00:17:59 · Aditya Agarwal, Sridhar Ramaswamy
[00:17:59] S1: be nice if all of these skills were actually converted into simple code that you could then have essentially byte code that can run much more efficiently that a compiler could then essentially optimize? [00:18:09] S0: Percent, yeah. 100%. And so you see this cycle coming back. And so in that sense, it's actually pretty good news for companies like Snowflake. But it also, again, I think there are many fundamental lessons that will continue to be true. The harness is the new browser regardless of what anyone tells you. Being like buried under someone else's harness, some of us might have to do it. It carries a bunch of risk. And so we have consistently believed this is partly my consumer background. I have my co founder who also worked in a consumer background. So we place a lot of value on create products that directly touch the end users. And so that comes in very handy in moments like this.
00:18:50 · Aditya Agarwal, Sridhar Ramaswamy
[00:18:50] S1: If I can push you a little bit here to make a forward looking kind of prognostication about how do you think that the model ecosystem evolves over the next twelve to twenty four months? Do you think that... Or maybe even the next few years, do you think that there is enough of a concentration of capitalchipsmaybe research talent at the best three or four organizations, again, OpenAI, Anthropic, Google, maybe Meta, such that they will reliably have a six to twelve month lead? Or do you think that the gap between open source and closed actually starts to narrow? I don't actually have a point of view on this myself. I think it comes in cycles, you're right, that you kind of get a big closed model release and then open source kind of chips away and then you kind of get a big and we'll see how Fable does. Obviously, I think it's a little hard to say. So it's hard to know how big a jump Fable is right now. [00:19:45] S0: You either make a lot of predictions or you make none. So I think it's just hard to tell. I think what's been unique about like both chips and AI models has been the infinite demand for all kinds of different chips and for all kinds of different models. I think it's pretty unique to have a lot of jobs to be run on top of A10 chips. Like they're perfectly good. We used them six years ago at Neeva, five years ago at Neeva. They're still super useful today to the latest B200s. [00:20:22] S1: And I think their price per compute hour has actually gone up relative to same because there's enough kind of demand for it.
00:20:22 · Aditya Agarwal, Sridhar Ramaswamy
[00:20:22] S1: And I think their price per compute hour has actually gone up relative to same because there's enough kind of demand for it. [00:20:27] S0: This will just continue to evolve. But I think what feels hard right now is the ability for frontier models to truly have like a big, big, big gap. The fact that there are two of them, first of all, or more potentially, plus a bunch of basically open source labs that are able to catch up relatively quickly. I think that's the most interesting phenomenon. And honestly, again, that is good for all of us because it means that there's not concentration. All of us talk a big game about things like open source while quite forgetting that Google effectively put an end to all search research. What fool academic wanted to work on search if you couldn't get your hands on one thousandth of the data that the biggest practical product that was being used by everyone on the planet had. Similarly, it's not exactly like the algorithms that powered social networks, where a big source of open source, even though Meta itself was a big source of open source for lots of other things. And so I think we could easily have gone down into a world in which one of the model providers was the only model provider. I personally think that we are all damn lucky that it's going to be three, five or seven companies.
00:21:56 · Aditya Agarwal
[00:21:56] S1: Yeah. That's a good observation. I mean, obviously, as somebody who was at Facebook pretty early, you know, and I think there were three of us who actually released Facebook's first open source package, but it was all infrastructure. It was basically an RPC layer thing. But you're totally right that the more broad category of essentially how do you do basically content ranking and content like we had there was a lot of research on it in the early 2000s and then everybody stopped because if you can't get access to the newsfeed data, then what are you doing? There's no point, right? Netflix did its Netflix challenge for a while and then they made it closed source and nobody wanted to do anything. This is totally mean, so I think it's actually just really good for us as users that all these open source models exist. And certainly for me, I'm running my personal agents and I think, it was just like a week ago, I don't know, I just out of nowhere, my Hermes agent decided that it was going to take up $600 of Opus 4.8. I'm like $600 a day, that's a little expensive for even a really good assistant. Then it's pretty easy. You swap in kind of like a cheap open source model and you let it run and you see if you're happy with the results and it's like, oh, actually, I can't tell the difference. And it was fantastic. Switching gears into perhaps Snowflake, you know, like, I'm curious, you know, Snowflake, I think technically was the world's ever biggest software IPO. That's correct. That's correct, right? If I remember correctly, that's right. Was an IPO In terms of dollars raised. In terms of dollars raised. Yeah, it was like the world's biggest ever software IPO. And historically, I think you kind of made this point earlier, Snowflake is in the business of writing really complex software and then essentially selling it and deploying it essentially, right? But we also live in an age where software is a lot easier to kind of create, to produce. And so far, we talk a lot about software that we might wipe code. Maybe it's websites. Maybe it's apps. Maybe it's internal tools. Maybe it's dashboards. Maybe now it's like V1 of my startup idea. But we also have people who are now starting to build file systems. We have a couple of people in our portfolio who are like, actually, five of us traditionally a file system is pretty complex. But you know what?
00:24:00 · Aditya Agarwal
We can write a file system because we can kind of do it a lot. So, at which kind of the logical question here is that there's probably somebody out there who is along with the help of kind of these coding agents building a modern kind of analytics kind of like storage and database solution. How do you think about that?
00:24:17 · Sridhar Ramaswamy
[00:24:17] S0: Yeah. There's absolutely someone out there building an analytics solution, building... Well, there's actually Postgres. It's already open source. There's lots of stuff that people are building on top of it. There are also people building CRM systems and every other kind of software that there is. I think every company needs to look really hard at what their enduring value is. It used to be that getting enterprises to adopt your software, to getting your software embedded in how they operated day to day, that familiarity was enough of a moat That they could not, that companies would not think of switching very quickly. I think all of that is changing. So we definitely think a lot about what that enduring value is. A big part of it is getting people to get value from their investments in something like a data platform be a whole lot faster? What do we know about usage? I look at Cortex a key strategic enabler for the future, not because it can get people to implement stuff on Snowflake fast, but it can learn how what a particular company needs to do in order to get the jobs that they want done on Snowflake fast. It has the beginnings of a feedback loop for what does it mean to optimize for a particular customer. I was talking to folks, for example, this is not even a wild theory anymore, but I think all software can be reduced to a handful of skills on a data substrate. And these skills can be self evolving. We have done this for a bunch of our own internal systems. We rebuilt an SRE system from scratch. That's basically Snowflake data. Day zero, there was nothing. At the end of week one, there were like two dozen skills. At the end of week two, there were more skills than I could reasonably parse and understand what was going on. And the structures kept evolving for the last three months. And it's a full on system at this point. And so we think very much in terms of what is the deployment lifecycle? What does value creation mean? What does Snowflake bring to the table in a way that like anyone else cannot? And so my take is that we need to be faster to value than say a Claude Code or a Codex plus some open source systems. That's the kind of competition that we're going to be facing. But the ideas are very much that of think in terms of durable value, think in terms of how does usage of the product make future usage go much faster.