VC: How Benchmark Picks AI Winners - Max 10 Bets a Year, 5 Partners | Chetan Puttagunta (GP)

1 00:00:00,160 --> 00:00:03,359 SPEAKER_02: We are roughly a$600 million fund with five partners, 2 00:00:03,439 --> 00:00:05,919 and each of us makes about two investments a year. 3 00:00:06,080 --> 00:00:09,919 And so the fund is making on the order of 710 investments a year. 4 00:00:10,160 --> 00:00:12,320 SPEAKER_05: What AI native companies are going to be the 5 00:00:12,320 --> 00:00:14,240 winners of that window period of time? 6 00:00:14,560 --> 00:00:16,480 SPEAKER_02: We're primarily doing Steent Series A 7 00:00:16,480 --> 00:00:16,960 investments. 8 00:00:17,120 --> 00:00:18,960 They're high conviction investments. 9 00:00:19,039 --> 00:00:20,559 We're taking board seats. 10 00:00:20,719 --> 00:00:23,679 So the last investment I did was data centers in space to Star 11 00:00:23,839 --> 00:00:24,079 Cloud. 12 00:00:24,320 --> 00:00:27,359 SPEAKER_05: Jason Puragunta, general partner at benchmark. 13 00:00:27,600 --> 00:00:30,719 How does that kind of speed to revenue change the way that you 14 00:00:30,719 --> 00:00:32,560 are looking at companies as an investor? 15 00:00:32,880 --> 00:00:36,240 SPEAKER_02: Are companies now going to dramatically decrease 16 00:00:36,240 --> 00:00:38,079 the amount of time to get to$100 million? 17 00:00:38,479 --> 00:00:38,880 Probably. 18 00:00:49,600 --> 00:00:51,280 SPEAKER_05: What do you see in specific founders? 19 00:00:51,439 --> 00:00:55,039 Whether it's Domanics, Blanking, Ligora, any of the companies 20 00:00:55,039 --> 00:00:57,679 that you're backing, you're like, this is the right person 21 00:00:57,679 --> 00:00:58,799 to take it. 22 00:01:13,680 --> 00:01:16,079 Max Altschuler: All right, we're back with another special 23 00:01:16,079 --> 00:01:18,799 episode of the GTM Now podcast. 24 00:01:18,879 --> 00:01:20,879 This is the VC Bonus Edition. 25 00:01:21,040 --> 00:01:24,719 I'm joined by my general partner, Paul Irving. 26 00:01:24,879 --> 00:01:26,079 Paul, how's it going? 27 00:01:26,560 --> 00:01:27,200 Doing well. 28 00:01:27,359 --> 00:01:30,719 SPEAKER_01: I'm really excited to talk about this episode as I 29 00:01:30,719 --> 00:01:33,040 was listening to Chaythan talk. 30 00:01:33,120 --> 00:01:37,760 I think probably as furious as I was taking notes in any episode 31 00:01:37,760 --> 00:01:38,480 that we've done so far. 32 00:01:38,560 --> 00:01:41,760 So I'm really excited for people to listen into it and uh and and 33 00:01:41,760 --> 00:01:44,799 learn from some incredible investments, uh, unsurprisingly, 34 00:01:44,879 --> 00:01:47,280 that benchmark has made over the last uh couple of months. 35 00:01:47,519 --> 00:01:49,920 Max Altschuler: Yeah, they've been on a tear recently, for 36 00:01:49,920 --> 00:01:50,239 sure. 37 00:01:50,480 --> 00:01:52,560 You know, we're in a couple of great deals with them. 38 00:01:52,799 --> 00:01:55,680 One that just got marked up, not sure if we'll be announced yet 39 00:01:55,760 --> 00:01:59,439 when this when this airs, but um phenomenal what they do. 40 00:01:59,599 --> 00:02:03,359 He's a great follow on X, always very insightful. 41 00:02:03,599 --> 00:02:09,360 And yeah, I mean, talk about a little kind of wave in the 42 00:02:09,360 --> 00:02:10,000 ecosystem. 43 00:02:10,159 --> 00:02:13,599 They've gone from uh the the fund is changing, right? 44 00:02:13,680 --> 00:02:18,080 They got uh the Jack Altman move was a big move, you know, a 45 00:02:18,080 --> 00:02:19,039 couple months ago. 46 00:02:19,280 --> 00:02:22,159 Now they just announced a$2 billion fund, which is very 47 00:02:22,159 --> 00:02:24,800 different strategy than they have they they've always been 48 00:02:24,800 --> 00:02:28,479 kind of the historically like, nope, we're gonna stay in our 49 00:02:28,479 --> 00:02:32,240 kind of half a billion dollar range, they're kind of their 50 00:02:32,479 --> 00:02:33,199 their model. 51 00:02:33,520 --> 00:02:36,319 Uh, and so there's definitely a lot of commentary on them, you 52 00:02:36,319 --> 00:02:38,319 know, doing a bigger fund than ever before. 53 00:02:38,400 --> 00:02:39,680 I mean, what are your thoughts on that? 54 00:02:40,080 --> 00:02:42,719 Is is that just a sign of the times where the outcomes are 55 00:02:42,719 --> 00:02:44,479 much bigger, and so the fund size should grow with that. 56 00:02:44,639 --> 00:02:46,960 I mean, I saw a post the other day that said, you know, we and 57 00:02:46,960 --> 00:02:49,039 we've spoken about this many times, but like, yeah, in the 58 00:02:49,039 --> 00:02:52,000 2010s, you you were like a three, four billion dollar 59 00:02:52,000 --> 00:02:53,840 company was like a big company. 60 00:02:53,919 --> 00:02:55,199 That was like a big startup. 61 00:02:55,280 --> 00:02:56,560 It took 10 years to get there. 62 00:02:56,719 --> 00:02:59,599 You know, now it's like in five years you could build a 63 00:02:59,599 --> 00:03:01,360 potentially a four or five trillion dollar company. 64 00:03:01,439 --> 00:03:04,080 Like we'll see, we'll see where anthropic nets out, right? 65 00:03:04,240 --> 00:03:08,400 So, you know, does that is that a is that just inflation? 66 00:03:08,639 --> 00:03:11,280 SPEAKER_01: I think when you see the headline, because I mean, 67 00:03:11,439 --> 00:03:14,879 one of the most historic and revered funds in history of 68 00:03:14,879 --> 00:03:17,919 venture capital for good reason, you can see it across the ledger 69 00:03:18,000 --> 00:03:20,240 of portfolio companies that they've had through funds and in 70 00:03:20,240 --> 00:03:21,280 the door over the years. 71 00:03:21,439 --> 00:03:22,879 And it's their first growth fund. 72 00:03:22,960 --> 00:03:24,000 They've never done it before. 73 00:03:24,240 --> 00:03:26,800 And I think on the surface you see the headline, you know,$2 74 00:03:26,879 --> 00:03:30,479 billion having the first growth fund, you know, one of the last 75 00:03:30,479 --> 00:03:34,319 kind of pure play elites of a certain stage in strategy now 76 00:03:34,319 --> 00:03:35,759 becoming more of a platform. 77 00:03:35,919 --> 00:03:38,159 But then you, you know, as more news came out and you start 78 00:03:38,159 --> 00:03:40,800 double-clicking on what the strategy is gonna look like, 79 00:03:40,960 --> 00:03:44,800 it's still very benchmarky in the sense that the intention is 80 00:03:44,800 --> 00:03:48,319 to have, from the sounds of it, you know, five or six or seven 81 00:03:48,319 --> 00:03:50,240 companies only in the growth fund. 82 00:03:50,319 --> 00:03:52,000 It's going to be highly concentrated. 83 00:03:52,159 --> 00:03:55,280 It's not going to change the sort of partner or or platform 84 00:03:55,280 --> 00:03:56,159 model, so to speak. 85 00:03:56,319 --> 00:03:59,280 Like it's still a small team making really concentrated, 86 00:03:59,360 --> 00:04:02,960 thesis-oriented bets, um, and ideally working with with, of 87 00:04:02,960 --> 00:04:04,240 course, generational founders. 88 00:04:04,319 --> 00:04:06,960 And uh, I think you just see it in the amount of capital that 89 00:04:06,960 --> 00:04:07,520 gets raised. 90 00:04:07,680 --> 00:04:11,280 You know, they were Series A, I think, lead and series B for 91 00:04:11,280 --> 00:04:15,120 Cerebrus companies that went public earlier this year, fairly 92 00:04:15,120 --> 00:04:17,600 recently, and is absolutely ripped in the public markets 93 00:04:17,759 --> 00:04:19,120 post-IPO. 94 00:04:19,519 --> 00:04:22,879 You know, they did multiple rounds of funding, you know, 95 00:04:23,120 --> 00:04:26,160 pre-IPO, post-the benchmark investment rounds. 96 00:04:26,240 --> 00:04:30,079 And you start to just see opportunities to put more 97 00:04:30,079 --> 00:04:32,560 capital to work into incredible companies that you have an 98 00:04:32,560 --> 00:04:33,439 inside track on. 99 00:04:33,600 --> 00:04:37,040 And to your point earlier, if we're compounding value to 100 00:04:37,040 --> 00:04:40,639 companies that are an order of magnitude larger than we saw in 101 00:04:40,639 --> 00:04:45,439 the previous generation of, you know, cloud B2B companies, or 102 00:04:45,519 --> 00:04:49,040 you know, even the generation before that, then you know, 103 00:04:49,199 --> 00:04:51,519 having another sleeve of capital to be able to put into those 104 00:04:51,519 --> 00:04:54,160 companies so you're not getting diluted and you're owning more 105 00:04:54,160 --> 00:04:56,800 and more of iconic businesses makes makes a ton of sense. 106 00:04:57,040 --> 00:04:57,360 Max Altschuler: Yeah. 107 00:04:57,600 --> 00:05:02,319 And he and we did end up um talking quite a bit about GTM in 108 00:05:02,319 --> 00:05:03,040 this episode. 109 00:05:03,360 --> 00:05:06,959 Sophie did a phenomenal job with it in my in my place. 110 00:05:07,120 --> 00:05:11,040 But um, you know, we talked about two things, which is POC 111 00:05:11,360 --> 00:05:13,040 to trial as an accelerant. 112 00:05:13,120 --> 00:05:16,720 So getting, you know, getting a demo and a trial as fast as 113 00:05:16,720 --> 00:05:19,360 possible leads to kind of bigger wins faster. 114 00:05:19,600 --> 00:05:22,879 And then uh the power of direct sales in the AI era. 115 00:05:23,040 --> 00:05:28,160 You know, now software is just so much more customizable than 116 00:05:28,160 --> 00:05:29,040 it ever has been. 117 00:05:29,199 --> 00:05:31,680 So you really do need kind of those forward-deployed 118 00:05:31,680 --> 00:05:33,759 engineers, that direct salesperson, the person who's 119 00:05:33,759 --> 00:05:37,600 gonna kind of, you know, in the past maybe be a masters of 120 00:05:37,600 --> 00:05:40,399 ceremony moving the deal forward, and now they're more of 121 00:05:40,399 --> 00:05:43,519 like, uh, hey, here's how we help you like help the software 122 00:05:43,519 --> 00:05:48,000 customize for your needs and tailor it to a certain degree. 123 00:05:48,160 --> 00:05:50,639 What were your thoughts on kind of those two pieces? 124 00:05:50,720 --> 00:05:53,519 Uh, because I think that those those were the big like, I don't 125 00:05:53,519 --> 00:05:56,560 know, pull the string on GTM parts of of this episode that I 126 00:05:56,560 --> 00:05:57,040 really liked. 127 00:05:57,360 --> 00:05:59,839 SPEAKER_01: Yeah, uh, because I think it's easy in the first 128 00:05:59,839 --> 00:06:02,800 generation of AI native companies that have grown really 129 00:06:02,800 --> 00:06:03,199 quickly. 130 00:06:03,439 --> 00:06:06,240 So many of them were PLG or sort of single user sign-ups. 131 00:06:06,319 --> 00:06:09,920 So you think of Cursor, Lovable, Replit, you know, these 132 00:06:09,920 --> 00:06:13,199 companies that have grown as fast as we've ever seen, but 133 00:06:13,199 --> 00:06:16,399 it's a lot of individuals signing up who are, you know, 134 00:06:16,560 --> 00:06:19,519 sophisticated enough buyers and swiping a credit card and 135 00:06:19,519 --> 00:06:20,000 getting to work. 136 00:06:20,160 --> 00:06:22,560 What what Chaythan mentioned, which I thought was fascinating, 137 00:06:22,720 --> 00:06:27,279 is that this next gigantic wave of AI companies is going to 138 00:06:27,279 --> 00:06:29,279 build a ton of value with direct sales. 139 00:06:29,360 --> 00:06:30,879 And then there's ways you can accelerate it. 140 00:06:30,959 --> 00:06:33,360 Um, so to separate those two things, to start on the direct 141 00:06:33,360 --> 00:06:37,120 sales side of the house, uh, and he mentions Lagora as a good 142 00:06:37,120 --> 00:06:37,600 example of it. 143 00:06:37,680 --> 00:06:40,560 And I I think it is a good example where, you know, there's 144 00:06:40,560 --> 00:06:43,040 big corners of the economy, whether you're building a 145 00:06:43,040 --> 00:06:46,000 horizontal or vertical specific platform that know that they 146 00:06:46,000 --> 00:06:47,120 need to adopt AI. 147 00:06:47,279 --> 00:06:50,800 These are C level, board level mandates to pull their business 148 00:06:50,800 --> 00:06:53,120 into the AI era or they're going to get left behind. 149 00:06:53,199 --> 00:06:56,639 So there's budget available, but there's not necessarily 150 00:06:56,639 --> 00:06:59,439 playbooks that everyone can replicate on how to do that and 151 00:06:59,439 --> 00:07:00,000 what it looks like. 152 00:07:00,160 --> 00:07:03,920 And so if you're building the best AI native product for that 153 00:07:03,920 --> 00:07:07,920 particular category, um, direct sales becomes this very 154 00:07:08,160 --> 00:07:08,800 consultative. 155 00:07:08,879 --> 00:07:11,040 And this is why we're seeing the explosion of forward-deployed 156 00:07:11,040 --> 00:07:14,800 engineers as well of like, let me help you take your business 157 00:07:14,800 --> 00:07:15,920 into the AI era. 158 00:07:16,000 --> 00:07:17,680 And there has to be trust. 159 00:07:18,000 --> 00:07:21,680 People are investing in vendors for what should be multi-year 160 00:07:21,680 --> 00:07:24,240 relationships, where it's not just what does the product do 161 00:07:24,240 --> 00:07:27,439 right now, which should be magical to the point of demos 162 00:07:27,439 --> 00:07:31,519 and POCs, but uh what can you do for a business over the long 163 00:07:31,519 --> 00:07:31,759 term? 164 00:07:31,920 --> 00:07:33,519 How can you transform it? 165 00:07:33,759 --> 00:07:37,279 And that's just a really difficult motion to execute if 166 00:07:37,279 --> 00:07:38,160 you're PLG. 167 00:07:38,319 --> 00:07:41,600 You need the executive buy-in, you need the trusted direct 168 00:07:41,600 --> 00:07:46,560 sales relationship, and then you need this post-sales FDE pretty 169 00:07:46,560 --> 00:07:49,680 hands-on implementation and go live just to make sure that 170 00:07:49,680 --> 00:07:52,399 they're having success because this is a huge investment, as it 171 00:07:52,399 --> 00:07:53,759 should be on both sides of the equation. 172 00:07:54,000 --> 00:07:56,480 Max Altschuler: And then, you know, I I think you're getting 173 00:07:56,800 --> 00:08:01,040 as as a leader these days, uh, a million different AI pitches. 174 00:08:01,360 --> 00:08:03,920 So, you know, he talked about how hard it is to kind of break 175 00:08:03,920 --> 00:08:06,959 in and how you got to separate yourself and differentiate 176 00:08:06,959 --> 00:08:07,439 yourself. 177 00:08:07,600 --> 00:08:11,600 And then if you even do get the the meeting, you know, the the 178 00:08:11,600 --> 00:08:15,600 demo is that kind of that hook, that draw. 179 00:08:15,680 --> 00:08:16,560 Hey, can I get in? 180 00:08:16,720 --> 00:08:17,279 Can I use it? 181 00:08:17,439 --> 00:08:20,959 Can I see like my product in the demo? 182 00:08:21,199 --> 00:08:23,600 And this was this was something in the 2021s. 183 00:08:23,680 --> 00:08:24,480 You saw a bunch of companies. 184 00:08:24,560 --> 00:08:27,279 It was Demo Stack, Reprise, Arcade. 185 00:08:27,360 --> 00:08:29,439 There were a bunch in this in this space where it was like 186 00:08:29,439 --> 00:08:33,120 demo environments where you can like show somebody a demo of 187 00:08:33,120 --> 00:08:36,879 your product in real time using their product or their 188 00:08:36,879 --> 00:08:38,159 information, stuff like that. 189 00:08:38,320 --> 00:08:42,480 And now with AI, it's like I think all those companies might 190 00:08:42,480 --> 00:08:43,200 have been early. 191 00:08:43,360 --> 00:08:46,080 Like you can finally kind of do this at scale. 192 00:08:46,320 --> 00:08:48,399 And um, while all those companies were early, I mean 193 00:08:48,480 --> 00:08:51,039 it'd be it'd be interesting to see kind of how that how it 194 00:08:51,039 --> 00:08:51,759 comes to version. 195 00:08:51,919 --> 00:08:54,879 Text box test box is another one in that in that space. 196 00:08:55,039 --> 00:08:57,440 So yeah, I think there's just so much more you can do in the 197 00:08:57,440 --> 00:09:04,720 sales process now where you're engaging the buyer in a more 198 00:09:04,879 --> 00:09:09,679 customized manner, and you can convert them to being in the 199 00:09:09,679 --> 00:09:12,320 product very quickly and have a hyper-customized product. 200 00:09:12,639 --> 00:09:14,960 SPEAKER_01: Yeah, and and that's where the compression comes 201 00:09:14,960 --> 00:09:15,120 from. 202 00:09:15,279 --> 00:09:17,440 Because you're still seeing, so now we're seeing a generation, 203 00:09:17,519 --> 00:09:19,840 you know, Lagora being the example that we talk about in 204 00:09:19,840 --> 00:09:25,120 depth in the actual episode, but of AI direct sales companies 205 00:09:25,120 --> 00:09:27,679 with with the direct sales go to market motion growing incredibly 206 00:09:27,679 --> 00:09:28,080 quickly. 207 00:09:28,159 --> 00:09:31,600 And the compression comes from this, okay, instead of doing 208 00:09:31,600 --> 00:09:36,159 this long 180D sales cycle where we do, you know, enterprise-wide 209 00:09:36,159 --> 00:09:38,240 deployment and we'll train everybody and have 210 00:09:38,240 --> 00:09:38,879 implementation. 211 00:09:39,039 --> 00:09:40,720 What's a POC that we can start with? 212 00:09:40,960 --> 00:09:43,120 And then to your point, you always talk to our founders 213 00:09:43,120 --> 00:09:45,519 about this, which I think is such an important call-out of 214 00:09:45,679 --> 00:09:47,919 don't just say let's start a trial or POC. 215 00:09:48,080 --> 00:09:51,360 Be very clear about what the goals are, what success looks 216 00:09:51,360 --> 00:09:54,159 like, what conversion looks like when we hit all those goals and 217 00:09:54,159 --> 00:09:57,440 metrics, and then what it looks like to expand, you know, post 218 00:09:57,440 --> 00:09:58,960 that first set of gates. 219 00:09:59,279 --> 00:10:01,919 But if you can do that correctly of in let's remove some 220 00:10:01,919 --> 00:10:04,080 friction, I'm gonna show you a magical demo. 221 00:10:04,320 --> 00:10:07,440 You're going to be inspired to, you know, at all the different 222 00:10:07,440 --> 00:10:09,440 ways you can roll this across your business and how much value 223 00:10:09,440 --> 00:10:10,080 it's gonna create. 224 00:10:10,159 --> 00:10:11,519 And let's just start somewhere. 225 00:10:11,679 --> 00:10:15,519 And if we can start somewhere, the expansion um as you 226 00:10:15,519 --> 00:10:18,000 implement and you roll it out and you invest a bunch of 227 00:10:18,000 --> 00:10:21,440 resources in implementation is really, really compelling. 228 00:10:21,600 --> 00:10:24,000 So you you know, we see it in our portfolio as well, these 229 00:10:24,240 --> 00:10:27,919 starting contracts, which um they're not small in their own 230 00:10:27,919 --> 00:10:31,840 right either, but the speed to upsell and expansion is you know 231 00:10:32,000 --> 00:10:33,840 not with not a 12-month cycle anymore. 232 00:10:33,919 --> 00:10:36,080 Like we're seeing it in one month, two months three month, 233 00:10:36,320 --> 00:10:39,759 four month increments um where these accounts just continue to 234 00:10:39,759 --> 00:10:40,159 grow. 235 00:10:40,559 --> 00:10:42,639 Max Altschuler: Well, really excited to dig into it uh 236 00:10:42,720 --> 00:10:43,919 because it's a great episode. 237 00:10:44,000 --> 00:10:47,840 So without further delay, we've got Chase in, general partner 238 00:10:48,080 --> 00:10:49,759 from Benchmark on the show. 239 00:10:49,840 --> 00:10:50,720 Let's get into it. 240 00:10:51,039 --> 00:10:54,080 Our LP based spans from individual operators to 241 00:10:54,080 --> 00:10:57,200 institutional allocators, and Angelist has been instrumental 242 00:10:57,200 --> 00:10:58,240 in supporting all of them. 243 00:10:58,399 --> 00:11:00,559 They handle everything from investor onboarding and 244 00:11:00,559 --> 00:11:03,759 accreditation to distribution and tax documentation, creating 245 00:11:03,759 --> 00:11:06,879 a seamless experience across geographies and fund types. 246 00:11:07,039 --> 00:11:10,080 Plus, all of this is available on a single modern platform. 247 00:11:10,240 --> 00:11:14,799 For an LP-based like ours with over 300 C-Suite and VP level 248 00:11:14,799 --> 00:11:17,519 operators, this kind of white glove service and seamless 249 00:11:17,519 --> 00:11:18,639 workflows is so important. 250 00:11:18,799 --> 00:11:21,919 Also instrumental that we support our institutional LPs 251 00:11:22,080 --> 00:11:24,879 that we're fortunate to work with, and Angelist is able to do 252 00:11:24,879 --> 00:11:25,919 so every step of the way. 253 00:11:26,080 --> 00:11:29,039 If you're looking for a platform that can support any type of LP 254 00:11:29,039 --> 00:11:32,879 investing in your fund, learn more at angelist.com slash GTM 255 00:11:32,879 --> 00:11:33,200 Fund. 256 00:11:33,519 --> 00:11:35,200 SPEAKER_05: Jason, welcome to GTM now. 257 00:11:35,519 --> 00:11:35,840 Thank you. 258 00:11:36,080 --> 00:11:38,080 Yeah, thanks for having me here in the benchmark office. 259 00:11:38,240 --> 00:11:38,879 It's great to be here. 260 00:11:39,120 --> 00:11:39,440 Welcome. 261 00:11:39,679 --> 00:11:40,159 Yeah. 262 00:11:40,320 --> 00:11:42,399 Well, let's start off there because you meet a lot of 263 00:11:42,399 --> 00:11:43,840 founders in this scary office. 264 00:11:43,919 --> 00:11:47,440 And one founder in particular that you met by the name of Max 265 00:11:47,679 --> 00:11:49,440 dates back to early 2024. 266 00:11:49,519 --> 00:11:52,000 I believe it might have even just been down the hallway from 267 00:11:52,000 --> 00:11:52,320 here. 268 00:11:52,559 --> 00:11:57,519 That company, Lagora, now in 2026, you know, just raised a$50 269 00:11:57,519 --> 00:12:01,440 million extension on their Series D, is now valued at$5.6 270 00:12:01,440 --> 00:12:01,919 billion. 271 00:12:02,639 --> 00:12:04,159 And you met him very early. 272 00:12:04,240 --> 00:12:07,279 So take us back to you know that conversation when you're sitting 273 00:12:07,279 --> 00:12:09,360 down with him in the office here, you and Peter. 274 00:12:09,840 --> 00:12:10,159 SPEAKER_02: Yeah. 275 00:12:10,240 --> 00:12:15,840 Um, we met Max originally uh I think February of 2024. 276 00:12:16,240 --> 00:12:20,000 Um the company was five people, it was still in Y Combinator, 277 00:12:20,159 --> 00:12:22,159 and it hadn't gone through demo day yet. 278 00:12:22,320 --> 00:12:27,600 And um, you know, Max came in and had very specific ideas 279 00:12:27,600 --> 00:12:30,159 about how the legal AI market was gonna evolve. 280 00:12:30,320 --> 00:12:33,279 Um, the couple things he talked about, which really resonated 281 00:12:33,279 --> 00:12:36,000 with us, was that you know the intelligence layer was gonna get 282 00:12:36,000 --> 00:12:38,639 a lot smarter than what we were dealing with at the moment. 283 00:12:38,879 --> 00:12:41,840 And the right way to go about building an application for 284 00:12:41,840 --> 00:12:46,080 lawyers was to really ride the curve of intelligence over the 285 00:12:46,080 --> 00:12:49,039 next year or so and beyond. 286 00:12:49,360 --> 00:12:53,200 And the company executed beautifully against that vision. 287 00:12:53,360 --> 00:12:58,799 You know, we invested um in March of 2024, and then the 288 00:12:58,799 --> 00:13:01,519 product launched in October of 2024. 289 00:13:01,759 --> 00:13:05,919 And at the time that the company launched, their you know, direct 290 00:13:05,919 --> 00:13:10,000 competitor had raised a devaluation of$3 billion. 291 00:13:10,720 --> 00:13:13,919 Um, they had been market in market for a really long time. 292 00:13:14,320 --> 00:13:20,240 Um, and so from that moment of launching the product in October 293 00:13:20,240 --> 00:13:25,679 of 2024, uh, and 18 months later, at the end of March uh 294 00:13:25,679 --> 00:13:30,720 2026, the company grew from a million dollars of ARR to$100 295 00:13:30,720 --> 00:13:34,559 million of ARR, setting all sorts of speed records for 296 00:13:34,559 --> 00:13:35,679 enterprise selling. 297 00:13:35,919 --> 00:13:37,919 Um, and it's been a remarkable journey. 298 00:13:38,080 --> 00:13:43,840 But if you fast forward to that moment in time in 2024, the 299 00:13:43,840 --> 00:13:48,799 thing that Max talked about that really resonated with us was 300 00:13:49,279 --> 00:13:52,639 purely that product differentiation and a technology 301 00:13:52,639 --> 00:13:55,200 trend and betting on that technology trend. 302 00:13:55,440 --> 00:13:59,200 And at that moment in time, it was unique insight. 303 00:13:59,360 --> 00:14:03,360 If you'll recall just two years ago, what everybody was doing 304 00:14:03,360 --> 00:14:04,720 was building custom models. 305 00:14:04,879 --> 00:14:09,200 The models were not that capable yet of doing agentic tasks. 306 00:14:09,600 --> 00:14:13,039 And everybody was trying to build lots of custom frameworks 307 00:14:13,039 --> 00:14:14,000 on top of these models. 308 00:14:14,080 --> 00:14:16,559 And Max just had the view that the models were just gonna get 309 00:14:16,559 --> 00:14:16,960 really good. 310 00:14:17,120 --> 00:14:20,159 And so just betting on the foundational models was the way 311 00:14:20,159 --> 00:14:21,519 to go and build that application. 312 00:14:21,600 --> 00:14:24,720 And that just turned out to be the right bet at the right time. 313 00:14:25,039 --> 00:14:27,759 Um, but it's been a it's been an amazing journey to date. 314 00:14:28,559 --> 00:14:29,200 SPEAKER_05: Definitely. 315 00:14:29,360 --> 00:14:32,000 And I mean, you met him in February of 2024. 316 00:14:32,080 --> 00:14:36,720 He said you invested in March of 2024, and they've just taken 317 00:14:36,720 --> 00:14:36,879 off. 318 00:14:37,039 --> 00:14:40,399 And that's not the first company, like far from you've 319 00:14:40,399 --> 00:14:43,440 got many different examples of times that you've built 320 00:14:43,440 --> 00:14:46,960 conviction quickly to, you know, win that partnership with a 321 00:14:46,960 --> 00:14:50,240 founder and then subsequently help them scale considerably to, 322 00:14:50,320 --> 00:14:52,960 you know, the likes of Lagora's growth and so forth. 323 00:14:53,120 --> 00:14:56,159 So, you know, another example, uh, which is a bit of a crazy 324 00:14:56,159 --> 00:14:57,519 story, is the Manus one. 325 00:14:57,759 --> 00:15:01,279 So we'd love to hear a little bit more about when you met 326 00:15:01,279 --> 00:15:02,000 Manus. 327 00:15:02,080 --> 00:15:04,720 Like what was that meeting like and how did things progress 328 00:15:04,720 --> 00:15:04,879 there? 329 00:15:05,200 --> 00:15:08,000 SPEAKER_02: Yeah, I originally met them in Tokyo, and the 330 00:15:08,000 --> 00:15:13,440 company at the time was pre-revenue pre-launch, uh, and 331 00:15:13,600 --> 00:15:17,759 you know, launched, I think soon after that meeting, probably a 332 00:15:17,759 --> 00:15:18,720 month after that. 333 00:15:18,879 --> 00:15:22,159 And they scaled zero to a hundred million in eight months. 334 00:15:22,320 --> 00:15:26,639 And so um, that was a different scaling factor because they 335 00:15:26,639 --> 00:15:28,799 scaled primarily through product-led growth. 336 00:15:28,879 --> 00:15:31,679 So that was users coming to the website, signing up, putting in 337 00:15:31,679 --> 00:15:34,320 their credit cards and paying for tokens basically. 338 00:15:34,639 --> 00:15:37,519 Lagora, in contrast, is enterprise selling. 339 00:15:37,600 --> 00:15:40,720 So they have AEs, they sell directly. 340 00:15:41,200 --> 00:15:46,080 And so I think the interesting thing to zoom out is just how 341 00:15:47,360 --> 00:15:50,879 fast companies can grow in the AI era. 342 00:15:51,519 --> 00:15:56,159 And, you know, there's lots of companies growing with just pure 343 00:15:56,159 --> 00:15:57,360 PLG motion. 344 00:15:57,519 --> 00:16:01,440 But what's really interesting to me in this AI world is that the 345 00:16:01,440 --> 00:16:03,759 direct sewing can move so quickly. 346 00:16:04,000 --> 00:16:08,240 And so I think that's more about the macro pull in these 347 00:16:08,240 --> 00:16:10,960 different industries and different verticals of companies 348 00:16:10,960 --> 00:16:14,720 for the first time wanting to change their software stack to 349 00:16:14,720 --> 00:16:15,919 be AI native. 350 00:16:16,159 --> 00:16:20,559 And so they are much more willing to experiment, they're 351 00:16:20,720 --> 00:16:22,240 much more willing to try. 352 00:16:22,399 --> 00:16:25,279 And so you're looking at verticals that haven't adopted a 353 00:16:25,279 --> 00:16:29,360 new stack in maybe 15 to 20 years that are finally willing 354 00:16:29,360 --> 00:16:31,519 to adopt solutions from startups. 355 00:16:31,679 --> 00:16:35,360 So, what that opens up is just dramatic velocity opportunity 356 00:16:35,360 --> 00:16:36,320 for new entrants. 357 00:16:36,480 --> 00:16:39,600 And that's all, you know, that's the thing that's really cool 358 00:16:39,600 --> 00:16:43,759 about this moment in time with AI, is just how fast you can 359 00:16:43,759 --> 00:16:44,799 move through the market. 360 00:16:45,039 --> 00:16:50,159 Even if you're going direct through salespeople and AEs and 361 00:16:50,159 --> 00:16:53,759 just doing direct selling, which could be considered a 362 00:16:53,759 --> 00:16:57,840 traditional playbook, but in the AI world, it's just experiencing 363 00:16:57,840 --> 00:17:01,120 a velocity that's like previously software companies 364 00:17:01,279 --> 00:17:02,000 and experience. 365 00:17:02,879 --> 00:17:04,799 SPEAKER_05: Do you see an end point there then? 366 00:17:04,960 --> 00:17:07,039 Because we're in this window where people are looking to 367 00:17:07,039 --> 00:17:09,039 disrupt their stats and go AI native. 368 00:17:09,359 --> 00:17:10,480 Will that window shut? 369 00:17:10,880 --> 00:17:15,440 SPEAKER_02: So if you look at the internet and the cloud, in 370 00:17:15,440 --> 00:17:20,960 the early days of both trends, was this huge acceleration of 371 00:17:20,960 --> 00:17:24,400 the companies that established themselves as the dominant 372 00:17:24,400 --> 00:17:25,920 enterprise software players. 373 00:17:26,160 --> 00:17:29,759 And there was a moment in time where if you captured a certain 374 00:17:29,759 --> 00:17:35,200 category, you actually were able to build escape velocity and 375 00:17:35,200 --> 00:17:36,160 dominate the market. 376 00:17:36,400 --> 00:17:39,599 So, a very specific example, and perhaps the best example of the 377 00:17:39,599 --> 00:17:42,160 cloud era is of course Salesforce, which when it showed 378 00:17:42,160 --> 00:17:46,319 up, SIBL was the dominant um CRM provider. 379 00:17:46,559 --> 00:17:51,759 And so if you just looked at how fast Salesforce grew in the 380 00:17:51,759 --> 00:17:55,279 initial innings of cloud, it kind of looks like the growth of 381 00:17:55,279 --> 00:17:56,160 AI companies. 382 00:17:56,319 --> 00:17:59,680 And what's amazing is how capital efficient Salesforce was 383 00:18:00,000 --> 00:18:01,920 through that entire growth cycle. 384 00:18:02,079 --> 00:18:05,440 Like they were an extraordinarily lean team 385 00:18:05,680 --> 00:18:09,839 selling licenses at an average of$5,000, then$10,000, and 386 00:18:09,920 --> 00:18:14,000 $20,000, which at the time was an unbelievable price point to 387 00:18:14,000 --> 00:18:15,359 be selling enterprise software. 388 00:18:15,519 --> 00:18:19,359 And so if you just look at how fast that velocity went for 389 00:18:19,359 --> 00:18:23,119 Salesforce, it looks a lot like initial innings of AI. 390 00:18:23,200 --> 00:18:25,920 Um and I think AI is even faster, especially at the 391 00:18:25,920 --> 00:18:26,880 foundation model level. 392 00:18:26,960 --> 00:18:30,480 Like those companies are growing at unprecedented rates. 393 00:18:31,279 --> 00:18:34,720 But if you're in the early innings of these markets, I 394 00:18:34,720 --> 00:18:37,599 think the velocity is actually much faster. 395 00:18:37,920 --> 00:18:41,759 And I think one of the analogies to the foundation models growth 396 00:18:41,759 --> 00:18:44,880 themselves is if you just look at the cloud hyperscaler growth 397 00:18:44,880 --> 00:18:45,440 in the initial days. 398 00:18:45,519 --> 00:18:49,119 So if you look at AWS growth in the initial days, Azure growth 399 00:18:49,119 --> 00:18:51,839 in the initial days, Google Cloud growth in the initial 400 00:18:51,839 --> 00:18:54,720 days, well, actually, and then Google Cloud more recently too. 401 00:18:56,640 --> 00:19:02,319 There are these early windows in a big macro trend where I do 402 00:19:02,319 --> 00:19:05,519 think a lot of enterprises re-examine their stack. 403 00:19:05,680 --> 00:19:08,720 And those are unique opportunities in time. 404 00:19:09,599 --> 00:19:11,599 How long will that window last? 405 00:19:12,079 --> 00:19:15,759 You know, you can sort of approximate that based on the 406 00:19:15,759 --> 00:19:20,319 addressable market, both from a software spend perspective and 407 00:19:20,720 --> 00:19:22,559 addressable market based on services. 408 00:19:22,720 --> 00:19:26,160 So for example, we're talking about um legal AI. 409 00:19:26,400 --> 00:19:31,359 Legal software as a market is about 40 billion per annum, and 410 00:19:31,359 --> 00:19:34,240 legal services as a market is about a trillion dollars per 411 00:19:34,240 --> 00:19:34,720 annum. 412 00:19:35,039 --> 00:19:40,400 And so, you know, the windows open until somebody can capture 413 00:19:40,720 --> 00:19:45,279 some significant share of both the software plus services 414 00:19:45,279 --> 00:19:45,519 market. 415 00:19:45,680 --> 00:19:52,079 And so in 2026, we're in the very, very early days of that 416 00:19:52,079 --> 00:19:53,359 market transforming. 417 00:19:53,680 --> 00:19:58,799 You know, like five to ten years from now, I would say the window 418 00:19:58,799 --> 00:20:01,039 is gonna be much narrower than it is now. 419 00:20:01,200 --> 00:20:06,960 Because a lot of the the incumbent um stack providers 420 00:20:06,960 --> 00:20:09,759 will have been replaced by the AI native ones. 421 00:20:09,839 --> 00:20:10,160 Yeah. 422 00:20:10,319 --> 00:20:13,920 And those companies will now become the incumbents. 423 00:20:14,079 --> 00:20:16,319 And so you'll have that stack replacement. 424 00:20:16,480 --> 00:20:19,440 And then at that point, those companies and the customers of 425 00:20:19,440 --> 00:20:21,759 the AI native companies are gonna be pretty happy with what 426 00:20:21,759 --> 00:20:22,640 they're doing. 427 00:20:22,880 --> 00:20:27,039 And then to dislodge them again is gonna be much tougher in five 428 00:20:27,039 --> 00:20:27,839 to seven years' time. 429 00:20:28,000 --> 00:20:31,359 And then I'm sure there will be another disruptive wave in a 430 00:20:31,359 --> 00:20:32,240 decade's time. 431 00:20:32,480 --> 00:20:32,640 Yes. 432 00:20:32,799 --> 00:20:34,559 And then you'll have another chance at the stack. 433 00:20:34,960 --> 00:20:39,839 So the it's not that it's a sort of a window that closes, it's 434 00:20:39,839 --> 00:20:41,920 just a window that sort of narrows over time. 435 00:20:42,720 --> 00:20:45,119 And so if you just look at what happened, you know, from my 436 00:20:45,119 --> 00:20:53,839 perspective at cloud, between 2009 to 2012 was like a great 437 00:20:53,839 --> 00:20:55,279 window for applications. 438 00:20:55,599 --> 00:20:59,440 And the application window just became narrower and narrower 439 00:20:59,440 --> 00:21:02,000 from 2012 to about 2022. 440 00:21:02,160 --> 00:21:04,000 It just became narrower and narrower. 441 00:21:04,079 --> 00:21:09,279 And so you got more verticalized apps, you got more narrow apps, 442 00:21:09,599 --> 00:21:13,359 you got more focused apps, and then of course, AI is open 443 00:21:13,440 --> 00:21:15,759 things up, and then you can now do horizontal software again, 444 00:21:15,839 --> 00:21:16,640 which is exciting. 445 00:21:17,039 --> 00:21:19,359 SPEAKER_05: That is, yes, yes, certainly. 446 00:21:19,599 --> 00:21:23,759 And so now there's this window in period of time that we talked 447 00:21:23,759 --> 00:21:23,920 about. 448 00:21:24,240 --> 00:21:27,440 So for founders thinking of building, it's a very exciting 449 00:21:27,440 --> 00:21:29,680 time because you can capitalize on that window. 450 00:21:30,000 --> 00:21:32,799 But what comes with that excitement is also just a surge 451 00:21:32,799 --> 00:21:36,160 of the amount of companies being built for yourself assessing 452 00:21:36,160 --> 00:21:39,119 many different companies, but only allocating capital towards 453 00:21:39,200 --> 00:21:41,759 like a very s few select companies. 454 00:21:41,920 --> 00:21:46,799 Like how are you kind of deciding and looking at what AI 455 00:21:46,799 --> 00:21:49,680 native companies are going to be the winners of that window 456 00:21:49,680 --> 00:21:50,400 period of time? 457 00:21:50,880 --> 00:21:53,680 SPEAKER_02: So to give you a sense of benchmark, we are 458 00:21:53,680 --> 00:21:56,720 roughly a six hundred million dollar fund with five partners, 459 00:21:56,880 --> 00:21:59,759 um, and each of us makes about two investments a year. 460 00:22:00,160 --> 00:22:04,240 And so the fund is making on the order of 70 to 10 investments a 461 00:22:04,240 --> 00:22:04,480 year. 462 00:22:04,720 --> 00:22:07,759 We're primarily doing C and Series A investments, they're 463 00:22:07,759 --> 00:22:11,279 high conviction investments, we're taking board seats. 464 00:22:11,599 --> 00:22:20,319 Um and so in that in moments of great disruption, all of us are 465 00:22:20,319 --> 00:22:27,039 very much tuned to founders coming up with visions. 466 00:22:27,440 --> 00:22:29,680 I think it has to be technology-led, technology-led 467 00:22:30,000 --> 00:22:36,240 vision that seems really interesting and perhaps radical, 468 00:22:36,720 --> 00:22:41,200 and perhaps the sort of spear that can cut through all of this 469 00:22:41,200 --> 00:22:45,039 enterprise demand and have this huge pull-through in this moment 470 00:22:45,039 --> 00:22:45,599 in time. 471 00:22:45,839 --> 00:22:49,920 And so I think if you look at the common thread amongst all 472 00:22:49,920 --> 00:22:54,079 the investments that I've led here over the last couple of 473 00:22:54,079 --> 00:22:56,960 years in AI, I think that's the common thread across all of 474 00:22:56,960 --> 00:22:59,680 them, which is that the founders have come up with some 475 00:23:00,000 --> 00:23:03,839 technically interesting angle at the market they're going after 476 00:23:04,000 --> 00:23:08,319 in a very AI-native way, which then has created a demand pull 477 00:23:08,319 --> 00:23:13,119 from customers and has created like just an immense velocity 478 00:23:13,119 --> 00:23:13,680 for them. 479 00:23:14,319 --> 00:23:16,880 I think that's broadly true of all of the investments that 480 00:23:16,880 --> 00:23:19,519 we've done over the last couple of years, which is that there 481 00:23:19,519 --> 00:23:23,359 was some kind of foundational technical element to it. 482 00:23:23,599 --> 00:23:26,960 So you look for that technical differentiation, and then that 483 00:23:26,960 --> 00:23:29,039 technical differentiation ultimately creates a 484 00:23:29,039 --> 00:23:30,319 distribution advantage. 485 00:23:30,880 --> 00:23:35,359 So, you know, of course, the large incumbent software vendors 486 00:23:35,359 --> 00:23:40,319 and hyperscalers have way more AEs than any of our startups can 487 00:23:40,319 --> 00:23:41,519 ever get, period. 488 00:23:41,759 --> 00:23:45,200 And when, you know, we go to an enterprise account like 489 00:23:45,200 --> 00:23:51,279 Coca-Cola, you know, some the incumbent vendors are going to 490 00:23:51,279 --> 00:23:56,000 have account coverage teams of maybe 100 people against one 491 00:23:56,000 --> 00:23:57,039 specific use case. 492 00:23:57,440 --> 00:24:02,319 And our startups can at most put five people on that account. 493 00:24:02,559 --> 00:24:05,920 And so there is no way you're going to be able to outsell an 494 00:24:05,920 --> 00:24:09,920 incumbent unless the customer realizes the value and is 495 00:24:09,920 --> 00:24:11,680 pulling you organically. 496 00:24:11,920 --> 00:24:14,240 And so that has to start product out. 497 00:24:14,400 --> 00:24:19,599 And so there's only so much distribution, you know, muscle 498 00:24:19,599 --> 00:24:22,240 you can build and hacks you can do and all that kind of stuff. 499 00:24:22,319 --> 00:24:24,799 But at the end of the day, if you don't have differentiated 500 00:24:24,799 --> 00:24:27,440 technology and differentiated product, it's going to be really 501 00:24:27,440 --> 00:24:30,400 hard to break through incumbent distribution. 502 00:24:31,920 --> 00:24:38,319 And right now, because of this AI wave, these large enterprises 503 00:24:38,319 --> 00:24:40,640 are all being forced to re-examine their stacks. 504 00:24:40,720 --> 00:24:42,480 So there's some kind of top-down mandate. 505 00:24:42,720 --> 00:24:46,720 So maybe somebody on the board or maybe the executive 506 00:24:46,720 --> 00:24:50,000 leadership team has told everybody in the company, we 507 00:24:50,000 --> 00:24:51,359 need to re-examine stacks. 508 00:24:51,440 --> 00:24:55,200 Let's go AI native where we can, because we see great ROI, we can 509 00:24:55,200 --> 00:24:57,440 save money, we can expand revenue, whatever it is. 510 00:24:57,599 --> 00:25:00,160 And so they're telling everybody in the organization, examine 511 00:25:00,240 --> 00:25:02,480 your stack, examine all the AI companies. 512 00:25:02,640 --> 00:25:07,440 And so that's unique in that everybody's now coming out and 513 00:25:07,440 --> 00:25:11,279 saying, okay, what are the interesting AI companies across 514 00:25:11,279 --> 00:25:14,079 these different functional areas inside the company? 515 00:25:14,319 --> 00:25:19,359 That gives you a reason as a startup or chance as a startup 516 00:25:19,519 --> 00:25:20,640 to pitch them. 517 00:25:21,200 --> 00:25:26,799 And then how you run from that initial pitch to a closed 518 00:25:26,799 --> 00:25:31,920 contract to a production deal, that's really interesting to me. 519 00:25:32,240 --> 00:25:37,359 And can this moment in time allow you to accelerate that 520 00:25:37,359 --> 00:25:41,759 time from initial contact to production in a way that wasn't 521 00:25:41,759 --> 00:25:43,839 possible five, six years ago? 522 00:25:44,160 --> 00:25:47,839 So traditionally, you would go sell an enterprise, they would 523 00:25:47,839 --> 00:25:51,039 put you in a pilot, it'd be a pretty big and complicated 524 00:25:51,039 --> 00:25:51,440 pilot. 525 00:25:51,759 --> 00:25:54,480 You'd get through that pilot, there would be some kind of 526 00:25:54,480 --> 00:25:57,440 security review at some point, you'd get bogged down in the 527 00:25:57,440 --> 00:25:58,319 security review. 528 00:25:58,480 --> 00:26:01,680 And by the time you put the system in production and 529 00:26:01,680 --> 00:26:04,480 integrate it with all their internal systems, you would look 530 00:26:04,480 --> 00:26:07,680 at these sales cycles in our board meetings, and it would be 531 00:26:07,680 --> 00:26:10,880 like a 180-day sales cycle, a 360-day sales cycle. 532 00:26:11,039 --> 00:26:11,279 SPEAKER_03: Yeah. 533 00:26:11,599 --> 00:26:13,920 SPEAKER_02: Can't do that if you're especially at the kind of 534 00:26:13,920 --> 00:26:15,920 speed that these AI companies want to go at. 535 00:26:16,000 --> 00:26:20,640 Like that is sort of like as frictionful of a sales motion as 536 00:26:20,640 --> 00:26:21,440 you can get. 537 00:26:21,759 --> 00:26:26,400 And so the key then becomes how do you design a sales motion 538 00:26:27,279 --> 00:26:32,480 that is in concert with the enterprise that wants to try 539 00:26:32,480 --> 00:26:32,960 things? 540 00:26:33,279 --> 00:26:34,799 So that's a big challenge right now. 541 00:26:34,880 --> 00:26:37,440 And I think this is like a moment in time where there's a 542 00:26:37,440 --> 00:26:40,640 lot of innovative things happening and go to market that 543 00:26:40,640 --> 00:26:44,640 could really help startups break the distribution advantage that 544 00:26:44,640 --> 00:26:46,720 the incumbents have because they have better products. 545 00:26:47,759 --> 00:26:49,920 SPEAKER_05: And what are you hearing in your board meetings 546 00:26:49,920 --> 00:26:54,000 around how they're actually, you know, consolidating that time to 547 00:26:54,000 --> 00:26:57,519 value of going through the pipeline for potential 548 00:26:57,519 --> 00:26:58,000 customers? 549 00:26:58,160 --> 00:27:00,960 Like, what are they actually doing to speed up the sales 550 00:27:00,960 --> 00:27:02,000 process along that way? 551 00:27:02,400 --> 00:27:05,039 SPEAKER_02: So none of this is gonna sound particularly um 552 00:27:05,359 --> 00:27:06,240 complicated. 553 00:27:06,400 --> 00:27:06,720 SPEAKER_03: Yeah. 554 00:27:06,960 --> 00:27:09,039 SPEAKER_02: But I think implementing it is tough. 555 00:27:09,200 --> 00:27:13,920 I think number one, you have to have a clear value proposition 556 00:27:13,920 --> 00:27:18,640 that you can show people within five to 15 minutes. 557 00:27:19,039 --> 00:27:23,440 You have to understand that enterprises now in 2026 are 558 00:27:23,440 --> 00:27:28,079 likely getting somewhere between five to 10 demos of AI products 559 00:27:28,079 --> 00:27:29,440 every single day. 560 00:27:29,680 --> 00:27:30,960 And so they see everything. 561 00:27:31,039 --> 00:27:35,119 And so you've got five to 15 minutes to make an impression of 562 00:27:35,200 --> 00:27:38,319 like what does your thing do and why is it magical? 563 00:27:38,480 --> 00:27:42,559 And so there is that hook of like the demo has to make sense, 564 00:27:42,720 --> 00:27:45,839 the demo has to be magical, the demo has to really deliver. 565 00:27:46,079 --> 00:27:49,200 And then immediately, one of the interesting things a lot of 566 00:27:49,200 --> 00:27:52,559 companies have done is how do you go from demo to a quick 567 00:27:52,559 --> 00:27:53,200 pilot? 568 00:27:53,920 --> 00:27:57,599 And and then how do you set up that pilot criteria in a pretty 569 00:27:57,599 --> 00:28:01,440 defined way, such that you, you know, in a 30-day pilot or a 570 00:28:01,440 --> 00:28:05,039 60-day pilot or a 90-day pilot, you have a clear rubric of 571 00:28:05,039 --> 00:28:08,160 what's going to be measured and why this is better than an 572 00:28:08,160 --> 00:28:09,519 incumbents product. 573 00:28:09,839 --> 00:28:13,599 The part where traditional software was very frictional, 574 00:28:13,759 --> 00:28:17,279 frictionful, if you had a great demo and you wanted to do a 575 00:28:17,279 --> 00:28:21,200 30-day pilot, there would be an integration phase where you had 576 00:28:21,200 --> 00:28:25,279 to like go in, get a security check, security compliance 577 00:28:25,279 --> 00:28:26,480 review, all this kind of stuff. 578 00:28:26,559 --> 00:28:29,839 And then you'd have to integrate into their enterprises data 579 00:28:29,839 --> 00:28:31,759 systems, and then you could run the pilot. 580 00:28:31,920 --> 00:28:32,240 Yeah. 581 00:28:32,400 --> 00:28:37,039 And so you have to figure out a way of can you run a pilot in 582 00:28:37,039 --> 00:28:39,839 sort of like a safe zone environment or whatever, where 583 00:28:39,920 --> 00:28:42,559 like those integrations may not be necessary. 584 00:28:42,880 --> 00:28:46,400 And if you can do that and then run a 30-day pilot and show 585 00:28:46,400 --> 00:28:52,240 value, you've now collapsed what was previously a six-month 586 00:28:52,240 --> 00:28:56,160 process between initial contact to the end of pilot to now, 587 00:28:56,400 --> 00:28:59,039 hopefully, a 15 to 30-day process. 588 00:28:59,279 --> 00:28:59,519 SPEAKER_03: Right. 589 00:28:59,759 --> 00:29:03,359 SPEAKER_02: And so now then, and then your pilot goes well, you 590 00:29:03,440 --> 00:29:07,039 show success, and then you're now on the other side of that 591 00:29:07,200 --> 00:29:10,079 where you have a champion in your organization in this 592 00:29:10,079 --> 00:29:12,480 enterprise that's saying, we need to use this. 593 00:29:12,640 --> 00:29:16,000 I'm going to clear a path to get this into production as quickly 594 00:29:16,000 --> 00:29:18,799 as possible because I see value, et cetera, et cetera. 595 00:29:19,440 --> 00:29:23,920 So these are these are not particularly complex techniques, 596 00:29:24,000 --> 00:29:28,880 but it is about taking a 180-day sales cycle and turning it into 597 00:29:28,880 --> 00:29:30,880 a 30-day sales cycle. 598 00:29:31,200 --> 00:29:36,400 And part of it is ultimately it's humans doing business with 599 00:29:36,400 --> 00:29:36,720 humans. 600 00:29:36,799 --> 00:29:40,880 And I think that's the part that may be lost, is that ultimately 601 00:29:41,039 --> 00:29:45,200 you as a startup have to realize that we're all vendors, 602 00:29:45,359 --> 00:29:49,680 commercial vendors, helping a company do their business 603 00:29:49,680 --> 00:29:50,079 better. 604 00:29:50,319 --> 00:29:55,279 Um, and so you have to become a trusted commercial vendor to the 605 00:29:55,279 --> 00:29:56,000 enterprise. 606 00:29:56,240 --> 00:30:01,440 And when you do that, you start to get these big lump uh big 607 00:30:01,519 --> 00:30:04,720 excuse me, big jumps in efficiency. 608 00:30:04,880 --> 00:30:10,319 So you can go from 180-day sales cycle, 30-day sales cycle, you 609 00:30:10,319 --> 00:30:14,640 know, 100 days to get pilot into production to two days to get 610 00:30:14,640 --> 00:30:18,000 pilot into production, integration time that used to 611 00:30:18,000 --> 00:30:22,079 be, you know, 50 days, 60 days, integration time now is seven 612 00:30:22,079 --> 00:30:22,319 days. 613 00:30:22,480 --> 00:30:27,039 So it's all of the stuff put together is what makes this 614 00:30:27,039 --> 00:30:28,400 thing so much faster. 615 00:30:28,720 --> 00:30:28,960 SPEAKER_05: Wow. 616 00:30:29,119 --> 00:30:29,279 Yeah. 617 00:30:29,440 --> 00:30:32,240 Compression of time to value, all the other areas too, 618 00:30:32,319 --> 00:30:34,720 collapsing from a timeline perspective. 619 00:30:34,960 --> 00:30:36,640 What about Lagore specifically? 620 00:30:36,720 --> 00:30:38,319 Because they had an enterprise sales motion. 621 00:30:38,400 --> 00:30:38,640 Yes. 622 00:30:38,799 --> 00:30:41,759 What did you see that kind of amazed you about their process 623 00:30:41,759 --> 00:30:44,480 and how they were able to scale at the tremendous rate that they 624 00:30:44,480 --> 00:30:45,279 were able to scale? 625 00:30:45,759 --> 00:30:50,079 SPEAKER_02: So the the big thing that they did initially that I 626 00:30:50,079 --> 00:30:52,160 think created a big differentiation from that for 627 00:30:52,160 --> 00:30:56,079 them is that they they, as I said, we invested in March and 628 00:30:56,079 --> 00:30:58,720 they didn't go GA with their product until October. 629 00:30:58,880 --> 00:31:04,720 And the company had been investigating AI for lawyers for 630 00:31:04,720 --> 00:31:06,880 like a year prior to even the seed round. 631 00:31:07,039 --> 00:31:12,799 And so they were just doing a ton of research on the industry 632 00:31:12,799 --> 00:31:13,119 itself. 633 00:31:13,279 --> 00:31:16,799 So you had the the founders were three computer scientists, three 634 00:31:16,799 --> 00:31:19,759 AI people that didn't know law. 635 00:31:20,000 --> 00:31:20,640 SPEAKER_04: Yeah. 636 00:31:21,200 --> 00:31:23,200 SPEAKER_02: And so they weren't lawyers by background, they 637 00:31:23,200 --> 00:31:24,559 weren't lawyers by training. 638 00:31:24,720 --> 00:31:28,160 And so the legal sector was new to them. 639 00:31:28,400 --> 00:31:31,440 And one of the things that they did before they even started Y 640 00:31:31,599 --> 00:31:36,079 Combinator is that they, you know, basically set up an office 641 00:31:36,079 --> 00:31:40,240 inside of a law firm and shadowed lawyers and just tried 642 00:31:40,240 --> 00:31:43,839 to understand what lawyers did every day and what was like the 643 00:31:43,839 --> 00:31:46,319 daily tasks that they did, and what were the tasks that they 644 00:31:46,480 --> 00:31:48,559 they needed to do every single day. 645 00:31:48,799 --> 00:31:52,480 And how could AI do stuff for them? 646 00:31:52,799 --> 00:31:53,039 Right. 647 00:31:53,279 --> 00:31:59,279 So there was a ton of internal data collection that they did on 648 00:31:59,279 --> 00:32:00,720 just the industry itself. 649 00:32:00,880 --> 00:32:05,519 Like they just wanted to learn about what legal professionals 650 00:32:05,519 --> 00:32:07,920 were doing in these large law firms. 651 00:32:08,240 --> 00:32:13,680 And then once from um March to October, in March they had a 652 00:32:13,680 --> 00:32:14,720 working prototype. 653 00:32:14,880 --> 00:32:19,839 And so the journey from March to October was really about taking 654 00:32:19,839 --> 00:32:23,920 a working prototype and making it production ready. 655 00:32:24,160 --> 00:32:27,680 And this whole time, because they were embedded in customers 656 00:32:27,680 --> 00:32:32,319 for so long and doing so much research about how lawyers 657 00:32:32,319 --> 00:32:36,079 evaluated software, what they used it for, how they did their 658 00:32:36,079 --> 00:32:44,079 jobs, etc., that allowed them to set up pilot programs and demos 659 00:32:44,400 --> 00:32:47,839 in such a way that resonated with lawyers in a really 660 00:32:47,839 --> 00:32:49,119 spectacular way. 661 00:32:49,359 --> 00:32:55,200 And so that doesn't happen accidentally or with like a, you 662 00:32:55,200 --> 00:32:58,319 know, some kind of like strike of genius. 663 00:32:58,480 --> 00:33:02,640 It was just purely that they had spent so much time with lawyers. 664 00:33:02,799 --> 00:33:08,319 I mean, we're talking like well over a year of just interviews 665 00:33:08,319 --> 00:33:11,119 with lawyers and just understanding what lawyers did 666 00:33:11,119 --> 00:33:15,920 every day to come up with these insights to say, okay, when we 667 00:33:15,920 --> 00:33:19,279 meet a specific kind of group inside of a law firm, here's 668 00:33:19,279 --> 00:33:22,640 what we're gonna demo them, and we're gonna say, with authority, 669 00:33:22,799 --> 00:33:25,440 because we've done so much research, these are the five 670 00:33:25,440 --> 00:33:26,640 things you do every day. 671 00:33:26,799 --> 00:33:29,680 And this is what we can automate with our software. 672 00:33:30,000 --> 00:33:32,720 And we know that you don't believe us right away. 673 00:33:32,880 --> 00:33:33,039 Yeah. 674 00:33:33,279 --> 00:33:34,079 And you shouldn't. 675 00:33:34,240 --> 00:33:35,359 You should be skeptical. 676 00:33:35,599 --> 00:33:38,880 So set us up in a pilot, and here's the rubric that you can 677 00:33:38,880 --> 00:33:39,440 follow. 678 00:33:39,680 --> 00:33:43,680 And we'll just give you this pilot for 30, 60, 90 days. 679 00:33:43,839 --> 00:33:46,960 And it's not going to be a traditional software pilot where 680 00:33:47,039 --> 00:33:52,480 you know you use the software and then the 30 days expire, and 681 00:33:52,480 --> 00:33:56,559 whatever sort of like internal assets you've built to make this 682 00:33:56,559 --> 00:33:59,920 pilot work, you know, the lights just get turned off and you've 683 00:33:59,920 --> 00:34:00,960 wasted all this effort. 684 00:34:01,119 --> 00:34:04,640 Their view was if you build IP around this thing, if you build 685 00:34:04,640 --> 00:34:07,440 integrations around this piece of software, or you build 686 00:34:07,519 --> 00:34:11,519 workflow around it, whatever documentation you build to use 687 00:34:11,519 --> 00:34:12,880 our software, that's yours. 688 00:34:13,519 --> 00:34:16,480 And at the end of the pilot, if you don't like what we're doing, 689 00:34:16,559 --> 00:34:17,119 it's okay. 690 00:34:17,280 --> 00:34:20,000 And you've like learned to use AI as a result of like working 691 00:34:20,000 --> 00:34:21,440 with us for 30 days, great. 692 00:34:21,519 --> 00:34:22,880 Like it's fantastic. 693 00:34:23,039 --> 00:34:24,400 We're glad to have helped. 694 00:34:24,559 --> 00:34:28,000 And so there was a collaborative nature to their approach, which 695 00:34:28,000 --> 00:34:32,559 was you know, you're looking for commercial vendors and AI 696 00:34:32,559 --> 00:34:35,119 vendors to take you into this AI world, and we want to be that 697 00:34:35,119 --> 00:34:38,320 trusted vendor to take you into this AI world. 698 00:34:38,559 --> 00:34:41,760 And so, how do we design pilot programs that enable you to do 699 00:34:41,760 --> 00:34:41,920 that? 700 00:34:42,079 --> 00:34:44,480 How do you design a pilot program that enables you to 701 00:34:44,480 --> 00:34:45,840 trust the commercial vendor? 702 00:34:46,000 --> 00:34:46,320 SPEAKER_03: Yeah. 703 00:34:46,719 --> 00:34:50,719 SPEAKER_02: And then there's also, you know, a big investment 704 00:34:50,719 --> 00:34:54,800 that we made under data security, data sovereignty. 705 00:34:55,119 --> 00:34:59,039 I think this is part of the advantage of being in Europe, is 706 00:34:59,039 --> 00:35:05,440 that they were keenly aware of how regulatory complexity around 707 00:35:05,440 --> 00:35:10,320 the legal world, just how complex it all was, and how you 708 00:35:10,320 --> 00:35:13,679 had to design the software from day zero to be compliant with 709 00:35:13,679 --> 00:35:14,079 anything. 710 00:35:14,239 --> 00:35:17,920 And because they started in Europe, you know, they weren't 711 00:35:17,920 --> 00:35:20,320 building legal AI just for Sweden. 712 00:35:20,480 --> 00:35:20,719 Right. 713 00:35:20,960 --> 00:35:23,920 They were building legal AI for Sweden, France, Germany, the 714 00:35:23,920 --> 00:35:26,079 United States, Singapore, etc. 715 00:35:26,400 --> 00:35:29,679 And so from day zero, they were thinking multi-jurisdiction. 716 00:35:30,000 --> 00:35:30,159 Yeah. 717 00:35:30,480 --> 00:35:31,440 There's a lot of nuance there. 718 00:35:31,679 --> 00:35:31,920 Yes. 719 00:35:32,000 --> 00:35:36,880 And they were thinking data complexity and um, you know, 720 00:35:37,519 --> 00:35:41,679 lots of data compliance around the world, depending on these 721 00:35:41,679 --> 00:35:41,920 regions. 722 00:35:42,079 --> 00:35:44,159 And so they were thinking multi-region from day zero. 723 00:35:44,320 --> 00:35:48,880 So they they would come to these lawyers and say, you know, you 724 00:35:49,039 --> 00:35:54,320 you are in a complex industry with complex requirements, and 725 00:35:54,320 --> 00:35:56,400 we know that we're keenly aware of that. 726 00:35:56,480 --> 00:35:59,199 We've built this product from day zero, thinking through all 727 00:35:59,199 --> 00:35:59,599 of that. 728 00:35:59,760 --> 00:36:02,559 So all of this establishes you as a trusted vendor pretty 729 00:36:02,559 --> 00:36:02,960 quickly. 730 00:36:03,119 --> 00:36:07,199 You run the pilot, and because you're running a very tight 731 00:36:07,199 --> 00:36:11,280 pilot, three days with like pretty exacting criteria, you 732 00:36:11,280 --> 00:36:15,440 come out of the other side, and the customers absolutely love 733 00:36:15,440 --> 00:36:20,000 you because they now understand how much homework you've done 734 00:36:20,000 --> 00:36:21,599 before you showed up with a product. 735 00:36:21,760 --> 00:36:22,000 Right. 736 00:36:22,159 --> 00:36:25,760 Like none of this is just kind of happening. 737 00:36:25,840 --> 00:36:27,760 Like we're not throwing spaghetti at a wall. 738 00:36:27,840 --> 00:36:28,000 Yeah. 739 00:36:28,159 --> 00:36:29,760 We're not figuring it out on the fly. 740 00:36:29,920 --> 00:36:33,039 We're not surprised by any of the things that you require. 741 00:36:33,280 --> 00:36:36,079 You know, if there's anything that we don't have in the 742 00:36:36,079 --> 00:36:39,360 product that you need, you know, we're probably aware of it 743 00:36:39,360 --> 00:36:39,920 already. 744 00:36:40,159 --> 00:36:42,880 We're already telling you, like, you probably, you know, are 745 00:36:42,880 --> 00:36:44,480 thinking about this specific thing. 746 00:36:44,639 --> 00:36:47,360 And look, it's in our roadmap, it's coming out next week or in 747 00:36:47,360 --> 00:36:48,320 two weeks or whatever. 748 00:36:48,400 --> 00:36:55,440 And so just being that prepared with that much knowledge is such 749 00:36:55,440 --> 00:36:58,960 a big advantage to somebody just showing up into a new vertical 750 00:36:59,280 --> 00:37:01,360 and just saying, I'll learn on the fly. 751 00:37:01,599 --> 00:37:06,960 And and that just, you know, I I it could be luck, it could be 752 00:37:06,960 --> 00:37:08,239 skill, whatever. 753 00:37:08,400 --> 00:37:11,280 It just so happened that the Lagoro founders had spent so 754 00:37:11,280 --> 00:37:14,559 much time researching the legal industry, embedded themselves in 755 00:37:14,559 --> 00:37:18,239 a law firm for so long, that now it's just turned into a huge 756 00:37:18,239 --> 00:37:18,800 advantage. 757 00:37:18,960 --> 00:37:19,119 SPEAKER_03: Yeah. 758 00:37:19,280 --> 00:37:23,679 SPEAKER_02: And the other part that they've done internally is 759 00:37:23,920 --> 00:37:29,840 they've hired a lot of um lawyers to be the people that 760 00:37:29,840 --> 00:37:31,920 are doing deployments for customers. 761 00:37:32,159 --> 00:37:32,400 Right. 762 00:37:32,639 --> 00:37:36,880 And so they they're, you know, they've created a role called 763 00:37:36,880 --> 00:37:41,360 legal engineers, and they're essentially highly technical 764 00:37:41,360 --> 00:37:42,079 lawyers. 765 00:37:42,400 --> 00:37:44,960 These are lawyers with law degrees that have practiced for 766 00:37:44,960 --> 00:37:48,159 a couple years at the top firm that also happen to be very 767 00:37:48,159 --> 00:37:48,880 technical people. 768 00:37:49,119 --> 00:37:51,199 So they can implement software very much. 769 00:37:51,360 --> 00:37:52,960 SPEAKER_04: And they're coming into this kind of four-deployed 770 00:37:52,960 --> 00:37:53,840 engineer role 100%. 771 00:37:54,239 --> 00:37:55,599 Like a trusted partner then interesting. 772 00:37:56,159 --> 00:37:59,679 SPEAKER_02: So they can sit with you know the senior partner at 773 00:37:59,920 --> 00:38:03,039 one of the world's leading firms, and that partner can 774 00:38:03,039 --> 00:38:05,360 describe their workflows and their work. 775 00:38:05,599 --> 00:38:09,760 And this person who has both a foot in product and engineering 776 00:38:09,840 --> 00:38:16,400 and in the legal world can translate that into AI-powered 777 00:38:16,400 --> 00:38:20,159 flows in a way that I think is really, really spectacular. 778 00:38:20,400 --> 00:38:24,880 And so they're working on this motion where you're taking AI 779 00:38:25,519 --> 00:38:29,440 and doing so much of the last mile to deliver value to the 780 00:38:29,440 --> 00:38:34,800 customer that you actually end up making the customer business 781 00:38:34,800 --> 00:38:35,440 better. 782 00:38:36,079 --> 00:38:41,519 And you know, everybody that uses Ligora sees much more 783 00:38:41,519 --> 00:38:44,400 efficiency in how their lawyers use time. 784 00:38:44,480 --> 00:38:48,480 Their lawyers are able to now stop doing all the rope manual 785 00:38:48,480 --> 00:38:51,039 work, they're able to spend way more time on the strategic 786 00:38:51,039 --> 00:38:55,280 stuff, they're able to deliver way better services uh to their 787 00:38:55,440 --> 00:38:56,639 to their clients. 788 00:38:57,119 --> 00:38:59,039 You know, Benchmark uses Ligora. 789 00:38:59,280 --> 00:39:05,440 We use Ligora with all of our outside firm partners. 790 00:39:05,679 --> 00:39:09,760 And you know, every legal firm that does work with Benchmark 791 00:39:09,760 --> 00:39:10,880 does it via Ligora. 792 00:39:11,280 --> 00:39:14,639 And so the the collaborative nature of Ligora itself, you 793 00:39:14,639 --> 00:39:16,400 know, just makes everything so much faster. 794 00:39:16,559 --> 00:39:20,639 So when we're, for example, you know, just earlier today I was I 795 00:39:20,639 --> 00:39:24,719 was reviewing uh uh an agreement that we have with one of our 796 00:39:24,719 --> 00:39:26,480 companies because they're raising more money. 797 00:39:26,639 --> 00:39:29,440 Normally that would have been several emails back and forth. 798 00:39:29,599 --> 00:39:29,920 SPEAKER_03: Yeah. 799 00:39:30,159 --> 00:39:33,840 SPEAKER_02: But you can just sort of see it evolve in Lagora. 800 00:39:34,400 --> 00:39:38,320 And if I have any questions about where the document is, 801 00:39:38,639 --> 00:39:41,360 what were the changes that were done in the last week, I'm now 802 00:39:41,360 --> 00:39:46,800 talking to Lagora and not having to sort of these send these like 803 00:39:46,800 --> 00:39:50,880 black, uh these emails into black holes and waiting for 804 00:39:50,880 --> 00:39:51,440 responses. 805 00:39:51,920 --> 00:39:57,360 And so um, so it's a complete once you get used to working in 806 00:39:57,360 --> 00:40:01,840 this system, it's just such a differentiated experience that 807 00:40:01,840 --> 00:40:05,039 you now have completely different expectations for all 808 00:40:05,039 --> 00:40:06,320 your software vendors. 809 00:40:06,639 --> 00:40:09,679 And again, looping back to the the beginning, which is like 810 00:40:09,840 --> 00:40:13,440 that ends up creating even more opportunity for startups. 811 00:40:13,519 --> 00:40:18,480 So I think once an enterprise buys one AI software vendor, 812 00:40:18,719 --> 00:40:22,559 they're gonna buy way more AI applications. 813 00:40:22,880 --> 00:40:25,119 And I think it's one of those things that just ripple. 814 00:40:25,199 --> 00:40:29,280 And we saw this in cloud, which was people originally bought 815 00:40:29,280 --> 00:40:33,599 Salesforce, then they bought ServiceNow, then they bought 816 00:40:33,599 --> 00:40:34,480 Workday. 817 00:40:34,639 --> 00:40:38,400 And once they bought all three, it created this exponential 818 00:40:38,400 --> 00:40:38,880 effect. 819 00:40:39,119 --> 00:40:45,039 And all of a sudden, it was like we went from three SaaS apps to 820 00:40:45,039 --> 00:40:49,440 50 to 100 to 500 inside one enterprise. 821 00:40:49,760 --> 00:40:54,719 And that's that similar trend is gonna happen in AI, in my view, 822 00:40:54,880 --> 00:41:00,559 which is people are gonna start with a Lagora, a Sierra, and 823 00:41:00,559 --> 00:41:03,199 they're gonna have these AI applications. 824 00:41:03,280 --> 00:41:05,599 Of course, they'll have ChatGPT, they'll have Claude, they'll 825 00:41:05,599 --> 00:41:11,039 have Gemini, they'll have these AI applications generate real 826 00:41:11,039 --> 00:41:12,000 value for them. 827 00:41:12,400 --> 00:41:15,360 They're gonna get really excited about it, and then it'll just 828 00:41:15,360 --> 00:41:19,440 spark this again, refactoring of their stacks, and they'll go 829 00:41:19,440 --> 00:41:25,360 from having five AI vendors, in my view, to about a hundred very 830 00:41:25,360 --> 00:41:26,159 quickly. 831 00:41:27,119 --> 00:41:29,920 SPEAKER_05: So, I mean, a couple areas I want to dive down there, 832 00:41:30,000 --> 00:41:33,519 but you mentioned Workday as one of you know the first companies 833 00:41:33,519 --> 00:41:36,960 that kind of created this wave and ripple effect, and Sierra 834 00:41:36,960 --> 00:41:41,679 and Lagora uh now being part of the more kind of current workday 835 00:41:41,679 --> 00:41:44,559 took you know over five years to hit 100 million AR. 836 00:41:44,639 --> 00:41:47,920 And Sierra and Lagora both did it in less than two years. 837 00:41:48,239 --> 00:41:51,519 How does that kind of speed to revenue change the way that you 838 00:41:51,519 --> 00:41:53,840 are looking at companies as an investor? 839 00:41:54,400 --> 00:41:59,840 SPEAKER_02: You know, I think we're in a in a world where are 840 00:41:59,840 --> 00:42:05,440 companies now going to dramatically decrease the amount 841 00:42:05,440 --> 00:42:07,599 of time to get to$100 million? 842 00:42:08,400 --> 00:42:09,039 Probably. 843 00:42:09,360 --> 00:42:15,360 I mean, I think when Workday and ServiceNow and Salesforce um 844 00:42:15,360 --> 00:42:19,840 went to$100 million, they had compressed the window relative 845 00:42:19,840 --> 00:42:23,360 to on-prem vendors and had taken advantage of the cloud. 846 00:42:23,440 --> 00:42:26,480 And then now the AI vendors are taking advantage of this AI 847 00:42:26,480 --> 00:42:29,039 window and getting to$100 million even faster. 848 00:42:29,360 --> 00:42:36,639 I think what's interesting to me is not so much the time from one 849 00:42:36,800 --> 00:42:40,559 to$100 million, which is obviously happening really fast. 850 00:42:41,119 --> 00:42:44,239 But what's really interesting to me is how long it takes you to 851 00:42:44,239 --> 00:42:45,519 get to that first million. 852 00:42:46,480 --> 00:42:50,079 And that may take a lot longer in this AI world. 853 00:42:50,239 --> 00:42:51,440 And I'm I don't know. 854 00:42:51,679 --> 00:42:55,840 I just, it's one of these things that could be true, which is 855 00:42:55,840 --> 00:43:02,480 that because these last mile problems, these AI problems, are 856 00:43:02,480 --> 00:43:04,320 perhaps more complicated. 857 00:43:04,960 --> 00:43:08,400 It may just require the founders to just spend way more time 858 00:43:08,400 --> 00:43:14,159 doing research and building before launching than in the 859 00:43:14,159 --> 00:43:14,800 cloud world. 860 00:43:15,039 --> 00:43:21,119 And I think the part that we saw a lot in in traditional SaaS, 861 00:43:21,519 --> 00:43:26,079 which was you could have a very strong viewpoint on a market, 862 00:43:26,400 --> 00:43:31,199 build in a vacuum, not have it touch the reality of customers, 863 00:43:31,440 --> 00:43:34,960 and yet scale really quickly and get to 100 million in five, six 864 00:43:34,960 --> 00:43:35,920 years, whatever. 865 00:43:36,239 --> 00:43:40,239 But I think in the AI world, the amount of time it takes to build 866 00:43:40,239 --> 00:43:41,679 could end up being longer. 867 00:43:41,920 --> 00:43:45,039 And then once you're out, the amount of time it takes to scale 868 00:43:45,039 --> 00:43:45,840 is faster. 869 00:43:46,719 --> 00:43:48,960 SPEAKER_05: Um that's really interesting because that's 870 00:43:48,960 --> 00:43:52,239 almost like the opposite of some large narratives around, you 871 00:43:52,239 --> 00:43:55,679 know, product build, time is collapsing, and now it's, you 872 00:43:55,679 --> 00:43:58,000 know, the actual scaling part that's longer. 873 00:43:58,159 --> 00:43:59,760 So I'd love to hear more on that. 874 00:44:00,159 --> 00:44:02,480 Because clearly you're onto something with Lagora having 875 00:44:02,480 --> 00:44:04,079 done so much research ahead of time. 876 00:44:04,639 --> 00:44:08,880 SPEAKER_02: I think if you just look at what these coding agents 877 00:44:08,880 --> 00:44:10,639 can do, it's absolutely magical. 878 00:44:10,960 --> 00:44:11,519 SPEAKER_04: Yeah. 879 00:44:11,920 --> 00:44:16,960 SPEAKER_02: And so the ability to generate code is trending 880 00:44:16,960 --> 00:44:19,519 towards zero in terms of marginal cost. 881 00:44:19,679 --> 00:44:22,719 So the marginal cost of generating the next line of code 882 00:44:22,719 --> 00:44:24,639 is basically trending to zero. 883 00:44:24,880 --> 00:44:29,199 In that world where you and your competitors can generate a lot 884 00:44:29,199 --> 00:44:35,039 of code really quickly, the customers' expectations of how 885 00:44:35,039 --> 00:44:38,719 good the application is when they first try it is now 886 00:44:38,719 --> 00:44:40,639 ratcheting up very quickly. 887 00:44:40,960 --> 00:44:41,039 Right. 888 00:44:41,280 --> 00:44:42,800 SPEAKER_05: Gone are the days of the old MVPs. 889 00:44:43,119 --> 00:44:43,519 SPEAKER_02: That's right. 890 00:44:43,679 --> 00:44:47,199 So they and you know, if you if you're selling to sophisticated 891 00:44:47,199 --> 00:44:51,440 users, they themselves can vibe code simple applications. 892 00:44:51,840 --> 00:44:57,840 And so the ability for a company to show up with a simple 893 00:44:57,840 --> 00:45:02,400 application and really get somebody to pay anything for it 894 00:45:02,639 --> 00:45:04,000 is essentially gone. 895 00:45:04,800 --> 00:45:10,239 And so if the cost of developing a product as viewed by the 896 00:45:10,239 --> 00:45:15,039 customer is effectively zero, the vendor has to build 897 00:45:15,039 --> 00:45:19,679 something sufficiently complex that a customer says, okay, I 898 00:45:19,679 --> 00:45:25,440 see value in paying real dollars for this, because for my team to 899 00:45:25,440 --> 00:45:29,039 reproduce this and maintain it, even if the marginal cost of 900 00:45:29,039 --> 00:45:32,559 generating the next line of code is zero, is just way too high 901 00:45:32,719 --> 00:45:36,480 because of maybe the time that it takes to maintain such a 902 00:45:36,480 --> 00:45:39,840 product or like all these last mile services they figured out. 903 00:45:39,920 --> 00:45:43,039 It's like, wow, you figured out a whole bunch of stuff around 904 00:45:43,039 --> 00:45:45,519 the code that I perceive to be valuable. 905 00:45:45,679 --> 00:45:48,320 Therefore, I will pay you serious dollars for this piece 906 00:45:48,320 --> 00:45:49,519 of software. 907 00:45:49,920 --> 00:45:52,480 I think that's actually really hard now. 908 00:45:53,280 --> 00:45:58,000 And so, and so there's a lot of innovation here where you'll see 909 00:45:58,000 --> 00:46:01,039 some companies building custom models as an example for their 910 00:46:01,039 --> 00:46:01,679 application. 911 00:46:01,760 --> 00:46:04,480 And that's a mode of differentiation because those 912 00:46:04,480 --> 00:46:07,280 custom models are very different than the foundation models. 913 00:46:07,440 --> 00:46:10,400 So there's like custom image models, custom video models, 914 00:46:10,480 --> 00:46:13,440 wherever it is, those are differentiated against the large 915 00:46:13,440 --> 00:46:14,559 language models. 916 00:46:14,719 --> 00:46:17,599 And there's a business being built around the differentiation 917 00:46:17,599 --> 00:46:18,800 of the model itself. 918 00:46:19,039 --> 00:46:22,000 And then what we've talked about with companies like Ligora is 919 00:46:22,000 --> 00:46:26,000 that they've built so much tooling and expertise on the 920 00:46:26,000 --> 00:46:30,239 last mile of what lawyers need that it's not the next marginal 921 00:46:30,239 --> 00:46:32,559 line of code that people are paying for. 922 00:46:32,639 --> 00:46:36,000 It's really that expertise of helping lawyers do their job 923 00:46:36,000 --> 00:46:39,440 better, become better service providers to their clients. 924 00:46:39,679 --> 00:46:42,880 So that stuff I think actually takes a lot longer than the code 925 00:46:42,880 --> 00:46:43,199 itself. 926 00:46:43,360 --> 00:46:49,519 I think generating an application now maybe you know 927 00:46:49,760 --> 00:46:52,960 an hour's time, which used to take months. 928 00:46:53,199 --> 00:46:59,280 So the value then moves away from just the product build to 929 00:46:59,280 --> 00:47:00,800 what value can you articulate. 930 00:47:00,960 --> 00:47:06,639 And so, therefore, it actually might take longer to get 931 00:47:06,639 --> 00:47:11,039 customers to perceive this value in your application. 932 00:47:12,159 --> 00:47:16,239 Because they just don't ascribe any value to an application that 933 00:47:16,239 --> 00:47:17,039 they can think of. 934 00:47:17,199 --> 00:47:17,519 SPEAKER_03: Yeah. 935 00:47:17,760 --> 00:47:21,199 SPEAKER_02: And ask Claude or Codex to just code for them. 936 00:47:21,599 --> 00:47:21,920 SPEAKER_05: Right. 937 00:47:22,079 --> 00:47:26,320 And what I've kind of hearing from you is the value accrual 938 00:47:26,480 --> 00:47:31,360 now occurs and the proximity to a customer and understanding 939 00:47:31,360 --> 00:47:34,320 their pain and helping them do well in their job, as opposed to 940 00:47:34,320 --> 00:47:36,079 the software in any kind of capacity. 941 00:47:36,480 --> 00:47:36,880 SPEAKER_02: Absolutely. 942 00:47:37,039 --> 00:47:41,599 I think if you just um look at traditional business models, you 943 00:47:41,599 --> 00:47:44,800 have application vendor business models, and I think you had 944 00:47:44,800 --> 00:47:46,719 service provider business models. 945 00:47:47,679 --> 00:47:51,039 I imagine that software in the future looks a lot more like 946 00:47:51,039 --> 00:47:55,440 service provider business models in the sense that a service 947 00:47:55,440 --> 00:47:58,159 provider has to make your business better for them to get 948 00:47:58,159 --> 00:47:58,639 revenue. 949 00:47:58,880 --> 00:47:59,039 Yes. 950 00:47:59,199 --> 00:48:02,320 So they get revenue either on success or some outcome or 951 00:48:02,320 --> 00:48:03,199 whatever. 952 00:48:03,599 --> 00:48:09,519 And you can easily imagine a future where the value of code 953 00:48:09,599 --> 00:48:13,599 or the perception of value of code disappears, and all the 954 00:48:13,599 --> 00:48:16,079 perception of value is transferred to what service do 955 00:48:16,079 --> 00:48:16,800 you provide me? 956 00:48:16,880 --> 00:48:16,960 Yeah. 957 00:48:17,199 --> 00:48:19,519 And how are you getting my business to be better as a 958 00:48:19,519 --> 00:48:20,880 result of this service? 959 00:48:21,199 --> 00:48:24,800 Um, and that's probably where it trends, in my view. 960 00:48:25,039 --> 00:48:27,519 And who knows, in six months this might change pretty 961 00:48:27,519 --> 00:48:30,800 dramatically too, just given how fast everything is moving. 962 00:48:31,039 --> 00:48:37,360 But it's certainly not that you can ship an MVP and that MVP is 963 00:48:37,360 --> 00:48:42,960 gonna generate a lot of revenue and a lot of sales immediately. 964 00:48:43,199 --> 00:48:43,440 Right. 965 00:48:43,679 --> 00:48:45,199 I don't think that's how it's gonna be. 966 00:48:46,159 --> 00:48:48,880 SPEAKER_05: Do you see kind of a trend between more of a sales 967 00:48:49,039 --> 00:48:52,880 led motion and a PLG motion for companies excelling in the AI 968 00:48:52,880 --> 00:48:53,119 era? 969 00:48:53,519 --> 00:48:57,599 SPEAKER_02: I think sales led is um here to stay. 970 00:48:57,760 --> 00:49:01,519 I think the it's it creates these like really durable 971 00:49:01,519 --> 00:49:04,639 relationships between the vendor and the customer. 972 00:49:04,880 --> 00:49:06,159 And I'm a big fan of that. 973 00:49:06,320 --> 00:49:10,800 I think the direct sales motion now, especially in a time of 974 00:49:10,800 --> 00:49:16,559 great disruption, it's from a customer's lens, it's very 975 00:49:16,559 --> 00:49:18,960 chaotic about what's happening. 976 00:49:19,119 --> 00:49:21,760 So you don't want to make a mistake by picking the wrong 977 00:49:21,760 --> 00:49:25,760 stack or the wrong vendor or some wrong set of tools, which 978 00:49:25,760 --> 00:49:28,320 then sets you behind your competition. 979 00:49:28,880 --> 00:49:32,800 So the competition makes the right decisions, you make wrong 980 00:49:32,800 --> 00:49:35,360 decisions, you have now harmed your own business. 981 00:49:35,599 --> 00:49:36,400 Not good. 982 00:49:37,119 --> 00:49:43,840 And so in the cyber great disrupt disruption, you're 983 00:49:43,840 --> 00:49:46,400 really looking for trusted vendors to take you into the 984 00:49:46,400 --> 00:49:46,800 future. 985 00:49:46,960 --> 00:49:47,280 Yeah. 986 00:49:47,519 --> 00:49:51,519 And that's where I think direct sales enables you to create 987 00:49:51,519 --> 00:49:54,559 these relationships in a way that can actually be very 988 00:49:54,559 --> 00:49:55,199 durable. 989 00:49:55,440 --> 00:49:59,280 And so I think if you just look at a number of these incredible 990 00:49:59,280 --> 00:50:04,639 AI companies, their product engineering teams are incredibly 991 00:50:04,639 --> 00:50:09,599 efficient and lean, but their go-to-market workforces look a 992 00:50:09,599 --> 00:50:11,440 lot like traditional software. 993 00:50:11,760 --> 00:50:14,719 And those don't seem to be particularly changing. 994 00:50:14,800 --> 00:50:16,000 Maybe they're more efficient. 995 00:50:16,480 --> 00:50:21,039 Maybe each person can have, you know, a higher ratio of um quota 996 00:50:21,039 --> 00:50:22,000 to OTE. 997 00:50:22,400 --> 00:50:25,440 Maybe they're able to close a lot more deals, et cetera, et 998 00:50:25,440 --> 00:50:25,599 cetera. 999 00:50:25,679 --> 00:50:29,119 And maybe they they they're able to just have a much higher 1000 00:50:29,119 --> 00:50:31,599 economic upside because they're able to just go through these 1001 00:50:31,599 --> 00:50:32,800 deals much faster. 1002 00:50:33,119 --> 00:50:36,400 But the number of people, I think, is just going to be 1003 00:50:36,400 --> 00:50:40,559 really, really big because ultimately, I think enterprises 1004 00:50:40,559 --> 00:50:44,559 want to buy from vendors they trust, especially if you're 1005 00:50:44,559 --> 00:50:46,159 committing high-ticket budgets. 1006 00:50:46,400 --> 00:50:50,400 I think like million, two million, five million, ten 1007 00:50:50,559 --> 00:50:54,400 million dollar ACV contracts, you really want to be able to 1008 00:50:54,400 --> 00:50:57,360 buy some insurance from your vendor to make sure that they're 1009 00:50:57,360 --> 00:50:59,920 the ones that are going to be protecting you from all these 1010 00:50:59,920 --> 00:51:01,440 issues that come up with AI. 1011 00:51:01,599 --> 00:51:01,840 Yeah. 1012 00:51:02,000 --> 00:51:04,400 And what guarantees are you providing me? 1013 00:51:05,039 --> 00:51:09,199 And can I trust you as these intelligence, as the 1014 00:51:09,199 --> 00:51:12,000 intelligence layer gets better and better and better every 1015 00:51:12,000 --> 00:51:16,320 month, every week, whatever, will you be able to deliver a 1016 00:51:16,320 --> 00:51:21,840 service that enables my business to get better and also de-risk 1017 00:51:22,000 --> 00:51:24,320 my ability to take advantage of all these new models? 1018 00:51:24,639 --> 00:51:25,119 SPEAKER_05: Mm-hmm. 1019 00:51:25,360 --> 00:51:26,239 I love it. 1020 00:51:26,480 --> 00:51:29,519 Um, one thing you said there is take me to the future. 1021 00:51:29,679 --> 00:51:33,039 And I think that kind of rings very true of everybody's looking 1022 00:51:33,039 --> 00:51:35,280 for that guidance and support, especially as things are moving 1023 00:51:35,360 --> 00:51:36,000 so quickly. 1024 00:51:36,239 --> 00:51:37,519 Very interesting there. 1025 00:51:37,760 --> 00:51:40,880 And uh, you know, one last question on the and more the 1026 00:51:40,880 --> 00:51:43,679 founder side, because you mentioned you look for 1027 00:51:43,840 --> 00:51:46,880 differentiated product, and that will lead to a distribution 1028 00:51:46,880 --> 00:51:49,119 advantage, which we've just talked through. 1029 00:51:49,440 --> 00:51:52,320 But of course, you know, everybody's got a lot of grand 1030 00:51:52,320 --> 00:51:54,000 visions on the product side sometimes. 1031 00:51:54,159 --> 00:51:57,760 And how do you know and how do you tell if it's the right 1032 00:51:57,760 --> 00:52:01,039 founder to actually take that vision into a reality, 1033 00:52:01,119 --> 00:52:03,599 especially when they may not be coming from the industry? 1034 00:52:03,679 --> 00:52:05,760 You know, this could be broadly, you just have to apply to Max 1035 00:52:05,840 --> 00:52:07,280 and the Lagora team, for example. 1036 00:52:07,440 --> 00:52:09,840 But you know, in that circumstance, you're sitting 1037 00:52:09,840 --> 00:52:13,920 with, you know, three people and or five people that don't have 1038 00:52:14,079 --> 00:52:14,800 legal backgrounds. 1039 00:52:15,039 --> 00:52:15,679 Like, why them? 1040 00:52:15,840 --> 00:52:18,400 What do you see in specific founders, whether it's you know, 1041 00:52:18,639 --> 00:52:22,079 Manus, Langchain, Ligora, any of the companies that you're 1042 00:52:22,079 --> 00:52:25,039 backing, that you're like, this is the right person to take it. 1043 00:52:25,440 --> 00:52:27,599 SPEAKER_02: You know, a lot of it, you're betting on the 1044 00:52:27,599 --> 00:52:30,159 founder and betting on the people, and and I'm not sure 1045 00:52:30,159 --> 00:52:31,679 there's an exact science behind it. 1046 00:52:32,159 --> 00:52:35,920 Part of the great thing about early stage venture capital is 1047 00:52:35,920 --> 00:52:38,800 that we make a number of investments and a number of 1048 00:52:38,800 --> 00:52:43,519 founders, and some companies will work spectacularly well, 1049 00:52:43,760 --> 00:52:45,039 and some companies won't. 1050 00:52:45,119 --> 00:52:46,000 Yeah, and that's okay. 1051 00:52:46,159 --> 00:52:51,599 And it's part of the risk curve that where we invest, which is 1052 00:52:51,599 --> 00:52:55,599 that not everything is going to just rocket to$100 million. 1053 00:52:55,920 --> 00:52:58,880 Like money goes in 18 months later, we're at$100 million. 1054 00:52:59,199 --> 00:53:01,039 It's not how it works. 1055 00:53:01,280 --> 00:53:06,000 And of course, the journey is not done at$100 million of ARR. 1056 00:53:06,239 --> 00:53:07,440 The journey continues. 1057 00:53:07,599 --> 00:53:07,920 Yeah. 1058 00:53:08,320 --> 00:53:11,360 And one of the things that we have a great deal of experience 1059 00:53:11,360 --> 00:53:14,480 for on the table is not only getting to$100 million from 1060 00:53:14,480 --> 00:53:16,960 zero, but a billion and beyond. 1061 00:53:17,760 --> 00:53:22,880 And a lot of things happen between 000, 100 to 200, 200 to 1062 00:53:22,880 --> 00:53:24,000 a billion, et cetera, et cetera. 1063 00:53:24,079 --> 00:53:27,199 Like a lot of things have to happen, a lot of things have to 1064 00:53:27,199 --> 00:53:27,840 go right. 1065 00:53:28,079 --> 00:53:31,599 And it takes a lot of time, a lot of conversation, a lot of 1066 00:53:31,599 --> 00:53:32,239 effort. 1067 00:53:32,639 --> 00:53:36,880 And not every company can, you know, fly through all these 1068 00:53:36,880 --> 00:53:38,239 gates and get there. 1069 00:53:38,480 --> 00:53:41,679 Some will, and they're spectacular, but not all can. 1070 00:53:41,920 --> 00:53:43,039 And that's okay. 1071 00:53:43,199 --> 00:53:49,440 And so part of what I've spent a lot of time thinking about is 1072 00:53:49,440 --> 00:53:53,760 that the founders ultimately come up with these insights and 1073 00:53:53,760 --> 00:53:56,559 then are able to turn these insights into spectacular 1074 00:53:56,559 --> 00:53:57,039 products. 1075 00:53:57,199 --> 00:54:00,159 These spectacular products then create a distribution advantage. 1076 00:54:00,320 --> 00:54:02,800 That distribution advantage creates great businesses. 1077 00:54:03,280 --> 00:54:04,719 And those are all kind of linked. 1078 00:54:04,880 --> 00:54:08,719 And then the cycle has to continuously happen, which is 1079 00:54:08,960 --> 00:54:12,079 the product insights have to keep happening. 1080 00:54:12,559 --> 00:54:17,599 So it can't just be a single insight, and then you've built a 1081 00:54:17,599 --> 00:54:18,639 business for a decade. 1082 00:54:18,719 --> 00:54:20,000 I don't think that works. 1083 00:54:20,239 --> 00:54:21,760 The insights have to keep coming. 1084 00:54:21,920 --> 00:54:22,159 Right. 1085 00:54:22,400 --> 00:54:23,840 New products have to keep coming. 1086 00:54:24,000 --> 00:54:26,400 You have to keep delivering value, et cetera, et cetera, et 1087 00:54:26,400 --> 00:54:26,639 cetera. 1088 00:54:26,719 --> 00:54:29,519 So like the cycle has to happen constantly. 1089 00:54:29,760 --> 00:54:36,639 And so you have to see the founder spike in some way. 1090 00:54:36,800 --> 00:54:37,440 Yeah. 1091 00:54:37,760 --> 00:54:42,079 And of course, the founders are going to grow, evolve, get 1092 00:54:42,079 --> 00:54:42,719 better. 1093 00:54:43,039 --> 00:54:48,000 Um they're also going to grow as people as they build their 1094 00:54:48,000 --> 00:54:48,480 businesses. 1095 00:54:49,280 --> 00:54:53,199 And so it's a it's a journey that's unpredictable. 1096 00:54:53,519 --> 00:54:57,679 And part of being an early stage comp uh, an early stage focused 1097 00:54:57,679 --> 00:55:01,760 firm is that you accept that and you just go. 1098 00:55:02,239 --> 00:55:06,559 And things are evolving, and when you're on the board, you're 1099 00:55:06,559 --> 00:55:12,800 seeing all of these new issues come up daily or weekly, and you 1100 00:55:12,800 --> 00:55:15,840 just try to puzzle through all of them the best you can. 1101 00:55:16,159 --> 00:55:17,119 And just keep going. 1102 00:55:17,280 --> 00:55:17,840 And that's it. 1103 00:55:18,320 --> 00:55:18,960 Just keep going. 1104 00:55:19,119 --> 00:55:19,440 SPEAKER_05: I love it. 1105 00:55:19,599 --> 00:55:22,320 Well, I got a couple last quick questions for you. 1106 00:55:22,639 --> 00:55:25,760 Um, what comp what's a company that you wish somebody would 1107 00:55:25,760 --> 00:55:27,280 build or space? 1108 00:55:28,320 --> 00:55:34,079 SPEAKER_02: Um, I think that we have a pretty strong limitation 1109 00:55:34,159 --> 00:55:38,400 that's pretty obvious in how many data centers we can build 1110 00:55:38,400 --> 00:55:39,840 in the United States. 1111 00:55:40,320 --> 00:55:46,880 And I am really excited about every company that's unlocking 1112 00:55:48,159 --> 00:55:50,320 data centers everywhere. 1113 00:55:50,559 --> 00:55:53,519 So the last investment I did was data centers in space with 1114 00:55:53,519 --> 00:55:54,159 StarCloud. 1115 00:55:54,320 --> 00:55:54,639 Yeah. 1116 00:55:54,880 --> 00:55:59,119 Um, which is along this theme of just unlocking inference 1117 00:55:59,119 --> 00:56:01,360 capacity wherever it's possible. 1118 00:56:01,599 --> 00:56:07,519 And I think we just have a lot of room in the world to just 1119 00:56:07,519 --> 00:56:10,320 really think of very aggressive ideas about where we can do 1120 00:56:10,320 --> 00:56:11,760 inference for AI. 1121 00:56:12,159 --> 00:56:14,800 And, you know, I think five years ago, if somebody said 1122 00:56:14,880 --> 00:56:17,920 we're going to do inference in space, that would have sounded 1123 00:56:17,920 --> 00:56:18,800 outlandish. 1124 00:56:19,039 --> 00:56:22,079 But in 2026, that sounds pretty realistic. 1125 00:56:22,159 --> 00:56:22,320 Yeah. 1126 00:56:22,480 --> 00:56:24,719 And it's pretty easy to imagine the next decade. 1127 00:56:24,800 --> 00:56:24,880 Yeah. 1128 00:56:25,039 --> 00:56:27,199 A lot of data centers getting built in space. 1129 00:56:27,519 --> 00:56:28,079 Definitely. 1130 00:56:28,320 --> 00:56:32,320 And I think anybody that's thinking through all of the 1131 00:56:32,320 --> 00:56:38,320 physical issues around AI continuing to grow and unlocking 1132 00:56:38,320 --> 00:56:41,039 that are all really interesting companies at the moment in time 1133 00:56:41,119 --> 00:56:41,360 for me. 1134 00:56:41,760 --> 00:56:42,719 SPEAKER_05: Very cool. 1135 00:56:43,039 --> 00:56:45,920 And other than yourself, obviously, who's an investor 1136 00:56:45,920 --> 00:56:48,400 that you know founders and operators should follow? 1137 00:56:48,960 --> 00:56:52,559 SPEAKER_02: Well, I think uh I would recommend everybody follow 1138 00:56:52,559 --> 00:56:56,079 my four partners, Eric, Peter, Jack, and Ev. 1139 00:56:56,639 --> 00:57:00,559 Each has their own angle in the venture business that I think is 1140 00:57:00,559 --> 00:57:02,320 really, really spectacular. 1141 00:57:02,559 --> 00:57:07,199 And um one of the fortunate parts of being in this business 1142 00:57:07,199 --> 00:57:10,639 for me is I get to sit with them and learn from them every single 1143 00:57:10,639 --> 00:57:11,119 day. 1144 00:57:11,440 --> 00:57:18,639 And um any content or podcast or tweet that they put out, I'd 1145 00:57:18,639 --> 00:57:19,840 encourage everybody to follow that. 1146 00:57:20,159 --> 00:57:21,920 SPEAKER_05: Yeah, I would echo that also. 1147 00:57:22,159 --> 00:57:24,239 And you know, you listed everyone there, those will all 1148 00:57:24,239 --> 00:57:26,320 be in the show notes in addition to yourself for anyone 1149 00:57:26,320 --> 00:57:26,800 listening. 1150 00:57:26,960 --> 00:57:29,360 Um, but the newest edition of the benchmark team is Jack 1151 00:57:29,360 --> 00:57:29,920 Altman. 1152 00:57:30,159 --> 00:57:32,880 I'm curious, you know, what is it like now working with Jack, 1153 00:57:32,960 --> 00:57:35,519 who is also Sam Altman's brother, incredible investor. 1154 00:57:35,679 --> 00:57:38,239 But I'm curious what that dynamic's been like changing a 1155 00:57:38,480 --> 00:57:41,039 benchmark, other than having a great podcast also about. 1156 00:57:42,559 --> 00:57:46,480 SPEAKER_02: Uh I met Jack a long time ago. 1157 00:57:46,639 --> 00:57:51,280 Um, I met Jack, I believe, for the seed round and the series A 1158 00:57:51,280 --> 00:57:52,480 round for Lattice. 1159 00:57:52,639 --> 00:57:56,880 And so I've I've known Jack for a really long time. 1160 00:57:57,280 --> 00:58:01,599 And, you know, he's just a a really great entrepreneur, a 1161 00:58:01,599 --> 00:58:05,119 great investor, and has just been such a positive influence 1162 00:58:05,119 --> 00:58:07,440 in this ecosystem for a long time. 1163 00:58:07,599 --> 00:58:12,960 And so I've been able to observe his journey journey for a decade 1164 00:58:12,960 --> 00:58:14,960 prior to him being my partner. 1165 00:58:15,280 --> 00:58:18,239 And I'll tell you, he's one of the most spectacular people in 1166 00:58:18,239 --> 00:58:19,599 this entire ecosystem. 1167 00:58:19,760 --> 00:58:24,000 And so I just feel really lucky to be working with him every day 1168 00:58:24,000 --> 00:58:27,679 now, as opposed to, you know, trying to partner with him on 1169 00:58:27,679 --> 00:58:30,559 investments or opportunities or whatever, as we were trying to 1170 00:58:30,559 --> 00:58:31,679 do uh before. 1171 00:58:31,840 --> 00:58:33,920 Um, but he brings a lot to the table. 1172 00:58:34,079 --> 00:58:38,239 He's has an incredible um network. 1173 00:58:38,559 --> 00:58:42,880 He has an unbelievable uh depth of empathy for the founder 1174 00:58:43,119 --> 00:58:45,519 journey, and obviously he's a great investor. 1175 00:58:45,679 --> 00:58:48,800 So yeah, really excited um that he's here. 1176 00:58:49,119 --> 00:58:49,599 SPEAKER_05: Very cool. 1177 00:58:49,679 --> 00:58:51,840 Yeah, you've got an incredible team at Benchmark. 1178 00:58:51,920 --> 00:58:54,320 And where can people find you and follow along to learn more? 1179 00:58:54,559 --> 00:58:57,679 SPEAKER_02: I'm Chaithan P on Twitter, and so I I tweet from 1180 00:58:57,679 --> 00:59:00,480 time to time, so it's a good place to follow me. 1181 00:59:00,800 --> 00:59:01,760 SPEAKER_05: Perfect, perfect. 1182 00:59:01,840 --> 00:59:03,360 That will also be in the show notes. 1183 00:59:03,679 --> 00:59:05,119 This has been a ton of fun. 1184 00:59:05,280 --> 00:59:06,079 Thank you, Chaitan. 1185 00:59:06,400 --> 00:59:06,559 Thank you. 1186 00:59:06,800 --> 00:59:07,599 Really enjoyed the conversation. 1187 00:59:07,840 --> 00:59:08,159 Max Altschuler: Thank you. 1188 00:59:08,320 --> 00:59:11,199 That was another fantastic episode of the VC series on the 1189 00:59:11,199 --> 00:59:12,559 GTM Now podcast. 1190 00:59:12,719 --> 00:59:15,760 Head over to Apple, Spotify, or YouTube and give us a like and 1191 00:59:15,760 --> 00:59:17,360 subscribe, and we'll see you on the next one.