine based on your genetic data to autonomous factory floors, even virtual shopping experiences, these things aren't really science fiction anymore. They can become realities through the power of data and AI. And we are here to help you get there.
At Snowflake, we help remove friction, break down silos, and make the complex feel effortless. We help you unlock outcomes with data that were previously unimaginable. Snowflake is the place where data does more.
Take a company like Caterpillar. It's a century old. It's an iconic company. They've been building machines and engines for over a hundred years. Today, Caterpillar is using data to make their operations even smarter. They're using the Snowflake AI Data Cloud as part of their digital platform to create a unified view of customer and dealer operations. Turning siloed data into real-time insights. They're building capabilities that allow their customers to monitor and manage equipment availability, utilization, service, and so much more, all in real time.
And then there's pharmaceutical leader AstraZeneca who, using their data foundation, is accelerating productivity across businesses and getting critical drugs in the hands of patients that need them. Let's take a look at their amazing story.
I'm here at AstraZeneca to develop new medicines that can help treat and maybe someday cure diseases like cancer. We have 118 data products out there now that is now releasing and unlocking the value of, I would say, thousands of hours in terms of productivity and over $10 million in terms of savings to us. The data tells us that lung disease, we have early detection, we are able to improve survival rates by 90%. Faster innovation means faster breakthroughs in our science and that means greater outcomes for our patients.
Amazing. That's an incredible story. And we'll hear even more from AstraZeneca live in tomorrow's keynote. Stories like Caterpillar's and AstraZeneca's are enabled by the way we design our technology. That's our mission in action. That's what it looks like when your data does more.
Now, it's no secret that I'm a bit of a geek. Anyone who knows me will tell you I love tinkering. I love building. I love experimenting. But the true magic of a great technology is taking something that's very complicated and making it feel easy. The key to a great solution is simplicity. You can choose checkboxes on functionality, string together solutions, and pass along all of that complexity to all of you, the end users. The cracks show. Complexity creates risk. Complexity creates cost. Complexity creates friction and makes it harder to get the job done. Whereas simplicity, it drives results. And that's why we hold simplicity at the heart of how we design products, how we design Snowflake.
We give you an AI Data Cloud that's easy to use. We make it easy to implement. We make it easy to share, collaborate, use, and build with data. We make it easy for you to get value from your data, whether you are a data analyst or a data scientist or a business user. And that simplicity has never been more important than it is right now.
In the age of AI, you should be able to ask a question with a voice memo and get an answer on your enterprise data. You should be able to launch a customer app without having to write a line of code. You should be able to harness the world's best AI models to create agents that are tailored for your business. And that's how simplicity drives innovation. And that's why Snowflake AI is designed for ease of use.
We've helped companies like BlackRock build agentic tools that equip their client-facing teams with innovative solutions for even stronger client engagement. TripAdvisor uses Snowflake AI to cut through the noise, filter out unwanted spam so they can focus on real customer feedback and act on it faster. Thomson Reuters launched AI-powered agents that deliver real-time insights to business users, removing technical dependencies and bottlenecks that got in the way. And Penske built a summarization model in just weeks that improved not only supply chain performance but also the safety of all of their associates. Thousands of you are using the Snowflake AI and machine learning to accelerate things from insurance underwriting to analysis of investor reports.
Underpinning all of these results is a strong, capable data foundation. I've said it before, there is no AI strategy without a data strategy. Data is the fuel for AI. And Snowflake's AI Data Cloud is powered by a connected ecosystem of data. We have thousands of customers sharing data, apps, and models with one another on our Snowflake Marketplace. We have over 3,000 listings from over 750 partners. Your data spans teams, departments, even organizations using Snowflake. Industry leaders like Stripe, Rocket Mortgage, State Street, and more are securely sharing data with their partner and customer ecosystem. And in many cases, they have hundreds of live connections. These connections can now be enriched with AI chatbots to surface proprietary data faster and more efficiently, all using the power of conversational language. And that's the power of the AI Data Cloud.
It is a financial firm like S&P Global tapping into them for real-time market intelligence. A media titan like NBC Universal using Snowflake Data Clean Rooms to securely share and analyze data from across its businesses to help advertisers reach the right audience without compromising end-user privacy. That connectivity creates fluid access to data wherever it sits and it empowers you to elevate business performance.
And of course, enterprise-grade performance requires enterprise-grade trust. Every example I have shared is encased with built-in governance, always-on security, and automated compliance and recovery. Our recent Department of Defense IL5 authorization is powerful validation of the trust that's placed in our AI Data Cloud to support mission-critical government as well as defense workloads. And already we have leaders like our own city of San Francisco using Snowflake to get a unified view of their data to make informed decisions that better serve local constituents.
And New York City Health and Hospitals use Snowflake to get a holistic view of their patients so that they can deliver faster, better care to vulnerable populations, including the homeless. We know these are high-stakes scenarios, but you need best-in-class security and governance when it comes to sensitive and personal information, and that's what Snowflake provides. You need data you can trust. That's why Snowflake has placed such a huge emphasis on retrieval quality and accuracy in AI. It's why Cortex has a market-leading 90-plus percent accuracy when it comes to AI chatbots. And we're not stopping there. We know you need to move ahead with confidence that the right people are using the right data for the right purpose.
With Snowflake, we help you do more with your data end to end. From the inception of data to getting insights from it, we help you at each stage of the data lifecycle. Early on in the data journey, we helped companies like BPX Energy bring together analytical and transactional data into a single platform, empowering their petroleum engineers with a complete view of well data so that they can optimize development. And with Snowflake connectors, organizations like WorkWave and Securonix can ingest real-time events and transactions, transform them on the fly, and make that data analysis-ready instantly.
We also give you flexibility and control of your data. Snowflake supports modern open table formats. And now you can take advantage of the incredible Snowflake capabilities in the core, like data sharing, security, performance optimization, using Apache Iceberg. And companies like Booking.com, Illumina, Komodo Health, and Veeva are leveraging this flexibility to manage and query data at scale without compromising on security or performance.
From there, you can streamline and scale your data pipelines. Chicago Trading Company, for example, moved its transformation workloads into Snowflake using Snowpark. And by this consolidation, they saved 54%, amounting to millions of dollars annually. Once your data is in Snowflake, we help you unlock its full value with world-class analytics. BMW Group's decades of vehicle service data now runs on Snowflake, enabling dealerships and garages to instantly access service histories, work more efficiently, and deliver faster repairs to customers.
And AI takes it one step further. Using Cortex, global telecom leader Ericsson is laying the foundation for autonomous telecom networks. They're using Cortex to detect anomalies in real time, resolve them using agentic orchestrators, and streamline customer support along the way, improving both reliability as well as response time. And it's not just about what you do today. We help you figure out what's next. This is why I'm excited to share that Snowflake has agreed to acquire Crunchy Data, a leading provider of open-source Postgres technology, to help developers build, innovate, and scale using the tools they love.
Postgres is truly amazing and it's going to be right inside Snowflake so that you can get the performance, the governance, and scale that Snowflake is known for with the familiarity and openness that comes with Postgres. We are doubling down on that momentum behind Postgres and investing in its future while preserving the openness, the extensibility, and developer-first ethos that make it great.
Your data does more with Snowflake. No matter the industry, no matter the challenge or the opportunity ahead of you, we are helping our customers push the boundaries of innovation at each and every stage of the data and AI journey. And there's no better way to demonstrate that than from hearing from one who truly exemplifies what you can do by moving to this data-first mindset. When you think of instances where data is critical, you think of the New York Stock Exchange. Every day they manage staggering volumes of data that power global markets and impact millions of people and businesses. With Snowflake, they're not just managing the data, they're unifying it, collaborating across their ecosystem, and using AI to streamline operations and shape the future of trading. They're a true pioneer, and I can't wait for you to learn more about their story. Please welcome to the stage, president of the New York Stock Exchange Group, Lynn Martin.
Thanks so much for having me. Look at this crowd. The scale, this energy, it's unimaginable.
Oh, absolutely. Wow. Thank you for being here with us today. We've been partnering together for over six years now and I'm thrilled that you're here to share what we have done with all of our guests here. To start with, can you give us a perspective on where the New York Stock Exchange was a few years ago and what you set out to achieve with data and AI?
Yeah. You know, sometimes I take two steps back and think where we were 10 years ago, 6 years ago, and then even three and a half years ago when I took my seat. And I'm a data person. I have data running through my veins. And so I always look at numbers. So some benchmarks that I look at, and Sridhar you mentioned this in your intro, are the amount of incoming order messages to our platform. The amount of traffic going through our platform every day. A peak day for us in 2022, now this is on the back of COVID, was about 350 billion incoming order messages a day. In April of this year, we hit new records of 1.2 trillion incoming order messages a day. So from our perspective, we have two incredibly important jobs. One is around market integrity focused on transparency and the other is around efficient risk management. So ensuring that those messages get processed incredibly efficiently so that people know the risk in their portfolio. We can't do that without having incredible technology, AI, good technology that enables us to match those trades really quickly, but then importantly surveil our markets afterwards to ensure that we catch any nefarious behavior in our markets.
That's, you know, that's really great to hear. And that transition that you talk about from where you were even three years ago to here sounds pretty incredible. That's more than 3x. I know that one of your key focus areas has been to drive innovation with the exchange, but also throughout the ecosystem. You've done a lot of investments and obviously these investments came in handy as we started hitting record peaks this year. So, how are you continuing to drive this innovation forward and your investments, where are they yielding results in the ecosystem?
Yeah. So, it's not just about having really good hardware. You know, we've gone through a big hardware and platform refresh, which we completed in 2023, which had we not done that, we wouldn't have been able to process those messages. But now, as I think going forward, it's not just the New York Stock Exchange and the core matching engines. It's our other businesses, our fixed income and data services business, our mortgage tech business. And for us, it's all about the sanctity of data. Because what really powers AI is that good source of truth. If you don't have a source of truth, you're going to have unfortunate outcomes. Now, we've been working with you all for more than six years. Initially just from a data storage and then a data sharing and now more recently with Cortex and the analysts and search and LLM and all of those additional tools because the amount of volume of our data keeps growing exponentially and we are very focused on protecting the sanctity of that data, the privacy of that data. So for us, it's about how do we leverage our infrastructure and put really good AI-enabled tools on top of that infrastructure to allow us to parse through this voluminous data set across all of our businesses.
Yeah. Yeah. Yeah. Well, what's also unique about you is, like many of our customers here, you operate in a tightly regulated industry. What advice would you have for people from financial services or healthcare that are working their way through AI addendums trying to figure out how to get their folks to approve them?
Yeah, you know, that's where there have been some use cases that AI has demonstrated its efficiency. Now, parsing through rule sets, if I look back on the amount of rules that have been layered on top of at least the exchange, you know, we've had probably a 40% increase in rules over the last 10 years, right? And they've not gotten simpler, they've gotten a lot more complicated. So, trying to figure out how that dovetails into our system functionality. Humans can do some of that. But humans need good technology. Humans need AI in order to do that.
So you see that as actually an ally in being able to navigate these regulations.
Absolutely. Can do things that, so the search capabilities for example that you all provide with Cortex have been incredibly helpful there.
Interesting. Interesting. Well there are lots of enterprise leaders here. They want to do great things like you have. What advice would you have for them?
When it comes to AI, we have developed a very deliberate approach. We always come back to our principles of market integrity and transparency. So we've been using a version of AI, LLMs, so not generative AI, for more than a decade to add transparency to our markets. We continue to be very deliberate and not stray from our two core principles around transparency and market integrity. When you stick to your core principles, the use cases will follow when rolling out AI. It's not very difficult to come up with those targeted use cases. And we've been more of the approach of not throwing AI against a wall and seeing what sticks like spaghetti. We've been more deliberate about, okay, we know we can get efficiency from natural language processing, from sharing information across our mortgage data footprint for example to make the process of buying a house a lot more efficient for the end user. So if you think about where are the big problems and you layer that alongside what your principles are, your use cases will follow.
I love that. Make sure you follow and enunciate the principles that you care about that are inviolable and then look for where you get leverage from that. You'll get a ton of efficiency from following that kind of approach. And it'll be very deliberate. Well, thank you, Lynn. Folks, please give Lynn a huge round of applause.
Thank you so much. Thanks for having me.
It's remarkable to see a cornerstone of global finance like the New York Stock Exchange embracing innovation and setting the pace for the future. As we look ahead, we know AI has the potential to shape the future and shape it for the better. Generative AI has unlocked everything from code writing to philosophical reasoning. That journey started with foundation models. They've enabled billions of users to tap into the power of data they never would have had access to. They have changed the game for us all. And few have shaped that future more than OpenAI. Through the explosive rise of ChatGPT, used by over a billion people daily, they've redefined how we interact with data and intelligence and how we imagine the future of work. And at the center of the transformation is one of the most influential voices in technology today. He's led OpenAI from groundbreaking research to products now impacting billions, driving the evolution of AI on a truly global scale. Please join me in welcoming the founder and CEO of OpenAI, Sam Altman. And joining us to moderate the discussion, the founder and managing partner of Conviction and a good friend, Sarah Guo. Thank you. Welcome, Sam and Sarah.
Well, it's amazing to be back. I was saying in a street art, this looks like a rock concert, but for data people.
For data people. Yeah, you were here two years ago.
Yes, but it wasn't as amazing. So, to kick us off, Sam, what advice would you have for enterprise leaders navigating the AI landscape in 2025?
I think just do it. Like, there's still a lot of hesitancy and the models are changing so fast and there's all this reason to wait for the next model or you're going to sort of like wait and see if this is going to shake out this way or that or if you should build, you know, with thing A or thing B. I, as a general principle of technology, when things are changing quickly, the companies that have the quickest iteration speed and sort of make the cost of making mistakes the lowest and the learning rate the highest win. And certainly what we're seeing with enterprises and AI is the people that are making the early bets and iterating very quickly are doing much better than the people that are waiting to see how it's all going to shake out.
Straight. What would you say? I can't agree with that more. And the thing that I'll add on is curiosity. I think there's so much that we take for granted about how things used to work that just aren't true anymore. And going and experimenting, and lots of people, OpenAI, Snowflake have made the cost of experimenting very, very low. You can run lots of little experiments, get value from it, and build on that strength. I want to echo what Sam said again, which is it's the folks that can iterate the fastest that are going to get the most value from things because they know the things that are going to work, the things that are not going to work. They can navigate this rapidly changing future. There's never going to be, I think, not in the next few years, one perfect moment where everything is settled down and we can then figure out what we do.
How would your advice differ from what you would have said last year?
I think I'd have said the same last year in terms of I think curiosity is the most overlooked thing and I think it's fine to make mistakes. You need to figure out situations in which it
It doesn't matter that much and there are lots of situations like that. But the technology is also rapidly maturing. You know you can absolutely use ChatGPT now for getting information about the latest events because it knows when to use web search to provide that. And so there are lots of applications like chatbots, whether it's structured or unstructured data, the technology is mature, you can adopt it. Yes, you can always push the boundary on what else you can do with it. There are edgier agentic applications, but far away from the frontier, I think this technology is actually ready for mainstream use. I wouldn't have quite said the same thing last year. I would have said the same thing to a startup last year, but to like a big enterprise, I would say like you can experiment a little bit, but this is maybe not totally ready for production use in most cases. And that has really changed. Our enterprise business has gone like this. And we talk to big companies who are now like really using us for a lot of stuff and say like what's so different? And we're like, did it just take you a while to figure it out? And they say that was part of it, but it just works so much more reliably. It works, you know, it can do all these things that I just didn't think were going to be possible. And it does seem like sometime over the last year we hit a real inflection point for the usability of these models. Now an interesting question is what will we say differently next year? And I think we'll be at the point next year where you can not only use a system to sort of automate some business processes or build these new products and services, but you can really say I have this hugely important problem in my business. I will throw a ton of compute at it if you can solve it. And the models will be able to go figure out things that teams of people on their own can't do. And the companies that have gotten experience with these models are well positioned for a world where they can say okay, you know, AI system, whatever, go, you know, like redo my most critical project and here's a ton of compute, think really hard, just figure out the answer. People who are ready for that I think will have another big step change next year.
I think, you know, given reasoning and applying more compute to hard problems and, you know, the introduction of agents into some workflows, there's a view that memory and retrieval have to change a lot. What do you think is the role of memory and retrieval in this, you know, this next era?
I think things like retrieval have always played a key role in making generative AI technology grounded when it needs to be grounded. If you're asking a factual question, you want a reliable answer. So, you know, on GPT-3, we built web search scale systems back in early 2023. So, whenever you asked a question that needed a reference point from the real world to be able to answer like breaking news for example, you could provide that context. Similarly, knowing how you have tackled certain problems before, memory, your interactions with a particular system can greatly influence and make that system better for the future. I think their role will continue to increase as you use these models for more and more interesting tasks. And the more context you have, I think the better these systems get, both from an interactive perspective, but also from an agentic perspective.
Sam, is there a framework you can give every leader here to think about like what can agents do today and next year?
Um, I mean the coding agent we just launched called Codex has been one of my like feel the AGI moments. You like watch this thing. You can give it a bunch of tasks. It goes and works in the background. It's really quite smart. It can do these long horizon things and then you get to just sit there and say yes to this one, no to that one, try again. And it is able to just kind of like connect to your GitHub and you know at some point it'll be able to also watch your meetings if you want and look at your Slack and read all your internal documents and it's just doing incredibly impressive stuff. And you know maybe today it is like a sort of intern that can work for a couple of hours but at some point it'll be like an experienced software engineer that can work for days. And then we'll see this for many other categories of work. And so you see you hear from companies that are building agents to automate most of their customer support or their outbound sales or any number of other things. And you hear people that talk about their job now is to assign work to a bunch of agents. Look at the quality, figure out how it fits together, give feedback, and it sounds a lot like how they'd work with a team of, you know, still relatively junior employees. And that's here. It's not evenly distributed yet, but that's happening. Um, I would bet next year that in some limited cases, at least in some small ways, we start to see agents that can help us discover new knowledge or can figure out solutions to business problems that are kind of very non-trivial. Um, right now it's very much in the category of okay, if you've got some like repetitive cognitive work, we can automate it at a kind of a low level on a short time horizon. And as that expands to longer time horizons and higher and higher levels, you know, at some point you get an AI scientist, an AI agent that can go discover new science and that will be kind of a significant moment in the world.
Uh you said it was a moment, you know, Codex and experiencing, you know, coding agents was a moment you felt the AGI. So I have to ask you about that like what is the definition of AGI to you now and how far away are we from it? What will that mean for us?
