1 00:00:00,160 --> 00:00:03,840 Speaker 1: Joining us with more on Everything AI is longtime tech 2 00:00:04,000 --> 00:00:06,880 Speaker 1: entrepreneur Arjunseeti. He is the chairman and co founder of 3 00:00:07,000 --> 00:00:10,080 Speaker 1: VC firm Tribe Capital. He is also the co CEO 4 00:00:10,200 --> 00:00:14,000 Speaker 1: of Termina AI. Thanks for joining us, especially because your 5 00:00:14,080 --> 00:00:17,280 Speaker 1: firm has invested in Xai, so you have a very 6 00:00:17,320 --> 00:00:20,959 Speaker 1: close view on how these lms have been developing and 7 00:00:21,040 --> 00:00:24,439 Speaker 1: the competition among them. Very curious about what you think 8 00:00:24,480 --> 00:00:27,280 Speaker 1: about this latest open ai funding round and the corporate 9 00:00:27,320 --> 00:00:28,080 Speaker 1: interest around it. 10 00:00:29,080 --> 00:00:31,400 Speaker 2: Yeah, thanks for having me. I think the right way 11 00:00:31,440 --> 00:00:35,080 Speaker 2: to think about it is the corporate interest, especially from 12 00:00:35,720 --> 00:00:38,479 Speaker 2: a company like Nvidia, is more about if you take 13 00:00:38,479 --> 00:00:41,720 Speaker 2: a look at their portfolio differcification of where the revenue 14 00:00:41,760 --> 00:00:44,080 Speaker 2: comes from. The fastest growth in the future is going 15 00:00:44,120 --> 00:00:46,879 Speaker 2: to come from companies like OpenAI and the types of 16 00:00:46,960 --> 00:00:48,960 Speaker 2: developers that they're going to support moving forward. 17 00:00:49,440 --> 00:00:52,400 Speaker 1: So go ahead, please good. 18 00:00:53,040 --> 00:00:55,880 Speaker 2: So what that means is that Nvidia is not just 19 00:00:56,040 --> 00:00:59,200 Speaker 2: a hardware and a company. It's a hardware and software 20 00:00:59,200 --> 00:01:02,960 Speaker 2: company is attracting developers and they have to partner with 21 00:01:03,040 --> 00:01:05,280 Speaker 2: other developers and other companies similar to Opening I. So 22 00:01:05,319 --> 00:01:08,680 Speaker 2: Opening I being the biggest, it's a foregone conclusion that 23 00:01:08,720 --> 00:01:10,800 Speaker 2: they have to partner with them in some capacity similar 24 00:01:10,880 --> 00:01:11,279 Speaker 2: to Apple. 25 00:01:12,200 --> 00:01:16,040 Speaker 1: So given that you have invested in XAI, draw me 26 00:01:16,080 --> 00:01:19,479 Speaker 1: a map a little bit on where different companies are 27 00:01:19,520 --> 00:01:21,800 Speaker 1: going to play in this ecosystem. 28 00:01:23,000 --> 00:01:26,319 Speaker 2: So right now we're talking about llms, we're talking about 29 00:01:26,360 --> 00:01:29,160 Speaker 2: foundational models, but we're not talking about is similar to 30 00:01:29,160 --> 00:01:32,440 Speaker 2: the past, where you've built the foundation, you've built the infrastructure, 31 00:01:33,120 --> 00:01:35,360 Speaker 2: and the next layer of application companies are being built, 32 00:01:35,360 --> 00:01:38,000 Speaker 2: which is essentially what are the next trillion dollar companies 33 00:01:38,040 --> 00:01:40,959 Speaker 2: that are out there? So opening I being one foundational, 34 00:01:41,160 --> 00:01:43,759 Speaker 2: XAI could be another one that's foundational, Andthropic is another 35 00:01:43,760 --> 00:01:46,800 Speaker 2: one that's foundational, but they're supporting the next level of developers. 36 00:01:47,520 --> 00:01:50,360 Speaker 2: You take a look at what companies are doing today 37 00:01:50,440 --> 00:01:52,560 Speaker 2: similar to what a lot of venture firms are trying 38 00:01:52,560 --> 00:01:54,360 Speaker 2: to figure out, which is how do you leverage AI 39 00:01:54,480 --> 00:01:56,400 Speaker 2: to be more efficient or how do I say cost 40 00:01:56,520 --> 00:02:01,720 Speaker 2: or increase revenue? Today with the aspect of what you 41 00:02:01,760 --> 00:02:05,920 Speaker 2: see with open AI is that every single company, every 42 00:02:05,960 --> 00:02:09,280 Speaker 2: single company in our portfolio is leveraging these prompts and 43 00:02:09,320 --> 00:02:11,240 Speaker 2: leveraging AI in some way, and so what is in 44 00:02:11,240 --> 00:02:13,520 Speaker 2: net effect is that they're being twenty five to fifty 45 00:02:13,520 --> 00:02:16,040 Speaker 2: percent more efficient. It's not ten x yet. We're working 46 00:02:16,040 --> 00:02:18,880 Speaker 2: our way there as these companies get larger and they 47 00:02:18,919 --> 00:02:23,560 Speaker 2: build better developer tools, but that's an increased efficiency and 48 00:02:23,600 --> 00:02:27,120 Speaker 2: that has a high amount of impact. So any corporate 49 00:02:27,160 --> 00:02:29,640 Speaker 2: interest that's coming into open ai is looking at that 50 00:02:29,800 --> 00:02:32,080 Speaker 2: not just for themselves, but they're looking at that for 51 00:02:32,200 --> 00:02:34,920 Speaker 2: the future of their business, keeping the business that they 52 00:02:35,000 --> 00:02:37,680 Speaker 2: have with their developer community, and making sure that more 53 00:02:37,960 --> 00:02:41,440 Speaker 2: of these developers are going to continue to succeed on 54 00:02:41,480 --> 00:02:44,000 Speaker 2: their platforms because that's what's going to continue to drive 55 00:02:44,000 --> 00:02:46,440 Speaker 2: their business. So for Nvidia, it's a lock in because 56 00:02:46,440 --> 00:02:49,240 Speaker 2: they have software plus hardware, and that is the defect 57 00:02:49,280 --> 00:02:50,120 Speaker 2: of monopoly today. 58 00:02:50,800 --> 00:02:52,480 Speaker 1: Argene, can you talk to me a little bit about 59 00:02:52,520 --> 00:02:55,840 Speaker 1: valuation here? You have a report out from your company 60 00:02:55,880 --> 00:02:58,560 Speaker 1: about what you call a weather report about the supply 61 00:02:58,680 --> 00:03:01,400 Speaker 1: dynam and dynamics in price markets. When we talk about 62 00:03:01,400 --> 00:03:04,360 Speaker 1: open ai, we're talking about evaluation that could be over 63 00:03:04,400 --> 00:03:06,840 Speaker 1: one hundred billion dollars. But really there are only a 64 00:03:06,880 --> 00:03:10,079 Speaker 1: handful of companies in the world with that kind of 65 00:03:10,400 --> 00:03:13,720 Speaker 1: valuation underpinning them. So what else are you seeing in 66 00:03:13,760 --> 00:03:15,160 Speaker 1: the private markets right now? 67 00:03:16,760 --> 00:03:19,280 Speaker 2: So you know the way to start to think about 68 00:03:19,320 --> 00:03:24,760 Speaker 2: private markets is worldwide. So this take China, India, US, 69 00:03:24,800 --> 00:03:27,680 Speaker 2: and then Latin America as a whole. US and China 70 00:03:27,800 --> 00:03:30,240 Speaker 2: sort of dominated valuations for a while, and a lot 71 00:03:30,280 --> 00:03:32,760 Speaker 2: of capital has been flowing into anything that's software and 72 00:03:32,800 --> 00:03:36,160 Speaker 2: tech enabled. The next phase of that was anything that 73 00:03:36,320 --> 00:03:39,240 Speaker 2: software tech enabled and the label of machine learning and AI, 74 00:03:39,840 --> 00:03:41,480 Speaker 2: and so that's where a lot of the capital is going. 75 00:03:41,680 --> 00:03:45,800 Speaker 2: So far, most of the investments in the private world 76 00:03:45,920 --> 00:03:49,840 Speaker 2: that's been going into AI has been coming from corporates. 77 00:03:49,880 --> 00:03:52,960 Speaker 2: It hasn't been coming from venture capital. Venture capital traditionally 78 00:03:53,040 --> 00:03:56,640 Speaker 2: is invested in anything that's vertically integrated or vertical application, 79 00:03:56,960 --> 00:04:00,240 Speaker 2: which basically means like how do you leverage AI into 80 00:04:01,520 --> 00:04:04,160 Speaker 2: a fintech, how do you leverage AI into healthcare, etc. Etc. 81 00:04:05,120 --> 00:04:07,040 Speaker 2: Those are where most of the investments are going in. 82 00:04:07,120 --> 00:04:11,120 Speaker 2: So valuations for those types of opportunities in the beginning 83 00:04:11,120 --> 00:04:13,800 Speaker 2: are really high. Then midway through the cycle it becomes 84 00:04:13,840 --> 00:04:16,040 Speaker 2: low again, and then what you'll see is something that's 85 00:04:16,080 --> 00:04:18,920 Speaker 2: much more intrinsic versus options value related. 86 00:04:19,800 --> 00:04:22,160 Speaker 1: You know, when you think about the global view here, 87 00:04:22,240 --> 00:04:24,760 Speaker 1: there's a lot of concerns in the AI world about 88 00:04:24,760 --> 00:04:29,080 Speaker 1: that competition towards AI spending towards AI development between the 89 00:04:29,160 --> 00:04:34,000 Speaker 1: US and China. What do the dollars say, Well, most. 90 00:04:33,800 --> 00:04:37,640 Speaker 2: Of the dollars today are going towards development in the 91 00:04:37,720 --> 00:04:41,520 Speaker 2: United States. That's actually very very clear. That said, what 92 00:04:41,600 --> 00:04:44,600 Speaker 2: you have to look at in terms of what investments 93 00:04:44,600 --> 00:04:47,680 Speaker 2: had happened outside of the United States, especially in China, 94 00:04:47,760 --> 00:04:53,120 Speaker 2: was essentially for image recognition, anything that was related to 95 00:04:53,240 --> 00:04:56,360 Speaker 2: cybersecurity or surveillance. And so that's how they had spent 96 00:04:56,560 --> 00:04:58,919 Speaker 2: majority of their capital, let's call over the last five years, 97 00:04:59,240 --> 00:05:02,840 Speaker 2: and we spent that plus more over here that's been 98 00:05:02,880 --> 00:05:07,320 Speaker 2: around AI, AGI machine learning. So we're much further ahead 99 00:05:07,720 --> 00:05:09,520 Speaker 2: for now. And you can see that in the types 100 00:05:09,520 --> 00:05:11,320 Speaker 2: of companies that are being built, types of products that 101 00:05:11,360 --> 00:05:16,520 Speaker 2: are being built for every single vertical. So I always 102 00:05:16,560 --> 00:05:20,320 Speaker 2: go back to cybersecurity, healthcare, financial services. You're going to 103 00:05:20,320 --> 00:05:22,960 Speaker 2: see the first input there. You could call it GDP 104 00:05:23,160 --> 00:05:27,039 Speaker 2: per capital growth and then cost. So those are the 105 00:05:27,040 --> 00:05:29,680 Speaker 2: bifurcations between the investments that are really being made and 106 00:05:29,720 --> 00:05:32,440 Speaker 2: then all the hyperscalers that are out there. They are 107 00:05:32,480 --> 00:05:34,240 Speaker 2: going to benefit from all of this because you need 108 00:05:34,279 --> 00:05:36,679 Speaker 2: more compute, you need more space, you need more training. 109 00:05:36,720 --> 00:05:40,000 Speaker 2: You need more inference, which ends up being in net 110 00:05:40,000 --> 00:05:42,159 Speaker 2: benefit for all the companies that are part of that stack. 111 00:05:42,400 --> 00:05:44,920 Speaker 1: Speaking of hyperscalers, I'm glad you brought this up. Of course, 112 00:05:44,920 --> 00:05:48,600 Speaker 1: we had Nvidea earnings this week. The initial reaction was disappointment. 113 00:05:48,839 --> 00:05:51,320 Speaker 1: But something that struck me on the heels of Unvideo 114 00:05:51,400 --> 00:05:54,520 Speaker 1: earnings is, even when you saw a day of earnings 115 00:05:54,520 --> 00:05:57,599 Speaker 1: where the stock was down right after, you saw every 116 00:05:57,680 --> 00:06:02,000 Speaker 1: other company in the Philadelphia Semi Conductor Index immediately have 117 00:06:02,120 --> 00:06:05,160 Speaker 1: the reaction in the opposite direction, there is still love 118 00:06:05,240 --> 00:06:09,280 Speaker 1: for that AI boom. Is this argent because there is 119 00:06:09,400 --> 00:06:12,440 Speaker 1: such a lack of investment opportunity. You mentioned a little 120 00:06:12,440 --> 00:06:14,599 Speaker 1: earlier that a lot of this benure investment is actually 121 00:06:14,600 --> 00:06:18,159 Speaker 1: coming from corporations. So what does that mean in terms 122 00:06:18,160 --> 00:06:21,480 Speaker 1: of exit opportunities? Are there fewer in the future than 123 00:06:21,520 --> 00:06:22,360 Speaker 1: meets the eye? 124 00:06:24,040 --> 00:06:27,159 Speaker 2: Yeah, so there's multiple questions there. I'll take a step back. 125 00:06:27,320 --> 00:06:31,200 Speaker 2: So you look at Nvidia, and the way in which 126 00:06:31,200 --> 00:06:33,480 Speaker 2: you should think about it from our framework is that 127 00:06:33,520 --> 00:06:36,359 Speaker 2: they've have clear product market fit for hardware that they've built, 128 00:06:36,560 --> 00:06:38,640 Speaker 2: and they have clear product market for the software that 129 00:06:38,720 --> 00:06:41,560 Speaker 2: enables their hardware. A lot of the other companies that 130 00:06:41,600 --> 00:06:44,640 Speaker 2: compete with them today don't have that, and so they're 131 00:06:44,720 --> 00:06:49,440 Speaker 2: racing to be able to lock in developer interests. So 132 00:06:50,080 --> 00:06:54,560 Speaker 2: all of them, they're racing to be able to try 133 00:06:54,560 --> 00:06:57,599 Speaker 2: to commoditize that part of the stat stack that hasn't 134 00:06:57,600 --> 00:06:59,760 Speaker 2: happened yet. And I think that's a key point. So 135 00:07:00,240 --> 00:07:02,360 Speaker 2: when you think about year over year growth of what 136 00:07:02,480 --> 00:07:04,920 Speaker 2: Nvidia looks like, if you think about year over year 137 00:07:04,960 --> 00:07:06,880 Speaker 2: growth for the other companies that are on top of 138 00:07:06,880 --> 00:07:10,880 Speaker 2: their stack, you know, namely open Ai, Xai Andthropic, et cetera. 139 00:07:11,400 --> 00:07:13,000 Speaker 2: All of these companies have to rely on that, and 140 00:07:13,000 --> 00:07:14,240 Speaker 2: then you're going to look at the next part of 141 00:07:14,280 --> 00:07:16,400 Speaker 2: the stack, which is what do all the developers do, 142 00:07:17,200 --> 00:07:18,760 Speaker 2: how do they train, and what are the products that 143 00:07:18,800 --> 00:07:21,080 Speaker 2: they're going to use. Beyond just open source, they still 144 00:07:21,120 --> 00:07:24,760 Speaker 2: have to work off of hardware. So I think of 145 00:07:24,800 --> 00:07:27,880 Speaker 2: this very similar to when Apple came out. Everyone had 146 00:07:27,920 --> 00:07:30,679 Speaker 2: made a bet that you know, Apple's devices are too expensive, 147 00:07:30,680 --> 00:07:32,360 Speaker 2: it's going to be commoditized and all of the other 148 00:07:32,360 --> 00:07:33,960 Speaker 2: people are going to come in and compete with them, 149 00:07:34,680 --> 00:07:38,240 Speaker 2: and then you have software developers moving over to Android. 150 00:07:38,240 --> 00:07:40,480 Speaker 2: You have roughly about a fifty to fifty market. You 151 00:07:40,480 --> 00:07:43,320 Speaker 2: don't have that today. You don't have a clear competitor 152 00:07:44,000 --> 00:07:46,720 Speaker 2: in hardware, and you don't have a clear competitor in software. 153 00:07:46,800 --> 00:07:48,880 Speaker 2: So the question you have to ask is that what 154 00:07:49,080 --> 00:07:53,160 Speaker 2: point does that start diverging in terms of overall demand 155 00:07:53,240 --> 00:07:56,400 Speaker 2: for processing and demand for inference and demand for training. 156 00:07:56,960 --> 00:07:59,120 Speaker 2: And today it hasn't stopped. I do believe it will 157 00:07:59,440 --> 00:08:02,680 Speaker 2: will sort of now level out at some point, but 158 00:08:02,760 --> 00:08:04,720 Speaker 2: we don't know if that's six months from now or 159 00:08:04,720 --> 00:08:06,720 Speaker 2: two years from now or ten years from now. 160 00:08:07,600 --> 00:08:09,480 Speaker 1: Rgine, we have to leave it there. That is Termina 161 00:08:09,560 --> 00:08:13,080 Speaker 1: AI co CEO, Rgine Sethie. We appreciate having you today. 162 00:08:13,160 --> 00:08:14,600 Speaker 1: Have a great long weekend.