Snowflake Summit 2025 Opening Keynote With Sridhar Ramaswamy, Sam Altman, And Sarah Guo

[Music] Please welcome Snowflake Chief Executive Officer Shredar [Music] Ramaswami.

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.

Woo. 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.

[Applause] 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.

Woohoo. 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 hyperfocused 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 silo 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 Astroenica who using their data data foundation to accelerate productivity across businesses and get critical drugs in the hands of patients that need them.

Let's take a look at their amazing story.

I'm here at Astroenica 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 Astroenica live in tomorrow's keynote.

Stories like Caterpillars and Astroenica 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 crack 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 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 Black Rockck build agentic tools that equip their client-f facing teams with innovative solutions for even stronger client engagement.

Trip Adviser 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.

Thompson writers 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, Rocketin, 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 chat bots 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 data tapping into them for real time market intelligence.

Auto 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 data wherever it sits and it empowers you to elevate business performance.

And of course, enterprisegrade performance requires enterprisegrade 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 IS5 authorization is powerful validation of the trust that's placed in our AI data cloud to support missionritical 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 a 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 highstakes 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% accuracy when it comes to AI chat bots.

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 life cycle.

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 Securonics 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 VOP 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 Snow Park.

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 worldclass 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 Ericson is laying the foundation for autonomous telecom networks.

They're using Cartex 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 Postgress technology to help developers build, innovate, and scale using the tools. Yay!

Postgress 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 Postgress.

We are doubling down on that momentum behind Postgress 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 way but no 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's 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.

[Applause] [Music] [Applause] [Music] 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. Um, 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.

Um, and so I always look at numbers.

So some benchmarks that I look at and shred you mentioned this in your intro are the amount of incoming order messages to our platform.

The amount of traffic y going through our platform every day.

A peak day for us in 2022 now this is on the back of co 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.

Um we can't do that without having incredible technology.

um 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 uh 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.

Yes. 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, um, it's not just the New York Stock Exchange and the core matching engines.

It's our other businesses, our business, 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.

um 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.

Yep. 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 um 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 dovetales into our system functionality.

Humans can do some of that.

Yeah. 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 ah so the search capabilities for example that you all provide with uh 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?

Uh when it comes to AI, I 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 um LLMs, so um 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 uh 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.

uh we've been more deliberate about okay we know we can get efficiency from natural language processing from sharing um sharing information across our mortgage data footprint for example to make the process of buying a a a house a lot more efficient for the end user. So if you think about where are the big problems Yeah.

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 inviable and then look for where you get leverage from that.

Yep. You'll get a ton of efficiency from following that kind of approach.

Yeah. And it'll be very deliberate.

Well, thank you, Lynn.

Folks, please give Lynn a huge round of applause.

so much. 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 codew 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 open AI. Through the explosive rise of chat GPT 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 open AI 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 Open AI, Sam Alman.

[Music] [Applause] [Music] 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. Um, 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, um Samar, what advice would you have for enterprise leaders navigating the AI landscape in 2025?

I think just do it. Like the 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 that thing A or thing B.

I I I as a general principle of technology when things are changing quickly, the companies that have the quickest iteration speed um 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'll 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 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 chat GPT 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 application like chat bots 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 usely I I I wouldn't have quite said the same thing last year um I would have said the same thing to a startup last year but to like a big enterprise I would have say like I I would say like uh you 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 and 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 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?

Um 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 are 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 I think you know given reasoning and applying more compute to hard problems and you know uh the introduction of agents into some workflows uh 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 GPD3, 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 with 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 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 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 upbound 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.

Um 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 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 uh 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 codeex and experiencing you know coding agents uh was a moment you felt the AGI.

So I have to ask you about that like what is the what is the definition of AGI to you now and and um how far away are we from it what will that mean for us?

Um, I think if you could go back to most people, if you could travel back in time, just five years, 2020, let's say, uh, it's like the dark ages for AI, though.

Actually, that's a very interesting time because I I think that was, if we could go back exactly 5 years, I may get this wrong, but I think that was just before we launched GBT3.

Okay. So, the world had not yet seen like a good language model.

And if you could go back to that moment and show someone chat GBT today to say nothing of codeex or anything else but just chat GBT uh I think most people would say that's AGI for sure. Mhm. And you know so we're great at adjusting our uh expectations which I think is like a wonderful thing about humanity.

Um I think mostly the question of what AGI is doesn't matter.

It is a term that people define differently.

The same person often will define it differently. Um the the thing that matters is the rate of progress that we have seen year-over-year over the last 5 years should continue for at least the next five probably well beyond that but hard to say. And whether you declare the AGI victory in 24 or 26 or 28, um, and whether you declare the super intelligence victory in 28 or 30 or 32 is way less important than this one long beautiful, shockingly smooth exponential.

Um, all of that said, to me, a system that can either autonomously discover new science or be such an incredible tool to people that our rate of scientific discovery in the world like quadruples or something.

Um, that would that would satisfy any test I could imagine for an AGI.

Some other people would say it's got to be a system capable of self-improvement.

Plenty of people would say like chatbt with memory today very AGI like certainly across some of our early tests like touring test that people used to say was the was the target.

Um, okay. Scrolling back to 2020, Sudar, do you remember what the first OpenAI model you used when you were building search was what year?

We were actually using uh GPD3 playground and running little experiments with it and then with the and then with APIs we couldn't afford uh GPD3 or running it at web scale.

So we basically reverse engineered how we could do this with 7 billion 10 billion parameter models.

Yeah. But already you could see greatness for me when you saw this problem called um abstractive summarization actually get tackled nicely by GPD3. This is basically taking a blog that's 1500 words and writing three sentences to describe it.

It's really hard. People struggle with doing this and these models all of a sudden were doing it. That was like a bit of an aha moment for wait. If you could do this on the entirety of the web car corpus, you of course have search which can figure out which 10 pages to look at.

That was a bit of a aha moment when it came to oh my god there is incredible power here and of course it's kept adding up.

At at what point in you know your journey as an entrepreneur or a CEO of Snowflake did you think like wow I I mean I uh employ a former NEVA person as well and part of my premise was like everything is search or search plus in in this era. Did when did you have that thought?

It's about setting context.

Um once you look at and interact with these models or think about any problem, you also want to have a way to narrow down the lens of what you want it to operate in.

And it's a very powerful and generalizable technique.

Even if you look at many of the post-training techniques that have come, it's a little bit of okay, take this incredibly powerful model, give it context for what's worked, what's not work, and use it to improve what it is is producing.

I would say it's it's more a general concept than a specific tool for how do you make something happen. It's all it's all about the right context setting.

There's always an infinity of context.

Humans solve it by what we call attention, where we focus on something.

I think of search as a tool for setting attention for a model. And do you agree with Sam that it's really just, you know, being on this exponential capability curve or is the is there a definition of AGI that matters to you or matters to customers? I think it becomes a matter of debate like Sam is saying in uh I think sometimes it's also a philosophical question that I would liken to I don't know does a submarine swim?

um at one level it's absurd but of course it does and so I see these models as having incredible capabilities that we will like any person looking at what things are going to be like in 2030 would just declare that's that's AGI but remember you and I would to Sam's point would say the same thing in 2020 about what we are seeing in 25 to me it's the rate of progress that is truly astonishing and I sincerely believe that many great things are going to come out of it.

And similar to again, how do we feel about the fact that a pretty decent computer can beat every person in the world that can play chess?

Doesn't matter. We still have people that play chess that are very, very good at it.

So, I think the definition matters.

What's it? It's more popular now.

It's more popular now than before.

Um, it's the same for Go. So, I think there is a lot that we will learn from it.

The actual moment, I don't really think matters a whole lot.

Um, I have a hunch personally that when people ask about AGI, I think they're really asking about consciousness.

They just don't always frame it that way or at least some large subset is, which is like a more, as you said, philosophical question.

Um, I have to ask you because we have you, you're training more models.

You know, you see the next capabilities before anybody else does.

uh what emergent behaviors are you seeing in the next set of models that change you know how you operate what you want to build from a product perspective how you're running open AAI yeah the the models over the next year or two years are are going to be quite breathtaking um really there's a lot of progress ahead of us a lot of improvement to come and like we have seen in the previous big jumps you know from GBT T3 to GPT4, businesses can just do things that totally were impossible with the previous generation of models.

And and so what an enterprise will be able to do, we talked about this a little bit, but just like give it your hardest problem, if you're a chip design company, say, "Go design me a better chip than I could have possibly had before.

" Um if you're a biotech company trying to cure some disease, say just go work on this for me. Like that's not so far away.

Uh and these models ability to understand all the context you want to possibly give them, connect to every tool, every system, whatever, and then go think really hard like really brilliant reasoning and come back with an answer and and have enough robustness that you can trust them to go off and do some work autonomously.

Like that that I don't know if I thought that would feel so close, but it feels really close.

Is there any intuition you can give everyone here uh for like what knowledge is in scope or soon to be in scope because I when I think about core intelligence I'm like well you know I'm reasonably smart but I don't have a perfect physics simulator in my head.

So like how should I know what's what's possible?

The the framework that I like to think about this is not something we're about to ship but like the platonic ideal is a very tiny model that has superhuman reasoning capabilities.

It can run ridiculously fast and one trillion tokens of context and access to every tool you can possibly imagine. And so it doesn't kind of matter what the problem is.

Doesn't matter whether the model has the knowledge or the data in it or not.

Like the model using these models as databases is sort of ridiculous.

It's a very slow, expensive, very broken database.

But the amazing thing is they can reason.

And if and if you think of it as this reasoning engine that we can then throw like all of the possible context of a business or a person's life into and any tool that they need for that physics simulator or whatever else.

That's like quite amazing what people can do and I think you know directionally we're headed there.

Uh amazing. I um want to ask both of you uh for a more like conjecture question.

If you had a thousand times more compute, the the original thought was infinite, but that gets silly.

A thousand times more compute, what would you do with it?

I mean, I guess the super meta answer, I will give a helpful one after this, but maybe the real answer is I would ask it to work super hard on AI research, figure out how to build like much better models, and then ask that much better model what we should do with all the compute.

Doing your hardest problem.

Well, I mean, I think that's I think that would be the rational thing to do.

Um well it means you really believe the answer.

You do have to really believe that I I think the more helpful thing I would say is we see all of these cases now inside of chatbt or inside of enterprises that are using our latest models where there are real returns to test time compute.

You know if you let the model reason more if you try more times on a hard problem you can get much better answers already.

And a business that just said, "I'm going to throw a thousand times more compute at every problem would get some amazing results.

" Now, you're not literally going to do that and you don't have a thousandx compute.

But the fact that that's now possible, I think does point to an interesting thing people could do today, which is say, "Okay, I'm going to like really treat this as a power law and be willing to try a lot more compute for my hardest problems or most valuable things.

" Uh streetar, do you just do the same thing with snowflake and whatever your hardest problem is or you've built this amazing career of you know data infrastructure search optimization running snowflake is that is that just ask the question I think that would be a pretty cool way to use a lot of compute but just to give a an answer that's different from the world of tech that we live in you know there's a project called the Arnum project it's like the DNA uh sequencing project that we did 20 odd years ago, but it's about figuring out RNA expression. Turns out they control pretty much how proteins work in our body.

And a breakthrough there, knowing exactly how RNA controls DNA expression, it's likely to solve a ton of diseases and put humanity forward so much more.

That would be a cool use of basically the equivalent of the DNA project done with with language models.

That would be a pretty cool outcome if you have a lot of compute to throw at something.

Inspiring. and one of our, you know, humanity's biggest problems. Thank you so much, sweetheart.

Thank you. Thank you, Sarah.

Thank you.

Thank you. I couldn't be more energized about the future.

We are at a defining moment where AI and data are converging to fundamentally reshape how we work and how we solve global challenges.

And data is the foundation of this transformation.

And this perspective matters far beyond business.

From optimizing cell coverage at massive events to enabling city governments to respond to real-time weather data.

Data is powering the experiences that are shaping our daily lives.

And when it comes to the ultimate test of coordination, precision, and global collaboration, it's hard to imagine a greater example than the Olympic and Parolympic Games.

And that's why I'm incredibly proud to share that Snowflake is the official data collaboration partner for the Olympic and Parolympic Games and Team USA.

Together, we'll help deliver a seamless data sharing and collaboration experience for one of the world's most iconic events, uniting athletes, fans, and partners across the globe.

to share more about our partnership.

I'm excited to welcome LA28 chairperson Casey Werman and CEO of the US Olympic and Parolympic properties John Slusher to the stage. Casey.

[Applause] [Music] John.

How you doing?

Casey, John, excited to have you.

Olympics are amazing and we're thrilled to have you here and be your partner.

Casey, it seems like uh every four years the bar gets set way higher, exponentially higher for the Olympic and Parolympic games.

You deliver an incredible sporting spectacle in terms of athletic performance, spectator moments, and in the way these global events come to life in front of us. So, we are thrilled to be a part of it. But tell us what can we expect from the LA28 games? Oh.

Oh, first I want to say thank you.

um your obviously sponsorship uh of Team USA and LA28, but also the support you will give us in actually delivering those games uh are really instrumental. And I just want everyone in this room to know that the entire Olympic movement in this country is privately funded. So the dollars from snowflake when you watch an American athlete in Milan Cortina in February of next year or LA in 2028 in the Olympics and the parolympics just know that their performance their success is directly linked to investments like this.

So thank you for that. We appreciate that.

Um, look, the Olympics are truly the greatest spectacle on earth. And, uh, we think, uh, the Olympic Games coming back to America is, uh, really special.

The first time the Summer Games coming back since 1996.

So, you're going to see without a question the greatest collection of athletes in one city the world has ever seen.

Um, more competitors in more incredible venues in a city that's used to telling stories to the world.

And we think all of that is really an incredible platform um to showcase and shine a light on these incredible athletes, their incredible stories.

Uh and we are thrilled as a as a leadership team and as a city to be able to uh be the stewards of that and uh and we certainly are excited to have partnerships like this to make it possible.

Thank you. Thank you.

John, you're leading commercial and sales business for the upcoming Olympic Games and uh you previously headed global sports marketing at Nike. So, you have seen firsthand how data collaboration can shape the world of sports. So, as we look ahead to the LA28 games, why does data collaboration matter more than ever?

Yes, and just want to reiterate uh what Casey said is huge thanks to you and the entire Snowflake team.

Uh the partnership is incredibly important to us and incredibly appreciated and especially in the area of data collaboration.

Um so crucial.

We are going to run one of the most, if not the most complex sporting event in the history of the world. Um, we've said this before.

I think Casey often says it's seven Super Bowls a day for 17 consecutive days when we talk about the complexity of this.

It's really unbelievable. And the line I like to use is if you think about it's the biggest sporting event in the history of the world and and just think about how long the world has been around, how many sporting events there are, and this is by far the biggest, by far the most complex.

And having partners like you that we can trust um to help us achieve all of our goals incredibly important.

whether it's helping the athletes achieve at a higher level like Casey mentioned, whether it's uh all the logistics we have to do. And then just one example, uh if there are some San Francisco 49er fans here, uh they'll sell about 600,000 seats to a season.

We're going to be selling 12 million tickets and it's over a 17-day window.

And when you think about how you can help us understand those consumers and which fans want to attend which event on which day, how they can get there.

Is it a high price ticket one location, a low price ticket another location? But as we try to work the complexity around just that example, but so many others, having a partner like Snowflake is going to be incredibly important as we try to pull off and and will pull off really the greatest sporting event in the history of the world, which is which is super exciting.

That's amazing. And it's also a matter of national pride for all of us to be able to host such an event at this scale.

We are thrilled to be a part of it.

Well, Casey John, we're thrilled about our partnership and we have a little something of a gift for you.

So, I'm going to welcome on to the stage our chief marketing officer, Denise Personson.

[Applause] Denise, you thank you.

Well, at Snowflake, we celebrate every special moment with a custom snowboard, and this is definitely one of these moments.

I know Team USA has no shortage of snowboards, but this one is really a symbol of our commitment, excitement, uh, on the journey ahead towards LA 28.

That's awesome. Thank you. And all the snow is missing. Oh, look at that.

There you go. Thank you very much. Thank you.

Yeah, we're truly honored to be a part of this journey with you and we just can't wait to see what we're going to build together.

Thank you. Thank you.

Woo.

Well, Denise, thank you so much.

Um, maybe Team USA has a lot of snowboards, but LA, we don't have a lot of snowboards in LA for the Olympics, so you'll be one of one, I promise.

So, we're used to getting surfboards in LA.

Um, and we have something for you.

Our team uh has created these custom LA28 Nikes.

Wow. There we go.

It's a big moment. Thank you.

Amazing. Wow. Thank you so much.

Thank you. As a small token of our appreciation, we are proud to be on this journey with you and together we're going to make history. Thank you.

Thank you. Thank you. Thank you.

Thank you, John. Thank you, Casey.

Thank you, Denise. Thank you very much. Thank you.

Thank you.

Gosh, what an act to follow.

Thanks again, Casey, John, and Denise.

I can't wait to see this come to life over the next few years.

Everyone, it's been amazing to be with you today.

Let me leave you with this.

Snowflake is the destination where data does more for your end to end data and AI journey.

We help power your critical business operations.

We simplify complex processes to accelerate your innovation and we partner with you to fuel AI transformation and help you achieve your potential.

We have an incredible week ahead.

Major product announcements, deep dive sessions, customer stories, partner showcases.

This community is here to build what's next together.

Thank you all for being here.

We'll see you tomorrow. Woo!

[Applause]