1 00:00:02,520 --> 00:00:07,080 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:07,360 --> 00:00:10,639 Speaker 2: AI startup Revo is emerging from stealth with the aim 3 00:00:10,720 --> 00:00:14,720 Speaker 2: of unifying the steps and data necessary for businesses to 4 00:00:14,760 --> 00:00:17,680 Speaker 2: develop and execute go to market strategies. The company has 5 00:00:17,760 --> 00:00:20,919 Speaker 2: eighty million dollars from a combination seed and Series A 6 00:00:21,040 --> 00:00:25,360 Speaker 2: funding round co led by Coaslaventures and Kleine Perkins. Revo 7 00:00:25,440 --> 00:00:29,800 Speaker 2: CEO David Zoo and investor Vinokosler are with us now. David, 8 00:00:29,800 --> 00:00:32,000 Speaker 2: I would love to start with you. You're trying to 9 00:00:32,680 --> 00:00:36,640 Speaker 2: tackle the concept that you have labeled the Franken stack. 10 00:00:37,760 --> 00:00:40,559 Speaker 2: Even though the term is new to me, the concept 11 00:00:40,640 --> 00:00:42,960 Speaker 2: is something that's been discussed on this program a lot. 12 00:00:43,320 --> 00:00:45,640 Speaker 2: Just to explain what Revo wants to achieve here. 13 00:00:46,720 --> 00:00:49,479 Speaker 3: Sure, well, first off, thanks for having me. It's a 14 00:00:49,479 --> 00:00:52,200 Speaker 3: big day for us. Look, AI is the biggest shift 15 00:00:52,200 --> 00:00:55,840 Speaker 3: in enterprise software since the cloud, but the reality is, 16 00:00:55,920 --> 00:00:58,279 Speaker 3: revenue teams are still stuck in like you said, what 17 00:00:58,280 --> 00:01:00,840 Speaker 3: we call the Franken stack, which is a dozen different 18 00:01:00,880 --> 00:01:04,200 Speaker 3: tools that don't really talk to each other, causing sellers 19 00:01:04,200 --> 00:01:07,000 Speaker 3: to waste over seventy percent of their time wrangling software 20 00:01:07,200 --> 00:01:08,600 Speaker 3: instead of talking to their customers. 21 00:01:08,800 --> 00:01:09,800 Speaker 4: And that's really painful. 22 00:01:10,520 --> 00:01:14,440 Speaker 3: Revo is looking to fix that via one unified revenue 23 00:01:14,480 --> 00:01:17,160 Speaker 3: operating system built from the ground up with AI at 24 00:01:17,160 --> 00:01:20,120 Speaker 3: its core, and with this eighty million raise, we're scaling 25 00:01:20,200 --> 00:01:22,800 Speaker 3: quickly to help all companies win in the age of AI. 26 00:01:24,280 --> 00:01:26,840 Speaker 2: Vinod I was talking with my team this morning about 27 00:01:26,959 --> 00:01:30,080 Speaker 2: the structure of this round and so it's basically a 28 00:01:30,080 --> 00:01:33,720 Speaker 2: combined seed in Series A and Revo is coming out 29 00:01:33,760 --> 00:01:35,760 Speaker 2: and kind of announcing itself to the world. 30 00:01:36,319 --> 00:01:38,240 Speaker 4: Would you kind of give us the backstory? 31 00:01:38,640 --> 00:01:41,559 Speaker 2: You know you are an experienced investor in the world 32 00:01:41,560 --> 00:01:44,480 Speaker 2: of technology. When this crossed your desk, How did you 33 00:01:44,640 --> 00:01:47,160 Speaker 2: know that you needed to get involved and how did 34 00:01:47,200 --> 00:01:50,840 Speaker 2: you determine at this very early stage the potential that 35 00:01:51,160 --> 00:01:51,840 Speaker 2: Revo had. 36 00:01:53,840 --> 00:01:59,160 Speaker 1: The opportunity didn't actually cross our desk in a traditional way. 37 00:01:59,200 --> 00:02:02,480 Speaker 1: It'd be've known for a long time from door dash 38 00:02:02,560 --> 00:02:07,320 Speaker 1: to open door to open store. Sorry, and now Revo. 39 00:02:08,080 --> 00:02:11,720 Speaker 1: We've been incubating ventures with him. My partner Samar has 40 00:02:11,760 --> 00:02:17,919 Speaker 1: worked closely with him on Revo, and we cooked up 41 00:02:17,919 --> 00:02:22,040 Speaker 1: the concept, rainstormed it and thought it was important enough 42 00:02:22,080 --> 00:02:25,400 Speaker 1: to do in the age of Hey, I think most 43 00:02:25,520 --> 00:02:29,760 Speaker 1: applications and application stacks need to be re taught and 44 00:02:29,800 --> 00:02:32,600 Speaker 1: this is David just a superstar armed to know, so 45 00:02:33,120 --> 00:02:34,079 Speaker 1: it was pretty easy. 46 00:02:36,400 --> 00:02:39,840 Speaker 2: That is this an example of you investing in the 47 00:02:39,880 --> 00:02:42,960 Speaker 2: known quantity founder then rather than the idea? 48 00:02:44,560 --> 00:02:47,200 Speaker 4: Absolutely, he wan we invested. 49 00:02:47,760 --> 00:02:51,280 Speaker 1: The idea wasn't clear that was a year and a 50 00:02:51,320 --> 00:02:55,080 Speaker 1: half or two years ago, but the founder was very clear. 51 00:02:56,960 --> 00:02:59,960 Speaker 2: David, I'm trying to track the growth that you've had. 52 00:03:00,160 --> 00:03:03,600 Speaker 2: So spring of twenty twenty four you kind of come 53 00:03:03,680 --> 00:03:06,520 Speaker 2: up with the idea, I guess, incorporate a business, and 54 00:03:06,560 --> 00:03:09,919 Speaker 2: you've hired I guess aggressively. You know you're sub one 55 00:03:10,000 --> 00:03:13,360 Speaker 2: hundred people still, but you've basically gone to companies that 56 00:03:13,400 --> 00:03:16,400 Speaker 2: are well known globally, not just here in the valley, 57 00:03:16,720 --> 00:03:18,399 Speaker 2: and taken the best people you can. 58 00:03:18,520 --> 00:03:24,200 Speaker 4: What was that like, Well, yeah, you're spot on there. 59 00:03:24,800 --> 00:03:27,800 Speaker 3: The company you build taking a page out of Vano's book, 60 00:03:27,840 --> 00:03:30,360 Speaker 3: but the company you built as the team you build, 61 00:03:30,360 --> 00:03:32,359 Speaker 3: as a company you build, not the plans you make. 62 00:03:32,760 --> 00:03:35,520 Speaker 3: And when we thought about tackling the space of unifying 63 00:03:35,600 --> 00:03:39,760 Speaker 3: all go to market tech in one system, spanning marketing, sales, 64 00:03:39,800 --> 00:03:42,400 Speaker 3: and success, we really needed to make sure that we 65 00:03:42,480 --> 00:03:45,640 Speaker 3: had the talent density both from a technical perspective, but 66 00:03:45,680 --> 00:03:48,280 Speaker 3: also from a go to market to really bring together 67 00:03:48,480 --> 00:03:52,520 Speaker 3: the combination of the depth of domain knowledge plus the 68 00:03:52,640 --> 00:03:55,360 Speaker 3: ability to access the first party data to really pull 69 00:03:55,400 --> 00:03:58,840 Speaker 3: this vision off. So global talent is necessary for us 70 00:03:58,920 --> 00:04:01,040 Speaker 3: to make this a reality. 71 00:04:01,920 --> 00:04:04,160 Speaker 2: David, what else do you need the eighty million dollars for? 72 00:04:06,440 --> 00:04:10,800 Speaker 3: Yeah, well it's over the next few months and quarters 73 00:04:10,800 --> 00:04:14,760 Speaker 3: and years. Our vision is clear. We need a continue 74 00:04:14,760 --> 00:04:18,280 Speaker 3: accelerating our product role map to build out both the 75 00:04:18,320 --> 00:04:21,680 Speaker 3: breadth of the surface area to serve our customers, but 76 00:04:21,760 --> 00:04:25,839 Speaker 3: also making sure that every single capability is the best 77 00:04:25,839 --> 00:04:28,680 Speaker 3: possible from a death perspective, but more importantly, in the 78 00:04:28,680 --> 00:04:32,000 Speaker 3: age of AI, context matters, and it's very important for 79 00:04:32,080 --> 00:04:34,279 Speaker 3: us to double down on making sure that our AI 80 00:04:34,560 --> 00:04:38,599 Speaker 3: operating system is able to work seamlessly across every single 81 00:04:38,640 --> 00:04:41,520 Speaker 3: one of these modules capabilities. Job to be done, so 82 00:04:41,640 --> 00:04:45,200 Speaker 3: we're not replicating another Franken stack for the future world. 83 00:04:45,400 --> 00:04:46,600 Speaker 4: So that's what we're going to use. 84 00:04:46,520 --> 00:04:48,520 Speaker 3: The proceeds to to do. 85 00:04:50,279 --> 00:04:52,359 Speaker 2: Vinod, I go back to you with the concept of 86 00:04:52,440 --> 00:04:57,520 Speaker 2: the Franken stack. You know you're invested everywhere. One of 87 00:04:57,520 --> 00:05:00,600 Speaker 2: the first checks into open AI the world is changed 88 00:05:00,640 --> 00:05:04,400 Speaker 2: a law since then you know, we had yesterday Shrida 89 00:05:04,480 --> 00:05:08,040 Speaker 2: from Snowflake on the program talking about the problems right 90 00:05:08,040 --> 00:05:10,640 Speaker 2: now and in the data layer. What do you make 91 00:05:10,680 --> 00:05:13,320 Speaker 2: of the frankenstack? Is that a real thing or this 92 00:05:13,400 --> 00:05:15,599 Speaker 2: is just a marketing opportunity around this round. 93 00:05:16,680 --> 00:05:19,040 Speaker 1: No, I think it's a real thing. Of course, AI 94 00:05:19,240 --> 00:05:21,960 Speaker 1: enables each startup to do a lot more than it 95 00:05:22,000 --> 00:05:25,400 Speaker 1: could do. Twenty engineers can do the work of sixty 96 00:05:25,440 --> 00:05:30,080 Speaker 1: engineers if you use the productivity tools AI enables. But 97 00:05:30,480 --> 00:05:34,440 Speaker 1: more than that, the user experience can be dramatically different 98 00:05:34,520 --> 00:05:37,920 Speaker 1: with the help of AI. As I like to say, 99 00:05:38,279 --> 00:05:42,039 Speaker 1: in the old world of applications, users had to learn 100 00:05:42,080 --> 00:05:46,000 Speaker 1: an application and they learned each vertical stack. In the 101 00:05:46,120 --> 00:05:49,800 Speaker 1: new world of AI, the application learns the human and 102 00:05:49,920 --> 00:05:53,520 Speaker 1: responds the way each ship. It's my favorite way to 103 00:05:53,600 --> 00:05:56,719 Speaker 1: think about the transition AI makes possible. You don't have 104 00:05:56,800 --> 00:06:01,520 Speaker 1: to be trained on an application, get SAP or pubs Jack. 105 00:06:02,080 --> 00:06:03,400 Speaker 4: The application gets to. 106 00:06:03,440 --> 00:06:06,760 Speaker 1: Know you and are the humans on your team, and 107 00:06:06,800 --> 00:06:11,120 Speaker 1: so facilitates not only the function, but also work for 108 00:06:11,360 --> 00:06:15,040 Speaker 1: across people and across applications. 109 00:06:15,839 --> 00:06:19,960 Speaker 4: I think it's a pretty important shift in software to 110 00:06:20,000 --> 00:06:20,280 Speaker 4: both you. 111 00:06:20,440 --> 00:06:22,920 Speaker 2: I kind of like to end the conversation by going 112 00:06:22,960 --> 00:06:24,760 Speaker 2: a little bit bigger on the world that we're in 113 00:06:24,839 --> 00:06:27,080 Speaker 2: right now. You know, David, if you could reflect on 114 00:06:27,160 --> 00:06:30,320 Speaker 2: what it was like doing this round. There is a 115 00:06:30,320 --> 00:06:33,400 Speaker 2: lot of energy behind AI right now, but at the 116 00:06:33,440 --> 00:06:36,960 Speaker 2: other end of the spectrum there is valuation concern. You know, 117 00:06:37,000 --> 00:06:40,320 Speaker 2: there's no difficulty in open AI or anthropic those kinds 118 00:06:40,320 --> 00:06:43,640 Speaker 2: of frontier labs gaining capital, but there is a lot 119 00:06:43,640 --> 00:06:45,560 Speaker 2: going on right now. Would you just reflect on what 120 00:06:45,600 --> 00:06:48,000 Speaker 2: the last few weeks has been like in trying to close. 121 00:06:47,800 --> 00:06:52,719 Speaker 3: This well, you know, the fundamental difference is clear with 122 00:06:52,760 --> 00:06:56,800 Speaker 3: our approach. We're AI native and purpose built for remy teams, 123 00:06:57,400 --> 00:06:59,000 Speaker 3: and so you know when you talk about the old 124 00:06:59,000 --> 00:07:01,840 Speaker 3: players that are trying to bowl AI onto legacy stack, 125 00:07:01,920 --> 00:07:05,400 Speaker 3: it just doesn't work. What we're building with Revo is 126 00:07:05,440 --> 00:07:09,000 Speaker 3: akin to how Tesla build its cars vertically integrated with 127 00:07:09,120 --> 00:07:12,119 Speaker 3: AI at its core. This way, our AI is able 128 00:07:12,120 --> 00:07:16,400 Speaker 3: to see, to see, and act across the full customer journey. 129 00:07:17,440 --> 00:07:19,640 Speaker 3: So the old players are held back by their past, 130 00:07:19,760 --> 00:07:22,640 Speaker 3: and the new players, to your point, you know, don't 131 00:07:22,640 --> 00:07:25,640 Speaker 3: have the depth of domain knowledge nor the access to 132 00:07:25,680 --> 00:07:27,720 Speaker 3: the full suite of first party data to pull this 133 00:07:27,840 --> 00:07:31,160 Speaker 3: vision off. So Revo's building with both and yes it's hard, 134 00:07:31,280 --> 00:07:33,800 Speaker 3: but that's really what makes it defensible and worth doing. 135 00:07:35,200 --> 00:07:38,280 Speaker 2: You know, you're co leading this round with your friends 136 00:07:38,480 --> 00:07:42,560 Speaker 2: at Kleiner Perkins. Again the same question, like what does 137 00:07:42,600 --> 00:07:45,160 Speaker 2: the environment look like to you right now? This is 138 00:07:45,200 --> 00:07:49,520 Speaker 2: a very sizable debut round. You had the conviction to 139 00:07:49,600 --> 00:07:50,000 Speaker 2: do that. 140 00:07:51,320 --> 00:07:51,560 Speaker 4: Well. 141 00:07:52,240 --> 00:07:54,400 Speaker 1: It was a while ago we did the seed round 142 00:07:54,400 --> 00:07:57,320 Speaker 1: of ten million, there wasn't the nuts, and now we 143 00:07:57,520 --> 00:08:01,720 Speaker 1: just participated in the seventy million dollars around. Obviously we 144 00:08:01,840 --> 00:08:05,240 Speaker 1: think highly of the team, but I think it's the 145 00:08:05,320 --> 00:08:09,600 Speaker 1: magnitude of the opportunity this opens up for AI native companies. 146 00:08:10,200 --> 00:08:14,080 Speaker 1: I like to say most valuations won't hold up in 147 00:08:14,120 --> 00:08:17,400 Speaker 1: the AI space, but the ones that are really high 148 00:08:17,520 --> 00:08:23,760 Speaker 1: quality companies will return disproportionate returns. In twenty eighteen, people 149 00:08:23,840 --> 00:08:26,760 Speaker 1: said to me a billion dollar valuation for open air. 150 00:08:26,680 --> 00:08:28,720 Speaker 4: Hours too high. 151 00:08:29,280 --> 00:08:31,880 Speaker 1: Then they said twenty six billion was too high, and 152 00:08:31,920 --> 00:08:34,719 Speaker 1: then one hundred billion, then three hundred billion, and then 153 00:08:35,080 --> 00:08:38,880 Speaker 1: five hundred billion were all too high. So a small 154 00:08:38,960 --> 00:08:43,440 Speaker 1: percentage of the companies I suspect, much less than five percent, 155 00:08:44,280 --> 00:08:48,679 Speaker 1: will actually do extremely well and return ten fifty or 156 00:08:48,679 --> 00:08:51,119 Speaker 1: one hundred times their money invested. 157 00:08:52,280 --> 00:08:54,040 Speaker 4: Most companies will still lose. 158 00:08:53,840 --> 00:08:55,960 Speaker 1: Money, and I think that's why you want to back 159 00:08:56,120 --> 00:09:00,720 Speaker 1: quality founders like David. David's a superstar, right back almost 160 00:09:00,720 --> 00:09:01,560 Speaker 1: anything you did. 161 00:09:03,800 --> 00:09:07,800 Speaker 2: David Zo, CEO of Revo Ai Vino Cosla, co founder Coasadventures, 162 00:09:07,840 --> 00:09:10,040 Speaker 2: and as we just discussed, co led that around. Great 163 00:09:10,080 --> 00:09:11,600 Speaker 2: to have you both on the program. Frank, you