1 00:00:02,520 --> 00:00:10,760 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Snowflake another of the 2 00:00:10,800 --> 00:00:13,200 Speaker 1: tech names that released earnings yesterday after the bell and 3 00:00:13,200 --> 00:00:16,480 Speaker 1: as investors look for signs of AI adoption or disruption. 4 00:00:16,920 --> 00:00:19,520 Speaker 1: It's forecast product revenue for the current quarter will be 5 00:00:19,560 --> 00:00:21,800 Speaker 1: about one point two six billion dollars. That's up twenty 6 00:00:21,800 --> 00:00:24,639 Speaker 1: seven percent. They reported more than nine thousand accounts using 7 00:00:24,680 --> 00:00:27,760 Speaker 1: Snowflake AI features shares you can see of three point 8 00:00:27,800 --> 00:00:31,680 Speaker 1: seven percent. Let's bring in the Snowflake CEO Shida Ramaswami. Shida, 9 00:00:31,800 --> 00:00:34,760 Speaker 1: I'm reading notes from zoo Ho saying bookings were a standout. 10 00:00:34,760 --> 00:00:37,320 Speaker 1: They're talking about the seven nine figure deals that have come. 11 00:00:37,880 --> 00:00:40,519 Speaker 1: Where are those deals coming from? What are those customers 12 00:00:40,520 --> 00:00:41,199 Speaker 1: demanding of you? 13 00:00:43,159 --> 00:00:44,320 Speaker 2: Great to see you, Caroline. 14 00:00:44,640 --> 00:00:47,479 Speaker 3: Yes, we had seven nine figure deals, including a man 15 00:00:47,560 --> 00:00:52,400 Speaker 3: of four hundred million dollar deal. It reflects the confidence 16 00:00:52,440 --> 00:00:56,760 Speaker 3: that our customers have both in where Snowflake is right now, 17 00:00:56,840 --> 00:01:00,400 Speaker 3: but importantly where we are going. We all understand and 18 00:01:00,440 --> 00:01:03,520 Speaker 3: that software is being disrupted by AI in. 19 00:01:03,360 --> 00:01:04,880 Speaker 2: A very very big way. 20 00:01:05,360 --> 00:01:08,320 Speaker 3: But what our customers understand is that for enterprise AI 21 00:01:08,400 --> 00:01:12,199 Speaker 3: to truly succeed, they need a single source of enterprise true. 22 00:01:12,200 --> 00:01:16,360 Speaker 3: They need built in security, auditibility, trust and access. Of course, 23 00:01:16,360 --> 00:01:19,640 Speaker 3: you also need the best models. That's what Snowflakes provides 24 00:01:19,680 --> 00:01:22,440 Speaker 3: for them. And we're creating great products, products like Snowflake 25 00:01:22,440 --> 00:01:25,360 Speaker 3: Intelligence that put the power of data into the hands 26 00:01:25,400 --> 00:01:30,720 Speaker 3: of every business user. The healthcare company, I mean the 27 00:01:30,720 --> 00:01:34,319 Speaker 3: health company loves using us, and there are lots of 28 00:01:34,480 --> 00:01:38,800 Speaker 3: partners that are using products like Cortex Code to speed 29 00:01:38,880 --> 00:01:41,920 Speaker 3: up what can be done with Snowflake. They're really looking 30 00:01:41,959 --> 00:01:44,240 Speaker 3: to the future and making sure that we can deliver 31 00:01:44,400 --> 00:01:47,600 Speaker 3: value with Snowflake, and we are creating the products that 32 00:01:47,640 --> 00:01:50,360 Speaker 3: help them deliver that kind of value, Dane, and they are. 33 00:01:50,480 --> 00:01:52,720 Speaker 3: That's why you are seeing companies, as you said, make 34 00:01:52,720 --> 00:01:56,320 Speaker 3: commitments of four hundred plus million dollars with Snowflick. 35 00:01:56,640 --> 00:01:59,000 Speaker 1: I'm really interested in codex Code with something that was 36 00:01:59,000 --> 00:02:02,360 Speaker 1: talked a lot about and people are adopting swiftly. But 37 00:02:02,440 --> 00:02:04,360 Speaker 1: that partnership model that you have, the fact that you 38 00:02:04,440 --> 00:02:08,040 Speaker 1: have integrations with Anthropic, OpenAI Cloud, also Google Cloud, but 39 00:02:08,120 --> 00:02:10,600 Speaker 1: some of these have very good coding tools of their own. 40 00:02:11,040 --> 00:02:13,960 Speaker 1: How do you see this ecosystem evolving? Because customers get it, 41 00:02:14,000 --> 00:02:17,200 Speaker 1: but the investments have been questioning whether they'll take away 42 00:02:17,200 --> 00:02:17,920 Speaker 1: your market. 43 00:02:17,639 --> 00:02:21,600 Speaker 3: Shaed Well, so there are a lot of things that 44 00:02:21,680 --> 00:02:25,839 Speaker 3: are specific to Snowflake and to data. Absolutely, there are 45 00:02:26,639 --> 00:02:30,160 Speaker 3: coding agents that are often provided by the model companies themselves, 46 00:02:30,320 --> 00:02:32,600 Speaker 3: but we know a lot about how data systems are 47 00:02:32,600 --> 00:02:35,240 Speaker 3: supposed to work, about how Snowflake is supposed to work, 48 00:02:35,440 --> 00:02:39,600 Speaker 3: and Cortex Code is super tightly integrated with the customer's 49 00:02:39,600 --> 00:02:40,400 Speaker 3: Snowflake account. 50 00:02:40,639 --> 00:02:43,639 Speaker 2: Data does not go anywhere. And remember we have earned the. 51 00:02:43,480 --> 00:02:46,440 Speaker 3: Trust of all of these customers with certifications, with the 52 00:02:46,560 --> 00:02:51,079 Speaker 3: guarantee around things like AIUS. We never use customer data 53 00:02:51,200 --> 00:02:55,400 Speaker 3: for things like training models. They get the superior models 54 00:02:55,440 --> 00:02:57,400 Speaker 3: that we get through partnerships with these folks. 55 00:02:57,600 --> 00:03:00,880 Speaker 2: But we also add the secret sauce of data of. 56 00:03:00,880 --> 00:03:04,840 Speaker 3: How Snowflake works into products like Cortex Code, and we're 57 00:03:04,880 --> 00:03:08,760 Speaker 3: seeing amazing wins both internally and also externally. One of 58 00:03:08,800 --> 00:03:11,960 Speaker 3: our partners told us that having Cortex Code was like 59 00:03:12,240 --> 00:03:15,320 Speaker 3: Snowflake supplying them with bulldozers were previously they had shovels. 60 00:03:16,520 --> 00:03:18,840 Speaker 4: SHOULDA One of the core pillars for you to grow 61 00:03:19,040 --> 00:03:21,480 Speaker 4: is to go out and find new customers. And I 62 00:03:21,480 --> 00:03:24,079 Speaker 4: wondered if you just give some detail on what's happening 63 00:03:24,120 --> 00:03:27,760 Speaker 4: in the world of technology in AI that would bring 64 00:03:27,880 --> 00:03:30,400 Speaker 4: a customer to Snowflake for the first time. What is 65 00:03:30,440 --> 00:03:32,200 Speaker 4: it that they need that they didn't before. 66 00:03:35,360 --> 00:03:38,200 Speaker 3: Typically they come to us because they need better insight 67 00:03:38,440 --> 00:03:41,520 Speaker 3: into data it is sitting somewhere. It's hard for them 68 00:03:41,560 --> 00:03:44,880 Speaker 3: to get these insights. But increasingly what we're able to 69 00:03:44,880 --> 00:03:49,080 Speaker 3: do is have our sales team build an honest to 70 00:03:49,120 --> 00:03:53,240 Speaker 3: goodness customize demo of something like a Snowflake Intelligence on 71 00:03:53,640 --> 00:03:56,320 Speaker 3: data that on the kind of data that a customer. 72 00:03:55,960 --> 00:03:56,520 Speaker 2: Is going to have. 73 00:03:57,000 --> 00:04:00,840 Speaker 3: It is that easy access that really is the big 74 00:04:00,840 --> 00:04:04,840 Speaker 3: winner for our customers. And smart customers are also quickly 75 00:04:04,880 --> 00:04:09,560 Speaker 3: realizing that having data in Snowflake means that they can 76 00:04:09,600 --> 00:04:11,640 Speaker 3: think about how this data is going to be used 77 00:04:11,680 --> 00:04:13,320 Speaker 3: in ways that they had not done before. 78 00:04:13,640 --> 00:04:14,720 Speaker 2: Santa Fe, which is an. 79 00:04:14,600 --> 00:04:18,360 Speaker 3: Existing customer, is now is now using Snowflake Intelligence our 80 00:04:18,440 --> 00:04:22,200 Speaker 3: AI products to redefine a lot of workflows, replacing a 81 00:04:22,200 --> 00:04:25,440 Speaker 3: lot of existing software. These are the use cases that 82 00:04:25,640 --> 00:04:28,480 Speaker 3: drive these customers to come to Snowflake and adopt it. 83 00:04:29,360 --> 00:04:29,760 Speaker 2: Shriidah. 84 00:04:29,839 --> 00:04:32,880 Speaker 4: Last night, Jensen Wang, who you know very well, talked 85 00:04:32,920 --> 00:04:36,760 Speaker 4: about profitable tokens, the idea that the output of an 86 00:04:36,760 --> 00:04:40,440 Speaker 4: AI model is worth paying for. Customers do pay, and 87 00:04:40,480 --> 00:04:43,200 Speaker 4: they pay it a price that is greater than the 88 00:04:43,240 --> 00:04:46,160 Speaker 4: compute used to generate it. Are you able to give 89 00:04:46,160 --> 00:04:49,160 Speaker 4: me any evidence through snowflakes lens that you actually see 90 00:04:49,200 --> 00:04:50,120 Speaker 4: that in the real world. 91 00:04:51,600 --> 00:04:56,760 Speaker 3: Well, what I can assure you is getting projects done 92 00:04:57,160 --> 00:05:01,400 Speaker 3: as being changed dramatically. Something like setting up a pipeline 93 00:05:01,839 --> 00:05:04,200 Speaker 3: used to be a multi week task, We can get that. 94 00:05:04,200 --> 00:05:06,000 Speaker 2: Done in a small number of hours. 95 00:05:06,279 --> 00:05:11,400 Speaker 3: My team's come to me just last weekend with speed ucks, 96 00:05:11,440 --> 00:05:14,960 Speaker 3: going from four weeks for a software engineering project that 97 00:05:15,040 --> 00:05:17,400 Speaker 3: they did down to forty minutes. 98 00:05:17,480 --> 00:05:19,840 Speaker 2: That is one hundred x speed up, and we. 99 00:05:19,800 --> 00:05:21,880 Speaker 3: Are happy to spend any number of tokens in those 100 00:05:21,920 --> 00:05:25,000 Speaker 3: forty minutes to save that kind of time. I think 101 00:05:25,040 --> 00:05:27,839 Speaker 3: coding agents are really quite magical in the value that 102 00:05:28,040 --> 00:05:31,520 Speaker 3: they deliver, and I think it's only going to accelerate 103 00:05:31,560 --> 00:05:34,240 Speaker 3: from here, and so these investments are going to be 104 00:05:34,279 --> 00:05:38,799 Speaker 3: pretty foundational in every company you know succeeding and thriving. 105 00:05:38,839 --> 00:05:41,440 Speaker 2: And that's why we're so bullish about codex. 106 00:05:41,120 --> 00:05:43,200 Speaker 1: Code and how much you have to invest in your 107 00:05:43,200 --> 00:05:45,680 Speaker 1: own business. I mean, the bullet case for many is 108 00:05:45,839 --> 00:05:48,160 Speaker 1: you need to keep up with a furious pace of innovation. 109 00:05:48,360 --> 00:05:49,839 Speaker 1: Can you at this moment? 110 00:05:49,880 --> 00:05:55,680 Speaker 3: Briefly, we can because we are organized to drive rapid innovation. 111 00:05:56,160 --> 00:05:59,000 Speaker 3: The team that is driving a product like Snowflake Intelligence 112 00:05:59,120 --> 00:06:01,479 Speaker 3: is not that, but we have structured it in such 113 00:06:01,480 --> 00:06:05,240 Speaker 3: a way that they can make rapid progress. And it 114 00:06:05,279 --> 00:06:07,920 Speaker 3: is more the meta structure of how you set up 115 00:06:07,960 --> 00:06:11,400 Speaker 3: environments where people can get work done quickly and effectively. 116 00:06:11,480 --> 00:06:13,880 Speaker 3: That matters a lot more than things like how much 117 00:06:13,880 --> 00:06:17,520 Speaker 3: hardware you are investing in and things like that. The 118 00:06:17,560 --> 00:06:20,080 Speaker 3: current moment is magical because all of us have access 119 00:06:20,080 --> 00:06:23,680 Speaker 3: to great tools. It really comes down to how effectively 120 00:06:23,760 --> 00:06:26,360 Speaker 3: we set up teams on projects to get things done. 121 00:06:26,480 --> 00:06:28,919 Speaker 3: And that's why we are very bullish on how we 122 00:06:28,960 --> 00:06:31,479 Speaker 3: have set things up at Snowflake because we now have 123 00:06:31,600 --> 00:06:35,599 Speaker 3: a demonstrated capability to be right at the cutting edge 124 00:06:35,640 --> 00:06:37,839 Speaker 3: of where AI is having impact. 125 00:06:38,640 --> 00:06:41,280 Speaker 4: Snowflake CEO Shreeta Ramaswami. Great to have you back on 126 00:06:41,320 --> 00:06:42,880 Speaker 4: Bloomberg Tech. Thank you very much.