1 00:00:02,640 --> 00:00:26,520 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:31,200 --> 00:00:34,559 Speaker 2: How efficient are private markets? As it turns out, it 3 00:00:34,640 --> 00:00:37,839 Speaker 2: depends where you look. In areas where VC money is 4 00:00:37,880 --> 00:00:40,840 Speaker 2: plentiful and there are lots of vcs tripping over each 5 00:00:40,880 --> 00:00:44,760 Speaker 2: other to fund deals thanks San Francisco, Boston, New York, 6 00:00:45,040 --> 00:00:48,360 Speaker 2: and other parts of the country where there are fewer vcs, 7 00:00:48,800 --> 00:00:54,080 Speaker 2: there are enormous market inefficiencies. As it turns out, fishing 8 00:00:54,120 --> 00:00:58,040 Speaker 2: and ponds overlooked by everyone else has been a great strategy. 9 00:00:58,480 --> 00:01:03,520 Speaker 2: Inefficient markets can lead two unexpectedly better returns. I'm Barry 10 00:01:03,560 --> 00:01:06,720 Speaker 2: Ritoltson on today's edition of At the Money. We're going 11 00:01:06,800 --> 00:01:11,600 Speaker 2: to discuss how investors can identify overlook startups to help 12 00:01:11,680 --> 00:01:13,520 Speaker 2: us unpack all of this and what it means for 13 00:01:13,600 --> 00:01:17,720 Speaker 2: your portfolio. Let's bring in Sarria Darabi of the venture 14 00:01:17,720 --> 00:01:22,240 Speaker 2: firm TMV. She's been an early investor in seven unicorns, 15 00:01:22,280 --> 00:01:25,920 Speaker 2: including firms that went public like Figgs, Casper, and cloud Flair, 16 00:01:26,440 --> 00:01:30,119 Speaker 2: and startups like Gimlet and Lightwell that were later acquired 17 00:01:30,200 --> 00:01:33,720 Speaker 2: by Spotify and Twitter. So Soria, let's begin with the 18 00:01:33,760 --> 00:01:38,759 Speaker 2: basic premise. AOL founder Steve Case observed seventy five percent 19 00:01:38,760 --> 00:01:42,360 Speaker 2: of venture funding has gone to just three states, California, 20 00:01:42,560 --> 00:01:46,160 Speaker 2: New York, and Massachusetts. How does this affect VC investing? 21 00:01:46,400 --> 00:01:49,680 Speaker 3: About half the time VC firms are concentrated into three 22 00:01:49,760 --> 00:01:53,240 Speaker 3: metropolitan areas, California, New York, and Massachusetts. As you said, 23 00:01:53,400 --> 00:01:56,640 Speaker 3: this is just a fact. Recently, some well known LPs, 24 00:01:56,720 --> 00:02:00,240 Speaker 3: these are Clarkson and Jamie Road reported that only three 25 00:02:00,280 --> 00:02:02,440 Speaker 3: percent of VC funds have been in more than three 26 00:02:02,480 --> 00:02:04,480 Speaker 3: percent of unicorns at the seed stage out of eight 27 00:02:04,600 --> 00:02:07,240 Speaker 3: hundred and forty five that they measured. The TLDR of 28 00:02:07,240 --> 00:02:11,440 Speaker 3: that insightful research is that seed stage investing remains completely fragmented. 29 00:02:11,840 --> 00:02:15,400 Speaker 3: WhatsApp was created by Ukrainian dropbox, by an Iranian Tesla, 30 00:02:15,480 --> 00:02:17,760 Speaker 3: by a South African cloud flare, as you mentioned, by 31 00:02:17,800 --> 00:02:20,160 Speaker 3: a Canadian woman. And by the way, one quarter of 32 00:02:20,280 --> 00:02:22,919 Speaker 3: US billion dollar startups have a founder who came here 33 00:02:22,960 --> 00:02:25,920 Speaker 3: as a student. So we could talk today about some 34 00:02:25,960 --> 00:02:29,040 Speaker 3: of the exceptional opportunity and really just looking for people 35 00:02:29,120 --> 00:02:32,000 Speaker 3: who are non obvious to live from a Silicon valley 36 00:02:32,080 --> 00:02:36,200 Speaker 3: term and coming from geographies or backgrounds that have been 37 00:02:36,320 --> 00:02:37,320 Speaker 3: largely overlooked. 38 00:02:37,400 --> 00:02:40,320 Speaker 2: So let's start with geography for a second. So San 39 00:02:40,360 --> 00:02:44,600 Speaker 2: Francisco and Silicon Valley, Boston and the surrounding areas New 40 00:02:44,680 --> 00:02:48,760 Speaker 2: York City. If that's three quarters of the funding, that 41 00:02:48,919 --> 00:02:51,679 Speaker 2: means that huge amounts of the rest of the country 42 00:02:51,960 --> 00:02:56,760 Speaker 2: are not getting capital. Competition has to be much less there. 43 00:02:57,200 --> 00:02:59,560 Speaker 2: Tell us about what you see in the rest of 44 00:02:59,760 --> 00:03:03,560 Speaker 2: the United States outside of those big three VC regions. 45 00:03:03,760 --> 00:03:07,200 Speaker 3: I'd brought in that to North America and globally. Great opportunity, 46 00:03:07,440 --> 00:03:10,280 Speaker 3: but you're absolutely right. Areas with less capital and less 47 00:03:10,280 --> 00:03:14,799 Speaker 3: competition reflect less efficiency and market returns. But these inefficiencies 48 00:03:14,840 --> 00:03:17,520 Speaker 3: typically mean that startups in the regions can be undervalued 49 00:03:17,560 --> 00:03:20,800 Speaker 3: and overlooked. So we at TMV have invested in the 50 00:03:20,840 --> 00:03:25,360 Speaker 3: last decade in very specific and academically researched areas but 51 00:03:25,480 --> 00:03:28,639 Speaker 3: overlooked verticals as well as overlooked founders, talking about maritime 52 00:03:28,760 --> 00:03:33,240 Speaker 3: tech in India and Singapore and Greece, and some of 53 00:03:33,280 --> 00:03:36,080 Speaker 3: our last most particular deals were sent to us by 54 00:03:36,680 --> 00:03:40,480 Speaker 3: large organizations like MERSK that said, hey, there's this really 55 00:03:40,520 --> 00:03:42,840 Speaker 3: interesting company, but would you invest in Athens And as 56 00:03:42,880 --> 00:03:44,920 Speaker 3: a matter of fact, we would as well as we'd 57 00:03:44,920 --> 00:03:49,480 Speaker 3: invest of course in Boston or Toronto or Austin. You 58 00:03:49,600 --> 00:03:51,680 Speaker 3: think about some of the best engineering schools in the US. 59 00:03:51,760 --> 00:03:53,480 Speaker 3: Just to focus on the United States for a second, 60 00:03:53,720 --> 00:03:57,560 Speaker 3: got Carnegie Melon in Pennsylvania, which produced Dulingo, where our 61 00:03:57,680 --> 00:04:00,880 Speaker 3: venture partner Tim Shay just ended up five year stint 62 00:04:01,000 --> 00:04:03,360 Speaker 3: and help them take that business public and it's going 63 00:04:03,400 --> 00:04:05,400 Speaker 3: to be one of the best AI ed tech companies 64 00:04:05,440 --> 00:04:08,280 Speaker 3: of all time. But it began on Carnegie Mellon's campus, 65 00:04:08,400 --> 00:04:11,840 Speaker 3: and notably that wasn't Stanford's campus or Harvard. At TMV, 66 00:04:11,920 --> 00:04:15,080 Speaker 3: we recently found a terrific AI company in the medical 67 00:04:15,120 --> 00:04:19,560 Speaker 3: scribe space out of Toronto by two Iranian immigrants, and 68 00:04:19,600 --> 00:04:22,039 Speaker 3: I'm very happy to share that if you invest in 69 00:04:22,080 --> 00:04:25,000 Speaker 3: AI in the ambient scribes space, particularly for a company 70 00:04:25,000 --> 00:04:27,800 Speaker 3: that has a path of profitability as ours does TALLYAI, 71 00:04:28,160 --> 00:04:31,479 Speaker 3: we're looking at potentially upwards of twenty million in capital 72 00:04:31,520 --> 00:04:33,880 Speaker 3: next year, the third year out of the run. Typically 73 00:04:34,040 --> 00:04:37,520 Speaker 3: the valuations are just hyperbolic in the US, They're really insane, 74 00:04:37,760 --> 00:04:40,599 Speaker 3: And we were able to invest one million US for 75 00:04:40,680 --> 00:04:43,040 Speaker 3: ten percent of the company just a year ago. That's 76 00:04:43,080 --> 00:04:45,920 Speaker 3: how sensible the valuations are outside of the major terrains. 77 00:04:46,080 --> 00:04:49,200 Speaker 3: So we're very happy to ignore San Francisco altogether. 78 00:04:49,480 --> 00:04:52,360 Speaker 2: So how do you go about looking for potential investments 79 00:04:52,600 --> 00:04:56,520 Speaker 2: in these other geographies. What's your process like, Well. 80 00:04:56,440 --> 00:05:02,600 Speaker 3: Our process is one part empirical and one part cowboy, 81 00:05:03,080 --> 00:05:06,640 Speaker 3: and so you have to kind of go where terrific 82 00:05:06,640 --> 00:05:08,680 Speaker 3: founders are and you need to seek them out. But 83 00:05:08,800 --> 00:05:10,919 Speaker 3: also you can reap the benefit of having been in 84 00:05:10,960 --> 00:05:13,960 Speaker 3: this industry as long as we have collectively to some extent. So, 85 00:05:13,960 --> 00:05:16,640 Speaker 3: for instance, the last deal I did this month investing 86 00:05:16,640 --> 00:05:18,960 Speaker 3: significantly into around that injuries and Horowitz, a very well 87 00:05:18,960 --> 00:05:21,320 Speaker 3: known VC firm out of sand Hill Road, is leading 88 00:05:21,920 --> 00:05:24,480 Speaker 3: and it's a seed round, but the founder had previously 89 00:05:24,520 --> 00:05:26,719 Speaker 3: built a unicorn. That founder happens to be an LP 90 00:05:26,800 --> 00:05:28,800 Speaker 3: in our fund, so we have an unfair advantage there. 91 00:05:28,960 --> 00:05:31,600 Speaker 3: But the unfit advantage in terms of the relationship, which 92 00:05:31,720 --> 00:05:35,440 Speaker 3: one might label as cronyism, is really just about having 93 00:05:35,520 --> 00:05:38,719 Speaker 3: been in this game for quite a long time. We 94 00:05:38,760 --> 00:05:41,359 Speaker 3: look to our LPs, which don't just include you know, 95 00:05:41,400 --> 00:05:44,120 Speaker 3: well known tech folks, but they do includes you know, 96 00:05:44,160 --> 00:05:48,520 Speaker 3: five corporate five hundreds and two pension funds and five banks, 97 00:05:48,560 --> 00:05:51,400 Speaker 3: and sometimes we get terrific deal flow from these organizations. 98 00:05:52,279 --> 00:05:54,800 Speaker 3: And sometimes it really just comes down to being in 99 00:05:54,839 --> 00:05:57,240 Speaker 3: the right building at the same time as the right 100 00:05:57,400 --> 00:06:00,599 Speaker 3: fantastic founder. And so to that end, the building in 101 00:06:00,600 --> 00:06:04,600 Speaker 3: which I work now hosts innumerable you know, terrific but 102 00:06:04,960 --> 00:06:08,279 Speaker 3: sort of out of work successful folks who are dreaming 103 00:06:08,320 --> 00:06:11,440 Speaker 3: up their next things. And then track Star. Trackstar is 104 00:06:11,600 --> 00:06:14,280 Speaker 3: a universal API for warehouse Management, a company that we 105 00:06:14,360 --> 00:06:16,839 Speaker 3: seeded last year. The founders happened to live in the 106 00:06:16,880 --> 00:06:20,680 Speaker 3: same apartment complex as our Star principle at TMVMMA Silverman. 107 00:06:21,040 --> 00:06:24,000 Speaker 3: So you really can't imagine adventure where your next deal 108 00:06:24,120 --> 00:06:26,440 Speaker 3: is going to come from. You have to be open 109 00:06:26,720 --> 00:06:29,400 Speaker 3: to the serendipity, but you have to be practiced in 110 00:06:29,440 --> 00:06:31,400 Speaker 3: your approach to deal flow. So for us, that comes 111 00:06:31,400 --> 00:06:35,120 Speaker 3: down to our tech stack, our CRM, our outreach initiatives 112 00:06:35,120 --> 00:06:38,320 Speaker 3: to other gps, and also relying on the kindness of 113 00:06:38,320 --> 00:06:41,440 Speaker 3: strangers and those big institutional vcs who happen to take 114 00:06:41,480 --> 00:06:44,440 Speaker 3: a shine to you. It's a mixed bag, Barry, But again, 115 00:06:44,640 --> 00:06:46,120 Speaker 3: you can't create this bag overnight. 116 00:06:46,320 --> 00:06:49,520 Speaker 2: The cliche is the traditional startup founders or a couple 117 00:06:49,520 --> 00:06:52,400 Speaker 2: of geeks who attended the same college and grad schools. 118 00:06:52,720 --> 00:06:55,240 Speaker 2: They create an idea, they put together a pitch deck, 119 00:06:55,640 --> 00:06:59,200 Speaker 2: and then they get funded. Is that cliche, accurate and 120 00:06:59,240 --> 00:06:59,960 Speaker 2: what's wrong with it? 121 00:07:00,120 --> 00:07:02,440 Speaker 3: Well, it's accurate and it's not so one of our 122 00:07:02,560 --> 00:07:05,400 Speaker 3: LPs at TMV Adam Grant I think he's highest rated 123 00:07:05,400 --> 00:07:08,960 Speaker 3: business school professor out of Wharton did some research for 124 00:07:09,000 --> 00:07:11,480 Speaker 3: his book Originals, where he said that actually, you do 125 00:07:11,520 --> 00:07:14,240 Speaker 3: have better odds if you're starting a business on a 126 00:07:14,240 --> 00:07:17,800 Speaker 3: college campus as an example, because it gives you access 127 00:07:17,840 --> 00:07:21,440 Speaker 3: to incredible talent, probably low cost talent, and freedom and 128 00:07:21,520 --> 00:07:24,640 Speaker 3: space to work on a problem while others aren't really 129 00:07:24,680 --> 00:07:28,120 Speaker 3: paying attention to it. But then ultimately people come to 130 00:07:28,160 --> 00:07:30,840 Speaker 3: your back door, be it venture capitalists for demo days. 131 00:07:31,000 --> 00:07:33,640 Speaker 3: I was recently at the Harvard Business School Entrepreneurship Demo 132 00:07:33,720 --> 00:07:36,120 Speaker 3: Day led by Julia Austin, who leads the Rock Center 133 00:07:36,120 --> 00:07:39,280 Speaker 3: of Entrepreneurship there. It's a terrific event, brought seventy different 134 00:07:39,600 --> 00:07:42,560 Speaker 3: vcs to her campus. But why doesn't every university in 135 00:07:42,560 --> 00:07:45,760 Speaker 3: the United States have a similarly run program. Harvard just 136 00:07:45,760 --> 00:07:48,600 Speaker 3: happens to be well tuned to the fact that billion 137 00:07:48,640 --> 00:07:51,840 Speaker 3: dollar businesses a la cloud flair, a la meta happened 138 00:07:51,880 --> 00:07:54,400 Speaker 3: to start, and so VC funds have been predicated on 139 00:07:54,440 --> 00:07:56,640 Speaker 3: that thesis alone. Let's have an index fund just to 140 00:07:56,640 --> 00:07:59,320 Speaker 3: invest in everything Harvard does. That was the X fund concept. 141 00:07:59,320 --> 00:08:03,080 Speaker 3: It's a good concept, but one would imagine that that 142 00:08:03,240 --> 00:08:07,320 Speaker 3: same practice could be applied for every great engineering program, 143 00:08:07,400 --> 00:08:09,640 Speaker 3: every great business school for that matter, in the US. 144 00:08:10,040 --> 00:08:12,640 Speaker 3: But it's just about the combination of a concentration of 145 00:08:12,680 --> 00:08:15,080 Speaker 3: talent and capital. And sand Hill Road, at the end 146 00:08:15,120 --> 00:08:16,760 Speaker 3: of the day, is really just a strip mall. It's 147 00:08:16,760 --> 00:08:19,160 Speaker 3: a strip mall where it is. It's a strip mall 148 00:08:19,280 --> 00:08:23,720 Speaker 3: of money. But it's also lazy fishing, honestly. And if you, 149 00:08:23,720 --> 00:08:27,520 Speaker 3: you know, think about every great ende program from UT 150 00:08:27,720 --> 00:08:31,720 Speaker 3: Austin to obviously MIT out of Boston and what they're 151 00:08:31,760 --> 00:08:34,360 Speaker 3: doing there with the Media Lab, you're going to find 152 00:08:34,480 --> 00:08:38,679 Speaker 3: some exceptional talent that doesn't have as rate of an 153 00:08:38,679 --> 00:08:42,079 Speaker 3: immediate access to capital. And there are some funds, Steve 154 00:08:42,120 --> 00:08:44,160 Speaker 3: Case's Fund, Rise of the Rest being a good example, 155 00:08:44,280 --> 00:08:48,200 Speaker 3: that are conditioned entirely to seek out those non obvious 156 00:08:48,240 --> 00:08:50,679 Speaker 3: geos and we're more than happy to co indust alongside. 157 00:08:50,800 --> 00:08:55,960 Speaker 2: So let's talk about some of those areas. Obviously, Harvard, Stanford, Wharton, MIT, 158 00:08:56,640 --> 00:08:59,640 Speaker 2: Big four, that's a lot when you're looking outside of 159 00:08:59,640 --> 00:09:02,400 Speaker 2: those three of four cities, where else are you looking at. 160 00:09:02,600 --> 00:09:06,320 Speaker 2: You mentioned Carnegie, mellon Is, I think Pittsburgh and Austin 161 00:09:06,400 --> 00:09:09,679 Speaker 2: in Texas. What other parts of the country are you 162 00:09:09,880 --> 00:09:15,160 Speaker 2: finding potentially unicorn ideas that could either get acquired or 163 00:09:15,160 --> 00:09:16,400 Speaker 2: go public eventually. 164 00:09:16,640 --> 00:09:19,800 Speaker 3: We're not ignoring California. We just think some better valuations 165 00:09:19,840 --> 00:09:22,480 Speaker 3: are available in Los Angeles or Berkeley for that matter, 166 00:09:22,679 --> 00:09:24,959 Speaker 3: versus San Francisco. Proper. We have a great company out 167 00:09:24,960 --> 00:09:28,400 Speaker 3: of Berkeley called Millie and it's an exceptional healthcare business 168 00:09:28,400 --> 00:09:31,280 Speaker 3: for women dealing with hig risk pregnancies, and their first 169 00:09:31,280 --> 00:09:33,640 Speaker 3: clinic was opened in Berkeley for the very fact that 170 00:09:33,679 --> 00:09:36,440 Speaker 3: it's less expensive to operate a business there, one zip 171 00:09:36,480 --> 00:09:39,680 Speaker 3: code away from probably the most expensive spot in America 172 00:09:39,720 --> 00:09:42,920 Speaker 3: to operate a business. So we're looking pretty much everywhere. 173 00:09:42,960 --> 00:09:46,360 Speaker 3: We have a diverse pool of founders and funds who 174 00:09:46,400 --> 00:09:49,920 Speaker 3: send us deals, but we're specifically not swimming in San 175 00:09:49,920 --> 00:09:52,880 Speaker 3: Francisco or Palo Alto for that matter, because we think 176 00:09:52,960 --> 00:09:57,960 Speaker 3: that it's overly commodified and the valuations are just dangerous. 177 00:09:58,000 --> 00:10:00,360 Speaker 2: At this point, that makes a lot of sense, So 178 00:10:00,559 --> 00:10:04,880 Speaker 2: this isn't just theory. You guys were early investors in figs. 179 00:10:05,240 --> 00:10:08,920 Speaker 2: You were an early investor in Casper. You were a 180 00:10:08,960 --> 00:10:12,760 Speaker 2: subsequent investor in cloud Flair, as well as startups like 181 00:10:12,800 --> 00:10:17,199 Speaker 2: Gimlet and Lightwell, were these companies from the traditional ivs? 182 00:10:17,280 --> 00:10:20,560 Speaker 2: Where else are you fishing outside of the well known 183 00:10:20,600 --> 00:10:21,280 Speaker 2: fishing halls? 184 00:10:21,440 --> 00:10:24,040 Speaker 3: Well, those examples you cited a couple of them, were 185 00:10:24,559 --> 00:10:27,240 Speaker 3: you know, Figs and cloud Flare. Three of those four 186 00:10:27,280 --> 00:10:30,040 Speaker 3: founders came from HBS specifically, so not just the top 187 00:10:30,120 --> 00:10:32,360 Speaker 3: university in the US, but the top business school or 188 00:10:32,480 --> 00:10:35,000 Speaker 3: among the top. But Casper. This is a fun story. 189 00:10:35,200 --> 00:10:37,920 Speaker 3: I met the founders at a concert in Williamsburg, I 190 00:10:37,960 --> 00:10:41,720 Speaker 3: think in Brooklyn, Brooklyn. Yeah, the band was Blonde Redhead, 191 00:10:41,720 --> 00:10:43,880 Speaker 3: I can't remember, but it was a good concert and 192 00:10:44,040 --> 00:10:47,680 Speaker 3: they were setting up their first ever display of the mattresses. 193 00:10:48,080 --> 00:10:50,440 Speaker 3: And by the way, I'm the first to admit that 194 00:10:50,480 --> 00:10:51,800 Speaker 3: I think I got in and got out at the 195 00:10:51,880 --> 00:10:53,560 Speaker 3: right time with Casper. I sold my shares at the 196 00:10:53,559 --> 00:10:55,720 Speaker 3: Series D, which was their peak val. But I met 197 00:10:55,760 --> 00:10:57,880 Speaker 3: them because they were giving out free beer for people 198 00:10:57,880 --> 00:11:00,000 Speaker 3: who would sit on the mattresses while listening to music, 199 00:11:00,160 --> 00:11:02,240 Speaker 3: and I thought that sounds like fun, and we started 200 00:11:02,240 --> 00:11:04,199 Speaker 3: talking about venture and I had been in the industry 201 00:11:04,200 --> 00:11:06,680 Speaker 3: for about five years at that point, and it led 202 00:11:06,720 --> 00:11:08,840 Speaker 3: to them sending over term sheets the next day and 203 00:11:08,880 --> 00:11:12,760 Speaker 3: I made a decision with thirty minutes notice, so no diligence. 204 00:11:13,040 --> 00:11:15,160 Speaker 3: That's how fast it was. With FIGS, I think is 205 00:11:15,160 --> 00:11:17,640 Speaker 3: more premeditated. That was the first deal I really diligenced 206 00:11:17,640 --> 00:11:20,520 Speaker 3: with my now partner, Marina Hajipaterras, and I'm very proud 207 00:11:20,559 --> 00:11:23,520 Speaker 3: of that original memo we wrote, which stated that a 208 00:11:23,520 --> 00:11:25,520 Speaker 3: lot of people are going to overlook this, not because 209 00:11:25,520 --> 00:11:27,600 Speaker 3: it's two women, but by the way, first two women 210 00:11:27,600 --> 00:11:29,240 Speaker 3: ever to take a company public on the New York 211 00:11:29,280 --> 00:11:31,920 Speaker 3: Stock Exchange, that's pretty powerful. We thought people were going 212 00:11:31,960 --> 00:11:34,440 Speaker 3: to overlook it because they would assume that it's a 213 00:11:34,480 --> 00:11:37,199 Speaker 3: consumer business and an e commerce business. And what Figgs 214 00:11:37,280 --> 00:11:40,960 Speaker 3: does is to this day very well. They make comfortable 215 00:11:40,960 --> 00:11:43,800 Speaker 3: and functional medical apparel, and we saw it more as 216 00:11:44,000 --> 00:11:47,720 Speaker 3: an enterprise play, selling into hospitals and giving back to 217 00:11:47,760 --> 00:11:51,480 Speaker 3: a community that's largely overlooked nurses, primarily. We continued to 218 00:11:51,480 --> 00:11:54,040 Speaker 3: invest along that thesis today. In fact, my last deal 219 00:11:54,120 --> 00:11:58,120 Speaker 3: was an AI nurse staffing company called in House Health, 220 00:11:58,520 --> 00:12:01,439 Speaker 3: led by a founder who previously built a tech unicorn 221 00:12:01,440 --> 00:12:04,480 Speaker 3: called Stellar Health, but going back to Figs. We saw 222 00:12:04,640 --> 00:12:06,560 Speaker 3: around corners with that deal, and we wrote in our 223 00:12:06,559 --> 00:12:09,920 Speaker 3: original memo that this could eventually end up in medspas 224 00:12:09,960 --> 00:12:12,720 Speaker 3: and dentist offices, which to this day it does, but 225 00:12:12,800 --> 00:12:14,439 Speaker 3: we also wrote it could be on the boiler room 226 00:12:14,440 --> 00:12:16,760 Speaker 3: of ships because Marina, my business partner, comes from a 227 00:12:16,760 --> 00:12:19,440 Speaker 3: two hundred year old shipping family, and sure enough her 228 00:12:19,480 --> 00:12:22,640 Speaker 3: family is buying Figs uniforms now to give to their workers. 229 00:12:22,679 --> 00:12:25,000 Speaker 3: And so it's really cool when you feel like a 230 00:12:25,080 --> 00:12:27,959 Speaker 3: profit or you have some sort of clairvoyance simply by 231 00:12:27,960 --> 00:12:28,760 Speaker 3: doing your homework. 232 00:12:28,960 --> 00:12:32,839 Speaker 2: So, when you're fishing in geographies outside of the Big three, 233 00:12:33,160 --> 00:12:37,160 Speaker 2: or investing in founders who are not what we think 234 00:12:37,240 --> 00:12:40,760 Speaker 2: of as typical founders, what have the returns been like? 235 00:12:40,880 --> 00:12:43,880 Speaker 2: What should VC investors be expecting? 236 00:12:44,400 --> 00:12:48,960 Speaker 3: Well on SPVs in non traditional founders. Before I started TMV, 237 00:12:49,160 --> 00:12:51,319 Speaker 3: it's one hundred and seventy two percent realized IRR on 238 00:12:51,360 --> 00:12:53,960 Speaker 3: those SPVs, and so I think most investors would like 239 00:12:54,040 --> 00:12:59,960 Speaker 3: those returns, and those are collective spbs, But more or less, 240 00:13:00,200 --> 00:13:02,480 Speaker 3: I think you're looking at the same returns and you're 241 00:13:02,559 --> 00:13:07,319 Speaker 3: underwriting for venture returns and traditionally BC's underwrite one hundred 242 00:13:07,440 --> 00:13:09,240 Speaker 3: x for a seed investment, ten x for a series 243 00:13:09,280 --> 00:13:12,200 Speaker 3: A investment. If you're talking about early stage specifically, we 244 00:13:12,240 --> 00:13:15,200 Speaker 3: do the same at TMB. You're also underwriting for a 245 00:13:15,240 --> 00:13:18,400 Speaker 3: forty percent fail rate, fifty percent success rate, and ten 246 00:13:18,440 --> 00:13:20,760 Speaker 3: percent super success rate. And it's those ten percent of 247 00:13:20,800 --> 00:13:24,040 Speaker 3: companies that really deliver all of the alpha for any 248 00:13:24,080 --> 00:13:25,360 Speaker 3: given fund, not just mine. 249 00:13:25,559 --> 00:13:30,840 Speaker 2: So to wrap up, markets are mostly kinda, sorta eventually efficient, 250 00:13:31,360 --> 00:13:35,520 Speaker 2: but not everywhere and not with everyone. Venture capitalists who 251 00:13:35,520 --> 00:13:39,319 Speaker 2: are looking at non traditional founders and in locations away 252 00:13:39,320 --> 00:13:42,720 Speaker 2: from New York, San Francisco and Boston are finding some 253 00:13:42,840 --> 00:13:51,400 Speaker 2: fantastic investment opportunities. You can listen to at the Money 254 00:13:51,640 --> 00:13:54,880 Speaker 2: every week, finding in our masters and business feed at 255 00:13:54,880 --> 00:13:59,240 Speaker 2: Bloomberg dot com, Apple Podcasts, and Spotify. Each week we'll 256 00:13:59,240 --> 00:14:02,120 Speaker 2: be here to discuss the issues that matter most to 257 00:14:02,200 --> 00:14:05,679 Speaker 2: you as an infestor. I'm Barry Rittolts. You've been listening 258 00:14:05,720 --> 00:14:07,959 Speaker 2: to Add the Money on Bloomberg Radio