The Exchanges

Q you think about, um, building your own models, which you've done, uh, to some extent with Arctic or to a very large extent with Arctic, uh, versus partnering? So you mentioned, uh, OpenAI and Cloud, and you just announced, uh, in the last few weeks, uh, major either partnership or expansion of partnerships with both. Microsoft to deploy OpenAI models, and Enthropic to deploy Claude. How does that all work?

A We got out of the foundation model business early last year. It just looked like an impossible challenge. At the end of the day, we are a smallish public company compared to the likes of Google and AWS and Microsoft, or even OpenAI in terms of how much money we are able to put for things like model training. We shifted our folks to focus more on things like post-training, where we felt we had, we still had leverage. We also have a very good inference research team that specializes in making inference super cheap and super fast for, for our needs. I think that's the right place to be. That also drove our partnerships. By the way, these are deep, meaningful partnerships, meaning the anthropic models run within our deployment, so I can With confidence, look at our customer and tell them that their data is not leaving the Snowflake deployment, and it's similarly with Microsoft and Azure and OpenAI. And so these are meaningful integrations that we have done working with these companies and the cloud providers. I feel like that's a much better use of kind of our resources, which we have to husband a little bit than continuing to Try to invest in foundation models. We got priced out. It's fine.

AI assessment note: “We got out of the foundation model business early last year.”

Q And just to complete this kind of, um, architecture, uh, product tour, uh, Cortex, uh, the analyst, like what, what are, what are those in, in just a few words?

A We said we wanted AI To be a core part of Snowflake. Roughly the way we did that in practice was we said we will host a model garden inside every Snowflake deployment. Snowflake essentially runs in what we call deployments, which you can think as a point of presence in every major data center that AWS and Azure and GCP have. These are instances of Snowflake that are running everywhere, but it's a single cloud. It's completely connected. Data can move seamlessly from one place to the other. And, um, we run a model garden inside each of these deployments, and we offer a set of products on top of it. That's the name Cortex. Cortex AI is an umbrella of products. In practice, what this means is anyone that can write SQL or Python in Snowflake can use language models just as part of their data processing. If you want to do sentiment detection on customer feedback you have in Snowflake, it's as easy as calling a single function. Similarly, if you want to create a chatbot on unstructured data, you create a Cortex search index, um, and then you use Streamlet, which we talked about, to create a user interface and an application that you can deploy. And Cortex Analyst is the, is a structured data solution. It's the idea of being able to ask a question about a structured data set that sits in Snowflake, and Snowflake Intelligence is the uber structure on top of all of this That helps with …

AI assessment note: “Cortex Analyst is the, is a structured data solution.”

Q And, uh, so you're coming from a very illustrious and sort of meaty and heavy, uh, search background from Google and then, and then Neva. To which extent has that background informed your product, uh, vision for Snowflake?

A I mean, I would say the aspects that have informed how I think about architecture and product, first of all, is with respect to elements of expertise that I talked about. How you think about delivering analyst-facing, end-user-facing functionality is a specialty unto itself. And so we are moving on to a modern micro front-end environment powered by Um, things like, things like Node with rapid iteration, half-hour deployments, so that we can develop UI faster. That's one aspect that is learning. But on the other side, Cortex Search is almost completely based on the Neva Search Index infrastructure. Um, Asim, who's one of, uh, our most amazing of engineers, called the system Koala, and Snowflake bought Neva, and so inherited all of that IP, and that code base is still there. I can spend an hour just talking about, uh, uh, the Cortex search infrastructure. It is all SSD based, and, uh, it offers the option, for example, to swap segments out so you can run massive experiments if you want very, very quickly. What happens when you run search systems at the scale of Google is experimentation, quick loading of new data becomes a big issue. And so there's a lot of cleverness that Asim put into how do we experiment? How do we put new bits of data into how search should work? How do you make it easy to try different aspects of search quality? How do you tune this? And so that has been ver…

AI assessment note: “Cortex Search is almost completely based on the Neva Search Index infrastructure.”

Q whole rabbit hole, that was not necessarily planned for this podcast. But anyway, thank you very much for sharing all of this. Before we go back to data infrastructure and AI, with your Neva hat on, what do you make of the state of search today? You know, we, the paradigm of the, you know, 12 blue links, or whatever it's called, seems under What do you make of it?

A First thing is the power of defaults still matter. I'm obviously a sophisticated user of products. I know which ones I should use for what. I've become a big fan of ChatGPT with search for any kind of quick reference. I still use Google via Safari quite a lot. For things I'm not quite sure that, um, ChatGPT is going to answer, all out of sheer force of habit. The fact of the matter is there are billions of people that have this habit. They're going to keep using Google. I don't think it's an immediate thing. But on the other hand, ChatGPT has numbers that begin to look like that of the big tech companies. My take is that any product that has a billion users

AI assessment note: “The fact of the matter is there are billions of people that have this habit.”

Q Because this is effectively what perplexity would be today, right? Is that fair?

A That's fair. That's, uh, that is fair. We started along similar lines. The best, the core thesis again was very simple, which was there must be a better way to do search. I'd worked on search and search-related products for 15 years, and I had come to the conclusion that if you start with things like I'm going to offer web links, I am going to show ads on top of those links, There was a limit to how much utility you could drive out of that product. I am, I am proud, especially looking back at how well we ran Google ads, especially search ads with that focus on quality. If you compare more recent platforms for search platforms, for example, app store searches, they're truly terrible. Even when you ask highly precise questions, they will show you completely irrelevant ads. We were proud of the work that we did in search ads. But I also knew that the model had its limits. There's only so much you can push something, and the pressure to make money is always there. You're always tempted to take that extra line of pixel because it was going to make you so much more money. In many ways, Neva was a pure intellectual exercise of search is a really important function, and we should be able to rethink it. But we didn't have the tools. I think we were two years too early in terms of creating, for example, a truly conversational experience. As soon as GPT-III came out in twenty-twenty-two, …

AI assessment note: “That's fair. That's, uh, that is fair. We started along similar lines.”

Q That's right. Business as well. Deep Seek. You, you were very quick to deploy Deep Seek. Um, any, any thoughts on sort of, um, open source and how that fits within Snowflake?

A We love open source. Why? It drives competition. It's that simple. And we launched DeepSeek quickly. It's more a demonstration of, you know, can you run that eleven-second sprint? It's models there, it appears to be good. If the worry is where it's hosted, we can host it. We actually hosted the full version of DeepSeek, not their small model. These are the ones that are not going to fit within NH-X. And, you know, we were proud to do it. Is it a company with staying power? I'm not yet sure in terms of their ability to innovate. If you were to compare them to XAI, I would say they are definitely one or two steps behind in terms of their ability to come from nothing and train a world-class foundation model. There are lessons for us in terms of how nimble they've been and how scarcity actually leads to innovation. Back to my point earlier about there is such a thing as having too much money, even as a startup. I think there are lessons that we all should take away from it. And open models are great. I'm super excited for Llama four, which I think is coming out imminently. So we partner with, uh, Meta as well. So I think the more models there are, the more options that you and I are going to have as consumers.

AI assessment note: “We love open source. Why? It drives competition. It's that simple.”

Q Yes, it's a partner, and then you have your own, um, uh, sort of real-time, um, uh, tools as part of the, of the platform. What, what do you make of the space? Uh, does all of this ultimately convert, you know, structured data, unstructured data, uh, BI, AI, batch, real-time, does all of this end up being, uh, part of the same platform?

A That's a lot. Of different things, but I do think that, uh, things like streaming ingest, which we are absolutely planning to support. We support something called Snowpipe streaming, which is, uh, low single digit second latency. I think these are things that we will continue to invest in. There is one aspect of real time, real time, which is, uh, call it sub-fifty millisecond real time for messages. And, uh, things like, um, a streaming solution as a backbone for agentic, uh, applications. Do they increase the reliability? Generally are partnering in this, uh, um, In this area to provide this. As I said, we have many customers that use Red Panda for the ingestion, and then it deposits data into, into, into Snowflake. I agree that it is an important area, and streaming as an AI backbone, I go back and forth. As you can imagine, if, uh, you have agents that take five seconds to come back, And that's not an unreasonable thing if you're going to use an AI model, consult some data and stuff like that. Reliability with RPCs is a little bit of an issue if you're operating at very high scale and you want to make sure that failure rates are low because the streaming solutions provide protection against random dropped RPCs because of overload and stuff like that. So I can imagine those kinds of, uh, use cases. So my rough take is it is an interesting area. Practical uses of true sub-fif…

AI assessment note: “my rough take is it is an interesting area. Practical uses of true sub-fifty millisecond”

Q that, uh, there's no AI strategy without a data strategy. Uh, can you, can you double click on this, uh, and, uh, perhaps put it in the context of, um, a company, one of your customers that does this well, uh, and why do it, do they do it well? And then what would you advise, uh, a company that wants to do AI, but has fragmented data to do?

A If you look In terms of what can AI enable today? And what do we think AI can enable in the near future? And break that down into things like, um, broadly into categories of structured and unstructured information. Snowflake, as you pointed out earlier, made its name as the best structured data store for analytical purposes that there is. And, um, you know, our use cases run broad and deep for everything from customers using us to close their books at the end of every, every month to get their financials in order, to running anti-money laundering systems, to things like next best prediction, which customers like Disney do on top of, uh, of Snowflake data. That, that, that list goes on and on. But back to my point about what are things you can do with AI. Business users inevitably want faster access to business data that there is. There's always been pain associated with getting data out to business users, especially new data, different kinds of insights very quickly, because they live in, in tables, and people have to write this language called SQL or bits of Python to get at this data, and so you have Analysts basically that understand both the business context of the data and the SQL language to then write queries to populate dashboards. And so there is a desire on the part of lots and lots of customers to short circuit this process. How do I get data to customers faster? And…

AI assessment note: “here is the rub. And here is where the entirety of the data strategy comes in.”

Q Switching to, um, a deeper dive into AI, which we've covered a little bit already, um, what's your sense of the reality of the markets in from the perspective of Snowflake customer, um, you know, in particular, the question of, uh, people going from, from POCs to actual implementations, where, where are we?

A I think it's useful to step back a little bit and talk about the priorities that we as a product team had for AI. Last year, remember, we were playing catch up with AI. And we wanted to bring products to market, but we needed to be deliberate. We could not be everywhere at the same time. And so the priorities that we set out for the team, first and foremost, were build amazing products that stand on their own. That we could blog about, that we could publish benchmarks about, and say these are world-class products. And, um, we have done that for everything from what we call AI SQL, which is the ability to seamlessly blend in both LLM functions, language functions, but soon multimodal models into how you write SQL and be able to run processing with it, batch benchmarks on what can you do with SQL. We published benchmarks on Cortex Search, which is our unstructured data solution. World-class, as I said, origins with things like Mustang at Google in terms of how we think about search. A team that has built search multiple times and really knows what it's doing. This is the third or fourth generation system that Asim has worked on with respect to search. And so a world-class product there. Then Cortex Analyst, as I said, a unique take with an early focus on maximum precision. How do you put in place a software system that can get better over, um, better over time? And our second pri…

AI assessment note: “I think it's useful to step back a little bit and talk about the priorities”

Q So fast forward to today, Snowflake calls itself the AI data cloud. So how do you define that in concrete terms? Uh, what does that mean in terms of of capabilities? Could you perhaps give us a little bit of a tour of what the, uh, whole, uh, aircraft carrier looks like?

A Yeah. This is a, this is a great question, and it also goes to your other point about how have we evolved as a company, especially over the past few years. Um, Snowflake came of age as a proprietary data format company. What I mean by that is we have a data format. It's called, uh, it's called FDN. The name doesn't matter, but basically only Snowflake can read and write that format. And it's been very helpful for us. We drove an enormous number of innovations. There are features that to this day blow my mind and blow our customers mind in terms of what is possible with Core Snowflake. You can write this, um, very funny, uh, query where you can, you can ask a question, you can ask an analytic question. How many users did I have? That's easy enough. You can translate that into SQL, but you can ask that question Um, with a time reference, you can say, how many users did I have as of February first, 2025? This is because this is the magic of our custom format where we not only remember what the latest version of the information is, but we keep a way to synthesize every possible time point or the previous end number of weeks or months. And so we got a lot out of this format, but increasingly what is happening is that the most progressive of the CIOs and the chief data officers What they want is independence with respect to vendors, including Snowflake. So they want their storage, th…

AI assessment note: “goes to your other point about how have we evolved as a company”

Q And was it the Streamlit acquisition that was part of it?

A So Streamlit was more a, it's a rapid prototyping platform. Streamlit was the version of that time, but for developing, for developing front ends for applications, um, including native applications. But as I said, the, the magic with native applications is more that, um, you know, we have one from T-Mobile, as you know, biggest, uh, phone carrier in the country, hundred and fifty million customers. They created a data cleaning application that, uh, that a different Snowflake customer can use to augment their data with the latest email address with the latest address. But done in a way that that customer cannot, does not really get their hands on, um, T-Mobile's database. But T-Mobile doesn't see those customers as well. Sort of do it in a privacy-safe, um, kind of way. And this actually has applications in the AI space that we will talk about. Now when you're talking about agents and what actions can you take, this becomes an interesting, um, interesting primitive. But, uh, in all of these, Snowflake remained a, a database-centric company. Now, I worked at Google for 15 years, and what you learn over time is that there are very different kinds of software engineering that happens in what looks like a monolith. The way in which you need to think about developing a core piece of infrastructure software, one that needs to run at incredibly high scale, especially if there are massi…

AI assessment note: “Streamlit was more a, it's a rapid prototyping platform.”

Q of, uh, you know, Snowflake was, was, even though that was separated, was compute and storage, uh, with, like, different revenue streams, uh, and, and, and if now people can move their data or keep the data where they are, Uh, what does that mean in terms of Snowflake just being a computer engine? Is that net positive, net negative? You mentioned that, uh, that was possibly a defensive move.

A It's easy to pass judgment in retrospect. I'm the first person to tell you that, but I like looking back and seeing whether we should have done things differently. I see, and, uh, I find that to be an intellectually rewarding exercise. I'm often critical of decisions that I make. You learn from them. Not to, not to place blame, but to just get to the bottom, perhaps, of why things went wrong. At Neva, for example, you know, for Neva, we talked about why I should have called it quits earlier, or what were the structural flaws in Neva. The thesis that there needed to be a better search engine was a fine one. But at the end of the day, if you were to ask me even back in 2019, I see Srila. So can you create a better search engine or at least show me you can create a better search engine for some slice of search with six people in six months? My answer would have been, that sounds hard. Probably not. But it illustrates the structural trap that I fell into. But at one level, it was a good idea. It's a big market. But on the other hand, it's not like I had the insight to create that NX product knowing that getting people to change behavior is always going to be hard. So, you know, as I said, I apply that kind of critical thinking to decisions that I have made in the hope of learning. My take is that us deciding to monetize storage was a long-term strategic mistake. I think we simply s…

AI assessment note: “My take is that us deciding to monetize storage was a long-term strategic mistake.”