01:10:15 路 Sridhar Ramaswamy
[01:10:15] S8: software, the cost of creating new software is going to keep going down. I tell people that the best analogy that I can give for them is to go back to, let's say, the 2 thousands when the cost of creating and distributing new content truly dropped precipitously. And you have to work through what is the implication of something like this. For example, I think simply getting deployed into an enterprise and being part of a workflow there is no longer guarantee that things will going to things are going to keep continuing. Because switching is also easier, I I would say that's the first thing. Every every company that's in the world of creating software as a big part of their business needs to think through what is the durable value that they are creating. I think it's a really hard question to internalize, and so we spend a lot of time just thinking about it. The second one, and this is this gets to the heart of your question, and I honestly just have a which is that this is a bad time to be making two and three year plans. Any time things are getting better or worse by 30% every month, all the human ability to predict these things, which are exponential in nature, is, is, you know, it just doesn't it doesn't really work. I joke to people that at this, point in time, we should treat coding agents the same way we treat traffic. Any person is always going to go get, look at Google Maps first before they go somewhere because it's an all seeing agents are sort of becoming like that when it comes to when it comes to software. And the more you have that open mentality that more things might be possible with a new model release than was possible before, the more you'll be pleasantly surprised and you'll be ready to take advantage of it. And so I stress that childlike quality in figuring out what is possible. Obviously, you know, there are nuances to how do you put in work in such a way that even if the model got better, you'll still be able to take advantage of what you have done before. There are practical problems like that. But the biggest thing that I ask for is that open mindedness to what is possible today because a lot different from what was possible, say, a month ago.
01:12:35 路 John Coogan, Sridhar Ramaswamy
[01:12:35] S1: Are you positioning Snowflake as a company that's in the token path or in the token stream? I don't know if you've heard this phrase that's sort of new coinage for a position in the AI value chain. And it feels like you're firmly in token stream, in the token path. But I'm wondering about what you think about that category. Is that narrowing you? Is that valuable to communicate to customers and investors? What do you think of the phrase like in the token path? [01:13:13] S8: It's a useful way to think about how value is going to get created. Mhmm. Remember, back in the web era, we talked about, you know, number of visits, number of unique visitors. [01:13:24] S1: Mhmm.
01:13:24 路 John Coogan, Sridhar Ramaswamy, Jordi Hays
[01:13:24] S1: Mhmm. [01:13:24] S8: These were the things, are weekly active versus monthly active. These were the abstractions that we used, to ground the numbers that various people would report in a reality. You can think of the token path as existing along this path that is using the power and knowledge that these models have, and creating value for customers. And, both in the data where our coding agent called Coco comes into play, where we want everyone that wants to get value from data to be using it to create all of the artifacts in that path, but also in the consumption path, where, our product is Snowflake Cowork, where I want every employee in every company to be able to access the valuable data that is in Snowflake, but also in other applications via things like MCP, to be using co work. And so we very much think about can we be in this path of coding up the things that are going to drive consumption and creating value, but also in the path of how does an end user query enterprise data, take an action on that data, and also creating value. So we very much think about this. [01:14:38] S1: So my my my last question is, you said it's a bad time to be making two and three year plans. How far are you looking into the future? Do you have a one year plan, a three month plan? What [01:14:51] S0: Well, as a public company, [01:14:52] S8: you you [01:14:52] S1: do have have to have forecast. But but but more qualitatively, like, what are you gunning for this year or over whatever time horizon you think is appropriate at this point in time?
01:14:52 路 John Coogan, Sridhar Ramaswamy, Farza Majeed, Jordi Hays
[01:14:52] S1: do have have to have forecast. But but but more qualitatively, like, what are you gunning for this year or over whatever time horizon you think is appropriate at this point in time? [01:15:06] S8: Our overall strategy is very clear. It's just what I described. Yeah. Anytime anyone wants to do something with data, I want them to use, agent tools that are created by Snowflake Mhmm. Cocoa, for example [01:15:18] S6: Yeah. [01:15:18] S8: To be able to do that. We wanna have the best data platform in the world so that when and whenever someone thinks, hey, I need a historical view of this, or I need an OLTP database to do this, they think of Snowflake. They can use any coding agent, not just our own, to be able to, to be able to do that. Similarly, we think a lot about how can we make sure that we sell co work to CROs and demonstrate what we have done internally within Snowflake, just transform our own sales organization. So we think a lot about how do we take our customers through the same journey that we have been through in terms of how can AI make a difference to the business. There'll be a lot of details along the way, but that's the path that we are headed in, AI leveraging the full power of data. [01:16:04] S1: Well, congratulations. Thank you so much for taking the time to [01:16:06] S0: and Great [01:16:08] S1: to meet you. [01:16:08] S0: Congrats to the team on all the momentum. [01:16:10] S1: Yes. Stay strong in the token pass. [01:16:12] S8: Thank you, guys.
01:16:12 路 Sridhar Ramaswamy, John Coogan, Jordi Hays
[01:16:12] S8: Thank you, guys. [01:16:12] S1: We'll talk to you soon. Have a good day. Goodbye. Let me tell you about agents meet the canvas. Your AI agents can now create and modify your Figma files with design system context. Yes. Apologies for [01:16:27] S0: technical issues. A lot of people said, is this AI? [01:16:31] S1: It's not AI. [01:16:31] S0: It's just AI. There was some type of clip.
01:16:34 路 John Coogan
[01:16:34] S1: We I I've never seen that exact technical failure before where it cuts out entirely. We've heard scratchy audio. We've heard degraded video. It was the video was coming through crystal clear, but the audio was cutting in and out a little bit. But I think we got a lot of good stuff. Coco, they should have called the AI agent Frosty. Snowflake? Frosty. Probably intellectual Frosty the agent. The jolly happy coder. Coder. Yeah. Something. Anyway, Citadel. Citadel Securities, former former employer of mine when I was an intern, wrote a post on tokenomics. They're one of the most significant hedge funds and they just dropped tokenomics. I have it here. I have a second printout. Tyler, you want to bring that over to me? It's not what you expected is what they say. So we've argued for some time that agentic and complex workflows delivered by frontier models would be expensive to run constrained by physical bottlenecks and vulnerable to unrealistic expectations of frictionless deployment cost. That judgment now looks less contrarian than it did when we set it out in February. They're taking a victory lap over at Citadel Securities. Amazon has now removed its token leaderboard. Microsoft has canceled clawed code subscriptions, and there have been multiple reports of unexpectedly large token bills. The salient point is that even the most powerful technologies must pass through the prosaic discipline of cost curves, capacity restraint constraints and marginal returns. Adoption is therefore becoming less about what frontier models can do in principle and more about the price and scarcity of the inputs required to make AI operational at scale. Compute power cooling, memory bandwidth and inference budgets are real and binding constraints. They go into economic theory talking about the the the three functions that prices provide. So they signal scarcity, create incentives for substitution, and ration scarce resources toward their highest value uses. This is the stuff that goes viral on TikTok. We're we're deep in it. You heard of the keeping the viewer watching. This is the most important thing. You read the Citadel Securities macro strategy report. But they do say they ration scarce capacity toward the areas where marginal productivity and AI justifies the marginal cost of using it. They're talking about ROI maxing.