Snowflake CEO Sridhar Ramaswamy on Using Data to Create Simple, Reliable AI for Businesses

27:41So I can't really do that. And so there's a bunch of stuff. Like even if you take Polaris, which is our cloud catalog, you know, done in a matter of three months. And so I think there's a lot of energy within within the team because, you know, it's a slow message, but it's getting through that you can have speed and quality. They're just different aspects of the same problem. And my firm belief all through my life is that virtuosity, Trump's strategy, all the law. What does that mean? Your speed of execution, your speed of reacting to situations is going to Trump's strategy very, very quickly.

28:25Yes, you need strategy. But life is never about fixed strategy because we live in a very, very dynamic world. It's hard to predict which product is going to be widely successful, what your competitor is going to do. Like we're going to talk about like GBD5. It's like it's a big unknown, whether it's going to come out and what impact that's going to have. So I place a huge amount of emphasis on, you just need to be really, really quick at what you do. And I would say like that's the message that I'm trying to convey to the team. That's very, I see nice continuity from the Sluteman era into the Shredar era because I know I've heard Frank say at least a few times the general patent quotes a good plan executed violently today is better than a perfect plan tomorrow.

29:12100 % 100 % and I said that adaptability, Napoleon has a famous court which roughly, I mean It's not his, it predates him. It roughly translates into, you know, I, I come it and I adapt, which is you go into an important area knowing that you're not going to know everything. And then you're adaptive to the situation that actually presents itself. Yeah. Are there any misconceptions about snowflake and AI that you have a debunk? We had a real player. It used to be that snowflake used to be taught off as somebody that didn't really get get AI, but early on, we relied on things like more of a partnership oriented strategy for AI, but my big sort of observation realization is that AI is a platform change in the sense that it is a new way in which you and I, and everybody else in the world is going to get to software, is going to get to applications.

30:21And so once we had that realization, out came a bunch of product consequences, which is AI needs to be central to Snowflay. We need to make it super easy to both build applications, but also build the most important applications ourselves. Cortex Analysts, for example, is a direct to business user application. We have never really done things like that before. It is driven by a strong belief that AI is going to disrupt how information is going to be consumed very, very broadly. And, you know, I am proud of having a world -class team from bottom to top, from foundation models to inference experts, to product engineers that integrate the AI, plus also the product engineers that are creating applications on top of AI.

31:14that combined with things like broad data access, which is Pilates, then iceberg, I think puts us in a very, very good position. Can we zoom out and ask a little bit about your, I guess your hypothesis and your hot takes on the future of AI? Absolutely. I just think you're so well positioned. You probably built one of the first, and not the first kind of LLM native consumer applications at Niva, and now obviously from your seat, that's no flake, you see so much. Maybe first on the LLM kind of race to scale. Like what do you think about all that? Are we reaching the limits of scale? Like what's next for those guys?

31:52I mean, obviously this can go in a couple of different directions. I talked to a lot of experts and you know, there is a collective belief that there is a GPD -5 in the horizon. What I don't think anyone has a a clear bar for is what that's going to represent. TBD4O was very cool, much faster. It also integrated multi -modality, natively in a way, that's pretty amazing. But when you think about reasoning capabilities, the ability to come up with plans for how to execute stuff, it didn't feel like it represented a step change. And while agents are very hot, similar to Cortex, you know, until Cortex Analyst came along, people didn't really believe that you could build reliable talk to your data application.

32:52They were always kind of hidden. And remember, the bar is very high. If you're giving data to a business user, like 75 % accuracy is like one out of four wrong. And so I think the big unknown is whether these models models are going to represent a big step forward in things like multi -step reasoning. And if they can, they're going to unleash like a whole new class of applications that you and I just cannot imagine right now. You know, on the other hand, I think when it comes to driving broad adoption, there is a lot that can be done with existing models. So many things that are useful for you and me every single day, whether it's a piece of mail that we're looking at or looking through a PDF.

33:42Just think about all the tedium that all of us have to go through. And so I think there is huge impact to be had. Simply in AI technology just permeating software as we know it, especially the user input part of software. So unlike other technologies, I think like there is enough that AI is already delivered that is going to have a meaningfully large impact on society. It's just going to take a while to run out. You know, I sincerely hope we don't get to a phase where you need a billion dollars to train a great new model. I actually think that while what that model can do is cool, I think it also reduces the number of people that can have models like that to a very small number.

34:28And I think competition is just overall healthy. So it's very hard to make a call. You've mentioned this a little bit, but I'm curious to get your take on it a bit more. You know, if GPT -5 is delayed or not a big step up or whatever the case might be, or if you just imagine a world in which the current capabilities of the foundation models, that's what we've got. And it comes down to how do we implement those, how do we optimize those, how do we tune those? is one of the things that we hear from a lot of people building an AI, the first couple of weeks are like magic. Everything is amazing. This is great.

35:04And then the next few months are pretty painful. Oh shoot, it can't do this corner case, it can't do that corner case, it's not quite accurate enough. And people get really frustrated. And sometimes they can engineer their way out of it, sometimes they can't. But sometimes it leaves people feeling kind of disillusioned. Like, yeah, this stuff's not as good as I thought it was. Maybe the time's not right. And so I'd love to get your take. if we froze the capabilities of the foundation models today, what sort of changes will we see in the enterprise landscape over the next handful of years? What sort of stuff will we not see because we're just not ready for it yet?

35:38To me, this is honestly the magic of software engine. Part of what I feel we have implicitly accepted with chat GPT is it's sort of like it's on nations. They're like it can do everything. They don't say it. In fact, they go to they take pains to not say it. But just like Google search never tells you that's a dumb query. You can't think about it, right? You're kind of fun if it did. If it is, but there are lots of dumb queries that people type into it. Google's like, oh, yeah. That's a dumb course. They're like, oh, here are a hundred million pages on the web. And here are the best pages for you Pat for your dumb query.

36:24And so I think it's like it's a some of it is good old fashioned, you know, AI enthusiasm. It can do everything. But some of it is just also plain dumb. You should not be doing that. To me, this is where things like, okay, let's actually make grounded chatbots the norm for, you know, like interacting with information. Yeah. The model is there, you know, this application should tell you where it got the information from. It should be very easy for you to verify said piece of information and feel good. Yeah. That you're actually getting something. Similarly, you need a test framework. You know, like Harrison talked about an observability framework to do this on an ongoing basis.

37:17But I think sometimes when it comes to things like chatbots, people forget, wait, like there is such a thing as a set of regression tests. There is such a thing as acceptance criteria for software. Everything that we have, like if somebody were to build a new application, like one of your founders, your expectation is that, you know, they got their clue together and are actually testing stuff before they give it to customers. And someone in the world of AI were like, no, no, no, no, it doesn't matter. And these models react pretty violently to the addition of a period in a prompt. And so I think there needs to be this idea that you need good old -fashioned software and you need to measure the performance of these things.

38:01And so I think this is where it goes away from these are hobby projects that can be hit our miss to hear somebody that can actually software engineer this for you. And we think of that as a core strength of what we bring to the table, which is like you should be able to have a predictable way to say, this chat part is going to work. Or this agent -like application, this is the success rate that it's going to have. or this is what Cortex Analyst is going to do for you in your domain so that you're like, okay, I feel good about deploying it. So even if GPD -5 did not happen, I think there is a lot of magic to be done, but it's also just work.

38:47Yeah, yeah, yeah, well put. Well, what's the, I forget who said it, there's a quote that we use every now and then, people miss most great opportunities because they tend to be wearing coveralls and they look like work. You know, I think this is one of those where like anything else, if you want it to be great, you got to work pretty hard on it. You got to sweat it out. And to me, this is also the place where the thinking of recall has something that you should tune. Thinking of recall as an important part of how you think about these applications. Any ML engineer worth their salt, will promptly come and tell you, it's like, okay, I have an AUC curve for you.

39:30What are they trying to say? They're basically trying to say there is a trade -off between how much you squeeze the model to do and how good it is. There's no perfect answer. That's really what the AUC curve represents. And the more we think of AI applications, as also having this AUC curve, there are trade -offs to be made between reliability and ability to respond. And that's a very conscious factor in how you should think about things. I think the better off we are going to be in terms of where can they deliver value. Yeah. Yeah. I'm going to go back to the point you said a little bit earlier about reasoning and kind of that delivering the next big leap, hopefully for GPT -5 and then Claude etc.

40:13It seems like the approach that most folks are taking is kind of bringing in search at inference time and a lot of more in -front -time compute and kind of this AlphaGo style search stuff. I'm curious, just given you are one of the best people in the world at search. Do you think that is the path to the promised land on the research side for bringing reasoning into these general models? Give me a little bit more context. I can certainly see how search plays a role in how these models operate. Can you just tell me a little bit more? Yeah, so I mean, if you take the example of, if you take AlphaGo, And you're trying to decide what move to do next.

40:50If you can kind of create a branching tree of, here are all the possible moves from here and do a search kind of over that, of like, here's what move I should do next. I think people are trying to bring that logic into, out of the gaming world, and into domains like, I don't know if you saw Devon's cognition, where they're effectively searching over different things that you can do when you're coding as well. And so just like at inference time just giving the model kind of the ability to like search possible paths to decide what to do Yeah, there have been a number of papers You know on this I think even newtops had a bunch of papers about searching over domains as you as you come up with a plan What I don't have To me it's important to understand I'm writing the name of the newtops paper, but it also had the same problem.

41:41They were doing tree search is that they fundamentally rely on a model, typically a neural network, being able to do things like grade a particular point in a state space. Basically, like, AlphaGo, for example, has pretty solid ideas about what is an advantage just position versus what is not. And the search is guided by that. What isn't clear in sort of very open -ended questions is as you come up with alternatives for the search space, can you actually grade them effectively for it's like an open -ended plan? Certainly, number of these techniques work well for games that have structure in which you can actually learn what does optimal mean and you can begin to optimize towards it.

42:39What I don't have as good a feel for is let's take like, you know, something as simple as cooking. You would think it's, you know, it's simple, but if you take, I don't know, 10 ingredients and 20 steps that you can take along the way, and various things that you can do in each of these 20 steps, and the steps themselves can be short, they can be long. You quickly end up with like this crazy the combinatorial explosion of different ways of doing things. And yet there is just one perfect recipe, R2, R3. That's the part honestly, I don't have a good feel for in terms of like, how do you even begin to measure the jump in terms of cognitive ability?

43:21It's easy in structured environments, but like out in the real world where you're trying to do some pretty complex things, I think it becomes trickier. And we've built prototypes for basically like agent analysts, but it's again a structured space. Yeah. So what we do, one thing, I've done numbers like pretty much all my life. I used to do whatever household finances for my dad when I was 10, same like we did in a notebook. And over the past 20 years, every day I get like this email that tells me how my company did the previous day. It used to be called Bean Contours at Google. Every day you got a report card.

43:58every few weeks something would go wrong. Like, you know, you made less money somewhere. And we would like start this predictable problem, like predictable exercise of some poor anglers would like go, drill down into a bunch of different things, blah, blah, blah, blah, look at sliced stuff. And then they would come back with like, oh, street art. It was like Easter in Germany and ascension day in Brazil. And that's why our numbers were off. And it took like a decade to model all of these complex things in the world into like a prediction model, so you're like, okay, I can, I can begin to predict.

44:31But if you think about it, the analysis that they do is constrained. It's pretty much, if a metric is wrong, go slice it by 10 different dimensions, go look at the results, see where likely the problem is. Certainly, we have built prototypes of this AI analyst that can remove 60, 70 % of the work that is needed in actually diagnosing problems. It's pretty freeform, but you can make a, you can make a language. If you can tell a language model, these are my attributes. Go call Cortex analysts with all of these parameters, get the output, take a look at it, and then tell me what I should do next.

45:03So you can begin to automate some of it so that this is actually useful. So you can do things like that, but a much more open -ended problem of here are a hundred different things, incomparable things you can do, and how do you judge, and how do you prune? I think that's the part I honestly don't have good intuition for. I'm gonna ask about search in a different sense if that's okay. You obviously have an incredible point of view on search given your time at Google and at NIVA. And it seems like right now, the consumer world is watching excitedly and nervously about, is there gonna be a new kind of search king crowned?

45:44I'm curious, you're a take on the whole AI search space right now. How about a hot take on perplexity? You have a hot take on perplexity? Like look, I'm happy for perplexity and it reminds you again that right time, you know, right time, right place matters a lot. At Niva, you know, which converged onto a view of what search should be that was very similar to perplexity, we were just two, three years early and timing ends up being everything. You can think of perplexity as like a consumer manifestation of how we want to deal with information. Let's face it, I want to look through an eight page doc to find the two lines that I really care about, said no one.

46:34But that's search. And so in that sense, it's absolutely the right place. I think the more important question is whether the business of search, which is carefully preserved with business contracts, not with consumer choice. Consumer choice is fiction. In a whole bunch of things that we do. We eat what's put in front of us, And we will search with the default search engine that came in our browsers. Okay, we might resist it, but on aggregate with humanity, that's the reality of the world. And so I, you know, I would say that that is the bigger challenge because search is mostly locked up by a few players that control the entry points.

47:26But I think that's the fundamental problem, which is very difficult to break into the business of search. Consumers don't like doing stuff. And this also gets to one of the kind of broader questions in the world of AI right now, which is incumbents versus startups. And historically, the battle is, can the incumbents with distribution build cool products before the startups with cool products build distribution? I think search is a great example of that. You might have the coolest products in the world. It's awfully hard to change consumer behavior. That's right. AI is an interesting test case for this because so much of the coolness of the products is available through the open source world or through third party models.

48:13And so it feels like it might be a scenario in which incumbents are advantaged versus the startups. But do you have a point of view on that? I would take two different lens to this one. One is what you said about models, open source models, plus players like Meta that basically have infinite budgets under the link to open source models. I think the world of creating models from scratch, unless you have an attached hyper -scaler and attached business looks very, very hard. Yeah. And so I think, as I said, I hope this doesn't go to like, and Ergo, three GPT -5 class models that the world has, because I think that's a bad ending for the world.

49:03So I would definitely say that foundation model companies without a strong business to accompany them. It can be a product, like I think OpenAI has created a pretty solid product. It's not just a foundation model. I think that's one thing to keep in mind. I'd answer your second question of sort of disruption slash innovation from a historical lens. I think of every generation of Silicon Valley companies as learning from the previous ones. They are smarter. they know the ways in which things can be disrupted. And they lean in pretty heavily. You know, we all know, for example, the IBM to the mid -range computer sort of disruption, and then the Dex and SGIs of the world, then getting disrupted by the microsoft of the world, and then the web coming along, leading to the rise of companies like Google or mobile.

50:13I would say that in each and every one of these transitions, powerful incumbents with very large pockets have shown an ability to lean in soon or lean in faster. At Google, for example, when I was there, we leaned in very heavily into the home assistance Because Alexa was going to take over the world. That was going to be the way in which you and I and everybody else's search, we were terrified. And we put a pile of money into it. And nothing came off it. And it didn't matter. Why? Because the cost of a disruption is way higher than the amount of investment that you have to make. I would say now this is generation five or something to that effect.

51:03I'd say all the incumbents are very aware of what can be disrupted and they lean into it. There's a bunch of strategic thinkers as I told you. I think of AI as basically shuffling the tiles on enterprise software. And a part of me goes like, you know, no way. Snowflake is going to be leading the charge when it comes to AI, not waiting for it to develop. But I think you see every enterprise AI company lean in the same way. And so this to me would be the question about how much disruption is AI going to drive in consumer software. Certainly there'll be new categories. To me, if I were to start up, I'd feel a lot more comfortable that I'm creating a new category.

51:47Image creation, like done in a mass scale, clearly amazing. But the same goes for videos, same goes for Wysers, a bunch of specializations that you can do here, adapt them to marketing. New things feel like a much safer bet in the AI world than, you know, take your pick. I can do XYZ faster because I am AI enabled. I don't think of that as having a whole lot of legs. Yeah. Do you think chatGPT has a chance of becoming the next Google? And to your point on consumer choice being a mirage and like, you know, business deals are where this stuff gets locked down. Like, I'm curious what you think of the Apple chatGPT deal.

52:25I think Chad GPD, I mean the phone is a pretty interesting place. To me, the phone, because it's a controlled environment, actually offers enormous potential for consumers. I tell people, something as ridiculous as copying, I don't know, an address like from your calendar or a piece of email or to Uber. So dumb. So hard. You know, like you think city would like, you know, do this. Copy the address from this email from Pat and stick it into, stick it into Uber so I can get an Uber. So to me, I think like there's a huge amount of potential again in like mundane applications. And because the mobile ecosystem is a, is a pretty closed one where Apple can mandate things like you must have APIs that make it possible to access your function out.

53:23using language models or else you might not get any traffic. That sounds like a pretty good incentive for everybody to kind of get in line. So I think there's a huge amount of potential there. I honestly wish there was more innovation in this space because again, all of this is super doable technology. You and I can argue about should this be done in the cloud? What can be done on the phone? But like, as a consumer, do you care? Like, you have great connections. I'm kind of like, if this thing works, actually only when I'm connected to the internet, I'll take it. And so to me, those are sort of, those are details.

53:59I actually think chat GPT is an amazing product. There's underlying technology, but in so many different ways, they've actually created a stunningly beautiful product experience that spans the gamut from, they've turned pretty much like visually illiterate people like me, into budding artists. I tell people, it's like, I'm good with words. I can talk all day long, I can write all day long. And the magic that I can do with chat GPs is truly amazing. That or even things like I, you know, for example, like I'm on this language kick, I'm learning Hindi and at some point I was like, oh, I'm struggling with these numbers.

54:42But off comes a prompt that says, hey, I want a CSV that translates numbers just a string of numbers to Hindi and can can you do that? Can you just give me a CSV file that I can import into Quizlet? That literally is faster for me to type than to describe to you. I type it in, out comes a CSV file in 10 seconds, I download it into Quizlet, I have a quiz. And so pretty much everything that I used to do with Python scripts on structured data, I just do like with English, you just upload the CSV file and you're like, oh, I add these two columns, do this other thing, format it into this nice table and get it out for me.

55:18It's magic. So I think there's absolutely a there there in terms of like is it a great product and a great business? But you know Being the king of search is like a few more zeros. They're easy to people. Yeah All right Should we close with a couple of quick fire questions rapid fire questions? Okay, who do you admire most in the world of AI? Who do I admire most in the world of AI?

55:53I admire the people that are, you know, like working on things like foundation models that are able to do it on the cheap without the infinity of resources. So for example, people like Arthur or Danny. I think they've gotten Danny Ogotama from Rekha. I think they've gotten just like a remarkable amount of things done. Or from our own team, folks like Samyaman Yushan, to me they represent so much creativity. Because I go and tell them, ah, limited budget. And what can you, you know, what can you do? I think there are a set of just like amazing earnest people that are driving research under tight constraints.

56:39So there's obviously lots and lots of people, but it's the doors that are doing the work imagining our future that I'm a huge fan of. What's your favorite AI application? I had GPT by far. Easy one. Easy one. Just the utility that I get from a day in and day out is just truly remarkable. Okay, follow up then. What's an AI app that you wish existed? like an actual talk to your phone that can actually mediate between apps. That would be super cool because remember as I said, like just flipping between applications, doing very little things, such a pain. All right, we're going to end on an optimistic question.

57:20Yeah. What is the best thing that can happen in the world of AI over the next five or 10 years? What would you be most excited to see coming out of the world of AI? Okay. Software which you can think of as encoding our thinking, capturing our ability to think an act in real world situation. Clearly has been transformational or the past 50 plus, you know, years. To me, AI as an enabler of access both to the act of creating software but using software to all of the people in the world would be a significant step up. And as I said, I don't think it's like lots of fancy new technology that you need.

58:10The newer technology can certainly help. Newer classes of applications. I was very proud of the fact that we put Google search thanks to things like Android into the hands of pretty much every human being on the planet. It is a gen. You can be cynical about technology. where is a genuine step forward for humanity. To me, just like AI models as like the new layer between humans and software and software, is actually a significant step forward just in having this functionality be vastly more accessible to lots more people. As I said, both in the creation aspects, but also in the consumption aspect.

58:51I think that's a pretty cool thing to look forward. Awesome. Thank you, Shridhar. Thanks for doing this. Thank you, Pat. Thank you, Sonia. Thank you.