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.
01:19:01 路 John Coogan, Jordi Hays
For the economy at large, simpler models may be more cost effective. Productivity augmenting pathway until physical constraints are eased. We hence see growing signs of a bifurcation in frontier versus everyday AI usage. They actually shared some data here around token expenditure index dropping, the log growth rate declining. And so folks are pulling back a little bit and shifting towards cheaper models as they figure out a new workflow. Something gets unlocked, and then they say, hey, we got to actually make this ROI positive. So that's the latest from Citadel. Let's see what Amazon Web Services is saying. They're black belting on the timeline. They said more AI generated code doesn't make your team faster. It might actually slow you down. [01:19:49] S0: Crazy This is such from AWS. [01:19:52] S1: Would have expected it from like some AI thought leader, podcaster, Twitter person. [01:19:57] S0: AI bear. [01:19:58] S1: But, yeah. It's funny coming from the AWS cloud gold check. [01:20:03] S0: It's funny because it's also like, if you were to write this, I think a lot of people would write this as more AI generated code doesn't necessarily make your team faster. No. Per No. More AI generated code doesn't make your team faster, which is just like factually incorrect for like a lot of teams. Yeah. Yeah. Right? That's true. We're actually moving faster than ever. Also, okay. Read the next post in this thread. [01:20:29] S1: It's like
01:20:29 路 John Coogan, Jordi Hays
[01:20:29] S1: It's like [01:20:30] S0: very clearly AI generated. Okay. The real bottleneck was never writing code. It's releasing it, debugging it, and keeping it running well. So when Honeycomb c Honeycomb IO [01:20:42] S1: CTO. [01:20:43] S0: CTO charity majors set a productivity target, she didn't chase 10 x. She chose two x and built from there. [01:20:50] S1: You think this is AI? Because the choose two x comma and ampersand built from there [01:20:55] S0: I think it was human edited but like that that [01:20:58] S1: Syntax Syntax. I agree. I agree. Her team also skipped the mandates and built a set of AI values instead. Every AI output has to have human owner. If you don't want [01:21:07] S0: your name on it, it's probably not good work. [01:21:09] S1: Yeah. That's reasonable. Quality first, quantity second. So they had a podcast about it. Coming from AWS, that's a wild statement. This is this is very funny. But, yes. Also funny with the backdrop of of a $500,000,000 budget. [01:21:27] S0: Well, yeah. And it's funny because Amazon is of course one of the largest investors in both OpenAI and Anthropic to Yeah. Leading coding model provider.