1 00:00:00,160 --> 00:00:03,677 this new wave of AI companies is is growing revenue like just like actual 2 00:00:03,760 --> 00:00:07,117 customer revenue, actual demand translated through to dollars showing up 3 00:00:07,200 --> 00:00:10,237 in bank accounts at like an absolutely unprecedented takeoff rate. We're seeing 4 00:00:10,320 --> 00:00:14,237 companies grow much faster. I'm very skeptical that the form and shape of the 5 00:00:14,320 --> 00:00:16,557 products that people are using today is what they're going to be using in 5 or 6 00:00:16,640 --> 00:00:19,037 10 years. I think things are going to get much more sophisticated from here. 7 00:00:19,120 --> 00:00:21,757 And so I think we probably have a long way to go. These are trillion dollar 8 00:00:21,840 --> 00:00:25,117 questions, not answers. But once somebody proves that it's capable, it 9 00:00:25,200 --> 00:00:28,317 seems to not be that hard for other people to be able to catch up, even 10 00:00:28,400 --> 00:00:32,397 people with far less resources. When a company is confronted with fundamentally 11 00:00:32,480 --> 00:00:36,797 open strategic or economic questions, it's often a big problem. Companies like 12 00:00:36,880 --> 00:00:39,197 need to answer these questions and if they get the answers wrong, they're 13 00:00:39,280 --> 00:00:42,797 really in trouble. Venture, we can bet on multiple strategies at the same time. 14 00:00:42,880 --> 00:00:46,077 We are aggressively investing behind every strategy that we've identified 15 00:00:46,160 --> 00:00:48,957 that we think has a plausible chance of working. If you want to understand 16 00:00:49,040 --> 00:00:51,677 people, there's basically two ways to understand what people are doing and 17 00:00:51,760 --> 00:00:55,597 thinking. One is to ask them and then the other is to watch them. And what you 18 00:00:55,680 --> 00:00:58,957 often see in many areas of human activity, including politics and many 19 00:00:59,040 --> 00:01:01,917 different aspects of society, the answers that you get when you ask people 20 00:01:02,000 --> 00:01:04,557 are very different than the answers that you get when you watch them. If you run 21 00:01:04,640 --> 00:01:08,877 a survey or a poll of what, for example, American voters think about AI, it's 22 00:01:08,960 --> 00:01:10,077 just like they're all in a total panic. 23 00:01:10,160 --> 00:01:11,437 It's like, "Oh my god, this is terrible. 24 00:01:11,520 --> 00:01:13,197 This is awful. It's going to kill all the jobs. It's going to ruin 25 00:01:13,280 --> 00:01:19,040 everything." If you watch the revealed preferences, they're all using AI. 26 00:01:22,320 --> 00:01:25,197 A lot of folks have sent questions ahead of time and and what I what I've done is 27 00:01:25,280 --> 00:01:28,957 kind of curated into a few different sections uh in in an AMA this morning 28 00:01:29,040 --> 00:01:32,877 with uh with Mark. So, what we thought we'd do is cover uh four big topics. So, 29 00:01:32,960 --> 00:01:36,957 AI and what's happening in the markets, policy and regulation, um all things 30 00:01:37,040 --> 00:01:40,717 816Z, and then we've got a a fun catchall which we're we're calling 31 00:01:40,800 --> 00:01:44,637 sandbox of things if we get to it. So, starting first maybe with uh with the 32 00:01:44,720 --> 00:01:48,077 biggest question. We're sitting in the middle of the AI revolution. Mark, what 33 00:01:48,160 --> 00:01:51,197 inning do you think we're in and and what are you most excited about? 34 00:01:51,280 --> 00:01:54,397 >> First of all, I I would say this is the biggest tech technological revolution of 35 00:01:54,480 --> 00:01:58,797 my life. Um and you know, hopefully I'll see more like this in the next whatever 36 00:01:58,880 --> 00:02:02,637 30 years, but I I mean this is the big one. Um and just in terms of order of 37 00:02:02,720 --> 00:02:06,557 magnitude, like this is clearly bigger than the internet. Um like the the the 38 00:02:06,640 --> 00:02:09,917 comps on this are things like the microprocessor and the steam engine and 39 00:02:10,000 --> 00:02:11,757 electricity. So that this is a really 40 00:02:11,840 --> 00:02:14,797 this is a really big one. um the wheel. 41 00:02:14,880 --> 00:02:19,197 Um the reason this is so big, I mean maybe obvious to folks at this point, 42 00:02:19,280 --> 00:02:23,197 but I'll just go through it quickly. So um if you kind of trace all the way back 43 00:02:23,280 --> 00:02:26,237 to the 1930s, uh there's a great book called Rise of the Machines that kind of 44 00:02:26,320 --> 00:02:29,037 goes through this. Um if you trace all the way back to the 1930s, there was 45 00:02:29,120 --> 00:02:32,477 actually a debate among the people who actually invented the computer. Um and 46 00:02:32,560 --> 00:02:35,757 it was this this sort of debate between whether computer they kind of understood 47 00:02:35,840 --> 00:02:38,637 the theory of computation before they before they they actually built the 48 00:02:38,720 --> 00:02:42,557 things. Um and um they they had this big debate over whether the computer should 49 00:02:42,640 --> 00:02:45,757 be basically built in the image of what at the time were called adding machines 50 00:02:45,840 --> 00:02:49,117 or calculating machines where you know think of sort of essentially cash 51 00:02:49,200 --> 00:02:52,877 registers. Um IBM is actually the successor company to the national cash 52 00:02:52,960 --> 00:02:57,117 register company uh of America. Um and so like and and and that was of course 53 00:02:57,200 --> 00:03:00,317 the path that the industry took which was building these kind of hyper literal 54 00:03:00,400 --> 00:03:03,757 you know mathematical machines you know that could execute mathematical 55 00:03:03,840 --> 00:03:06,877 operations billions of times per second but of course had no ability to kind of 56 00:03:06,960 --> 00:03:09,997 deal with human beings the way humans like to be dealt with and so you know 57 00:03:10,080 --> 00:03:13,597 couldn't understand you know human speech human language um and so forth 58 00:03:13,680 --> 00:03:16,317 and and that's the computer industry that got built over the last 80 years 59 00:03:16,400 --> 00:03:19,437 and that's the computer industry that's built all the wealth of uh uh and and 60 00:03:19,520 --> 00:03:22,797 financial returns of the computer industry uh over the last 80 years you 61 00:03:22,880 --> 00:03:25,757 know across all the generations of computers from mainframes through to 62 00:03:25,840 --> 00:03:29,357 smartphones. Um but but they knew at the time they knew in the 30s actually they 63 00:03:29,440 --> 00:03:32,077 understood the basic structure of the human brain. They understood they had a 64 00:03:32,160 --> 00:03:35,117 theory of sort of human cognition and and and actually they had the theory of 65 00:03:35,200 --> 00:03:39,677 neural networks. Um and so they they had this theory that um the there's actually 66 00:03:39,760 --> 00:03:43,837 the first neural network uh paper academic paper was published in 1943 you 67 00:03:43,920 --> 00:03:48,957 know which was over 80 years ago which is extremely amazing. Um there's an 68 00:03:49,040 --> 00:03:51,357 interview you can read an interview or you can watch an interview on YouTube 69 00:03:51,440 --> 00:03:54,957 with uh these two authors Makulla and Pitts and you can watch an interview I 70 00:03:55,040 --> 00:03:59,197 think with Makulla on YouTube from like I don't know 1946 or something. He was 71 00:03:59,280 --> 00:04:02,477 like on TV you know in the in the ancient past and it's literally like 72 00:04:02,560 --> 00:04:04,797 it's amazing interview because it's like him in his beach house and for some 73 00:04:04,880 --> 00:04:08,317 reason he's not wearing a shirt um and he's like you know talking about like 74 00:04:08,400 --> 00:04:11,117 this future in which computers are going to be you know built on on the model of 75 00:04:11,200 --> 00:04:15,037 a human brain through through neural networks. Um and and that was the path 76 00:04:15,120 --> 00:04:18,077 not taken. And basically what happened was right the computer industry got 77 00:04:18,160 --> 00:04:21,917 built in the in the image of of like the adding machine. Um but and the neural 78 00:04:22,000 --> 00:04:25,117 network basically didn't happen but the neural network as an idea continued to 79 00:04:25,200 --> 00:04:29,357 be explored in academia um and sort of advanced research by sort of a rump you 80 00:04:29,440 --> 00:04:32,717 know movement that was originally called cybernetics and then became known as as 81 00:04:32,800 --> 00:04:36,157 artificial intelligence uh basically for the last 80 years and and and 82 00:04:36,240 --> 00:04:39,037 essentially it didn't work like essentially it was basically decade 83 00:04:39,120 --> 00:04:43,117 after decade after decade of excessive optimism uh followed by disappointment. 84 00:04:43,200 --> 00:04:47,117 When I was in college in the 80s, there had been a famous kind of AI boom bust 85 00:04:47,200 --> 00:04:51,437 uh cycle in the 80s in venture and in Silicon Valley. Um I mean it was tiny by 86 00:04:51,520 --> 00:04:55,437 by modern standards, but it it at the time was a big deal. Um and um you know 87 00:04:55,520 --> 00:04:59,357 by the time I got to college in '89 um in computer science departments, AI was 88 00:04:59,440 --> 00:05:01,757 kind of a backwater field and everybody kind of assumed that it was never going 89 00:05:01,840 --> 00:05:04,637 to happen. But the scientists kept working on it to their credit and they 90 00:05:04,720 --> 00:05:08,237 they they built up this kind of enormous reservoir of of concepts and ideas and 91 00:05:08,320 --> 00:05:12,557 then basically we all saw what happened with the CHIGPT uh moment. all of a 92 00:05:12,640 --> 00:05:15,677 sudden it it sort of crystallized. It's like oh my god, right? It turns out it 93 00:05:15,760 --> 00:05:19,117 works. Um and and so you know that that's the moment we're in now. And then 94 00:05:19,200 --> 00:05:21,757 you know really significantly that was what you that was less than three years 95 00:05:21,840 --> 00:05:26,157 ago, right? That was the summer of 20 it was the the Christmas of 22. So, we're 96 00:05:26,240 --> 00:05:30,637 sort of three year we're we're sort of three years in um to, you know, 97 00:05:30,720 --> 00:05:34,957 basically what is effectively effectively an 80-year revolution um of 98 00:05:35,040 --> 00:05:38,077 actually being able to deliver on all the promise that the that the people on 99 00:05:38,160 --> 00:05:41,517 the all the on the alternate path, the sort of human cognition model path, you 100 00:05:41,600 --> 00:05:44,157 know, kind of saw from the very beginning and and then, you know, the 101 00:05:44,240 --> 00:05:47,757 great news with this technology is it's already it's kind of ultra democratized. 102 00:05:47,840 --> 00:05:50,797 you know, the best AI in the world is available. Launch at GPD or Grock or 103 00:05:50,880 --> 00:05:53,837 Gemini or or um you know, these other you know, these other products that you 104 00:05:53,920 --> 00:05:57,597 can just use um and you can just kind of see how they work and you know, same 105 00:05:57,680 --> 00:06:01,357 thing for video, you can see with Sora and VO kind of state-of-the-art uh with 106 00:06:01,440 --> 00:06:05,277 that you can see with music, you can see you know uh Suno and IDO and so forth. 107 00:06:05,360 --> 00:06:09,437 Um and so like you know we're basically seeing that happen and now and now 108 00:06:09,520 --> 00:06:13,997 Silicon Valley is responding with this just like incredible rush of enthusiasm. 109 00:06:14,080 --> 00:06:16,957 And you know, really critically this gets to the magic of Silicon Valley, 110 00:06:17,040 --> 00:06:20,477 which is, you know, Silicon Valley long since has ceased to be a place where 111 00:06:20,560 --> 00:06:23,517 people make silicon that, you know, that's that not long ago moved out out 112 00:06:23,600 --> 00:06:26,637 of the out out of California and then ultimately out of the US, although we're 113 00:06:26,720 --> 00:06:30,477 trying to bring it back now. Um but but but the great kind of virtue of Silicon 114 00:06:30,560 --> 00:06:34,077 Valley o over the last you know over the last you know 80 years of its existence 115 00:06:34,160 --> 00:06:38,077 is its ability to kind of uh recycle talent from previous waves of technology 116 00:06:38,160 --> 00:06:41,517 and new waves of technology uh and then inspire an entire new generation of 117 00:06:41,600 --> 00:06:45,277 talent you know to basically come join the you know join the project. Um and so 118 00:06:45,360 --> 00:06:47,997 Silicon Valley has this recurring pattern of being able to reallocate 119 00:06:48,080 --> 00:06:50,797 capital and talent and build enthusiasm and build critical mass and build 120 00:06:50,880 --> 00:06:53,997 funding support and build you know human capital and build you know everything 121 00:06:54,080 --> 00:06:56,877 enthusiasm um you know for each new wave 122 00:06:56,960 --> 00:07:01,517 of technology. So, so that's what's happening with AI. Um, you know, I I 123 00:07:01,600 --> 00:07:04,477 think probably the biggest thing I could just say is like I'm surprised I think 124 00:07:04,560 --> 00:07:08,637 essentially on a daily basis of what I'm seeing. Um, uh, and and you know, we 125 00:07:08,720 --> 00:07:11,597 we're we're in the fortunate position to kind of get to see it from from two 126 00:07:11,680 --> 00:07:15,517 angles. Uh, you know, one one is we track the underlying science and and, 127 00:07:15,600 --> 00:07:18,157 uh, and kind of research work very carefully. And so I would say like every 128 00:07:18,240 --> 00:07:22,317 day I see a new AI research paper that just like completely floores me um of 129 00:07:22,400 --> 00:07:25,677 some new capability um or some new discovery uh or some new development 130 00:07:25,760 --> 00:07:28,877 that I that I would have never anticipated that I I'm just like wow I 131 00:07:28,960 --> 00:07:31,757 you know I can't believe this is happening. And then um on the other side 132 00:07:31,840 --> 00:07:34,957 of course you know we see the flow of all of the new uh products uh and all 133 00:07:35,040 --> 00:07:39,597 the new startups. Um and you know I would say we're routinely um you know 134 00:07:39,680 --> 00:07:42,957 kind of seeing things and again kind of have my my jaw on the floor. Um, and so, 135 00:07:43,040 --> 00:07:47,677 you know, it feels like we we we've unlocked this giant vista. Um, I do 136 00:07:47,760 --> 00:07:50,877 think it's going to kind of come in fits and starts. Um, you know, the these 137 00:07:50,960 --> 00:07:54,797 things are messy processes. Um, you know, you know, this is an industry that 138 00:07:54,880 --> 00:07:58,477 kind of routinely gets out over risks and overpromises. Um, and and so, you 139 00:07:58,560 --> 00:08:00,717 know, there, you know, there will certainly be points where it's like, 140 00:08:00,800 --> 00:08:03,117 wow, you know, this isn't working as well as people thought, or you know, 141 00:08:03,200 --> 00:08:05,597 wow, this turns out to be too expensive and the economics don't work or 142 00:08:05,680 --> 00:08:08,397 whatever. But, you know, against that, I would just say the capabilities are 143 00:08:08,480 --> 00:08:11,277 truly magical. And and by the way, I think that's the experience that 144 00:08:11,360 --> 00:08:12,557 consumers are having when they use it. 145 00:08:12,640 --> 00:08:15,037 And I think that's the experience that businesses are having for the most part 146 00:08:15,120 --> 00:08:18,237 when they uh you know, when when they're working on their pilots and and looking 147 00:08:18,320 --> 00:08:21,757 at adoption and and and then and then it translates to the underlying numbers. I 148 00:08:21,840 --> 00:08:24,957 mean, we're we're just seeing a this new wave of AI companies is is growing 149 00:08:25,040 --> 00:08:29,117 revenue like just like actual customer revenue, actual demand uh translated 150 00:08:29,200 --> 00:08:32,637 through to dollars showing up in bank accounts. Um you know, at like an 151 00:08:32,720 --> 00:08:34,077 absolutely unprecedented takeoff rate. 152 00:08:34,160 --> 00:08:36,237 We're seeing companies grow much faster. 153 00:08:36,320 --> 00:08:39,597 um uh you the the key leading AI companies and the companies that have 154 00:08:39,680 --> 00:08:42,637 real breakthroughs um and have real have very compelling products are growing 155 00:08:42,720 --> 00:08:45,597 revenues that you know kind of faster than any any way I've certainly ever 156 00:08:45,680 --> 00:08:49,277 seen before. Um and so like just just from all that it kind of feels like it 157 00:08:49,360 --> 00:08:53,357 has to be early. Like it it's kind of hard to imagine that we've like we we've 158 00:08:53,440 --> 00:08:56,317 topped out in any way. It feels like everything is still developing. I mean 159 00:08:56,400 --> 00:08:59,117 quite frankly it feels like the products to me it feels like the products are 160 00:08:59,200 --> 00:09:03,437 still super early. Like I'm I'm I'm very skeptical that the form and shape of the 161 00:09:03,520 --> 00:09:05,517 products that people are using today is what they're going to be using in five 162 00:09:05,600 --> 00:09:08,157 or 10 years. I think I think things are going to get much more sophisticated 163 00:09:08,240 --> 00:09:11,037 from here. Um and so I think we probably have a long way to go. 164 00:09:11,120 --> 00:09:15,357 >> Maybe on that that topic. So one of the big knocks is yes the revenue is immense 165 00:09:15,440 --> 00:09:19,597 but the expenses seem to also be keeping pace. So like what are people missing as 166 00:09:19,680 --> 00:09:21,597 a part of that discussion and topic? 167 00:09:21,680 --> 00:09:25,517 >> Yeah. So just start with just like core business models, right? Um and so you're 168 00:09:25,600 --> 00:09:28,157 right. There's basically this industry basically has two two core business 169 00:09:28,240 --> 00:09:31,437 models. consumer business model and the quote unquote enterprise uh or 170 00:09:31,520 --> 00:09:35,037 infrastructure business model. Um you know look on the on the consumer side we 171 00:09:35,120 --> 00:09:38,317 we just live in a very interesting world now where where the internet exists and 172 00:09:38,400 --> 00:09:42,157 is fully deployed right. Um, and so I'll give you an example. Sometimes people 173 00:09:42,240 --> 00:09:44,717 ask us like, "Is AI like the internet revolution?" It's like, well, a little 174 00:09:44,800 --> 00:09:47,197 bit, but like the thing with the internet was we had to build the 175 00:09:47,280 --> 00:09:51,357 internet. Like we we like we had we had to actually build the network and we 176 00:09:51,440 --> 00:09:53,917 actually had to, you know, and ultimately it involved enormous amount 177 00:09:54,000 --> 00:09:56,637 of fiber in the ground and it involved enormous numbers of like mobile cell 178 00:09:56,720 --> 00:09:59,757 towers and, you know, enormous number of, you know, shipments of of of 179 00:09:59,840 --> 00:10:03,197 smartphones and tablets and and and laptops in order to get people on the 180 00:10:03,280 --> 00:10:06,637 internet. Like there was this like just like incredible physical lift um, you 181 00:10:06,720 --> 00:10:09,837 know, to do that. And and by the way, people forget how long that took. Uh 182 00:10:09,920 --> 00:10:12,957 right, the the the you know, the internet itself is a invention of the 183 00:10:13,040 --> 00:10:17,517 1960s, 1970s. Um the consumer internet, you know, was a new phenomenon in the 184 00:10:17,600 --> 00:10:21,277 early '90s. Um but, you know, we didn't really get broadband to the home until 185 00:10:21,360 --> 00:10:24,397 the 2000s. You know, that really didn't start rolling out actually until after 186 00:10:24,480 --> 00:10:26,317 the com crash, which is fairly amazing. 187 00:10:26,400 --> 00:10:30,397 And then we didn't get mobile broadband until like 2010. And and people actually 188 00:10:30,480 --> 00:10:32,397 forget the original iPhone dropped in 189 00:10:32,480 --> 00:10:34,877 2007. It didn't have broadband. it was 190 00:10:34,960 --> 00:10:37,757 on a it was on a narrowband 2G network. 191 00:10:37,840 --> 00:10:40,877 Um it did not have high speed like it did not have anything resembling 192 00:10:40,960 --> 00:10:44,477 high-speed data. Um and so it wasn't really until you know really about 15 193 00:10:44,560 --> 00:10:47,757 years ago that we even had mobile broadband. So so the internet was this 194 00:10:47,840 --> 00:10:50,957 massive lift but but the internet got built right and smartphones 195 00:10:51,040 --> 00:10:54,157 proliferated. And so the point is now you have 5 billion people on planet 196 00:10:54,240 --> 00:10:58,157 earth that are on some version of you know mobile broadband internet right um 197 00:10:58,240 --> 00:11:00,717 and you know smartphones all over the world are selling for you know as little 198 00:11:00,800 --> 00:11:04,317 as like 10 bucks. Um and you know you have these you know amazing projects 199 00:11:04,400 --> 00:11:07,437 like geo and India that are bringing you know you know the sort of the remaining 200 00:11:07,520 --> 00:11:09,917 you know kind of the remaining population of of planet earth that 201 00:11:10,000 --> 00:11:13,277 hasn't been online until now is coming online. So, you know, so we're talking 202 00:11:13,360 --> 00:11:17,037 five billion, six billion, you know, people and and then the consumer, the 203 00:11:17,120 --> 00:11:19,917 reason I go through that is the consumer AI products could basically deploy to 204 00:11:20,000 --> 00:11:24,237 all of those people basically as quickly as they want to adopt, right? Um, and so 205 00:11:24,320 --> 00:11:27,997 sort of the internet's the carrier wave for AI to be able to proliferate at kind 206 00:11:28,080 --> 00:11:31,917 of light speed uh uh into the broad base of the global population. And and that's 207 00:11:32,000 --> 00:11:35,277 a let's just say that's a potential rate of proliferation of a new technology 208 00:11:35,360 --> 00:11:38,317 that's just far faster than has ever been possible before. Like what you 209 00:11:38,400 --> 00:11:41,677 know, like you couldn't download electricity, right? you you couldn't 210 00:11:41,760 --> 00:11:45,517 download, you know, you couldn't download indoor plumbing. Um, you know, 211 00:11:45,600 --> 00:11:48,877 you couldn't download television, but you can download AI. Um, and and and 212 00:11:48,960 --> 00:11:52,077 this is what we're seeing, which is the AI consumer, you know, the AI consumer 213 00:11:52,160 --> 00:11:56,317 killer applications are growing at at at an incredible rate. Um, and then and 214 00:11:56,400 --> 00:11:59,677 then they're monetizing really well. Um, and and again, you know, we we I 215 00:11:59,760 --> 00:12:02,077 mentioned this already, but like generally speaking, the monetization is 216 00:12:02,160 --> 00:12:06,157 is very good. Um, by the way, including at higher price points. Um, one of the 217 00:12:06,240 --> 00:12:09,437 things I like about the um, you know, about watching the AI wave is the AI 218 00:12:09,520 --> 00:12:12,477 companies I think are are more creative on pricing than the SAS companies and 219 00:12:12,560 --> 00:12:13,517 the consumer internet companies were. 220 00:12:13,600 --> 00:12:16,957 And so it's it's for example now becoming routine to have $200 or $300 t 221 00:12:17,040 --> 00:12:20,237 per month tiers uh, for consumer AI which I which I think is very positive 222 00:12:20,320 --> 00:12:25,037 because I I think the I think a lot of companies cap their kind of opportunity 223 00:12:25,120 --> 00:12:28,157 by by capping their pricing uh, kind of too low and I think the AI companies are 224 00:12:28,240 --> 00:12:31,997 more willing to push that which I think is good. So anyway, so that you know I 225 00:12:32,080 --> 00:12:35,117 think that's reason for like I would say you know considerable rational optimism 226 00:12:35,200 --> 00:12:37,917 for the scope of of consumer revenues that we're going to be talking about 227 00:12:38,000 --> 00:12:41,117 here. And then on the enterprise side, you know, there the question is 228 00:12:41,200 --> 00:12:45,517 basically just, you know, what is intelligence worth, right? Um, and you 229 00:12:45,600 --> 00:12:48,157 know, if you have the ability to like inject more intelligence into your 230 00:12:48,240 --> 00:12:51,437 business and you have the ability to do, you know, even the most prosaic things 231 00:12:51,520 --> 00:12:55,437 like raise your customer service scores, uh, you know, increase upsells, um, uh, 232 00:12:55,520 --> 00:12:58,317 you know, or reduce churn or if you have the ability to, um, you know, run 233 00:12:58,400 --> 00:13:01,437 marketing campaigns more effectively, um, you know, all of which AI is 234 00:13:01,520 --> 00:13:04,717 directly relevant to, like, you know, these are like direct business payoffs, 235 00:13:04,800 --> 00:13:07,677 um, you know, that people are seeing already. Um, and then if you have the 236 00:13:07,760 --> 00:13:11,117 opportunity to infuse AI into new products and all of a sudden, you know, 237 00:13:11,200 --> 00:13:14,157 all of a sudden your car talks to you, um, and everything in the world kind of 238 00:13:14,240 --> 00:13:17,197 lights up and starts to get really smart. Um, you know, you know, what's 239 00:13:17,280 --> 00:13:20,077 that worth? And and again there you just you you kind of observe it and you're 240 00:13:20,160 --> 00:13:22,717 like, wow, the the leading AI infrastructure companies are growing 241 00:13:22,800 --> 00:13:26,797 revenues incredibly quickly. Um, you know, the pull is really tremendous. Um, 242 00:13:26,880 --> 00:13:30,877 and so, you know, again there it's just it feels like this just like incredible 243 00:13:30,960 --> 00:13:34,477 uh, you know, product market fit. Um and and and the core business model, right, 244 00:13:34,560 --> 00:13:37,437 is is is actually quite quite interesting. The core business model is 245 00:13:37,520 --> 00:13:41,117 is is basically is basically tokens by the drink, right? And so it's it's sort 246 00:13:41,200 --> 00:13:44,957 of tokens of intelligence uh you know, per dollar. And oh, and then by the way, 247 00:13:45,040 --> 00:13:47,837 this is the other fun thing is if you look at what's happening with uh the 248 00:13:47,920 --> 00:13:53,437 price of AI, the price of AI is falling much faster than Moore's law. And when I 249 00:13:53,520 --> 00:13:56,157 could go through that in great detail, but basically like all of the inputs 250 00:13:56,240 --> 00:14:01,117 into AI on a perunit basis, the costs are collapsing. Um and and and and then 251 00:14:01,200 --> 00:14:04,237 as a consequence there's kind of this hyperdelation of per unit cost and then 252 00:14:04,320 --> 00:14:08,237 that is like driving you know just like you know a more than corresponding level 253 00:14:08,320 --> 00:14:12,637 of demand growth you know with with with elasticity. Um and so you know even 254 00:14:12,720 --> 00:14:15,597 there we're like it feels like we're just at the very beginning of kind of 255 00:14:15,680 --> 00:14:18,797 you know figuring out exactly how you know expensive or cheap this stuff is 256 00:14:18,880 --> 00:14:21,357 getting. I mean look there's just no question tokens by the drink are going 257 00:14:21,440 --> 00:14:24,077 to get a lot cheaper from here. Um that's just going to drive I think 258 00:14:24,160 --> 00:14:27,437 enormous demand. Um and then everything in the cost structure is going to get 259 00:14:27,520 --> 00:14:31,757 optimized right? Um and so you know when when people talk about like you know the 260 00:14:31,840 --> 00:14:35,757 chips or you know whatever you know kind of the unit input costs for building AI 261 00:14:35,840 --> 00:14:40,077 you know you now have these like m the losses of blind demand are are going to 262 00:14:40,160 --> 00:14:44,077 are going to kick in right um what's the you know in any market that has sort of 263 00:14:44,160 --> 00:14:47,357 commodity like characteristics you know the number one cause of a of a of of a 264 00:14:47,440 --> 00:14:50,717 glut is a shortage and the number one cause of a shortage is the glut right um 265 00:14:50,800 --> 00:14:54,637 and so you have you know to the extent you have like shortage of GPUs or 266 00:14:54,720 --> 00:14:57,517 shortage of whatever infest chips or shortage of you know whatever data 267 00:14:57,600 --> 00:15:01,277 center case, you know, if you look at just the history of humanity building 268 00:15:01,360 --> 00:15:04,557 things in response to demand, you know, if there's a shortage of something that 269 00:15:04,640 --> 00:15:08,877 can be physically replicated, it it does get replicated. Um, and so there's going 270 00:15:08,960 --> 00:15:11,357 to be like just enormous build out of all I mean there is there's just 271 00:15:11,440 --> 00:15:14,077 hundreds of billions or at this point trillions of dollars maybe going into 272 00:15:14,160 --> 00:15:17,837 the ground um in all these things. And so the the per unit cost of the AI 273 00:15:17,920 --> 00:15:19,677 companies are going to drop like a rock 274 00:15:19,760 --> 00:15:24,877 um you know over the course of the next decade. Um and so like I yeah I mean the 275 00:15:24,960 --> 00:15:27,357 economic questions of course are very real and of course there's you know 276 00:15:27,440 --> 00:15:30,557 microeconomic questions around around all these businesses but the the sort of 277 00:15:30,640 --> 00:15:34,477 macro forces have been at least here I think are very strong um and and yeah I 278 00:15:34,560 --> 00:15:37,197 I just given the underlying value of the 279 00:15:37,280 --> 00:15:41,597 of of this technology to both the consumers the enterprise users. Um, and 280 00:15:41,680 --> 00:15:44,717 given the just like incredibly aggressive discovery that's happening of 281 00:15:44,800 --> 00:15:47,437 of all the ways that people can use this in their lives and in their businesses, 282 00:15:47,520 --> 00:15:50,157 like it's just it's really hard for me to see how it both doesn't grow a lot 283 00:15:50,240 --> 00:15:51,997 and generate just enormous revenue. 284 00:15:52,080 --> 00:15:55,277 >> Yeah. And actually, I think it was like two or three weeks ago where AWS was 285 00:15:55,360 --> 00:15:58,077 saying like the the GPUs that they've been using, they've been able to extend 286 00:15:58,160 --> 00:15:59,917 back to even like seven plus years. So 287 00:16:00,000 --> 00:16:02,317 like the shelf life also of the GPUs 288 00:16:02,400 --> 00:16:05,117 that they're using is now extending in 289 00:16:05,200 --> 00:16:09,757 ways of which they can optimize better than maybe perhaps the last couple of of 290 00:16:09,840 --> 00:16:12,637 cycles. as well. Is that the right way to think about it as well? 291 00:16:12,720 --> 00:16:15,277 >> Yeah, that's right. And then and then that's one that's that's one really 292 00:16:15,360 --> 00:16:18,317 important question and observation and and then by the way that also gets to 293 00:16:18,400 --> 00:16:21,837 this other kind of question um where there's different theories on it. Um 294 00:16:21,920 --> 00:16:24,317 which is basically big models versus small models. 295 00:16:24,400 --> 00:16:28,157 >> Um and so a a lot of the data a lot of the data center build is oriented around 296 00:16:28,240 --> 00:16:31,677 hosting um training and and and and serving the the big the big models, you 297 00:16:31,760 --> 00:16:33,357 know, for for all the obvious reasons. 298 00:16:33,440 --> 00:16:36,637 Um but there's also the small the small model revolution is happening at the 299 00:16:36,720 --> 00:16:39,597 same time and and and and if you just kind of track you know you can get get 300 00:16:39,680 --> 00:16:42,877 the various research firms have these charts you can get um but if you just 301 00:16:42,960 --> 00:16:45,437 kind of track the if you track the capability of the leading edge models 302 00:16:45,520 --> 00:16:48,397 over time what you find is after 6 or 12 months there's a small model that's just 303 00:16:48,480 --> 00:16:52,477 as capable um and so there there there's this kind of chase function that's 304 00:16:52,560 --> 00:16:55,517 happening which is the capabilities of the big models are basically being 305 00:16:55,600 --> 00:17:00,157 shrunk shrunk down uh and provided at at at at smaller size and then therefore 306 00:17:00,240 --> 00:17:03,357 smaller cost you know quite quickly. So, I I'll just give you the the most recent 307 00:17:03,440 --> 00:17:05,677 example that just got hit over the last two weeks. And again, this is a thing 308 00:17:05,760 --> 00:17:09,757 that's just kind of shocking. Um is there's this Chinese company that has a 309 00:17:09,840 --> 00:17:12,237 um well, I forget the name of the company, but it's it's uh the company 310 00:17:12,320 --> 00:17:15,517 that produces the model called Kimmy, which is spelled Kim Mi, which is one of 311 00:17:15,600 --> 00:17:19,677 the leading open source models out of China. Um and uh the new version of 312 00:17:19,760 --> 00:17:23,197 Kimmy is a reasoning model that is at least according to the benchmark so far 313 00:17:23,280 --> 00:17:27,437 is basically a replication of the reasoning capabilities of GPT5, right? 314 00:17:27,520 --> 00:17:31,117 and and and these new models of GPT5 were a big advance over GPT4 and of 315 00:17:31,200 --> 00:17:34,477 course GPT5 costs a tremendous amount of money to to develop and to serve and all 316 00:17:34,560 --> 00:17:37,197 of a sudden you know here we are whatever 6 months later and you have an 317 00:17:37,280 --> 00:17:40,317 open source model called Kimmy and I think I don't know if they had it's 318 00:17:40,400 --> 00:17:42,957 either shrunk down to be able to run on either it's like one MacBook or two 319 00:17:43,040 --> 00:17:47,997 MacBooks um right um and so you can all of a sudden if you have like an applica 320 00:17:48,080 --> 00:17:50,877 you if you're a business and you want to have a reasoning model that's GPT5 321 00:17:50,960 --> 00:17:53,997 capable um but you you know you're whatever you're not going to pay the 322 00:17:54,080 --> 00:17:57,197 whatever GPT5 cost or you're not going to want to have it be hosted and you 323 00:17:57,280 --> 00:18:01,117 want to run it locally, um, you know, you can do that. Um, and and and again, 324 00:18:01,200 --> 00:18:04,237 that's just like another just it's just like another, you know, it's another 325 00:18:04,320 --> 00:18:06,797 breakthrough. Like it's just it's another another Tuesday, another huge 326 00:18:06,880 --> 00:18:09,117 advance. It's like, oh my god. And then of course, it's like, all right, well, 327 00:18:09,200 --> 00:18:11,917 what is OpenAI going to do? Well, obviously they're going to go to GPT6, 328 00:18:12,000 --> 00:18:15,837 right? Uh, and you know, right? And so there there's this kind of lattering 329 00:18:15,920 --> 00:18:18,477 that's happening where the entire industry is moving forward. Um, the big 330 00:18:18,560 --> 00:18:21,517 models are getting more capable. The small models are kind of chasing them. 331 00:18:21,600 --> 00:18:24,797 Um uh and then um and then the small models provide you know completely 332 00:18:24,880 --> 00:18:30,157 different way to deploy um you know at at at at very low price points. Um and 333 00:18:30,240 --> 00:18:32,877 so yeah I think and and you know we'll we'll see what happens. I mean there 334 00:18:32,960 --> 00:18:35,037 there are some very smart people in the industry who think that ultimately 335 00:18:35,120 --> 00:18:37,517 everything only runs in the big models because obviously the big models are 336 00:18:37,600 --> 00:18:40,077 always going to be the smartest and so therefore you're always you know you're 337 00:18:40,160 --> 00:18:41,997 always going to want the most intelligent thing because why would you 338 00:18:42,080 --> 00:18:45,037 ever want something that's not the most intelligent thing for any application. 339 00:18:45,120 --> 00:18:47,837 You know the counterargument is just there's a huge number of tasks that take 340 00:18:47,920 --> 00:18:50,877 place in the economy and in the world that don't require Einstein. you know, 341 00:18:50,960 --> 00:18:54,557 where, you know, where, you know, 120 IQ person is great. You don't need a, you 342 00:18:54,640 --> 00:18:58,317 know, 160 IQ, you know, PhD in, you know, string theory. You just like have 343 00:18:58,400 --> 00:19:02,157 somebody who's competent and capable and it's great. Um, and so, you know, I I, 344 00:19:02,240 --> 00:19:05,517 you know, and I we've talked about this before. I tend to think the AI industry 345 00:19:05,600 --> 00:19:08,157 is going to be structured a lot like the computer industry ended up getting 346 00:19:08,240 --> 00:19:11,117 structured, which is you're going to have a small handful of basically the 347 00:19:11,200 --> 00:19:14,077 equivalent of supercomputers, which are these like giant, you know, kind of we 348 00:19:14,160 --> 00:19:17,517 call god models that are, you know, running in these giant data centers. Um 349 00:19:17,600 --> 00:19:21,517 and then and then you know I I I I I'm not like convinced on this but my my 350 00:19:21,600 --> 00:19:24,077 kind of working assumption is what happens is then you have this cascade 351 00:19:24,160 --> 00:19:27,837 down of smaller models all ultimately all the way the very small models that 352 00:19:27,920 --> 00:19:31,277 run on embedded systems right run on run on individual chips inside every you 353 00:19:31,360 --> 00:19:35,357 know physical item in the world. Um and that you know the smartest models will 354 00:19:35,440 --> 00:19:38,397 always be at the top but the volume of models will actually be the smaller 355 00:19:38,480 --> 00:19:41,357 models that proliferate out and right that's what happened with microchips. uh 356 00:19:41,440 --> 00:19:44,157 it's what happened with computers which became microchips and then it's what 357 00:19:44,240 --> 00:19:46,717 happened with operating systems and with with a lot of everything else that we 358 00:19:46,800 --> 00:19:51,277 built in software. Um so you know I tend to think that's what will happen. Um 359 00:19:51,360 --> 00:19:55,917 just quickly on the chip side um again like chips you if you look at the entire 360 00:19:56,000 --> 00:20:00,877 history of the chip industry uh uh shortages become gluts um and you get 361 00:20:00,960 --> 00:20:04,637 just you know like anytime there's a giant profit pool in a in a new chip 362 00:20:04,720 --> 00:20:08,877 category um you know somebody has a lead for a while and kind of gets you know um 363 00:20:08,960 --> 00:20:12,317 let's say the the the profits appropriate to what we u what we call 364 00:20:12,400 --> 00:20:16,637 robust market share um but in time what happens right is that that draws 365 00:20:16,720 --> 00:20:19,597 competition and of course you know that that that's happening right now. So 366 00:20:19,680 --> 00:20:22,397 Nvidia's, you know, Nvidia is an absolutely fantastic company, fully 367 00:20:22,480 --> 00:20:24,797 deserves the position that they're in, fully deserves the profits that they're 368 00:20:24,880 --> 00:20:27,997 generating, but they're now so valuable, generating so many profits that it's the 369 00:20:28,080 --> 00:20:30,637 bat signal of all time to the rest of the chip industry to figure out how to 370 00:20:30,720 --> 00:20:32,557 advance the state-of-the-art AI chips. 371 00:20:32,640 --> 00:20:35,277 Um, and that's, by the way, and that's already happening, right? And so you've 372 00:20:35,360 --> 00:20:38,077 got other major companies like AMD coming at them, and then you've got 373 00:20:38,160 --> 00:20:41,197 really significantly, you've got the hyperscalers building their own chips. 374 00:20:41,280 --> 00:20:44,157 Um, and so, you know, a bunch of the big a bunch of those kind of big tech 375 00:20:44,240 --> 00:20:45,357 companies are building their own ships. 376 00:20:45,440 --> 00:20:48,397 Um, and of course then the Chinese are building their own ships as well. Um and 377 00:20:48,480 --> 00:20:52,397 so it's just it's like pretty likely in 5 years that that you know AI chips will 378 00:20:52,480 --> 00:20:56,317 be you know cheap and plentiful at least in comparison to the situation today. Uh 379 00:20:56,400 --> 00:20:59,277 which again I think will you know will tend to be extremely positive for the 380 00:20:59,360 --> 00:21:02,237 economics of of the kinds of companies that we invest in. 381 00:21:02,320 --> 00:21:04,237 >> Yep. And that startups are also starting 382 00:21:04,320 --> 00:21:06,797 to go after new chip design as well which is exciting. 383 00:21:06,880 --> 00:21:09,037 >> Yeah. Well, that's the other thing is yeah, you have these disruptive startups 384 00:21:09,120 --> 00:21:11,597 and actually that just for a moment on the chips, they were not really big 385 00:21:11,680 --> 00:21:14,077 investors in chips because it's kind of a big it's kind of a big company thing, 386 00:21:14,160 --> 00:21:18,157 but um it's a little bit of historical happen stance that AI is running on 387 00:21:18,240 --> 00:21:23,197 quote unquote GPUs um you know which GPU stands for graphical processing unit. So 388 00:21:23,280 --> 00:21:25,997 um and basically just for people who haven't tracked this there were 389 00:21:26,080 --> 00:21:28,957 basically two kinds of chips that made the personal computer happen. the 390 00:21:29,040 --> 00:21:33,757 so-called CPU central processing unit which classically was the Intel x86 x86 391 00:21:33,840 --> 00:21:36,317 chip that's kind of the brain of the computer and then there was this other 392 00:21:36,400 --> 00:21:39,437 kind of chip called the GPU or graphical processing unit that was the sort of 393 00:21:39,520 --> 00:21:44,077 second chip in every PC that does all the graphics um and you know and this is 394 00:21:44,160 --> 00:21:47,357 graphics you know 3D graphics for gaming or for CAD CAM or for you know anything 395 00:21:47,440 --> 00:21:50,877 else you know Photoshop or for anything that involves you know lots of visuals 396 00:21:50,960 --> 00:21:54,557 and so the the kind of canonical architecture for a personal computer was 397 00:21:54,640 --> 00:21:58,797 a CPU and a GPU by the way same thing for smartphones um but by the way. And 398 00:21:58,880 --> 00:22:01,597 over time, you know, these have kind of merged and so like a lot of CPUs now 399 00:22:01,680 --> 00:22:04,957 have GPU capability built in. Actually, a lot of GPUs now have CPU capability 400 00:22:05,040 --> 00:22:07,517 built in. So this, you know, this has gotten fuzzy over time, but like that 401 00:22:07,600 --> 00:22:10,557 that was like the classic breakdown. But the fact that that was the classic 402 00:22:10,640 --> 00:22:13,837 breakdown, you know, kind of meant that while Intel had a you know, monopoly for 403 00:22:13,920 --> 00:22:18,877 a long time on CPUs, um there was this other market of GPUs which Nvidia um you 404 00:22:18,960 --> 00:22:22,477 know basically fought the GPU wars for 30 years and and and came out the winner 405 00:22:22,560 --> 00:22:25,357 like what was the best company in the space. But it was like a hyper 406 00:22:25,440 --> 00:22:27,917 competitive market for graphics processors. it was actually not that 407 00:22:28,000 --> 00:22:31,517 high margin and it was actually not that big. And then basically it just it 408 00:22:31,600 --> 00:22:35,677 turned out that there were two other um forms of computation that were 409 00:22:35,760 --> 00:22:39,837 incredibly valuable that happened to be massively parallel uh in how they 410 00:22:39,920 --> 00:22:43,837 operate which which happened to be very good fits for the GPU architecture. And 411 00:22:43,920 --> 00:22:46,477 those two basically highly lucrative additional applications were 412 00:22:46,560 --> 00:22:50,717 cryptocurrency starting about you know 15 years ago and then AI starting about 413 00:22:50,800 --> 00:22:53,197 you know whatever four years ago. Um, 414 00:22:53,280 --> 00:22:58,317 and so and and Nvidia like I would say very cleverly set itself up with an 415 00:22:58,400 --> 00:23:01,437 architecture that works very well for this, but it's also just a little bit of 416 00:23:01,520 --> 00:23:04,637 a twist of fate that it just turns out that if AI is the killer app, it just 417 00:23:04,720 --> 00:23:08,237 turns out that the GPU architecture is the best legacy architecture is devoted 418 00:23:08,320 --> 00:23:11,517 to it. And I go through that to say like if you were designing AI chips from 419 00:23:11,600 --> 00:23:15,277 scratch today, you wouldn't build a full GPU. you would build dedicated AI chips 420 00:23:15,360 --> 00:23:19,277 that were much more much more specifically adapted to AI um and would 421 00:23:19,360 --> 00:23:22,717 have I I think would just be much more economically efficient and you know John 422 00:23:22,800 --> 00:23:25,277 to your point there there there are startups that are actually building 423 00:23:25,360 --> 00:23:29,917 entirely new kinds of chips uh oriented specifically for AI and you know we'll 424 00:23:30,000 --> 00:23:32,477 have to see what happens there you know it's hard to build a new chip company 425 00:23:32,560 --> 00:23:35,597 from scratch um you know it's possible that one or more of those startups makes 426 00:23:35,680 --> 00:23:39,357 it on their own um and some of them are you know doing very well um it's also 427 00:23:39,440 --> 00:23:42,797 possible of course that they get bought um you know by big companies that that 428 00:23:42,880 --> 00:23:46,797 have the ability to scale them. Um, and so, you know, you know, we'll see 429 00:23:46,880 --> 00:23:50,557 exactly how that unfolds. Um, and of course, we'll also, by the way, see, you 430 00:23:50,640 --> 00:23:54,397 know, the Koreans are going to play here for sure. Um, uh, the Japanese are going 431 00:23:54,480 --> 00:23:58,317 to play. Um, and then, you know, the Chinese in a major way, uh, as well. 432 00:23:58,400 --> 00:24:00,637 And, you know, they have their own, you know, native chip ecosystem that they're 433 00:24:00,720 --> 00:24:03,677 that they're building up. And so there there there there are going to be many 434 00:24:03,760 --> 00:24:06,877 choices of AI chips in the future. Um, and it's going to be that, you know, 435 00:24:06,960 --> 00:24:09,837 that'll be a giant battle that'll be a giant battle that we observe very 436 00:24:09,920 --> 00:24:13,517 carefully. um and that we uh make sure that our our companies basically are 437 00:24:13,600 --> 00:24:15,757 able to take full advantage of. 438 00:24:15,840 --> 00:24:19,757 >> While while on the topic of of international um we you mentioned Kimmy 439 00:24:19,840 --> 00:24:22,877 earlier. So it seems like some of the best open source models today are from 440 00:24:22,960 --> 00:24:27,677 China. Should this be worrisome to to folks? How are you thinking and talking 441 00:24:27,760 --> 00:24:29,757 about this topic with with folks in DC? 442 00:24:29,840 --> 00:24:31,517 I know you were just there last week. 443 00:24:31,600 --> 00:24:34,877 How much of this is a concern for uh US 444 00:24:34,960 --> 00:24:37,117 companies particularly just having seen 445 00:24:37,200 --> 00:24:40,477 the rise of China do unnatural things in 446 00:24:40,560 --> 00:24:45,757 solar markets, car markets? Um are they kind of flooding the ecosystem so that 447 00:24:45,840 --> 00:24:49,837 they can eventually kind of take share and and increasingly uh own the the 448 00:24:49,920 --> 00:24:52,957 ecosystem? >> Yeah. So uh you know a couple things. So 449 00:24:53,040 --> 00:24:56,077 one is you know you know you want to start these discussions by just kind of 450 00:24:56,160 --> 00:24:58,637 saying like you know look there's there's vigorous debate in in the US and 451 00:24:58,720 --> 00:25:01,197 around the world of look like you know how much are we in a new cold war with 452 00:25:01,280 --> 00:25:04,637 China you know and exactly like how hostile you know should should we view 453 00:25:04,720 --> 00:25:07,437 them and it you know it's very tempting by the way it's very tempting and I 454 00:25:07,520 --> 00:25:10,637 think it's a very good case made that we're in like a new cold war that's like 455 00:25:10,720 --> 00:25:14,397 that in a lot of ways is like the US versus USSR um in the in the 20th 456 00:25:14,480 --> 00:25:18,397 century um you know it is the counter argument would be it is more complicated 457 00:25:18,480 --> 00:25:21,517 than that because the US and the USSR were never really intertwined from a 458 00:25:21,600 --> 00:25:25,677 trade standpoint Um and and a big part of that quite frankly was the USSR never 459 00:25:25,760 --> 00:25:29,757 really made anything that anybody else needed I guess other than weapons. Um 460 00:25:29,840 --> 00:25:33,357 but like you know the USSR's primary exports were literally like you know 461 00:25:33,440 --> 00:25:36,077 literally like wheat and and oil. Um 462 00:25:36,160 --> 00:25:38,237 whereas of course China exports just a 463 00:25:38,320 --> 00:25:43,357 tremendous number of physical things right um including like a huge part of 464 00:25:43,440 --> 00:25:45,917 like the entire supply chain of parts that basically go into everything that 465 00:25:46,000 --> 00:25:50,157 American manufacturers you know kind of make right and so by the time a US you 466 00:25:50,240 --> 00:25:53,357 know whatever by the time an American company brings a toy to market right or 467 00:25:53,440 --> 00:25:57,677 a uh you know or a car um or anything or a computer or a smartphone or whatever 468 00:25:57,760 --> 00:26:00,877 like it's got a lot of componentry in it that was made in China so there so there 469 00:26:00,960 --> 00:26:04,317 is a much tighter in interlinkage between the the American and Chinese 470 00:26:04,400 --> 00:26:08,317 economies than there as the American and Soviet economies and you know may maybe 471 00:26:08,400 --> 00:26:10,877 you know Adam Smith or whatever might say you know that's good news for peace 472 00:26:10,960 --> 00:26:13,677 and that you know both countries need each other by the way the other part of 473 00:26:13,760 --> 00:26:16,477 that argument is that the Chinese basically the Chinese you know the 474 00:26:16,560 --> 00:26:20,477 Chinese governance model is based on high employment um you know because you 475 00:26:20,560 --> 00:26:23,517 know if if if you know at least all the geopolitical people say if China ended 476 00:26:23,600 --> 00:26:26,877 up with like 25 or 50% unemployment that would cause civil unrest which is the 477 00:26:26,960 --> 00:26:30,637 one thing that the CCP doesn't want and so the corresponding part of the trade 478 00:26:30,720 --> 00:26:33,997 pressure is China needs the American export market you know the American 479 00:26:34,080 --> 00:26:37,197 consumer is like a third of the global economy. Uh a third of global consumer 480 00:26:37,280 --> 00:26:41,757 demand. Um and so you know China needs the US export market or it has high all 481 00:26:41,840 --> 00:26:44,397 of a sudden a lot of its factories would go kind of instantly bankrupt and you 482 00:26:44,480 --> 00:26:47,437 know would cause mass unemployment and unrest in China. So so anyway like you 483 00:26:47,520 --> 00:26:51,517 know we there is this complicated it's a it's a complicated intertwined um 484 00:26:51,600 --> 00:26:55,917 relationship. Um having said that you know the the mood in DC basically for 485 00:26:56,000 --> 00:27:00,557 the last 10 years on a bipartisan basis um has been that we need to take we the 486 00:27:00,640 --> 00:27:04,637 US need to take China more seriously as a geopolitical foe. And you know under 487 00:27:04,720 --> 00:27:07,597 under under that school of thought there's sort of the sort of you know 488 00:27:07,680 --> 00:27:10,957 there's there's the military dimension which is you know the sort of the you 489 00:27:11,040 --> 00:27:13,997 know the the risk of some kind of war in the South China Sea the risk of some 490 00:27:14,080 --> 00:27:17,677 kind of war around around Taiwan and so that you know that that has everybody in 491 00:27:17,760 --> 00:27:21,357 Washington on high alert um you know there's also this this economic question 492 00:27:21,440 --> 00:27:24,877 around the kind of de-industrialization of the US potential re-industrialization 493 00:27:24,960 --> 00:27:28,157 and what that means about you know dependence on China and then and then 494 00:27:28,240 --> 00:27:31,997 there's and then there's this this this AI question um and and the AI question 495 00:27:32,080 --> 00:27:35,437 is an economic question but It's also like a geopolitical question which is 496 00:27:35,520 --> 00:27:38,637 okay you know basically AI is essentially only being built in the US 497 00:27:38,720 --> 00:27:43,037 and in China. Um you know the rest of the world either you know can't build it 498 00:27:43,120 --> 00:27:46,397 or doesn't want to which which we could talk about. So it's basically US versus 499 00:27:46,480 --> 00:27:50,237 China. Um and then AI is going to proliferate all over the world and is it 500 00:27:50,320 --> 00:27:52,557 going to be American AI that proliferates all over the world or is it 501 00:27:52,640 --> 00:27:55,677 going to be Chinese AI that proliferates all over the world and so and I was 502 00:27:55,760 --> 00:28:00,077 saying just generally across party lines in DC this you know the the things I 503 00:28:00,160 --> 00:28:04,797 just went through are kind of how they look at it. Um and and the Chinese are 504 00:28:04,880 --> 00:28:07,117 in the game and so the you know the Chinese are in the game for sure you 505 00:28:07,200 --> 00:28:10,237 know with software u you know deepseek you know was kind of the big you know 506 00:28:10,320 --> 00:28:13,357 kind of fire the starting gun in the software race and now you've got I think 507 00:28:13,440 --> 00:28:17,917 it's I think you've got four it's like deepsek uh which is a deep so deepseek 508 00:28:18,000 --> 00:28:22,477 is an AI model from actually a hedge fund um in uh in China um it's a little 509 00:28:22,560 --> 00:28:26,317 bit uh kind of took a lot of people by surprise um then Quen is the model from 510 00:28:26,400 --> 00:28:28,077 Alibaba. Kimmy is from another startup. 511 00:28:28,160 --> 00:28:32,157 Oh, called Moonshot. The company's called Moonshot. Um, and then there's, 512 00:28:32,240 --> 00:28:35,757 you know, and then, um, you know, there's also Tencent and BU. Um, and, 513 00:28:35,840 --> 00:28:38,797 um, by Dance, um, you know, that are all primary, you know, companies doing a lot 514 00:28:38,880 --> 00:28:42,077 of work in AI. Um, and so, you know, there's somewhere between three to six, 515 00:28:42,160 --> 00:28:43,597 you know, kind of primary AI companies. 516 00:28:43,680 --> 00:28:47,197 And then there's, you know, tremendous numbers of of startups. Um, and so, you 517 00:28:47,280 --> 00:28:50,157 know, they're in the race on on, uh, you know, they're in the race on on on 518 00:28:50,240 --> 00:28:53,757 software. Um, they are, you know, working to catch up on chips. They're 519 00:28:53,840 --> 00:28:56,557 not there yet, but they're working incredibly hard to catch up. And just as 520 00:28:56,640 --> 00:29:00,397 an example of that, you know, the at least the common understanding um you 521 00:29:00,480 --> 00:29:03,357 know, in the US is that the reason you haven't seen the new version of DeepSeek 522 00:29:03,440 --> 00:29:05,837 yet is that basically the Chinese government has instructed them to build 523 00:29:05,920 --> 00:29:09,597 it only on Chinese chips um as a as a motivator to get the Chinese chip 524 00:29:09,680 --> 00:29:12,717 ecosystem up and running. Um and and then the main chip company there is 525 00:29:12,800 --> 00:29:17,517 Huawei, although there could be more in the future. Um and then there's um so 526 00:29:17,600 --> 00:29:20,397 you know, so so so there's that and then and then there's everything to follow 527 00:29:20,480 --> 00:29:24,477 which is basically AI in kind of robotic form, right? And so there there's this 528 00:29:24,560 --> 00:29:28,957 basically global technological economic robotics competition that's kicking off. 529 00:29:29,040 --> 00:29:32,877 Um and u you know China kind of starts out ahead on robotics because they're 530 00:29:32,960 --> 00:29:36,957 just ahead on so many of the so many of the components that go into robots u 531 00:29:37,040 --> 00:29:39,757 because the you know the sort of like I said this the kind of entire supply 532 00:29:39,840 --> 00:29:43,197 chain of like electromechanical things you know basically moved from the US to 533 00:29:43,280 --> 00:29:47,117 China 30 years ago and and has never come back. So so so that's kind of the 534 00:29:47,200 --> 00:29:49,997 the the the DC lens on it. Um and and I 535 00:29:50,080 --> 00:29:54,877 would say you know DC is watching it uh you know quite carefully. Um uh the the 536 00:29:54,960 --> 00:29:58,397 the the big kind of supernova moment this year was the deepseek release. The 537 00:29:58,480 --> 00:30:01,917 deepseek release was surprising on a number of fronts. Um one was just how 538 00:30:02,000 --> 00:30:06,157 good it was and again along this line of it took the capability set that were 539 00:30:06,240 --> 00:30:07,597 running in large models in the cloud and 540 00:30:07,680 --> 00:30:10,637 kind of shrunk it um onto a um you know 541 00:30:10,720 --> 00:30:15,757 into into a uh into a a sort of a a reduced size you know a smaller version 542 00:30:15,840 --> 00:30:18,397 of sort of equivalent capabilities that you could run on small amounts of local 543 00:30:18,480 --> 00:30:22,157 hardware. Um and so there was that and then it was also a surprise that it was 544 00:30:22,240 --> 00:30:25,277 released as open source uh and particularly open source from China 545 00:30:25,360 --> 00:30:29,437 because China China does not have a long history of open source. Um and then um 546 00:30:29,520 --> 00:30:33,677 it was also a surprise um that it actually came from a hedge fund. Um so 547 00:30:33,760 --> 00:30:36,797 it didn't come from a big R&D you know sort of university research lab. It 548 00:30:36,880 --> 00:30:39,597 didn't come from a you know from a big tech company. it it came from a hedge 549 00:30:39,680 --> 00:30:43,997 fund and it it like as as far as we can tell it it basically is this somewhat 550 00:30:44,080 --> 00:30:47,197 idiosyncratic situation where you just have this incredibly successful quant 551 00:30:47,280 --> 00:30:50,477 hedge fund with all these you know super geniuses um and the the founder of that 552 00:30:50,560 --> 00:30:54,557 hedge fund you know basically decided to build AI um and you know at least 553 00:30:54,640 --> 00:30:57,197 external indications are this was a surprise to even even the Chinese 554 00:30:57,280 --> 00:31:00,477 government it's it's impossible to prove you know what the Chinese government was 555 00:31:00,560 --> 00:31:03,597 surprised by or not but you know there's at least the atmospherics are that this 556 00:31:03,680 --> 00:31:06,957 was not exactly planned this was not a national champion tech company at the 557 00:31:07,040 --> 00:31:09,997 time that Deepseek was released it was it sort of came out of left field which 558 00:31:10,080 --> 00:31:13,197 by the way is very encouraging for the field that it was possible for somebody 559 00:31:13,280 --> 00:31:16,077 to do that kind of who was unknown right because it kind of means that maybe you 560 00:31:16,160 --> 00:31:19,197 don't need all these you know super genius superstar researchers maybe 561 00:31:19,280 --> 00:31:22,157 actually smart kids can just build this stuff which I think is is the direction 562 00:31:22,240 --> 00:31:26,717 things are headed um and so that kicked off I would say like this kind of I I 563 00:31:26,800 --> 00:31:29,757 don't know copycat's the wrong word but that that was sort of it feels like the 564 00:31:29,840 --> 00:31:33,117 success of deepseek and the success of deepseek from China as open source kind 565 00:31:33,200 --> 00:31:37,437 of kicked off a sort of trend in China releasing these open source models um 566 00:31:37,520 --> 00:31:41,437 you know Look, the cynics, you know, in DC would say, you know, yeah, like 567 00:31:41,520 --> 00:31:44,477 they're dumping, right? The the they're obviously dumping. They're trying to, 568 00:31:44,560 --> 00:31:46,797 you know, they see that the West has this opportunity to build this China 569 00:31:46,880 --> 00:31:49,357 industry. You know, they're trying to commoditize it right out of the gate. 570 00:31:49,440 --> 00:31:52,797 You know, there's probably something to that. Um, you know, the the Chinese 571 00:31:52,880 --> 00:31:55,757 industrial economy does have a history of, you know, sort of, let's say, 572 00:31:55,840 --> 00:31:59,117 subsidized production that leads to selling, you know, selling things below 573 00:31:59,200 --> 00:32:04,237 cost in some cases. Um, but I think also it's it like I think that's almost too 574 00:32:04,320 --> 00:32:06,637 cynical of a view also because it's just like all right wow like they're really 575 00:32:06,720 --> 00:32:09,917 in the race like open source closed source whatever like that you know 576 00:32:10,000 --> 00:32:13,037 they're actually really in the race. Um, you know, we we've talked in the past, I 577 00:32:13,120 --> 00:32:16,477 think, on on on LP calls about, you know, these policy fights that, you 578 00:32:16,560 --> 00:32:18,717 know, we've been having in DC for the last two years. And, you know, there was 579 00:32:18,800 --> 00:32:22,157 a big pretty pretty big push within the US government, you know, two years ago 580 00:32:22,240 --> 00:32:25,597 to basically, you know, restrict, uh, you know, or outright ban, you know, a 581 00:32:25,680 --> 00:32:30,077 lot of AI. Um, and, you know, it's very easy for a country that is the only game 582 00:32:30,160 --> 00:32:31,517 in town to have those conversations. 583 00:32:31,600 --> 00:32:34,557 It's quite another thing if you're actually in a foot race with China. Um, 584 00:32:34,640 --> 00:32:37,197 and so I think actually the the the the 585 00:32:37,280 --> 00:32:41,597 policy landscape in DC has I would say has improved dramatically as a 586 00:32:41,680 --> 00:32:44,477 consequence of sort of an awareness now that this is actually a two- horse race, 587 00:32:44,560 --> 00:32:47,357 not a one-horse race. >> For sure. Yeah. Actually on on the point 588 00:32:47,440 --> 00:32:48,797 I'll I'll jump ahead here to policy and 589 00:32:48,880 --> 00:32:51,117 regulation just because it seems like uh 590 00:32:51,200 --> 00:32:54,797 the current stance on on 50 different 591 00:32:54,880 --> 00:32:57,277 set of AI laws by state seems like a 592 00:32:57,360 --> 00:33:02,397 catastrophic uh way to to put us effectively with a 593 00:33:02,480 --> 00:33:06,717 uh or one of our our hands tied behind our our back here in terms of the the AI 594 00:33:06,800 --> 00:33:08,797 race. What's a state of plan on that? 595 00:33:08,880 --> 00:33:12,477 Are folks recognizing that that would be catastrophic for progress and 596 00:33:12,560 --> 00:33:16,397 development? Where do most people at least stand on that topic today? 597 00:33:16,480 --> 00:33:17,997 Yeah. So it's a little bit complicated. 598 00:33:18,080 --> 00:33:21,197 So I'll rewind to say like two years ago I was very worried about like really 599 00:33:21,280 --> 00:33:24,637 ruinous federal federal legislation on AI and there was there was we you know 600 00:33:24,720 --> 00:33:27,117 we engaged you know kind of very heavily at that point which we've talked about 601 00:33:27,200 --> 00:33:29,997 in the past and I think the good news on that is I think the risk of that sitting 602 00:33:30,080 --> 00:33:34,237 here today is very low. Um I there's very little mood in DC on either side of 603 00:33:34,320 --> 00:33:38,157 the aisle uh to really you know essentially there's very little there's 604 00:33:38,240 --> 00:33:40,637 very little interest in doing anything that would prevent us from beating 605 00:33:40,720 --> 00:33:46,077 China. Um so so you know on the federal side things things are much better now. 606 00:33:46,160 --> 00:33:48,557 There there will there will be issues and there are tensions in the system but 607 00:33:48,640 --> 00:33:53,437 like things are looking looking pretty good. Um that has translated Jen to your 608 00:33:53,520 --> 00:33:55,837 point that's translated a lot of the attention to the states and basically 609 00:33:55,920 --> 00:33:59,197 what's happened is you know under our system of of federalism uh you know the 610 00:33:59,280 --> 00:34:02,717 states get to pass their own laws on a lot of things. Um and so uh yeah, 611 00:34:02,800 --> 00:34:05,597 basically you know a lot of you know and and you know with these things it's 612 00:34:05,680 --> 00:34:07,757 always a combination. A lot of well-meaning people are trying to figure 613 00:34:07,840 --> 00:34:09,757 out what to do at the state level and then of course there's a lot of 614 00:34:09,840 --> 00:34:13,437 opportunism where AI is just the hot topic. And so if you're a you know 615 00:34:13,520 --> 00:34:15,917 aggressive up and cominging state legislator or whatever in some state and 616 00:34:16,000 --> 00:34:18,077 you want to run for governor and then president you know you want to kind of 617 00:34:18,160 --> 00:34:21,437 attach yourself to the heat. Um and so there's like a political motivation to 618 00:34:21,520 --> 00:34:25,437 to do state level stuff. Um yeah and sitting here today like we're tracking 619 00:34:25,520 --> 00:34:29,757 on the order of,200 bills across the 50 states. And by the way, um, not just the 620 00:34:29,840 --> 00:34:33,197 blue states, also the red states. Um, and so, you know, I'm I've, you know, 621 00:34:33,280 --> 00:34:35,997 for the last like 5 years or whatever, I spent a lot of time complaining about, 622 00:34:36,080 --> 00:34:38,397 uh, you know, kind of what Democratic politicians are threatening to do to 623 00:34:38,480 --> 00:34:42,077 attack. There's also a lot of Republicans, like Republicans are not a 624 00:34:42,160 --> 00:34:44,877 block on this. And there are quite a few like local Republican officials in 625 00:34:44,960 --> 00:34:47,917 different states, um, that that also, I think, have, you know, let's say, you 626 00:34:48,000 --> 00:34:49,917 know, misinformed or ill-advised, um, 627 00:34:50,000 --> 00:34:55,677 views and are trying to put together, uh, put out bad bills. um you know it's 628 00:34:55,760 --> 00:34:58,077 a little bit weird that this is happening and that you know the federal 629 00:34:58,160 --> 00:35:01,677 government does have regulation of interstate commerce um and you know 630 00:35:01,760 --> 00:35:05,437 technology AI kind of by definition is interstate like you know there's there's 631 00:35:05,520 --> 00:35:07,197 no AI company that just operates in 632 00:35:07,280 --> 00:35:09,437 California or just operates in you know 633 00:35:09,520 --> 00:35:13,037 Colorado or Texas um you know AI of all 634 00:35:13,120 --> 00:35:16,797 technologies AI is obviously something this this sort of national in scope um 635 00:35:16,880 --> 00:35:19,197 you know it's sort it's sort of obvious that the federal government should be 636 00:35:19,280 --> 00:35:22,877 the regulator not not not the states um but but the federal government need 637 00:35:22,960 --> 00:35:24,877 needs to assert itself needs to step in. 638 00:35:24,960 --> 00:35:28,477 There there was actually an attempt to do that. There was a um there was an 639 00:35:28,560 --> 00:35:32,477 attempt to add a moratorium of state level AI regulation that basically would 640 00:35:32,560 --> 00:35:35,277 would reserve the right of the federal government to regulate AI and sort of 641 00:35:35,360 --> 00:35:38,317 prevent the states from moving forward with these bills. That was I think part 642 00:35:38,400 --> 00:35:41,917 of the negotiation for the quote one big beautiful bill and then that that there 643 00:35:42,000 --> 00:35:44,637 was a deal behind that and that deal kind of blew up at the at the last 644 00:35:44,720 --> 00:35:48,077 minute and that moratorium didn't happen and and you know in fairness the critics 645 00:35:48,160 --> 00:35:51,997 of that moratorum it probably was a was it was probably too much of a stretch. 646 00:35:52,080 --> 00:35:54,397 Well, it was I'm sorry. It was definitely too much of a stretch to get 647 00:35:54,480 --> 00:35:57,357 enough support to pass, but it was also probably too much of a stretch in terms 648 00:35:57,440 --> 00:35:59,757 of restricting the states from certain kinds of regulation that they really 649 00:35:59,840 --> 00:36:03,037 should be able to do. So, so it just it didn't quite come together. Um, there's 650 00:36:03,120 --> 00:36:06,317 a very active we're having very active discussions in DC right now about kind 651 00:36:06,400 --> 00:36:09,837 of the next, you know, the kind of the next turn on that. Um, you know, the 652 00:36:09,920 --> 00:36:12,317 administration is I would say the administration is very supportive of of 653 00:36:12,400 --> 00:36:15,197 the idea of of the federal government being in charge of this as part of it 654 00:36:15,280 --> 00:36:19,837 being an actual, you know, 50-st state issue. Um, and and and an issue of 655 00:36:19,920 --> 00:36:23,917 national importance. Um, and then, you know, I'd say most most Congress people 656 00:36:24,000 --> 00:36:27,357 on both sides of the aisle, you know, kind of get this. Um, so we just we we 657 00:36:27,440 --> 00:36:29,917 kind of have to figure out a way to, you know, to land this, but but I think 658 00:36:30,000 --> 00:36:32,397 that'll happen. Um, some of the state 659 00:36:32,480 --> 00:36:35,197 level bills are wild. Um, the the 660 00:36:35,280 --> 00:36:38,077 Colorado passed a very draconian uh 661 00:36:38,160 --> 00:36:41,357 regulation bill uh last year. Um, and 662 00:36:41,440 --> 00:36:45,357 against like furious objections from the local startup ecosystem in in in around 663 00:36:45,440 --> 00:36:48,557 Denver and Boulder. Um, and actually they're they're now actually trying to 664 00:36:48,640 --> 00:36:51,997 reverse their way out of that bill. um you know a year later some of the the 665 00:36:52,080 --> 00:36:55,277 nuance of it like the algorithmic discrimination and like how to mitigate 666 00:36:55,360 --> 00:36:59,197 like what were some of the the extreme versions of what they they had proposed. 667 00:36:59,280 --> 00:37:01,997 >> Yeah. So the really draconian one was the the one that we really fought hard 668 00:37:02,080 --> 00:37:05,757 was the one in California which was called SB1047 and it wasn't it it it was 669 00:37:05,840 --> 00:37:09,757 basically it was modeled basically after the was called the EU AI act. So the 670 00:37:09,840 --> 00:37:13,037 European Union's AI act. Okay. And this is the backdrop to all the US stuff 671 00:37:13,120 --> 00:37:16,317 which is the EU passed this bill called the AI act I don't know whatever two 672 00:37:16,400 --> 00:37:19,677 years ago and it basically has killed AI development in well it's actually killed 673 00:37:19,760 --> 00:37:24,317 AI development in Europe to a large extent. Um and then it even it it's so 674 00:37:24,400 --> 00:37:27,997 draconian that even even big American companies like Apple and Meta are not 675 00:37:28,080 --> 00:37:31,597 launching leading edge AI capabilities in their products in Europe. Like that 676 00:37:31,680 --> 00:37:34,317 that's how that's how like draconian that bill was. And it's it's sort of a 677 00:37:34,400 --> 00:37:38,557 classic it's a classic kind of European thing where they like you know like they 678 00:37:38,640 --> 00:37:41,357 just thought that you know they they have this kind of view that it's just 679 00:37:41,440 --> 00:37:43,917 like well you know we if we can't be the leader they literally say this by the 680 00:37:44,000 --> 00:37:45,917 way if we can't be the leaders in innovation at least we can be the 681 00:37:46,000 --> 00:37:48,477 leaders in regulation. Um and and and 682 00:37:48,560 --> 00:37:53,037 then they pass this like incredibly you know kind of ruinous uh selfharm you 683 00:37:53,120 --> 00:37:55,837 know kind of thing and then you know a few years pass and they're like oh my 684 00:37:55,920 --> 00:37:58,397 god what have we done and so they're you know they're kind of going through their 685 00:37:58,480 --> 00:38:02,957 own version of that. Um, by the way, you know, I I you know, when I talk about 686 00:38:03,040 --> 00:38:05,357 Europe, I I tend to be very dark about the whole thing. I will tell you the 687 00:38:05,440 --> 00:38:07,997 darkest people I know about Europe are the European entrepreneurs who moved to 688 00:38:08,080 --> 00:38:10,957 the US. Um, are just like absolutely 689 00:38:11,040 --> 00:38:15,197 furious about what's happening in in in Europe on this stuff. Um, but but even 690 00:38:15,280 --> 00:38:17,757 there, like it it's it's so bad in Europe, like they they shot themselves 691 00:38:17,840 --> 00:38:20,717 in the foot so badly that there's actually a process now at the at the EU 692 00:38:20,800 --> 00:38:22,077 to try to unwind that. They're trying to 693 00:38:22,160 --> 00:38:27,757 unwind the GDPR. So u anyway for people tracking Europe uh Mario Draghi um is 694 00:38:27,840 --> 00:38:30,397 the former I guess prime minister of Italy did this thing about a year ago 695 00:38:30,480 --> 00:38:33,677 called the Draghy report which is the report on European competitiveness and 696 00:38:33,760 --> 00:38:36,157 he kind of outlined kind of in great detail all the ways that Europe was 697 00:38:36,240 --> 00:38:39,277 holding itself back and part of it was overregulation areas like AI. So so 698 00:38:39,360 --> 00:38:43,277 they're trying to reverse out of that or making gestures you know we'll we'll see 699 00:38:43,360 --> 00:38:48,157 what happens. Um it in the middle of all that, California 700 00:38:48,240 --> 00:38:51,517 sort of inexplicably decided to basically copycat the EU AI act and try 701 00:38:51,600 --> 00:38:55,277 to apply it to California. Um which might strike you as completely insane. 702 00:38:55,360 --> 00:38:59,597 To which I would say yes, welcome to California. Um uh and um you know, it 703 00:38:59,680 --> 00:39:03,597 was this basically this like Sacramento political dynamic that kind of got got 704 00:39:03,680 --> 00:39:06,877 crazy. Um it would have you know completely killed you know AI 705 00:39:06,960 --> 00:39:10,877 development in California. Um unfortunately our governor vetoed it at 706 00:39:10,960 --> 00:39:14,237 the last minute. Um it did pass both houses legislature that he vetoed at the 707 00:39:14,320 --> 00:39:17,997 last minute. Um it to Jen to your point it would have done for it would have 708 00:39:18,080 --> 00:39:20,717 done a whole bunch of things that were ruinously uh bad. But one of the things 709 00:39:20,800 --> 00:39:21,757 it would have done is it would have 710 00:39:21,840 --> 00:39:24,717 assigned downstream liability um uh to 711 00:39:24,800 --> 00:39:28,957 open source developers. Um and so you know we talked about you know this 712 00:39:29,040 --> 00:39:31,277 Chinese open source thing. Okay so you got Chinese out there with open source. 713 00:39:31,360 --> 00:39:34,237 Now you're gonna have American companies that have open source AI. And by the way 714 00:39:34,320 --> 00:39:36,717 you're also going to have American academics and just like independent 715 00:39:36,800 --> 00:39:40,077 people in their nights and weekends developing open source. um you know 716 00:39:40,160 --> 00:39:43,117 which is a key way that all this technology proliferates and and so this 717 00:39:43,200 --> 00:39:46,477 this law would have assigned downstream liability to any misuse of open source 718 00:39:46,560 --> 00:39:49,997 to the original developer of the open source and so you know you're an 719 00:39:50,080 --> 00:39:53,197 independent developer or you're an academic or you're a startup you develop 720 00:39:53,280 --> 00:39:56,477 and release an AI model the AI model works fine the day you release it it's 721 00:39:56,560 --> 00:39:59,677 great but like 5 years later it gets built into a nuclear power plant and 722 00:39:59,760 --> 00:40:02,557 then there's a meltdown of the nuclear power plant and then somebody says oh 723 00:40:02,640 --> 00:40:05,357 it's the fault of the AI um the the the 724 00:40:05,440 --> 00:40:09,757 the legal liability for that nuclear meltdown or for anything any other 725 00:40:09,840 --> 00:40:12,557 practical real world thing that would follow in the out years would then be 726 00:40:12,640 --> 00:40:15,277 assigned back to that open source developer. Of course, this is completely 727 00:40:15,360 --> 00:40:18,557 insane. It would completely kill open source. It would completely kill 728 00:40:18,640 --> 00:40:21,357 startups doing open source. It would completely kill academic research like 729 00:40:21,440 --> 00:40:25,677 in its entirety. Um, you know, anything in the field. Um, and so, you know, that 730 00:40:25,760 --> 00:40:28,957 like that's the level of playing with fire. Um, you know, kind of that these 731 00:40:29,040 --> 00:40:32,557 state level politicians have become enamored with. Um, like I said, I think 732 00:40:32,640 --> 00:40:35,117 the good news is the feds understand this. I suspect that this is going to 733 00:40:35,200 --> 00:40:38,557 get resolved, but it but it does need to get resolved because, you know, just as 734 00:40:38,640 --> 00:40:40,957 a country, it just doesn't make any sense to let let the states kind of 735 00:40:41,040 --> 00:40:46,077 operate suicidally like this. Um, and so that's what we're doing. You know, we we 736 00:40:46,160 --> 00:40:49,277 talk about this, we call this our little tech agenda. Um, we're extremely focused 737 00:40:49,360 --> 00:40:52,957 on on on the freedom and starters innovate. We are not trying to argue, 738 00:40:53,040 --> 00:40:56,557 you know, many many other issues. We operate in a completely bipartisan 739 00:40:56,640 --> 00:40:59,997 fashion. We have extensive um support, you know, on both sides of the aisle and 740 00:41:00,080 --> 00:41:04,397 for both sides of the aisle. Um, so it's it's a truly bipartisan effort. um very 741 00:41:04,480 --> 00:41:07,277 policy based and you know I think very much aligned with the interests of the 742 00:41:07,360 --> 00:41:12,077 country uh broadly um and so that is what we're doing and then and then the 743 00:41:12,160 --> 00:41:15,357 other question we get we we get actually you know in some cases from LP but in a 744 00:41:15,440 --> 00:41:17,357 lot of cases actually from employees um 745 00:41:17,440 --> 00:41:22,557 is like okay why us right like you know you know with with any sort of you know 746 00:41:22,640 --> 00:41:25,117 policy question like this there's always this collective action question which is 747 00:41:25,200 --> 00:41:28,237 just like you know tragedy of the commons which is in theory like 748 00:41:28,320 --> 00:41:31,117 everybody every venture firm every tech company whatever should be weighing in 749 00:41:31,200 --> 00:41:34,877 on these things in practice what happens is mo most them just simply don't. Um, 750 00:41:34,960 --> 00:41:37,437 and so at some point it falls on somebody's shoulders to fight these 751 00:41:37,520 --> 00:41:41,117 things. And we we we Ben and I just basically concluded that the stakes here 752 00:41:41,200 --> 00:41:44,157 were just way too high. You know, if if we're going to be the industry leader, 753 00:41:44,240 --> 00:41:48,237 we just have to take responsibility for our own destiny. You know, for better or 754 00:41:48,320 --> 00:41:50,797 for worse, I think that's the cost of doing business uh for being the leader 755 00:41:50,880 --> 00:41:53,597 in the field right now. >> Before we get off the topic of of AI, I 756 00:41:53,680 --> 00:41:56,477 want to go back to one question that that was submitted in. So, do you think 757 00:41:56,560 --> 00:42:01,677 usage based or utility is a right way to price an AI compared to seeds? 758 00:42:01,760 --> 00:42:05,117 Ah that is a fantastic question. So this is one of these giant this is in my my 759 00:42:05,200 --> 00:42:08,157 list of what I call the trillion dollar questions u where you know depending on 760 00:42:08,240 --> 00:42:11,357 how this is answered will drive you know trillions of dollars of market value. So 761 00:42:11,440 --> 00:42:15,037 yeah so usage based pricing it's it's actually 762 00:42:15,120 --> 00:42:17,437 it's actually fairly amazing if you think about this from a startup 763 00:42:17,520 --> 00:42:20,397 standpoint from a venture standpoint it's actually fairly amazing what's 764 00:42:20,480 --> 00:42:22,957 happened and I'm trying I'm not really talking about this in public because I 765 00:42:23,040 --> 00:42:25,597 don't really I because I don't want it to stop. I think it's actually quite 766 00:42:25,680 --> 00:42:29,997 amazing. Um, which is you have these technology companies, you know, these 767 00:42:30,080 --> 00:42:32,797 big tech companies with these like incredible R&D capabilities that are 768 00:42:32,880 --> 00:42:35,677 building these big models, these big AI models with this incredible, you know, 769 00:42:35,760 --> 00:42:39,357 new new kind of new new kind of intelligence. And then it it turns out 770 00:42:39,440 --> 00:42:42,637 that they were already in a war. They were already in the cloud war, right? 771 00:42:42,720 --> 00:42:45,357 And so they were already in the war for kind of cloud services. And this is like 772 00:42:45,440 --> 00:42:48,797 AWS versus Azure versus uh Google Cloud. 773 00:42:48,880 --> 00:42:51,837 Um, you know, and then all the all these other all these other cloud efforts. And 774 00:42:51,920 --> 00:42:55,517 so what what what what actually happened was they sort of like there's an 775 00:42:55,600 --> 00:42:58,877 alternate universe in which they basically just kept all of their magic 776 00:42:58,960 --> 00:43:03,197 AI secret and captive and just used it in their own business um or used it to 777 00:43:03,280 --> 00:43:06,797 just compete with more companies um you know in more in more categories but 778 00:43:06,880 --> 00:43:10,397 instead what they've done is they've basically you know if I commod 779 00:43:10,480 --> 00:43:13,757 commoditize is too strong a word but they they have they have proliferated 780 00:43:13,840 --> 00:43:17,837 their magic new technology through their cloud business um which is which is this 781 00:43:17,920 --> 00:43:20,557 business that just has these like incredible scale you know kind of kind 782 00:43:20,640 --> 00:43:23,757 of components to But um you know and sort of this hyper competition between 783 00:43:23,840 --> 00:43:26,797 the providers and these you know these these prices that that come down very 784 00:43:26,880 --> 00:43:29,837 fast. Um, and so you've got like the most magic new technology in the world 785 00:43:29,920 --> 00:43:34,157 and then it's basically being served up by those companies in in in a as a cloud 786 00:43:34,240 --> 00:43:37,037 business and made made basically available to everybody on the planet to 787 00:43:37,120 --> 00:43:40,797 just click and use and for like relatively small amounts of money and 788 00:43:40,880 --> 00:43:44,077 then on on a usage basis which means and usage is great for startups because you 789 00:43:44,160 --> 00:43:47,197 it means you can start easily right you the the the you know there's very you 790 00:43:47,280 --> 00:43:50,477 know there's basically no fixed co for a startup building an AI app they don't 791 00:43:50,560 --> 00:43:53,597 have giant fixed cost because they could just tap into the open AI or anthropic 792 00:43:53,680 --> 00:43:57,117 or Google or Microsoft or whatever you know cloud you know tokens by the you 793 00:43:57,200 --> 00:44:00,397 know, intelligence tokens by the drink offering and just get going. Um, and so 794 00:44:00,480 --> 00:44:03,517 it's it's kind of this this from this from the startup standpoint, it's like 795 00:44:03,600 --> 00:44:06,077 this marvelous thing where like the most magical thing in the world is available 796 00:44:06,160 --> 00:44:11,357 by the drink. You know, it's absolutely amazing. Um, uh, I, you know, and, you 797 00:44:11,440 --> 00:44:13,517 know, that model, you know, by the way, that model's working and those companies 798 00:44:13,600 --> 00:44:15,757 are happy and they're growing really fast and they're, you know, happily 799 00:44:15,840 --> 00:44:18,557 reporting massive cloud revenue growth and, you know, they they're happy with 800 00:44:18,640 --> 00:44:21,837 the margins and so forth and so, you know, I think generally it's working. 801 00:44:21,920 --> 00:44:25,357 Um, and those businesses are, I think, likely to get much larger. Um and so I 802 00:44:25,440 --> 00:44:28,637 think you know generally that's going to work but but to to to the question like 803 00:44:28,720 --> 00:44:32,397 that doesn't mean that the optimal pricing model for for example all of the 804 00:44:32,480 --> 00:44:35,757 applications should be tokens by the drink and in fact very much I think not 805 00:44:35,840 --> 00:44:39,357 the case. Um you know we spend a lot of time working we actually have you know 806 00:44:39,440 --> 00:44:42,797 dedicated you know experts on on pricing in our firm. We spend a lot of time with 807 00:44:42,880 --> 00:44:45,757 our companies working on pricing because it's you know it's really this magical 808 00:44:45,840 --> 00:44:48,077 art and science that that a lot of companies don't take don't take 809 00:44:48,160 --> 00:44:50,797 seriously enough. So we spend a lot of time with other companies on this. And 810 00:44:50,880 --> 00:44:54,397 of course, you know, a core principle of pricing is you don't want to price by 811 00:44:54,480 --> 00:44:58,317 cost if you can avoid it. You want to price by value, right? Like you want to 812 00:44:58,400 --> 00:45:01,757 price you price where you're getting a percentage of the business value um of, 813 00:45:01,840 --> 00:45:04,397 you know, especially when you're selling two businesses, you want to price as a 814 00:45:04,480 --> 00:45:07,837 percentage of the business value that you're getting. And so so you do have 815 00:45:07,920 --> 00:45:10,557 some AI startups that are that are pricing by the drink for certain things 816 00:45:10,640 --> 00:45:13,517 that they're doing, but you have many others that are exploring other pricing 817 00:45:13,600 --> 00:45:17,037 models. uh you know some that are just like replications of SAS pricing models 818 00:45:17,120 --> 00:45:19,677 but you also have other companies are explor exploring pricing models for 819 00:45:19,760 --> 00:45:22,077 example of well if the AI can actually 820 00:45:22,160 --> 00:45:24,717 do the job of a coder or the AI could do 821 00:45:24,800 --> 00:45:26,957 the job of a doctor or a nurse or a 822 00:45:27,040 --> 00:45:29,677 radiologist or a lawyer or a parallegal 823 00:45:29,760 --> 00:45:34,157 right or whatever or a teacher. Um you know basically can you can could can you 824 00:45:34,240 --> 00:45:37,517 price by value and can you get a percentage of the value of what of what 825 00:45:37,600 --> 00:45:40,397 of of of what otherwise would would would have been you know would have been 826 00:45:40,480 --> 00:45:44,237 literally a person. um you know or or by the way equivalently can you price by 827 00:45:44,320 --> 00:45:47,197 marginal productivity. So if you can take a human doctor and make them much 828 00:45:47,280 --> 00:45:49,917 more productive because you give them AI, you know, can you price as a 829 00:45:50,000 --> 00:45:53,117 percentage of kind of the productivity uplift, uh, you know, from the from from 830 00:45:53,200 --> 00:45:56,557 the from the augment, you know, the comb symbiotic relationship between the the 831 00:45:56,640 --> 00:46:00,797 human being and and the AI. Um, and so I I think what we see in startup land is 832 00:46:00,880 --> 00:46:04,397 like a lot of experimentation happening on on these pricing models. And I and I 833 00:46:04,480 --> 00:46:08,877 and I think again I I think that's like super healthy. Um, I I you know, I was 834 00:46:08,960 --> 00:46:11,517 in this little speech on this is like high prices are really underappreciated. 835 00:46:11,600 --> 00:46:14,877 High prices are often a favorite of the customer. It's actually really funny. A 836 00:46:14,960 --> 00:46:17,597 lot of like the naive view on pricing is the lower the price, the better it is 837 00:46:17,680 --> 00:46:19,997 for the customer. The the more sophisticated looking at it is higher 838 00:46:20,080 --> 00:46:22,637 prices are often good for the customer because a higher price means that the 839 00:46:22,720 --> 00:46:26,957 vendor can make the product better faster, right? Like you can actually 840 00:46:27,040 --> 00:46:29,917 companies with higher prices, higher margins can actually invest more in R&D 841 00:46:30,000 --> 00:46:33,277 and they can actually make the product better. Um and you know most people who 842 00:46:33,360 --> 00:46:35,677 buy things aren't just looking for the cheapest price. They want something 843 00:46:35,760 --> 00:46:38,957 that's really that's going to work really well. Um and so often high 844 00:46:39,040 --> 00:46:42,957 prices, you know, the customer doesn't ever say this. it'll never show up in a 845 00:46:43,040 --> 00:46:46,397 survey. Um, but but the high price can actually be a gift for the customer 846 00:46:46,480 --> 00:46:48,557 because it can make the vendor better, can make the product better, and 847 00:46:48,640 --> 00:46:49,757 ultimately make the customer better off. 848 00:46:49,840 --> 00:46:53,677 And so I I'm I'm very encouraged by the degree to which the AI entrepreneurs are 849 00:46:53,760 --> 00:46:55,997 willing to run these experiments. And I, you know, we'll have to see where it 850 00:46:56,080 --> 00:46:59,197 pans out. But at least so far, I feel I feel good about the the uh, you know, at 851 00:46:59,280 --> 00:47:00,957 least the attitude of the industry about it. 852 00:47:01,040 --> 00:47:04,077 >> Awesome. I actually uh I was, as you were gone through, I had probably 10 853 00:47:04,160 --> 00:47:07,437 more follow-up questions, but I'm actually going to go back to um a topic 854 00:47:07,520 --> 00:47:09,837 you had uh briefly, the trillion dollar 855 00:47:09,920 --> 00:47:14,557 questions. Will open source or close source win? Feels like we we've come out 856 00:47:14,640 --> 00:47:17,677 on this this debate or where do you where do you put that? 857 00:47:17,760 --> 00:47:20,957 >> No, I think this is still open. I I think this is still very open. Um you 858 00:47:21,040 --> 00:47:25,517 know that like the the the closed source models keep getting better. Um uh by the 859 00:47:25,600 --> 00:47:28,557 way if you generally if you just like take the temperature of the people 860 00:47:28,640 --> 00:47:31,677 working at the big labs who work on the big proprietary models like generally 861 00:47:31,760 --> 00:47:35,437 what they'll tell you is progress is continuing at a very rapid pace. Um you 862 00:47:35,520 --> 00:47:37,597 know there's there's this you know there's this periodic concern that kind 863 00:47:37,680 --> 00:47:40,557 of shows up on online which is or in the in the market which is like you know 864 00:47:40,640 --> 00:47:43,757 maybe the capabilities these models are topping out um and you know there's 865 00:47:43,840 --> 00:47:46,397 certain there's there's certain areas in which you know there's there's you know 866 00:47:46,480 --> 00:47:49,757 people are working but like the people working at the big labs are like oh no 867 00:47:49,840 --> 00:47:52,797 we have like 800 new idea like we have tons of new ideas we have tons of new 868 00:47:52,880 --> 00:47:56,397 ways of doing things. We we might need to find new ways to scale but like we we 869 00:47:56,480 --> 00:47:57,997 have a lot of ideas on how to do that. 870 00:47:58,080 --> 00:48:00,157 We know a lot of ways to make these things better and you know we're 871 00:48:00,240 --> 00:48:02,957 basically making new discoveries all the time. So like I would say you know 872 00:48:03,040 --> 00:48:06,237 generally the people working in the like across all the big labs are are pretty 873 00:48:06,320 --> 00:48:10,077 optimistic. Um and so like I I think the big models are going to continue to get 874 00:48:10,160 --> 00:48:13,597 better you know very quickly here and then you know overall um and then the 875 00:48:13,680 --> 00:48:16,717 open source models continue to get better. Um and like I said you know you 876 00:48:16,800 --> 00:48:19,117 know every every every I don't know every month or something there's like 877 00:48:19,200 --> 00:48:22,157 another big release of like something like this Kimmy thing. Um where it's 878 00:48:22,240 --> 00:48:24,877 just like wow like you know that's amazing and you know wow they really 879 00:48:24,960 --> 00:48:27,517 like shrunk that down and got that capability on a very small form factor. 880 00:48:27,600 --> 00:48:30,957 Um uh and so um yeah that's the case and 881 00:48:31,040 --> 00:48:34,557 then you know I maybe just the third kind of thing to bring up is um the 882 00:48:34,640 --> 00:48:38,477 other really nice benefit of open source um is that uh open source is the thing 883 00:48:38,560 --> 00:48:43,437 that's easy to learn from right um and so if you're a you know computer sc if 884 00:48:43,520 --> 00:48:47,437 you're a computer science professor who wants to teach a class on on CS on AI or 885 00:48:47,520 --> 00:48:49,917 if you're a computer science student that's trying to learn about it or if 886 00:48:50,000 --> 00:48:53,837 you're just like a normal engineer in a normal company trying to learn this new 887 00:48:53,920 --> 00:48:57,757 thing um or just somebody in your you know by the way somebody in basement at 888 00:48:57,840 --> 00:49:00,797 night with a startup idea. Um the existence of these of these 889 00:49:00,880 --> 00:49:03,837 state-of-the-art open source models is amazing because that's the education 890 00:49:03,920 --> 00:49:06,797 that you need. Like they actually these open source models actually show you how 891 00:49:06,880 --> 00:49:11,837 to do everything. Um right. Um and so like and and what that's leading to 892 00:49:11,920 --> 00:49:14,717 right is the proliferation of the knowledge about how to build AI is like 893 00:49:14,800 --> 00:49:18,557 expanding very fast. Um again as compared to a counterfactual world in 894 00:49:18,640 --> 00:49:22,077 which it was all basically bottled up in two or three big companies. And so, you 895 00:49:22,160 --> 00:49:25,277 know, the open source thing is also just proliferating knowledge and then that 896 00:49:25,360 --> 00:49:29,037 knowledge is generating a lot of new people. Um, and so I I you know, you 897 00:49:29,120 --> 00:49:32,397 know, as you guys have all seen sitting here today, AI researchers are at an 898 00:49:32,480 --> 00:49:35,357 enormous premium. You know, AI researchers today are getting paid more 899 00:49:35,440 --> 00:49:37,277 than professional athletes. Um, right? 900 00:49:37,360 --> 00:49:40,797 Like, you know, and that's right, that's a supply demand imbalance there. There 901 00:49:40,880 --> 00:49:45,197 aren't enough of them to go around. But, you know, again, shortages create glut. 902 00:49:45,280 --> 00:49:48,957 um the the number of the number of smart people in the world who are coming up to 903 00:49:49,040 --> 00:49:52,397 speed very quickly on how to build these things u I mean some of the best AI 904 00:49:52,480 --> 00:49:55,917 people in the world are like 22 23 24 like they you know kind of by definition 905 00:49:56,000 --> 00:49:58,957 they haven't been in the field that long you know you know they they can't have 906 00:49:59,040 --> 00:50:02,317 been experts their whole lives right so you know they they kind of have to have 907 00:50:02,400 --> 00:50:05,197 come up to speed over the course of the last four or five years and and if if 908 00:50:05,280 --> 00:50:07,757 they if they've been able to do that then then there's going to be a lot more 909 00:50:07,840 --> 00:50:11,277 in the future that are going to do that um and so just the the the sort of 910 00:50:11,360 --> 00:50:14,637 spread of the level of expertise on this technology is happening now very quickly 911 00:50:14,720 --> 00:50:18,077 Um, so I yeah, I mean I think it's still like I said, I think it's I think it's 912 00:50:18,160 --> 00:50:20,877 still a race. And and by the way, you know, look, the long-term answer may 913 00:50:20,960 --> 00:50:24,957 well just be both. Um, you know, like I said, if you if you believe my pyramid 914 00:50:25,040 --> 00:50:28,397 industry structure, then there will then there will certainly be a large business 915 00:50:28,480 --> 00:50:32,477 of whatever is the smartest thing almost regardless of how of how much it costs. 916 00:50:32,560 --> 00:50:35,917 Um, and then there but there will also be this just giant volume market of of 917 00:50:36,000 --> 00:50:38,717 smaller models everywhere, which which is what we're also seeing. 918 00:50:38,800 --> 00:50:42,557 >> Yep. Yep. The another question you had posed at at that point in time was will 919 00:50:42,640 --> 00:50:45,837 incumbents versus startups went and at that point in time I think there was a 920 00:50:45,920 --> 00:50:50,077 mixed bag of where the incumbents were approaching AI. I think that's radically 921 00:50:50,160 --> 00:50:52,157 changed in the last two years. Um and 922 00:50:52,240 --> 00:50:54,477 then on the counter example the the 923 00:50:54,560 --> 00:50:58,077 blossoming of startups increasingly now 924 00:50:58,160 --> 00:51:01,677 maybe migrating into the incumbent category just how big they since that 925 00:51:01,760 --> 00:51:05,357 time. You you want to take that uh question and and give uh your assessment 926 00:51:05,440 --> 00:51:07,757 of where where the state of the world is? 927 00:51:07,840 --> 00:51:09,917 >> Yeah. Yeah. So, I mean, look, you know, big companies that are definitely, you 928 00:51:10,000 --> 00:51:12,957 know, playing hard. You know, Google's playing hard. Meta's playing hard. Um, 929 00:51:13,040 --> 00:51:16,557 Amazon, um, Microsoft, um, you know, there's a bunch of these companies that 930 00:51:16,640 --> 00:51:19,517 are, you know, that are kind of in in in there, um, you know, very aggressively. 931 00:51:19,600 --> 00:51:21,677 And then you've got these, you know, what we call the new incumbents like 932 00:51:21,760 --> 00:51:25,917 Anthropic and and, uh, and Open AI. Um, but you also have like, you know, even 933 00:51:26,000 --> 00:51:28,557 in the last two years, you've had this birth of all of a sudden like brand new 934 00:51:28,640 --> 00:51:31,517 companies that are almost instant incumbents. And you, you could say XAI 935 00:51:31,600 --> 00:51:36,477 is one of those. Uh, ML, by the way, ML is the great outlier to my Europe thing 936 00:51:36,560 --> 00:51:39,837 from earlier. like Mald is actually doing very well as sort of the European 937 00:51:39,920 --> 00:51:43,677 kind of uh you know French national European uh continental you know kind of 938 00:51:43,760 --> 00:51:48,237 AI champion um sort of the you know the exception that proves the rule um but 939 00:51:48,320 --> 00:51:50,317 you know there's there's a bunch of these now that are like you know doing 940 00:51:50,400 --> 00:51:53,357 quite well and are kind of becoming new incumbents um and then of course there's 941 00:51:53,440 --> 00:51:56,077 tons of startups by the way there's and then there's there's actual foundation 942 00:51:56,160 --> 00:52:00,077 model startups right and so you know we funded uh you know we funded Ilas out of 943 00:52:00,160 --> 00:52:03,597 open AAI to do a new foundation model company we funded Miriam Maratti also 944 00:52:03,680 --> 00:52:06,877 out of open AI we funded Faith Ali out of Stanford to do a world 945 00:52:06,960 --> 00:52:09,837 foundation model company and so you know there you know there's there are new 946 00:52:09,920 --> 00:52:14,077 swings all all you know all early but very promising um for to kind of build 947 00:52:14,160 --> 00:52:17,837 you know new incumbents quickly um and so you know that's all happening and 948 00:52:17,920 --> 00:52:20,317 then and then you know what and then on top of that there's just this giant 949 00:52:20,400 --> 00:52:23,277 explosion of AI application companies right and so there there's basically 950 00:52:23,360 --> 00:52:27,197 companies that then usually startups that basically take the technology and 951 00:52:27,280 --> 00:52:30,877 then you know field it in a specific domain whether that's law or medicine or 952 00:52:30,960 --> 00:52:33,997 education or you know creativity um or 953 00:52:34,080 --> 00:52:36,957 or or or whatever Um but again here it's 954 00:52:37,040 --> 00:52:39,117 just like it's amazing kind of how how 955 00:52:39,200 --> 00:52:43,997 sophisticated things are getting very quickly. So 956 00:52:44,080 --> 00:52:46,717 talk about the application companies for a moment. So like an application company 957 00:52:46,800 --> 00:52:49,437 like classic example is like a cursor is like an application company. So they 958 00:52:49,520 --> 00:52:53,517 take the core AI capability which they purchase by the drink from you know 959 00:52:53,600 --> 00:52:57,037 anthropic or open AI or Google um you know to tokens by the drink and then 960 00:52:57,120 --> 00:53:01,997 they they they build a code basically a code editor what we used to call an IDE 961 00:53:02,080 --> 00:53:05,357 um integrated development environment or basically like a a software creation 962 00:53:05,440 --> 00:53:09,917 system um so they build like an AI coding system um on on top of the 963 00:53:10,000 --> 00:53:13,037 anthropic or open AAI or whatever you know kind of kind of big models feel 964 00:53:13,120 --> 00:53:16,077 that and that the the critique of those companies in the industry has been oh 965 00:53:16,160 --> 00:53:19,437 those are what are called called GPT rappers is kind of the pjorative And the 966 00:53:19,520 --> 00:53:22,557 idea basically being is well they're not actually like they're not actually doing 967 00:53:22,640 --> 00:53:26,237 anything that's going to preserve value because the the actual the the whole 968 00:53:26,320 --> 00:53:29,117 point of what they're doing is they're surfacing AI but it's not their AI. The 969 00:53:29,200 --> 00:53:32,157 the AI that's being surfaced is from somebody else. And so these are kind of 970 00:53:32,240 --> 00:53:35,357 these pass pass through shell things that ultimately won't have value. It 971 00:53:35,440 --> 00:53:38,077 actually turns out what's happening is kind of the opposite of that which is 972 00:53:38,160 --> 00:53:42,397 the the leading uh AI application companies like Cursor I mean f first of 973 00:53:42,480 --> 00:53:45,117 all what they're discovering is they they're not just using a single AI 974 00:53:45,200 --> 00:53:48,237 model. they're actually they actually as these products get more sophisticated 975 00:53:48,320 --> 00:53:50,877 they actually end up using many different kinds of models that are kind 976 00:53:50,960 --> 00:53:54,957 of customtailored to the specific aspects of how these products work. Um 977 00:53:55,040 --> 00:53:57,437 and so they may start out using one model but they end up using a dozen 978 00:53:57,520 --> 00:54:00,157 models and then in the fullness of time it might be 50 or 100 different models 979 00:54:00,240 --> 00:54:03,677 for different aspects of the product. A and then B they end up building a lot of 980 00:54:03,760 --> 00:54:07,517 their own models. Um and so they they a lot of these the leading edge 981 00:54:07,600 --> 00:54:09,837 application companies are actually backward integrating and actually 982 00:54:09,920 --> 00:54:12,717 building their own AI models because because they have the deepest 983 00:54:12,800 --> 00:54:14,957 understanding of their domain. and they're able to build the model that's 984 00:54:15,040 --> 00:54:19,597 best suited to that. Um, and then by the way, also AI open source, they're also 985 00:54:19,680 --> 00:54:23,437 able to pick up and run an open source models. Um, and so if they don't like 986 00:54:23,520 --> 00:54:27,437 the economics of of buying intelligence, you know, by the drink from a from a 987 00:54:27,520 --> 00:54:30,157 from a cloud service provider, you know, they can pick up one of these open 988 00:54:30,240 --> 00:54:32,477 source models and implement it instead, which, you know, which these companies 989 00:54:32,560 --> 00:54:36,397 are also doing. Um, and so the the best of the best of the AI application 990 00:54:36,480 --> 00:54:39,757 companies are they are actually full-fledged deep technology companies 991 00:54:39,840 --> 00:54:43,357 actually building their own AI. Um and so that you know that's I think 992 00:54:43,440 --> 00:54:46,397 >> small models though right Mark when you think about god models versus small 993 00:54:46,480 --> 00:54:49,117 models as you were describing that but that would be small would you categorize 994 00:54:49,200 --> 00:54:51,997 that as a small >> well some of them I mean we I will let 995 00:54:52,080 --> 00:54:54,477 them I will let them announce you know whatever they're doing whenever it's 996 00:54:54,560 --> 00:54:58,077 appropriate but some of them are now also doing big model development um and 997 00:54:58,160 --> 00:55:00,797 again this this is also part of what this is also part of the learning just 998 00:55:00,880 --> 00:55:04,157 in the last two years well so like here's a big learning just from the last 999 00:55:04,240 --> 00:55:06,477 two years which is very interesting which is two years ago or three years 1000 00:55:06,560 --> 00:55:10,157 ago for sure you would have said wow open AI is like way out ahead um and 1001 00:55:10,240 --> 00:55:12,237 like it's probably going to be impossible for anybody to catch up and 1002 00:55:12,320 --> 00:55:14,717 then it's like okay well Anthropic caught up and so but you know they came 1003 00:55:14,800 --> 00:55:17,677 out of open AI and so they had all the secrets you know whatever and so knew 1004 00:55:17,760 --> 00:55:20,477 how to do it and so okay they caught up but surely nobody can catch up after 1005 00:55:20,560 --> 00:55:23,837 them and then very quickly after that there were a raft of other companies 1006 00:55:23,920 --> 00:55:27,357 that caught up very fast and and XAI is maybe the best example of that which is 1007 00:55:27,440 --> 00:55:32,157 like you know XAI you know Elon's company XAI is the company name gro is 1008 00:55:32,240 --> 00:55:35,917 the consumer product version of it um XAI basically caught up to you know 1009 00:55:36,000 --> 00:55:39,597 state-of-the-art openai anthropic level in in like less than 12 months from a 1010 00:55:39,680 --> 00:55:43,757 standing start right and So, and again that that kind of argues against any 1011 00:55:43,840 --> 00:55:47,037 kind of permanent lead, right, by by any one incumbent that's just going to 1012 00:55:47,120 --> 00:55:49,837 basically be able to lock the entire market down like if you can catch up 1013 00:55:49,920 --> 00:55:52,877 like that. And then and then as we as we've discussed the you know the China 1014 00:55:52,960 --> 00:55:54,797 part is all new in the last year, right? 1015 00:55:54,880 --> 00:55:58,877 The deepseek uh this the deepseek moment I think was in January or February of 1016 00:55:58,960 --> 00:56:02,877 this year, right? So less than 12 months ago. Um and so and now you've got like 1017 00:56:02,960 --> 00:56:06,077 four Chinese companies that have effectively caught up. And so, you know, 1018 00:56:06,160 --> 00:56:09,277 so it's like, all right, I mean, again, this is these are these are trillion 1019 00:56:09,360 --> 00:56:13,037 dollar questions, not answers. But it's just like, wow, okay, like it's one of 1020 00:56:13,120 --> 00:56:16,717 these things where once somebody proves that it's capable, it seems to not be 1021 00:56:16,800 --> 00:56:19,837 that hard for other people to be able to catch up, even people with far less 1022 00:56:19,920 --> 00:56:23,517 resources. Um, and so, you know, I don't know what that does. Maybe it makes you 1023 00:56:23,600 --> 00:56:27,117 slightly more skeptical in the long run economics of of the big players. On the 1024 00:56:27,200 --> 00:56:30,797 other hand, maybe it makes you like more bullish about the startup ecosystem. Uh, 1025 00:56:30,880 --> 00:56:33,597 it certainly should make you more bullish about uh startup application 1026 00:56:33,680 --> 00:56:36,317 companies, right? being able to do interesting things, which is why we're 1027 00:56:36,400 --> 00:56:39,917 so excited about that. Um, you know, it should make you probably, you know, a 1028 00:56:40,000 --> 00:56:44,477 bit more excited about about certainly about China. Um, 1029 00:56:44,560 --> 00:56:47,357 on the other hand, the Chinese competition putting pressure on the 1030 00:56:47,440 --> 00:56:49,997 American system to not screw itself up is very positive. So, it should probably 1031 00:56:50,080 --> 00:56:53,597 make you a little bit more bullish on the US. Um, and so, yeah, I think, you 1032 00:56:53,680 --> 00:56:56,797 know, the these are, yeah, these are yeah, these are are live dynamics and I 1033 00:56:56,880 --> 00:56:59,757 I think we still need more time to pass before we know the exact answer. I 1034 00:56:59,840 --> 00:57:01,997 should say this, but sometime because sometimes I don't sometimes I freak 1035 00:57:02,080 --> 00:57:06,237 people out when I say these are open questions. Um, when a company is 1036 00:57:06,320 --> 00:57:09,757 confronted with fundamentally open strategic or economic questions, it's 1037 00:57:09,840 --> 00:57:14,157 often a big problem because a company needs to have a strategy and the 1038 00:57:14,240 --> 00:57:18,077 strategy needs to be very specific. Um, and a company has to make like very 1039 00:57:18,160 --> 00:57:21,837 specific concrete choices about where it like deploys investment dollars and 1040 00:57:21,920 --> 00:57:25,277 personnel and like the strategy has to be like logical and coherent or the 1041 00:57:25,360 --> 00:57:26,637 company kind of collapses into chaos. 1042 00:57:26,720 --> 00:57:29,597 And so like companies like need to answer these questions and if they get 1043 00:57:29,680 --> 00:57:30,797 the answers wrong, they're really in 1044 00:57:30,880 --> 00:57:34,637 trouble. Um, venture We have our issues 1045 00:57:34,720 --> 00:57:38,317 and venture but a huge advantage that we have is we don't have to we we can bet 1046 00:57:38,400 --> 00:57:42,397 on multiple strategies at the same time right um and and we are doing this so we 1047 00:57:42,480 --> 00:57:45,437 are betting on big models and small models and prepared train models and 1048 00:57:45,520 --> 00:57:48,797 open source models right and and you know and foundation models and 1049 00:57:48,880 --> 00:57:53,677 applications right uh and consumer and enterprise and so the portfolio approach 1050 00:57:53,760 --> 00:57:57,997 the nature of it is like we we are aggressively basically uh we we are 1051 00:57:58,080 --> 00:58:01,277 aggressively investing behind every strategy that we've identified that we 1052 00:58:01,360 --> 00:58:04,637 think has a plausible chance of even when that even when that's 1053 00:58:04,720 --> 00:58:07,917 contradictory to another strategy that we're investing in and one is just like 1054 00:58:08,000 --> 00:58:10,637 the world's messy and probably a bunch of things are going to work and so like 1055 00:58:10,720 --> 00:58:13,517 there's not going to be clean yes or no answers to a bunch of this like a lot a 1056 00:58:13,600 --> 00:58:15,757 lot of the answers to this I think are just going to be and answers but the 1057 00:58:15,840 --> 00:58:19,117 other is like if one of these strategies doesn't work like you know we're not 1058 00:58:19,200 --> 00:58:22,637 we're not trying to hedge per se but you know we're going to have representation 1059 00:58:22,720 --> 00:58:25,357 in the portfolio of the alternate strategy and and so we're going to have 1060 00:58:25,440 --> 00:58:28,717 mult multiple ways to win. So anyway, that's that's the goal. That's the 1061 00:58:28,800 --> 00:58:32,317 theory of why we are, you know, kind of taking the approach in the space that 1062 00:58:32,400 --> 00:58:35,757 we're taking. Um, and that's why I have a big smile on my face when I say that 1063 00:58:35,840 --> 00:58:37,757 there are these big open questions because I think that actually works to 1064 00:58:37,840 --> 00:58:42,237 our advantage. >> It's a good seg uh to A16Z questions 1065 00:58:42,320 --> 00:58:45,997 because we we've gotten a few in so far and and uh we had a few that uh were 1066 00:58:46,080 --> 00:58:50,877 were sent in ahead as well. So uh I'll start one with a with a broad topic. 1067 00:58:50,960 --> 00:58:52,717 What is something you and Ben disagree 1068 00:58:52,800 --> 00:58:57,997 and commit on? disagree commit. Um, you know, we agree. 1069 00:58:58,080 --> 00:59:00,557 I mean, we we Ben I was going to say, you know, we're an old married couple, 1070 00:59:00,640 --> 00:59:05,197 so we argue argue constantly, but we've been >> where the romance is dead. 1071 00:59:05,280 --> 00:59:07,357 >> The romance is long dead. Yes. Yes. Yes. 1072 00:59:07,440 --> 00:59:08,957 Yes. The light the fire the fire has 1073 00:59:09,040 --> 00:59:14,157 long since gone out. Um, but um uh yes, 1074 00:59:14,240 --> 00:59:15,837 if you Yes. We're in the park squabbling 1075 00:59:15,920 --> 00:59:20,557 all the time. Um so, um yeah, I mean, so 1076 00:59:20,640 --> 00:59:23,197 look, we debate everything. We we argue about everything. We that that said like 1077 00:59:23,280 --> 00:59:25,517 you know one of the things that's made our partnership work is like we do we do 1078 00:59:25,600 --> 00:59:28,637 tend to come to the same conclusion like each of us is open to being persuaded by 1079 00:59:28,720 --> 00:59:31,197 the other one and so we we end up coming you know we end up coming to the same 1080 00:59:31,280 --> 00:59:34,637 conclusion most of the time. Um so I would say there there aren't like a 1081 00:59:34,720 --> 00:59:37,757 there aren't I said specifically sitting here today there are like zero issues 1082 00:59:37,840 --> 00:59:40,637 where I'm sitting here and I'm like I can't believe you know I just I can't 1083 00:59:40,720 --> 00:59:43,917 believe I'm you know I'm putting up with this crazy thing on on his on his part 1084 00:59:44,000 --> 00:59:46,637 that he's doing um that I really disagree with but I feel like I have to 1085 00:59:46,720 --> 00:59:48,797 commit to or I I don't think vice versa. 1086 00:59:48,880 --> 00:59:50,717 Um and so so we don't have any of those. 1087 00:59:50,800 --> 00:59:54,477 Um, you know, quite honestly, the biggest thing I say the biggest thing 1088 00:59:54,560 --> 00:59:59,197 that I that he and I the biggest thing that he and I discuss, this this by the 1089 00:59:59,280 --> 01:00:01,597 way, this is not this is not the most important thing we're doing, but it is a 1090 01:00:01,680 --> 01:00:02,957 topic since somebody asked the question. 1091 01:00:03,040 --> 01:00:06,317 The biggest thing he and I discuss where I I don't know, maybe I'm always like 1092 01:00:06,400 --> 01:00:09,197 second guessing myself or I I I never quite know where I should come out on it 1093 01:00:09,280 --> 01:00:13,037 that he and I talk about a lot is just like basically the public footprint of 1094 01:00:13,120 --> 01:00:17,357 the company. Um so like our pres our presence in the our presence in the 1095 01:00:17,440 --> 01:00:19,757 world in terms of like public statements 1096 01:00:19,840 --> 01:00:23,437 uh controversy um uh you know uh how we 1097 01:00:23,520 --> 01:00:27,597 vocalize and express our views on things um and I would just say there like you 1098 01:00:27,680 --> 01:00:30,237 know there's a real there's a tension there's a real it's you know maybe 1099 01:00:30,320 --> 01:00:33,917 obvious but like a very important tension like generally speaking the more 1100 01:00:34,000 --> 01:00:37,277 out there we are and the more outspoken we are and the more controversial we are 1101 01:00:37,360 --> 01:00:39,677 the better for the better for the business in the sense of the 1102 01:00:39,760 --> 01:00:43,197 entrepreneurs love it. Uh the the the 1103 01:00:43,280 --> 01:00:45,677 founders want to work with is very clear 1104 01:00:45,760 --> 01:00:50,317 at this point. The founders want to work with uh uh people who basically are 1105 01:00:50,400 --> 01:00:54,077 brave and controversial and take controversial stands uh and articulate 1106 01:00:54,160 --> 01:00:56,397 things clearly and and they want that for a bunch of reasons. One is because 1107 01:00:56,480 --> 01:00:59,277 it's a demonstration of courage which they appreciate. But the other is 1108 01:00:59,360 --> 01:01:03,677 because it it it it teaches them who we are before they even meet us. Um and and 1109 01:01:03,760 --> 01:01:07,037 and that has just proven to be just like this incredible competitive advantage. 1110 01:01:07,120 --> 01:01:10,077 you know, long long-term LPs will know like this is why we started with a very 1111 01:01:10,160 --> 01:01:13,357 active marketing strategy from the very beginning and like it completely worked. 1112 01:01:13,440 --> 01:01:16,637 Like the the whole thing was if we're able to broadcast our message and we're 1113 01:01:16,720 --> 01:01:20,077 able to basically be very clear in what we believe even to the point where it's 1114 01:01:20,160 --> 01:01:22,957 controversial, like the best founders in the world are going to understand us 1115 01:01:23,040 --> 01:01:25,597 before they even walk in the door, right? And they're going to they're 1116 01:01:25,680 --> 01:01:28,317 going to know us even before they've met us as opposed to everybody else in 1117 01:01:28,400 --> 01:01:31,117 venture, at least at the time, that was basically just like keeping everything 1118 01:01:31,200 --> 01:01:34,637 quiet. Um where they, you know, the founder just has no idea who these 1119 01:01:34,720 --> 01:01:37,117 people are and what they believe. And so that that like worked incredibly well. 1120 01:01:37,200 --> 01:01:41,197 It continues to work incredibly well. Um it's by the way it's you know it's 1121 01:01:41,280 --> 01:01:43,917 generally true across the industry. It's it's it's like generally the case. On 1122 01:01:44,000 --> 01:01:47,437 the other hand, there are externalities to being you know publicly visible and 1123 01:01:47,520 --> 01:01:49,917 and and and to being controversial um on 1124 01:01:50,000 --> 01:01:54,717 many fronts. Um we are I would say this we are we're very much we're trying very 1125 01:01:54,800 --> 01:01:57,277 hard to thread this needle. So like we're we're not backing off of generally 1126 01:01:57,360 --> 01:02:00,237 being a a company that does a lot of outbound. we, you know, we Eric 1127 01:02:00,320 --> 01:02:02,797 Worenberg and the team that he's built, you know, that we've talked to you guys 1128 01:02:02,880 --> 01:02:06,637 about in the past, um, you know, is I is already off to the races. Um, you know, 1129 01:02:06,720 --> 01:02:09,037 we're we're going to, you know, we're tripling down on the idea of basically 1130 01:02:09,120 --> 01:02:12,317 being the leaders and articulating the tech and business issues that matter. 1131 01:02:12,400 --> 01:02:14,957 You know, the, you know, the issues for sure that people need to be able to 1132 01:02:15,040 --> 01:02:19,117 understand. Um, and and that's proven to be very effective. By the way, a fair 1133 01:02:19,200 --> 01:02:23,197 amount of our coms are actually aimed at Washington. Um because again it's like 1134 01:02:23,280 --> 01:02:27,517 if you're a policy maker in Washington and you're sitting there 3,000 mi away 1135 01:02:27,600 --> 01:02:30,477 and your entire information source is like East Coast newspapers that hate 1136 01:02:30,560 --> 01:02:34,557 Silicon Valley. Like that's bad. Um and so you know our ability to like 1137 01:02:34,640 --> 01:02:38,317 broadcast, you know, inform points of view on technology. We just we meet 1138 01:02:38,400 --> 01:02:41,597 people in DC all the time um who say, "Yeah, I you know, most of what I know 1139 01:02:41,680 --> 01:02:43,917 about this topic I learned from you guys because I listened to the podcast, I 1140 01:02:44,000 --> 01:02:46,797 read the articles, I watched the YouTube channel." Um and so, you know, we're 1141 01:02:46,880 --> 01:02:49,837 we're going to continue to do that. And so we, you know, over over over overall 1142 01:02:49,920 --> 01:02:52,557 we have a, you know, we're kind of on our front foot on that stuff. But yeah, 1143 01:02:52,640 --> 01:02:55,997 he he and I do he and I do go back and forth a bit on exactly how, yeah, how 1144 01:02:56,080 --> 01:02:57,597 many third rail topics should we touch? 1145 01:02:57,680 --> 01:03:01,597 Um, and uh and how frequently. And I I would say we're we're we are trying to 1146 01:03:01,680 --> 01:03:03,117 we are trying to moderate that. 1147 01:03:03,200 --> 01:03:07,197 >> As Elizabeth Taylor said, as long as I spell our name right, um, it's 1148 01:03:07,280 --> 01:03:09,997 oftentimes could be good in most 1149 01:03:10,080 --> 01:03:14,797 scenarios, particularly when it comes to little tech. uh double uh and also I 1150 01:03:14,880 --> 01:03:18,877 think embedded in that question is probably uh some degree of of uh uh the 1151 01:03:18,960 --> 01:03:22,317 relationship that you and Ben have which is now going on 30 plus years at this 1152 01:03:22,400 --> 01:03:24,317 point. Uh so much so that that Mark has 1153 01:03:24,400 --> 01:03:27,757 become uh one person representing both 1154 01:03:27,840 --> 01:03:29,837 uh some people refer to Mark as Andre 1155 01:03:29,920 --> 01:03:35,197 and Horowitz no lost the mark have combined just into one person. Uh 1156 01:03:35,280 --> 01:03:37,677 >> yes >> that's the result of 30 plus years 1157 01:03:37,760 --> 01:03:41,437 working together. Okay. Um, so it's been 2 years since you've reorganized around 1158 01:03:41,520 --> 01:03:45,917 AI, launched AD. What do you think you got most right? Uh, and in hindsight, is 1159 01:03:46,000 --> 01:03:51,037 there anything that you underestimated or or missed in that decisioning process? 1160 01:03:51,120 --> 01:03:52,717 >> No, I mean, look, we made we made plenty 1161 01:03:52,800 --> 01:03:55,597 of mistakes. I think those were I think those were the right calls. I mean, AI 1162 01:03:55,680 --> 01:03:59,917 was like I said, like you know, the whole theor back up the whole theory of 1163 01:04:00,000 --> 01:04:02,637 venture the whole theory of venture that we've had from the beginning is that you 1164 01:04:02,720 --> 01:04:06,797 know, many people before us have had as well. that's very correct I think is the 1165 01:04:06,880 --> 01:04:09,517 whole theory is like the money adventure is made when there's like a a 1166 01:04:09,600 --> 01:04:12,557 fundamental architecture shift like when there's like a fundamental change in the 1167 01:04:12,640 --> 01:04:16,077 technology landscape. Um and and that's been true for you know adventure 1168 01:04:16,160 --> 01:04:19,677 basically forever. Um uh and and the reason is because if you have a 1169 01:04:19,760 --> 01:04:23,037 fundamental change in technology then you have this period of creativity in 1170 01:04:23,120 --> 01:04:26,237 which you can have basically aggressive you know very aggressive kind of people 1171 01:04:26,320 --> 01:04:28,717 you know kind of start these new companies and and they have this kind of 1172 01:04:28,800 --> 01:04:32,077 shot to kind of come in and you kind of win categories before big companies can 1173 01:04:32,160 --> 01:04:35,997 respond. um if there's no fundamental change in technology, it's very hard to 1174 01:04:36,080 --> 01:04:38,557 make startups work because the big companies just end up doing everything. 1175 01:04:38,640 --> 01:04:42,637 And so you so venture kind of, you know, sort of lives or dies on on the basis of 1176 01:04:42,720 --> 01:04:47,037 these of these waves of these transitions. Um and and so there's 1177 01:04:47,120 --> 01:04:49,757 always there there's always this question. It's always this question. I 1178 01:04:49,840 --> 01:04:53,437 mean, I would just say the best venture capital firms in history, I I think are 1179 01:04:53,520 --> 01:04:56,637 the ones that were the most aggressive at being able to navigate from wave to 1180 01:04:56,720 --> 01:04:59,517 wave, right? And and and look, I was a beneficiary of this when I came to 1181 01:04:59,600 --> 01:05:03,677 Silicon Valley in ' 904. you know that there was no venture firm in 1994 that 1182 01:05:03,760 --> 01:05:07,277 was like the internet venture capital firm like that it just didn't exist. Um, 1183 01:05:07,360 --> 01:05:10,317 but there were a set of venture capital firms at the time, you know, at the time 1184 01:05:10,400 --> 01:05:13,757 our our firm Kleiner Perkins that said, "Oh, this is a new architecture. This is 1185 01:05:13,840 --> 01:05:17,197 a new technology change. It seems totally crazy. Everybody says you can't 1186 01:05:17,280 --> 01:05:18,717 make money on it. Whatever, whatever. 1187 01:05:18,800 --> 01:05:22,317 These kids are nuts." But like, we're going to make those bets. Um, and so 1188 01:05:22,400 --> 01:05:25,037 they were willing to invest. And by the way, you know, KP in the in the in the 1189 01:05:25,120 --> 01:05:28,797 '90s invested not only in us, but also in Amazon and then Google and like in, 1190 01:05:28,880 --> 01:05:31,757 you know, company after company after company. They invested in at home, which 1191 01:05:31,840 --> 01:05:33,917 basically made made home broadband work. 1192 01:05:34,000 --> 01:05:37,037 um you know they invested in in a fleet of companies and they were a venture 1193 01:05:37,120 --> 01:05:40,797 capital firm that had started in the 1970s around really around what was at 1194 01:05:40,880 --> 01:05:43,757 the time called Minicomputers which was like a you know three generations of 1195 01:05:43,840 --> 01:05:47,757 techn technology back and they had navigated from wave to wave um and and 1196 01:05:47,840 --> 01:05:49,997 you know the same thing is true for Sequoia the same thing is true for 1197 01:05:50,080 --> 01:05:53,197 basically any successful venture firm has been in business for you know 30 or 1198 01:05:53,280 --> 01:05:57,437 40 or 50 years and so I I think in this business like of all businesses like you 1199 01:05:57,520 --> 01:06:01,837 you just you need you need to get onto the new thing um you know it it was I 1200 01:06:01,920 --> 01:06:05,837 mean quite honestly it was I pretty amazing that most of the venture 1201 01:06:05,920 --> 01:06:07,597 ecosystem just decided to sit crypto 1202 01:06:07,680 --> 01:06:11,357 out. Um and and the number of VCs that 1203 01:06:11,440 --> 01:06:14,717 we talked to between call it, you know, the release of the Bitcoin white paper 1204 01:06:14,800 --> 01:06:18,557 in 2009 to the beginning of the crypto war in 2021 who just basically said, 1205 01:06:18,640 --> 01:06:22,557 "Oh, we're not going to do crypto." It was fairly it's I I like I don't I I 1206 01:06:22,640 --> 01:06:24,797 never quite know what to do with the VC who says, "Oh, there's a new wave of 1207 01:06:24,880 --> 01:06:27,197 technology and I'm very deliberately not going to participate in it." And I'm 1208 01:06:27,280 --> 01:06:30,957 always like like, "Is that not the job?" 1209 01:06:31,040 --> 01:06:34,957 Right? Like so so so like I was fairly amazed by the VCs that didn't make the 1210 01:06:35,040 --> 01:06:38,717 jump uh to crypto. You know they they looked briefly smart during the crypto 1211 01:06:38,800 --> 01:06:41,517 wars I would say of the last you know three or four years and I think they 1212 01:06:41,600 --> 01:06:45,357 they probably look maybe a little bit less smart now. Um you know AI is 1213 01:06:45,440 --> 01:06:47,837 another one of these where there are certain firms that are are jumping all 1214 01:06:47,920 --> 01:06:49,917 over it and there are certain firms that are just kind of sitting back and 1215 01:06:50,000 --> 01:06:53,917 letting it happen. Um and um and and by the way there were certain firms that 1216 01:06:54,000 --> 01:06:55,997 never made it to the internet. I mean there were there were firms that were 1217 01:06:56,080 --> 01:06:59,197 very well known in the 80s um and very successful that just like did not make 1218 01:06:59,280 --> 01:07:01,917 the jump uh to the internet and basically just petered out. And so 1219 01:07:02,000 --> 01:07:04,637 anyway long-winded way of saying I think I think in this business of all 1220 01:07:04,720 --> 01:07:07,757 businesses you have to jump you have to jump on the new wave. Um and I and I 1221 01:07:07,840 --> 01:07:10,397 think we got the magnitude of it of it right that this is like a fundamental 1222 01:07:10,480 --> 01:07:14,317 fundamental transformation inside the firm. Um you know AD is you know AD is 1223 01:07:14,400 --> 01:07:16,877 doing great. Um AD AD itself I believe 1224 01:07:16,960 --> 01:07:21,757 is also a beneficiary of AI. um right because in in two ways one is a lot of 1225 01:07:21,840 --> 01:07:25,357 the kinds of products that AD companies build themselves benefit from AI and 1226 01:07:25,440 --> 01:07:27,757 then also AI is a driver of demand in 1227 01:07:27,840 --> 01:07:32,557 other sectors of AD like like energy and materials. Um and so I you know I think 1228 01:07:32,640 --> 01:07:35,997 that that generally is is very consistent and you know is working well. 1229 01:07:36,080 --> 01:07:41,037 Um by the way you know crypto's back back to being a you know I would say an 1230 01:07:41,120 --> 01:07:44,637 exciting industry as a consequence of all the policy changes. Um and then and 1231 01:07:44,720 --> 01:07:46,957 then there's even going to be I think intersections. I I think there's 1232 01:07:47,040 --> 01:07:50,157 actually going to be quite a few intersections between AI and crypto. Um 1233 01:07:50,240 --> 01:07:54,157 and then and then biote you know biotech also bio and healthcare I think are 1234 01:07:54,240 --> 01:07:57,197 obviously going to be transformed by AI both on the healthcare side and on the 1235 01:07:57,280 --> 01:08:00,797 actual drug discovery side and you know and that's underway. And so any anyway 1236 01:08:00,880 --> 01:08:04,157 so like the the the individual efforts in the firm feel good um and suitable 1237 01:08:04,240 --> 01:08:08,717 for the time the inter the interactions between the teams um and the kind the 1238 01:08:08,800 --> 01:08:11,917 the hybrid ideas you know the companies that are coming at these things from 1239 01:08:12,000 --> 01:08:16,317 multiple angles uh you know feels really good um you know maybe the correlarying 1240 01:08:16,400 --> 01:08:19,037 question is like you know what do we feel like we're missing right now um and 1241 01:08:19,120 --> 01:08:22,637 I I think the answer is really not like I don't I don't think like right now 1242 01:08:22,720 --> 01:08:26,077 we're not missing a vertical like I I don't like as of right now like there 1243 01:08:26,160 --> 01:08:29,117 there's not like a specific vertical of like I don't know whatever that like 1244 01:08:29,200 --> 01:08:31,437 where we just like, oh, we just need, you know, we need the equivalent of a 1245 01:08:31,520 --> 01:08:34,397 new of a new unit or the equivalent of a new um you know, new fund or whatever. I 1246 01:08:34,480 --> 01:08:37,597 don't I don't see that at the moment. I think it's more executing extremely well 1247 01:08:37,680 --> 01:08:40,477 in the verticals that we have in front of us. Um and um and then, you know, 1248 01:08:40,560 --> 01:08:43,917 being the best possible partner to the to the portfolio companies. 1249 01:08:44,000 --> 01:08:46,157 >> Yeah. Actually, on the point of of AD, 1250 01:08:46,240 --> 01:08:49,997 um because uh AI is creating and there's 1251 01:08:50,080 --> 01:08:52,317 a lot of talk around AI taking jobs, 1252 01:08:52,400 --> 01:08:54,957 etc. Ironically enough, the jobs in AD 1253 01:08:55,040 --> 01:08:59,757 sectors have never been more in demand in the physical world related to energy, 1254 01:08:59,840 --> 01:09:03,277 related obviously to data center build, etc. So like the the pendulum it seems 1255 01:09:03,360 --> 01:09:05,757 like also is uh is swinging from just an 1256 01:09:05,840 --> 01:09:10,397 accelerant standpoint from from a society uh point of view. Um you talked 1257 01:09:10,480 --> 01:09:13,997 about the importance of society also needing to be ready for tech adoption. 1258 01:09:14,080 --> 01:09:17,517 Like have you seen that accelerating of recently? what's your sentiment of of 1259 01:09:17,600 --> 01:09:20,877 how to actually um increase that just to 1260 01:09:20,960 --> 01:09:25,037 also make sure the convergence of of adoption also falls in line with with 1261 01:09:25,120 --> 01:09:27,517 how quickly tech is is actually being implemented. 1262 01:09:27,600 --> 01:09:31,037 >> Yeah. So, you know, look, we've talked about this before, but um you know, 1263 01:09:31,120 --> 01:09:32,637 look, for a very long time, tech was 1264 01:09:32,720 --> 01:09:36,157 just not a very relevant look, if you go 1265 01:09:36,240 --> 01:09:39,997 back over like whatever 300 years, like there's just like recurring waves of 1266 01:09:40,080 --> 01:09:43,757 like total panic and freakout caused by new technology. Or even you go back 500 1267 01:09:43,840 --> 01:09:46,157 years, you go back to the printing press, you know, which basically was 1268 01:09:46,240 --> 01:09:48,717 handin-hand with the the sort of creation of Protest Pro Protestantism, 1269 01:09:48,800 --> 01:09:53,037 which really changed things. Um, and then um, you know, you you go back to 1270 01:09:53,120 --> 01:09:55,437 um, you know, there there were just always kind of, you know, continuous 1271 01:09:55,520 --> 01:09:57,837 panics there. You know, there have been m there have been multiple ways of 1272 01:09:57,920 --> 01:10:00,477 automation panics for the last 200 years. You know, a lot of the 1273 01:10:00,560 --> 01:10:04,397 foundational panic under Marxism was basically a fear of of of of the 1274 01:10:04,480 --> 01:10:08,637 elimination of jobs through the application of automation. um uh you 1275 01:10:08,720 --> 01:10:10,877 know a lot of the same arguments you hear today about like AI is going to 1276 01:10:10,960 --> 01:10:13,357 centralize all the wealth in a handful of a few people and everybody else is 1277 01:10:13,440 --> 01:10:16,397 going to be poor and emiserated like that that basically is what Markx used 1278 01:10:16,480 --> 01:10:21,037 to say um which I think was by the way wrong then is wrong now we can talk 1279 01:10:21,120 --> 01:10:24,797 about but um you know and then even like in the 1960s there was this whole panic 1280 01:10:24,880 --> 01:10:29,277 around around AI um uh replacing all the jobs there was this there's this great 1281 01:10:29,360 --> 01:10:32,077 uh it's long long forgotten but it was a big deal at the time during the Johnson 1282 01:10:32,160 --> 01:10:35,837 administration you read these AI pause letters today you know that this one 1283 01:10:35,920 --> 01:10:37,037 that just came out a few weeks ago that 1284 01:10:37,120 --> 01:10:39,997 Prince Harry uh headlined of all people. 1285 01:10:40,080 --> 01:10:43,517 Um and um uh uh you know he talks about 1286 01:10:43,600 --> 01:10:48,157 AI is going to ruin everything and it's like and 1964 there was basically a 1287 01:10:48,240 --> 01:10:52,957 group of like the leading lights in academia science and uh you know um kind 1288 01:10:53,040 --> 01:10:56,237 of public affairs that there was this thing called the triple committee or the 1289 01:10:56,320 --> 01:10:59,117 committee for the triple revolution. If you do a Google search on it's like 1290 01:10:59,200 --> 01:11:02,317 committee for the triple revolution Johnson white house or whatever you'll 1291 01:11:02,400 --> 01:11:06,557 this thing will pop up. Um and you know it was a very similar kind of manifesto 1292 01:11:06,640 --> 01:11:09,117 of like we need to stop the march of technology today or we're going to ruin 1293 01:11:09,200 --> 01:11:12,717 everything. Um and and then you know even in the course of the last 20 years 1294 01:11:12,800 --> 01:11:17,117 there was like a big panic around um actually outsourcing in the 2000s was 1295 01:11:17,200 --> 01:11:20,397 going to take all the jobs and then it was actually robots weirdly enough in 1296 01:11:20,480 --> 01:11:23,677 the 2010s which is amazing because robots didn't even work in the 2010s and 1297 01:11:23,760 --> 01:11:27,357 they kind of you know still don't. Um but uh you know there's a panic around 1298 01:11:27,440 --> 01:11:30,957 that and now there's kind of whatever level of AI panic. Um and so like you 1299 01:11:31,040 --> 01:11:33,997 know I would just say like look that you know the way I would describe it is you 1300 01:11:34,080 --> 01:11:37,837 know we in Silicon Valley have always wanted the work that we do to matter. Um 1301 01:11:37,920 --> 01:11:41,437 you know we spend most of our time quite honestly with people telling us that 1302 01:11:41,520 --> 01:11:44,557 everything that we're doing is stupid and won't work. Um like that's the 1303 01:11:44,640 --> 01:11:48,477 default position. Um you know and then basically that flips at some point into 1304 01:11:48,560 --> 01:11:52,317 panic about how it's going to ruin everything. Um you know it's it's easy 1305 01:11:52,400 --> 01:11:55,837 sitting out here to be cynical about that. Um especially when you kind of see 1306 01:11:55,920 --> 01:11:59,517 the patterns over time. I you know my view is we need to be actually very 1307 01:11:59,600 --> 01:12:02,397 respectful of that and we need to be very aware of that and and basically 1308 01:12:02,480 --> 01:12:05,757 that we you know I use the metaphor with the dog that caught the bus like we 1309 01:12:05,840 --> 01:12:07,917 always wanted to work on things that matter we are working on things that 1310 01:12:08,000 --> 01:12:11,677 matter uh people in the rest of society actually really do care about these 1311 01:12:11,760 --> 01:12:14,957 things um and you know and it's our responsibility to think that all through 1312 01:12:15,040 --> 01:12:18,237 very carefully and to do a good job um you know both not just building the 1313 01:12:18,320 --> 01:12:20,957 technology but also explaining it you know look you know I think we have a 1314 01:12:21,040 --> 01:12:24,157 real obligation to uh you know to to really explain ourselves and engage on 1315 01:12:24,240 --> 01:12:27,597 these issues um in terms of how to measure how going you know it's it's 1316 01:12:27,680 --> 01:12:28,957 sort of the classic social science 1317 01:12:29,040 --> 01:12:31,997 question um uh which is like okay if you 1318 01:12:32,080 --> 01:12:36,397 want to understand basically you know patterns of people there's basically two 1319 01:12:36,480 --> 01:12:41,037 ways to understand what people are doing and thinking um one is to ask them and 1320 01:12:41,120 --> 01:12:44,477 and then the other is to watch them um and like every social every social 1321 01:12:44,560 --> 01:12:47,357 scientist like every sociologist will will will will tell you this which 1322 01:12:47,440 --> 01:12:51,517 basically is you can you can ask people right and and the way you do that right 1323 01:12:51,600 --> 01:12:56,797 is like you know surveys focus groups polls um you know what they think Um but 1324 01:12:56,880 --> 01:12:59,197 then but then you can watch them and you can do what's you know called reveal 1325 01:12:59,280 --> 01:13:01,837 preferences. They're just observe behavior which is you can actually watch 1326 01:13:01,920 --> 01:13:04,557 their behavior and and and what you often see in many areas of human 1327 01:13:04,640 --> 01:13:08,237 activity including politics and many different aspects of society and culture 1328 01:13:08,320 --> 01:13:11,037 over time is the answers that you get when you ask people are very different 1329 01:13:11,120 --> 01:13:15,277 than the answers that you get when you watch them. Um and the reason is because 1330 01:13:15,360 --> 01:13:19,037 like I mean you could have a bunch of theories as to why this is the Marxists 1331 01:13:19,120 --> 01:13:22,157 claim that people have false consciousness. the the the the somewhat 1332 01:13:22,240 --> 01:13:24,957 the explanation I believe is just people have opinions on all kinds of things 1333 01:13:25,040 --> 01:13:27,997 particularly when they're in a context where they get to express themselves um 1334 01:13:28,080 --> 01:13:30,877 and they'll have a tendency to kind of express themselves in very heated ways 1335 01:13:30,960 --> 01:13:34,397 and then if you just watch their behavior they're often a lot calmer um 1336 01:13:34,480 --> 01:13:37,437 and a lot more measured and a lot more rational in in what they do and so the 1337 01:13:37,520 --> 01:13:41,517 AI that's playing out in AI right now which is if you pull if you run a survey 1338 01:13:41,600 --> 01:13:45,597 or a poll of what for example American voters think about AI it's just like 1339 01:13:45,680 --> 01:13:48,397 they're all in a total panic it's like oh my god this is terrible this is awful 1340 01:13:48,480 --> 01:13:51,597 it's going to kill all the jobs it's going to ruin thing. The whole thing, if 1341 01:13:51,680 --> 01:13:53,197 you watch the revealed preferences, 1342 01:13:53,280 --> 01:13:56,637 they're all using AI. So, they're like, 1343 01:13:56,720 --> 01:14:01,517 they're downloading the apps. They're using chat GPT in their job. They're, 1344 01:14:01,600 --> 01:14:04,877 you know, having an argument. You You see this online all the time now. I'm 1345 01:14:04,960 --> 01:14:07,197 having an argument with my boyfriend or girlfriend. I don't understand what's 1346 01:14:07,280 --> 01:14:10,797 happening. I take the text exchange. I cut and paste it into chat GPT and I 1347 01:14:10,880 --> 01:14:13,677 have chat GPT explain to me what my partner is thinking and tell me how I 1348 01:14:13,760 --> 01:14:16,557 should answer so that he's, you know, he or she is not mad at me anymore, right? 1349 01:14:16,640 --> 01:14:19,357 So, or like, you know, I have this thing, you know, I have a skin, you 1350 01:14:19,440 --> 01:14:22,317 know, I have a skin condition and doctors, you know, da da da, and I take 1351 01:14:22,400 --> 01:14:26,157 a photo and I and I'm finally like learning about my own health or I use it 1352 01:14:26,240 --> 01:14:29,117 in my job like I, you know, I had to get this report ready for Monday morning and 1353 01:14:29,200 --> 01:14:33,037 I ran out of time and like it, you know, chat GPT really saved my bacon. Um, and 1354 01:14:33,120 --> 01:14:36,957 so people in their daily lives are I would, you know, just you just look at 1355 01:14:37,040 --> 01:14:40,637 the just look at the data you just like they are not only using this technology, 1356 01:14:40,720 --> 01:14:43,757 they love this technology. Um, and they love it and they're adopting as fast as 1357 01:14:43,840 --> 01:14:47,517 they possibly can. So I I tend to think we're going to the public discussion of 1358 01:14:47,600 --> 01:14:49,917 this is going to ping pong back and forth for a while because there is this 1359 01:14:50,000 --> 01:14:53,037 divergence between what people are saying what people are doing. Um but but 1360 01:14:53,120 --> 01:14:56,237 I do think that the what people are doing part is is is obviously the part 1361 01:14:56,320 --> 01:14:59,357 the part ultimately that wins and and and I think this by the way I think this 1362 01:14:59,440 --> 01:15:02,397 technology is going to be exactly the same as every other one. Um which is the 1363 01:15:02,480 --> 01:15:04,557 thing that's going to happen here is this is just going to proliferate really 1364 01:15:04,640 --> 01:15:07,757 broadly. It's going to freak everybody out and then you know 20 years from now 1365 01:15:07,840 --> 01:15:10,717 everybody's going to be like oh thank god we've got it. Like wouldn't life be 1366 01:15:10,800 --> 01:15:15,117 miserable if we didn't have this? um and or you know 5 years from now or or one 1367 01:15:15,200 --> 01:15:19,037 year from now you know people are going to reach that conclusion. Um so I'm I'm 1368 01:15:19,120 --> 01:15:20,637 very optimistic about where this lands. 1369 01:15:20,720 --> 01:15:22,637 It's just that you know there will be turbulence along the way. 1370 01:15:22,720 --> 01:15:24,317 >> I'm I'm smiling because I also witnessed 1371 01:15:24,400 --> 01:15:27,837 that in the wild. Literally late last week I was on the plane. The guy next to 1372 01:15:27,920 --> 01:15:32,157 me was talking to his chat. I could see him and he was like help me draft an 1373 01:15:32,240 --> 01:15:35,677 escalation letter to United for the delay on this flight. I was like sir you 1374 01:15:35,760 --> 01:15:41,240 are on the flight right now. Like at least wait until it's over. 1375 01:15:42,000 --> 01:15:45,037 It was very good though. I'm sure he had a great email crafted as a as a part of 1376 01:15:45,120 --> 01:15:48,237 that. Uh so, okay, I'm going to switch 1377 01:15:48,320 --> 01:15:52,637 gears to uh a few fun questions that that were sent in uh that uh is intended 1378 01:15:52,720 --> 01:15:56,077 to be a lightning round. So, so uh what what is something you've changed your 1379 01:15:56,160 --> 01:15:59,117 mind on recently? Bonus points if it was someone younger than you. 1380 01:15:59,200 --> 01:16:03,597 >> I mean, it's like every day. Um it's just like it's just a constant, you 1381 01:16:03,680 --> 01:16:07,117 know, it's it's almost all like what's in the realm of the possible. Um, I I'm 1382 01:16:07,200 --> 01:16:09,837 I'm terrible at specific examples, so I don't I don't have one like ready at 1383 01:16:09,920 --> 01:16:13,277 hand, but like like I said, it's just it's it's always Yeah. No, it's it's 1384 01:16:13,360 --> 01:16:15,997 often somebody showing up. It's either something somebody writes or something 1385 01:16:16,080 --> 01:16:19,757 somebody says. Um, and yeah, it's almost Yeah, it's very frequently somebody 1386 01:16:19,840 --> 01:16:23,517 who's very young. Um, and um, yeah, it's just like I would say it's a it's a 1387 01:16:23,600 --> 01:16:28,157 routine experience. >> Good way to stay young. Um, do you plan, 1388 01:16:28,240 --> 01:16:32,477 speaking of young, do you plan to be cryogenically frozen? 1389 01:16:32,560 --> 01:16:34,797 Not with current not with current 1390 01:16:34,880 --> 01:16:37,597 cryogenic technology. Um the uh the the 1391 01:16:37,680 --> 01:16:39,677 the track record of that is not great. 1392 01:16:39,760 --> 01:16:44,397 Um uh and um the stories are somewhat horrifying, but uh you know, we'll see. 1393 01:16:44,480 --> 01:16:47,437 >> We'll see. You got we still got some time. 1394 01:16:47,520 --> 01:16:51,757 >> Um how do you stay grounded when your influence itself may distort reality 1395 01:16:51,840 --> 01:16:56,717 around you? >> Yeah. So I was just say the good news, you know, 1396 01:16:56,800 --> 01:16:59,437 I would say the good news on several front. So one is look the concern is 1397 01:16:59,520 --> 01:17:02,557 real. Um, and it's hard for me to it's hard for me to talk about with sort of 1398 01:17:02,640 --> 01:17:06,237 my Midwestern, you know, kind of, you know, Midwesterners, we we either are 1399 01:17:06,320 --> 01:17:09,837 very humble or we we're really good at faking it, but um, uh, you know, it's 1400 01:17:09,920 --> 01:17:12,397 hard to talk about, but requires some introspection. But yeah, I mean, look, 1401 01:17:12,480 --> 01:17:15,437 the the reality warping effect is definitely real. Um, by the way, there 1402 01:17:15,520 --> 01:17:18,717 is a very big advantage to the reality warping effect, um, which is being able 1403 01:17:18,800 --> 01:17:19,837 to get people to do what you want them 1404 01:17:19,920 --> 01:17:23,277 to do. Um, so that, you know, there is 1405 01:17:23,360 --> 01:17:27,597 there is another side to it. Um but it you know it is a concern in terms of 1406 01:17:27,680 --> 01:17:30,557 like having an actual accurate understanding of what's happening. I 1407 01:17:30,640 --> 01:17:33,597 guess I would say two things. I would say one is um you know I mean one is 1408 01:17:33,680 --> 01:17:36,797 just you know my partners I think are quite you know including Ben are quite 1409 01:17:36,880 --> 01:17:39,837 forthright um in telling me when I'm wrong but you know more generally like 1410 01:17:39,920 --> 01:17:43,997 we're just we are very exposed to reality. Um and so and this and again 1411 01:17:44,080 --> 01:17:46,957 you know you mentioned I don't know it's a way to stay younger, make sure their 1412 01:17:47,040 --> 01:17:49,917 hair never grows back or whatever. It's just like you know we run these 1413 01:17:50,000 --> 01:17:52,717 experiments you know cuz we make these decisions about whether to invest or not 1414 01:17:52,800 --> 01:17:55,597 invest and we work with these companies and all their things and like you know 1415 01:17:55,680 --> 01:17:59,357 reality kicks in quickly. You know the the the delusions don't last very long 1416 01:17:59,440 --> 01:18:02,477 in this business. Um because like you know these these things either work or 1417 01:18:02,560 --> 01:18:06,317 they don't. Um and you know you have these like long elaborate you know 1418 01:18:06,400 --> 01:18:08,797 discussions about you know theories on this and that and the other thing and 1419 01:18:08,880 --> 01:18:12,157 then reality just like completely smacks you square in the face you know like you 1420 01:18:12,240 --> 01:18:16,557 idiot right you know like you know what were you you like you know this is like 1421 01:18:16,640 --> 01:18:18,957 the you know the ultimate frustration of the business which is also very 1422 01:18:19,040 --> 01:18:21,357 motivating which is the number of times that you think that you've applied 1423 01:18:21,440 --> 01:18:24,717 superior analysis and then you've either invested or not invested based on that 1424 01:18:24,800 --> 01:18:27,277 analysis and it turns out it was just you the analysis was just completely 1425 01:18:27,360 --> 01:18:31,037 wrong right um and you know you just like completely overrated your ability 1426 01:18:31,120 --> 01:18:33,757 to epistemically you know kind of analyze these things you just you know 1427 01:18:33,840 --> 01:18:37,757 basically inflicted harm like I always the question is always you know it's 1428 01:18:37,840 --> 01:18:41,277 sort of you know any activity that we do is it value add or is it actually value 1429 01:18:41,360 --> 01:18:45,197 subtract right and and and I think in this business of all businesses is kind 1430 01:18:45,280 --> 01:18:49,117 of like that and and that applies to all of my own contributions as well so so 1431 01:18:49,200 --> 01:18:51,757 there is that and then and then I would say um you know maybe the final thing is 1432 01:18:51,840 --> 01:18:55,437 just like I do have the entire internet ready to tell me that I'm an idiot so 1433 01:18:55,520 --> 01:18:58,317 that also 1434 01:18:58,400 --> 01:19:03,680 that also doesn't doesn't hurt and it and it does on a regular basis 1435 01:19:04,080 --> 01:19:08,317 on on the point of uh your alluding to earlier about uh decisions on investing 1436 01:19:08,400 --> 01:19:11,677 in companies. My favorite line I think it was from the uh the Cheeky Point 1437 01:19:11,760 --> 01:19:15,597 interview that you did uh was you know when you invest in a company it doesn't 1438 01:19:15,680 --> 01:19:20,237 go well at least it goes bankrupt right if it does if it does well and it does 1439 01:19:20,320 --> 01:19:24,317 fantastically well you hear about it every single day 1440 01:19:24,400 --> 01:19:29,277 >> for the rest of your life. Yeah. For the next for the next 30 years. 1441 01:19:29,360 --> 01:19:32,317 validity smacking you in the face saying you fool. 1442 01:19:32,400 --> 01:19:35,597 >> You had it. It's literally It's literally you had it in your office. All 1443 01:19:35,680 --> 01:19:39,357 you had to do is say yes. And by the way, and this is the thing 1444 01:19:39,440 --> 01:19:43,117 like every great VC like if you this is this is the stories that you know the 1445 01:19:43,200 --> 01:19:46,957 VCs tell each other. Every great VC basically has this history of like my 1446 01:19:47,040 --> 01:19:50,077 god I had it was in my office. The thing was in my office and I said no and if I 1447 01:19:50,160 --> 01:19:55,117 had just said yes. Um and so it's yeah it's very hard to um yes the constant 1448 01:19:55,200 --> 01:19:57,997 reminders in the Wall Street Journal and on CNBC every day that you made a giant 1449 01:19:58,080 --> 01:20:02,237 mistake um is yes very good very good for the the old humility factor. 1450 01:20:02,320 --> 01:20:06,717 >> Yeah very humbling helps you stay grounded uh all the time. Uh last 1451 01:20:06,800 --> 01:20:11,517 question do you plan to go to Mars if and when that opportunity presents itself? 1452 01:20:11,600 --> 01:20:16,120 >> Probably not. 1453 01:20:16,480 --> 01:20:20,957 >> My subliminal Zoom background wasn't uh sending the positive vibes. This is what 1454 01:20:21,040 --> 01:20:22,237 it >> Well, I'm not even willing to leave 1455 01:20:22,320 --> 01:20:25,837 California. Um, 1456 01:20:25,920 --> 01:20:28,157 so I'm barely willing to leave my house. 1457 01:20:28,240 --> 01:20:32,397 So, um, uh, yeah, I may maybe by maybe by VR. 1458 01:20:32,480 --> 01:20:34,717 >> Yeah. >> Um, and then we'll see what happens. I 1459 01:20:34,800 --> 01:20:37,837 mean, look, having said that, I think Elon's going to pull it off. Um, and so 1460 01:20:37,920 --> 01:20:40,877 I think, you know, I don't know. I don't know. I don't want to predict. This is 1461 01:20:40,960 --> 01:20:43,117 not a prediction, but I, you know, I would not be surprised if within a 1462 01:20:43,200 --> 01:20:47,677 decade there's routine trips back and forth. Um, so, uh, yeah, we may, uh, 1463 01:20:47,760 --> 01:20:50,477 this this may actually become a a practical question. And and by the way, 1464 01:20:50,560 --> 01:20:53,277 I do know a lot of people who are probably going to go, 1465 01:20:53,360 --> 01:20:54,957 >> myself included. Put me on that. 1466 01:20:55,040 --> 01:20:58,317 >> Oh, fantastic. >> The the flights around the world have 1467 01:20:58,400 --> 01:21:05,160 prepared me for the six-month journey to Mars, so I will be just fine.