1
00:00:00,200 --> 00:00:12,000
Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. This is Masters in

2
00:00:12,080 --> 00:00:15,560
Speaker 1: Business with Barry Ritholts on Bloomberg Radio.

3
00:00:17,040 --> 00:00:20,200
Speaker 2: This week on the podcast, I have an extra special guest.

4
00:00:20,640 --> 00:00:23,759
Speaker 2: What can I say about Devin PreK, Managing director and

5
00:00:23,880 --> 00:00:30,040
Speaker 2: Insight Partners, major venture capital slash private equity shop that

6
00:00:30,160 --> 00:00:34,440
Speaker 2: has had just countless, countless exits. He was an early

7
00:00:34,440 --> 00:00:40,560
Speaker 2: investor in Twitter, Buddy Media, Eves men Apras, Insights, Website,

8
00:00:40,600 --> 00:00:46,360
Speaker 2: Pros Turnton. They focus on software, which is much broader

9
00:00:46,400 --> 00:00:50,080
Speaker 2: and more varied than you might imagine. They are global

10
00:00:50,600 --> 00:00:53,040
Speaker 2: in their footprint of where they put money to work,

11
00:00:53,520 --> 00:00:58,200
Speaker 2: and they're not just early stage investors. They do a rounds,

12
00:00:58,240 --> 00:01:02,320
Speaker 2: B rounds. They will help provide liquidity for a company

13
00:01:02,360 --> 00:01:06,880
Speaker 2: that's looking for a partial exit, as well as strategic

14
00:01:06,959 --> 00:01:10,360
Speaker 2: investments and m and a sort of from a private

15
00:01:10,360 --> 00:01:14,720
Speaker 2: equity shop. I think Insight Partners is unique because they

16
00:01:14,760 --> 00:01:18,160
Speaker 2: have a foot in both venture and pe worlds. I

17
00:01:18,200 --> 00:01:20,720
Speaker 2: thought this conversation was fascinating and I think you will

18
00:01:20,760 --> 00:01:25,679
Speaker 2: also with no further ado my discussion with Insight Partners.

19
00:01:26,520 --> 00:01:28,360
Speaker 3: Devin correct, thanks for having me.

20
00:01:29,000 --> 00:01:32,680
Speaker 2: So let's start out way back when you get a

21
00:01:32,720 --> 00:01:37,039
Speaker 2: bachelor's and economics from Wharton. What was your original career plan?

22
00:01:37,640 --> 00:01:38,560
Speaker 3: Not business?

23
00:01:38,920 --> 00:01:39,520
Speaker 2: Not business.

24
00:01:39,600 --> 00:01:44,840
Speaker 3: I actually in high school was a total science nerd. Know,

25
00:01:44,920 --> 00:01:50,080
Speaker 3: competed in Westinghouse and oh really what area? Biochemistry? Microbiology?

26
00:01:50,120 --> 00:01:53,000
Speaker 3: Actually one first place in microbiology the International Science Fair.

27
00:01:53,080 --> 00:01:56,160
Speaker 3: So my path was kind of being a doctor, probably

28
00:01:56,160 --> 00:02:01,400
Speaker 3: being an mdphd. I didn't actually start pen in Warden.

29
00:02:01,440 --> 00:02:03,560
Speaker 3: I actually started the College of Arts and Sciences. I

30
00:02:03,560 --> 00:02:07,040
Speaker 3: started as a biochemistry major. I was doing research at

31
00:02:07,040 --> 00:02:11,200
Speaker 3: the medical school my freshman year, and you know, I think,

32
00:02:11,400 --> 00:02:13,919
Speaker 3: like at everything in life, there's a lot of fate

33
00:02:13,960 --> 00:02:16,040
Speaker 3: and who your roommates are and the people you meet,

34
00:02:16,720 --> 00:02:20,240
Speaker 3: and you know, my roommates were all business and I

35
00:02:20,360 --> 00:02:24,120
Speaker 3: was the only kind of science person, and I thought, well,

36
00:02:24,160 --> 00:02:27,280
Speaker 3: maybe I should take maybe I should take a finance

37
00:02:27,440 --> 00:02:29,800
Speaker 3: course or an economics course. So I did freshman year.

38
00:02:29,880 --> 00:02:34,720
Speaker 3: Found it really interesting, And after my freshman year, I

39
00:02:34,800 --> 00:02:38,400
Speaker 3: decided rather than doing working in science for the summer,

40
00:02:38,639 --> 00:02:40,680
Speaker 3: I was going to work on Wall Street for the summer.

41
00:02:40,720 --> 00:02:43,280
Speaker 3: And I managed to get a job in Wall Street

42
00:02:43,520 --> 00:02:46,600
Speaker 3: between my freshmen and sophomore year, which was unusual at

43
00:02:46,639 --> 00:02:48,800
Speaker 3: the time, but but I did. I came back after

44
00:02:48,840 --> 00:02:51,120
Speaker 3: that and said, well, maybe I can put these two

45
00:02:51,120 --> 00:02:54,919
Speaker 3: interests together. And I was going to do biochemistry and finance.

46
00:02:55,360 --> 00:02:58,160
Speaker 3: I was going to do the dual degree, you know,

47
00:02:58,240 --> 00:02:59,960
Speaker 3: with a degree in Wharton and a degree in colleg

48
00:03:00,120 --> 00:03:03,080
Speaker 3: Now they have pre set programs for all of these things,

49
00:03:03,080 --> 00:03:06,560
Speaker 3: but at the time they didn't. But it would involve

50
00:03:06,680 --> 00:03:09,720
Speaker 3: taking you know, between six and seven courses every semester

51
00:03:10,240 --> 00:03:13,000
Speaker 3: and not not These were not easy classes. These were

52
00:03:13,040 --> 00:03:16,840
Speaker 3: like organic chemistry and quantitative finance. And I just thought,

53
00:03:16,840 --> 00:03:19,040
Speaker 3: this isn't going to be a great college experience if

54
00:03:19,040 --> 00:03:22,160
Speaker 3: I do both. I kind of needed to pick, and

55
00:03:22,400 --> 00:03:25,880
Speaker 3: so I ended up picking Wharton. And of course people

56
00:03:25,919 --> 00:03:27,880
Speaker 3: are like, well, what was the thought process you went

57
00:03:27,919 --> 00:03:30,280
Speaker 3: through when you did that? And the thought process probably

58
00:03:30,320 --> 00:03:34,120
Speaker 3: was not. It was I was impatient, and I saw

59
00:03:34,880 --> 00:03:37,440
Speaker 3: the route for medical school was I was gonna do

60
00:03:37,480 --> 00:03:39,280
Speaker 3: four years in medical school. I was thinking at that

61
00:03:39,320 --> 00:03:41,200
Speaker 3: point I also wanted to do research. I thought maybe

62
00:03:41,200 --> 00:03:43,360
Speaker 3: I was gonna get a PhD. It just seemed like

63
00:03:43,400 --> 00:03:45,760
Speaker 3: a long time in school before I could actually start

64
00:03:46,320 --> 00:03:49,960
Speaker 3: my career as opposed to business, I could I could

65
00:03:50,040 --> 00:03:52,240
Speaker 3: kind of jump in right away. And I always thought

66
00:03:53,000 --> 00:03:55,240
Speaker 3: that at some point in the future, I would somehow

67
00:03:55,240 --> 00:03:58,000
Speaker 3: bring these two interests together. I didn't know how that was.

68
00:03:58,160 --> 00:04:01,080
Speaker 2: That was the obvious question because I on the list

69
00:04:01,160 --> 00:04:03,440
Speaker 2: of areas you invest I don't see a whole lot

70
00:04:03,440 --> 00:04:07,120
Speaker 2: of healthcare or biotech or genomics. But have the Twain

71
00:04:07,240 --> 00:04:07,960
Speaker 2: ever met.

72
00:04:08,040 --> 00:04:11,440
Speaker 3: Or they actually have, and they have in two different ways.

73
00:04:11,440 --> 00:04:14,280
Speaker 3: We do now have a team at Insight that does

74
00:04:14,440 --> 00:04:18,960
Speaker 3: invest in in kind of therapeutics, biotechnology, kind of therapeutics.

75
00:04:19,600 --> 00:04:21,240
Speaker 3: We have a team that does it. I'm involved in it,

76
00:04:21,240 --> 00:04:23,440
Speaker 3: but I'm not the one doing those deals or leading

77
00:04:23,480 --> 00:04:26,680
Speaker 3: those deals. But it's actually probably also manifested a lot

78
00:04:26,760 --> 00:04:30,520
Speaker 3: more like philanthropically. I'm on the board of NYU langone.

79
00:04:30,560 --> 00:04:32,520
Speaker 3: We're funding a bunch of research there, as well as

80
00:04:32,520 --> 00:04:35,599
Speaker 3: a bunch of other, you know, kind of universities. So

81
00:04:35,839 --> 00:04:39,719
Speaker 3: philanthropically it's been a big focus of mine, and so

82
00:04:39,800 --> 00:04:43,240
Speaker 3: it's been enabled. I've been able to bring bring that

83
00:04:43,320 --> 00:04:46,600
Speaker 3: kind of interest back into my life in a way

84
00:04:46,640 --> 00:04:47,599
Speaker 3: that's been satisfying.

85
00:04:47,680 --> 00:04:51,480
Speaker 2: Huh really interesting. So from Wharton, how do you end

86
00:04:51,560 --> 00:04:53,200
Speaker 2: up on Wall Street? What's your first gig?

87
00:04:53,920 --> 00:04:56,640
Speaker 3: Well? I worked for the summers. I worked at a

88
00:04:56,680 --> 00:04:59,800
Speaker 3: small biotshop after my freshman year. After my sophomore year

89
00:04:59,839 --> 00:05:02,640
Speaker 3: work a credit sweets, and after my junior year, I

90
00:05:02,720 --> 00:05:05,600
Speaker 3: was actually first Boston at the time. After my junior year,

91
00:05:05,640 --> 00:05:09,719
Speaker 3: I worked at DLJ and then I started at blackstam and.

92
00:05:09,520 --> 00:05:12,080
Speaker 2: That was that's quite a laundry list, of it was

93
00:05:12,080 --> 00:05:12,760
Speaker 2: a laundry list.

94
00:05:12,960 --> 00:05:15,640
Speaker 3: It was a laundry list. And I started as an

95
00:05:15,680 --> 00:05:19,640
Speaker 3: analyst at Blackstone in nineteen ninety one and then had

96
00:05:19,640 --> 00:05:23,880
Speaker 3: the opportunity, even kind of before I finished my analyst

97
00:05:23,960 --> 00:05:26,160
Speaker 3: program to go to a startup. But it was just

98
00:05:26,200 --> 00:05:29,760
Speaker 3: not a tech startup. It was a investment banking startup

99
00:05:30,200 --> 00:05:33,920
Speaker 3: that was founded by Jeffrey Barnson and Raymondella, who used

100
00:05:33,920 --> 00:05:38,000
Speaker 3: to be the coheads of merchant banking at Merylnch And

101
00:05:38,120 --> 00:05:42,800
Speaker 3: so I left Blackstone to go to what was then

102
00:05:43,040 --> 00:05:45,400
Speaker 3: a no name and to some degree it's still not

103
00:05:45,520 --> 00:05:51,120
Speaker 3: well known firm. And I remember having a conversation with

104
00:05:51,160 --> 00:05:54,600
Speaker 3: my dad at the time, who was like, he didn't

105
00:05:54,640 --> 00:05:56,640
Speaker 3: really know who Blackstone was, and so when I took

106
00:05:56,680 --> 00:05:58,000
Speaker 3: that job, he was like, well, why would you take

107
00:05:58,000 --> 00:05:59,880
Speaker 3: Blackstone when you got all these offers from firms he'd

108
00:05:59,880 --> 00:06:01,480
Speaker 3: heard of, And I was like, well, I think it's

109
00:06:01,520 --> 00:06:03,920
Speaker 3: going to be a really good firm. And then finally

110
00:06:04,000 --> 00:06:05,680
Speaker 3: he got comfortable that was a good idea, and I

111
00:06:05,760 --> 00:06:07,240
Speaker 3: leave to go to this firm that no one's heard of.

112
00:06:07,240 --> 00:06:08,920
Speaker 3: And I said, well, my downside cases, I'll go to

113
00:06:08,920 --> 00:06:12,280
Speaker 3: business school. Like it's really not anyway. So I made

114
00:06:12,279 --> 00:06:16,960
Speaker 3: that leap and that was a It was a great experience.

115
00:06:18,000 --> 00:06:20,840
Speaker 3: They were primarily kind of M and A advisory, but

116
00:06:20,839 --> 00:06:22,680
Speaker 3: then over time they were trying to figure out how

117
00:06:22,720 --> 00:06:25,159
Speaker 3: to get into the principal business in some way.

118
00:06:26,000 --> 00:06:27,480
Speaker 2: How do you how do you go from M and

119
00:06:27,560 --> 00:06:29,280
Speaker 2: A to venture capital?

120
00:06:29,760 --> 00:06:34,719
Speaker 3: So the two co founders of Insight, Jeff Horing and

121
00:06:34,800 --> 00:06:41,839
Speaker 3: Jerry Murdoch started they're pre effectively the predecessor to Insight

122
00:06:42,520 --> 00:06:47,599
Speaker 3: at Baronsamonela and Baron. Samla was kind of a a

123
00:06:47,680 --> 00:06:50,600
Speaker 3: sponsor of these two guys who wanted to do something

124
00:06:50,600 --> 00:06:53,840
Speaker 3: in technology. Really early. We were not technology experts. We

125
00:06:53,880 --> 00:06:55,720
Speaker 3: didn't the firm didn't know anything about technology. But we

126
00:06:55,760 --> 00:06:57,440
Speaker 3: thought we could help them raise capital, or at least

127
00:06:58,480 --> 00:07:00,760
Speaker 3: the guys ran the firm thought they could help. But

128
00:07:01,000 --> 00:07:03,359
Speaker 3: we didn't really have a lot of competency in software.

129
00:07:04,200 --> 00:07:07,240
Speaker 3: I was the closest thing they had to somebody understood technology,

130
00:07:07,240 --> 00:07:10,480
Speaker 3: which just means that I used it. And so I

131
00:07:10,600 --> 00:07:15,400
Speaker 3: was kind of working with, you know, Jeffrey and jeff

132
00:07:15,400 --> 00:07:18,120
Speaker 3: Moring and Jerry Murdoch, and then they kind of came

133
00:07:18,160 --> 00:07:21,120
Speaker 3: to the conclusion that they were going to kind of

134
00:07:21,120 --> 00:07:23,880
Speaker 3: go do this on their own, that there wasn't really

135
00:07:24,480 --> 00:07:27,320
Speaker 3: like the partnership didn't make sense for them, so they

136
00:07:27,360 --> 00:07:30,720
Speaker 3: went off. They'd asked for you, was that they uh

137
00:07:31,320 --> 00:07:34,000
Speaker 3: ninety five? Yeah, And they asked me at that time

138
00:07:34,000 --> 00:07:37,000
Speaker 3: if I was interested in joining, and you know, I

139
00:07:37,040 --> 00:07:39,280
Speaker 3: was twenty five and a vice president, and I was like, oh, well,

140
00:07:39,280 --> 00:07:41,600
Speaker 3: why would I go join a startup? And now all

141
00:07:41,600 --> 00:07:43,960
Speaker 3: of a sudden I lost my startup kind of bug,

142
00:07:44,760 --> 00:07:46,960
Speaker 3: and so I didn't. Then I maintained a relationship with them,

143
00:07:46,960 --> 00:07:49,480
Speaker 3: and then in nineteen ninety nine, when I was thinking

144
00:07:49,520 --> 00:07:52,400
Speaker 3: of leaving parents to go do something on the principal side,

145
00:07:52,800 --> 00:07:54,880
Speaker 3: I ended up kind of joining them when they were

146
00:07:54,960 --> 00:07:56,320
Speaker 3: raising their first institutional fund.

147
00:07:56,840 --> 00:07:59,680
Speaker 2: So, so what was that process like going from what

148
00:07:59,800 --> 00:08:02,840
Speaker 2: was really a startup to going to something that was

149
00:08:04,120 --> 00:08:07,440
Speaker 2: barely no longer a startup? Or was that really their

150
00:08:07,480 --> 00:08:08,320
Speaker 2: first major rountud?

151
00:08:08,360 --> 00:08:10,120
Speaker 3: No, So they had so they at that point had

152
00:08:10,200 --> 00:08:14,400
Speaker 3: raised three funds, they were about to raise their fourth.

153
00:08:14,120 --> 00:08:15,760
Speaker 2: Fund, so somewhat seasons.

154
00:08:16,600 --> 00:08:20,200
Speaker 3: It was primarily at that point that very few institutional investors,

155
00:08:20,200 --> 00:08:22,360
Speaker 3: so their fourth fund fund for was going to be

156
00:08:22,360 --> 00:08:27,240
Speaker 3: their first institutional fund, and so was. The firm is

157
00:08:27,320 --> 00:08:30,520
Speaker 3: very small from a number of people standpoints, about ten people,

158
00:08:31,160 --> 00:08:33,000
Speaker 3: you know, today we're four and fifty people, so it's

159
00:08:33,200 --> 00:08:37,200
Speaker 3: a much larger firm today, but it was I think

160
00:08:37,240 --> 00:08:40,360
Speaker 3: the harder part of the transition is, you know, it's

161
00:08:40,480 --> 00:08:44,240
Speaker 3: very different being an advisor. I wanted this transition, but

162
00:08:44,280 --> 00:08:47,200
Speaker 3: it's very different being an advisor whose goal it is

163
00:08:47,240 --> 00:08:50,120
Speaker 3: to kind of get a deal done to being a principal,

164
00:08:50,160 --> 00:08:51,800
Speaker 3: where your goal is not just to get a deal done,

165
00:08:51,920 --> 00:08:54,840
Speaker 3: was to make sure it's a good deal. And that's

166
00:08:54,880 --> 00:08:58,120
Speaker 3: a that's a that's a shift, that's a shift of mentality.

167
00:08:59,400 --> 00:09:01,400
Speaker 3: You know, you it's not like an on off switch

168
00:09:01,440 --> 00:09:04,440
Speaker 3: for that. But really, the way I looked at it

169
00:09:04,480 --> 00:09:08,760
Speaker 3: is I was and the firm that I left very

170
00:09:08,800 --> 00:09:12,080
Speaker 3: generously offered me the opportunity to take a pool of

171
00:09:12,120 --> 00:09:14,320
Speaker 3: capital that they had and invest in technology is kind

172
00:09:14,320 --> 00:09:15,920
Speaker 3: of as a way to maybe get me to consider

173
00:09:15,960 --> 00:09:18,760
Speaker 3: staying there. And I said, No, it wasn't really an

174
00:09:18,760 --> 00:09:21,360
Speaker 3: economic decision. What I said was, I'm not really qualified

175
00:09:21,360 --> 00:09:23,880
Speaker 3: to do that at that point in time, h and

176
00:09:23,920 --> 00:09:26,920
Speaker 3: that I'm one of the reasons I'm making this shift

177
00:09:27,320 --> 00:09:30,360
Speaker 3: is to actually learn how to do something.

178
00:09:30,520 --> 00:09:35,840
Speaker 2: What was that learning curve like? Because I remember the

179
00:09:35,960 --> 00:09:40,120
Speaker 2: nineteen nineties in the late eighties, and it seemed like

180
00:09:40,480 --> 00:09:43,199
Speaker 2: a ton of people just jumping into the venture worlds

181
00:09:43,720 --> 00:09:47,599
Speaker 2: regardless of their credentials or academic qualifications.

182
00:09:47,600 --> 00:09:51,600
Speaker 3: Well, and I think in I joined at late ninety nine,

183
00:09:51,640 --> 00:09:54,680
Speaker 3: two thousand, you remember that time. Sure, in some ways

184
00:09:54,800 --> 00:09:56,719
Speaker 3: it was a great time. In some ways it was

185
00:09:56,720 --> 00:09:58,880
Speaker 3: a terrible time. I think in retrospect it ended up

186
00:09:58,920 --> 00:10:01,920
Speaker 3: being a very good time for the the following reason. Economically,

187
00:10:02,120 --> 00:10:06,880
Speaker 3: it was not a great decision for years because you know,

188
00:10:07,000 --> 00:10:09,520
Speaker 3: I think I told my wife when I took the job.

189
00:10:09,720 --> 00:10:12,360
Speaker 3: You know, she was we just bought an apartment and

190
00:10:12,480 --> 00:10:14,480
Speaker 3: she was pregnant with her first kid, and I said,

191
00:10:14,520 --> 00:10:16,280
Speaker 3: don't worry. I know I'm making less cash, but I'm

192
00:10:16,280 --> 00:10:19,520
Speaker 3: gonna have all this equity, and like that equity was

193
00:10:19,559 --> 00:10:21,320
Speaker 3: like five years I hadn't really she was like, I'm

194
00:10:21,360 --> 00:10:23,880
Speaker 3: not sure, it doesn't not sure. I feel like this

195
00:10:24,000 --> 00:10:27,360
Speaker 3: was the right trade. But so you get there in

196
00:10:27,480 --> 00:10:31,400
Speaker 3: ninety nine and the deal pace is frenetic, and so

197
00:10:31,440 --> 00:10:33,600
Speaker 3: you think like, oh, I'm learning so much. I'm getting

198
00:10:33,600 --> 00:10:37,520
Speaker 3: all these deals done. I also got put on a

199
00:10:37,559 --> 00:10:40,640
Speaker 3: ton of boards, you know, of companies, and the first

200
00:10:40,640 --> 00:10:42,640
Speaker 3: thing I figured out was, well, a lot of these

201
00:10:42,640 --> 00:10:45,000
Speaker 3: companies didn't really have a business model without raising a

202
00:10:45,040 --> 00:10:47,000
Speaker 3: lot more capital. It wasn't just us, it was just

203
00:10:47,080 --> 00:10:49,800
Speaker 3: that was that was that time. It was a land grad,

204
00:10:49,840 --> 00:10:52,440
Speaker 3: it was a lane grad in the early days, and

205
00:10:52,480 --> 00:10:55,120
Speaker 3: the market corrected very quickly, I think four or five

206
00:10:55,120 --> 00:10:57,760
Speaker 3: months after I got there, and we look back. I mean,

207
00:10:57,800 --> 00:11:01,679
Speaker 3: those were really really hard years. But I actually think

208
00:11:01,720 --> 00:11:04,480
Speaker 3: this way to learn the most. You know, it's easy

209
00:11:04,520 --> 00:11:07,400
Speaker 3: to be it's easy to be a cheerleader when things

210
00:11:07,400 --> 00:11:10,439
Speaker 3: are great. It's a lot harder to have to kind

211
00:11:10,440 --> 00:11:13,120
Speaker 3: of dig into a business, including businesses that aren't going

212
00:11:13,200 --> 00:11:14,840
Speaker 3: to make it, and try to get to the best

213
00:11:14,840 --> 00:11:18,480
Speaker 3: possible outcomes. So from a learning standpoint, you know, and

214
00:11:18,520 --> 00:11:20,840
Speaker 3: I think this is sometimes the things I tell my

215
00:11:20,920 --> 00:11:24,120
Speaker 3: kids is like the worst time sometimes are the ones

216
00:11:24,200 --> 00:11:26,720
Speaker 3: we're going to learn the most. And there's always going

217
00:11:26,800 --> 00:11:29,199
Speaker 3: to be you're going to get to the other side.

218
00:11:29,320 --> 00:11:31,560
Speaker 3: It might not be the side exactly the way you

219
00:11:31,600 --> 00:11:33,600
Speaker 3: wanted it, but there's no way you're going to look

220
00:11:33,600 --> 00:11:35,280
Speaker 3: back and say you didn't get something out of that expert.

221
00:11:35,320 --> 00:11:37,040
Speaker 2: It's so funny you say that. I started on a

222
00:11:37,080 --> 00:11:39,680
Speaker 2: trading desk. And one of the things you figure out

223
00:11:39,720 --> 00:11:43,880
Speaker 2: pretty early is you learn much more from your losers

224
00:11:43,920 --> 00:11:46,320
Speaker 2: than you do from your winners. Same thing in venture,

225
00:11:46,640 --> 00:11:47,439
Speaker 2: same thing in venture.

226
00:11:47,840 --> 00:11:50,319
Speaker 3: I think it's the same thing in life. Oh really, yeah,

227
00:11:50,320 --> 00:11:51,720
Speaker 3: I think it's true in lots of things.

228
00:11:51,720 --> 00:11:55,880
Speaker 2: It's stumbles and fails are more than it.

229
00:11:55,800 --> 00:11:58,959
Speaker 3: Could be jobs, it could be relationships, it could be

230
00:11:59,840 --> 00:12:02,959
Speaker 3: you know, even like you're you're like you know, right,

231
00:12:03,040 --> 00:12:06,320
Speaker 3: if you think about the world today, where your world today,

232
00:12:06,360 --> 00:12:08,920
Speaker 3: where there's a tendency for parents and I'll include myself

233
00:12:08,920 --> 00:12:11,720
Speaker 3: in this to be too involved, right, Oh, my son

234
00:12:11,800 --> 00:12:14,320
Speaker 3: got to be because he had a bad teacher. Like well, like,

235
00:12:14,400 --> 00:12:16,880
Speaker 3: guess what, we all have bad teachers and bad bosses

236
00:12:16,920 --> 00:12:21,040
Speaker 3: and bad roommates. And but you learn to adapt. And

237
00:12:21,320 --> 00:12:23,319
Speaker 3: I think sometimes you have to go through those things

238
00:12:23,559 --> 00:12:25,720
Speaker 3: and I think you learn from them. Right, bad relationships,

239
00:12:25,760 --> 00:12:27,760
Speaker 3: I think you learn something from So I think you

240
00:12:27,840 --> 00:12:30,520
Speaker 3: have to. If you take the mindset that you can

241
00:12:30,600 --> 00:12:32,839
Speaker 3: learn something in good times, you can learn something in

242
00:12:32,840 --> 00:12:34,400
Speaker 3: bad times. I'd argue you probably learn more in the

243
00:12:34,400 --> 00:12:37,959
Speaker 3: bad times. I think that's a it's a valuable mindset

244
00:12:37,960 --> 00:12:39,200
Speaker 3: to try to have. It's hard to have it when

245
00:12:39,200 --> 00:12:41,360
Speaker 3: you're in the bad time. You know.

246
00:12:41,559 --> 00:12:45,679
Speaker 2: You mentioned the role of serendipity earlier. Michael Mobison likes

247
00:12:45,720 --> 00:12:48,600
Speaker 2: to point out part of the reasons we may not

248
00:12:48,760 --> 00:12:51,960
Speaker 2: learn much from the good times is it's very hard

249
00:12:52,000 --> 00:12:55,480
Speaker 2: to distinguish between Hey, is this working out because I'm skillful,

250
00:12:55,520 --> 00:12:57,440
Speaker 2: or is this working out because I just got lucky.

251
00:12:57,559 --> 00:12:58,680
Speaker 3: Rising tide lifts all boats.

252
00:12:58,720 --> 00:12:59,559
Speaker 2: Yeah, that's right, and.

253
00:12:59,559 --> 00:13:01,360
Speaker 3: You don't know whether you're you're you know, you're on

254
00:13:01,440 --> 00:13:04,400
Speaker 3: a yacht or a boat with a hole. Uh and so,

255
00:13:04,600 --> 00:13:07,960
Speaker 3: but they all rise.

256
00:13:06,559 --> 00:13:08,040
Speaker 2: At least them, right.

257
00:13:08,160 --> 00:13:08,319
Speaker 3: Yeah.

258
00:13:08,360 --> 00:13:11,319
Speaker 2: So you mentioned you're on a ton of boards US

259
00:13:11,360 --> 00:13:17,160
Speaker 2: International develop Development Finance Corp, Council of Foreign Relations, Carnegie

260
00:13:17,280 --> 00:13:21,640
Speaker 2: Endowment for International Peace, and Yu Lang Gon. What's the

261
00:13:21,679 --> 00:13:23,199
Speaker 2: attraction to all these boards?

262
00:13:23,800 --> 00:13:26,160
Speaker 3: Well, those are the things I do, you know, outside

263
00:13:26,160 --> 00:13:28,719
Speaker 3: of the office. You know, I think I've always had

264
00:13:28,760 --> 00:13:34,480
Speaker 3: a belief that if you're successful, you kind of owe

265
00:13:34,520 --> 00:13:40,600
Speaker 3: it to give back. So that's one. Two is intellectual interest, right,

266
00:13:40,720 --> 00:13:43,319
Speaker 3: Like the things that I'm involved in are things I've

267
00:13:43,320 --> 00:13:46,040
Speaker 3: always been really interested in, and even in some of

268
00:13:46,080 --> 00:13:50,319
Speaker 3: these even in some of the I talked about how

269
00:13:50,320 --> 00:13:52,920
Speaker 3: I ended up going to Warden because of like who

270
00:13:52,960 --> 00:13:55,240
Speaker 3: my roommates were. Another story was when I was in

271
00:13:55,280 --> 00:13:57,959
Speaker 3: college that for my freshman year I went to go

272
00:13:58,040 --> 00:14:00,440
Speaker 3: right for the newspaper, the Daily Pennsylvania. It's a pretty

273
00:14:00,440 --> 00:14:03,800
Speaker 3: well known college newspaper, and my roommate at the time

274
00:14:04,640 --> 00:14:07,600
Speaker 3: went to go a volunteer for College Democrats. This is

275
00:14:07,640 --> 00:14:10,880
Speaker 3: my first semester of freshman year. Second semester freshman year,

276
00:14:11,320 --> 00:14:15,160
Speaker 3: I asked my roommate to come check out the DP,

277
00:14:15,520 --> 00:14:19,000
Speaker 3: the newspaper, and he came. He asked me to do

278
00:14:19,040 --> 00:14:21,840
Speaker 3: the same. And senior year, I was president of College

279
00:14:21,880 --> 00:14:23,800
Speaker 3: Democrats and he was editor in chief of the newspaper.

280
00:14:24,520 --> 00:14:27,720
Speaker 3: Neither would have happened without us kind of having totally

281
00:14:27,720 --> 00:14:32,000
Speaker 3: different interests. And he's now in journalism, right, So you know,

282
00:14:32,040 --> 00:14:34,760
Speaker 3: I just think that that there's a lot of these things,

283
00:14:34,800 --> 00:14:38,560
Speaker 3: and so those interests that interest policy related things as

284
00:14:38,600 --> 00:14:42,440
Speaker 3: interests I've had ever since college and kind of over time,

285
00:14:43,080 --> 00:14:45,280
Speaker 3: I've been able to engage in those things in a

286
00:14:45,320 --> 00:14:46,520
Speaker 3: more meaningful way.

287
00:14:46,800 --> 00:14:50,800
Speaker 2: Coming up, we continue our conversation with Devin Perek, Managing

288
00:14:50,840 --> 00:14:56,080
Speaker 2: director of Insight Partners, discussing how the firm developed its expertise.

289
00:14:56,600 --> 00:15:00,160
Speaker 2: I'm Barry Rittaults. You're listening to Masters in Business on

290
00:15:00,240 --> 00:15:03,920
Speaker 2: Bloomberg Radio. I'm Barry Redults. You're listening to Masters in

291
00:15:03,960 --> 00:15:08,000
Speaker 2: Business on Bloomberg Radio. My special guest today is Devin Prrect,

292
00:15:08,040 --> 00:15:11,880
Speaker 2: Managing director at Inside Partners, where he helped oversee over

293
00:15:11,920 --> 00:15:16,040
Speaker 2: one hundred and forty investments, several of which many of

294
00:15:16,080 --> 00:15:20,800
Speaker 2: which have had exits. So let's start chatting about Inside

295
00:15:20,800 --> 00:15:24,240
Speaker 2: Partners approach a little bit. You guys do everything from

296
00:15:24,360 --> 00:15:28,520
Speaker 2: software investing to AI. How do you differ from other

297
00:15:29,240 --> 00:15:30,720
Speaker 2: venture capitalists in the space.

298
00:15:31,480 --> 00:15:34,200
Speaker 3: So I think the approach that we take is we're

299
00:15:34,240 --> 00:15:37,440
Speaker 3: really software investors, but we're stage agnostic. And what does

300
00:15:37,480 --> 00:15:37,760
Speaker 3: that mean?

301
00:15:37,760 --> 00:15:40,040
Speaker 2: Meaning not just seed angel.

302
00:15:40,120 --> 00:15:43,200
Speaker 3: So probably the only stage that we don't really play

303
00:15:43,400 --> 00:15:47,440
Speaker 3: is seed in pre seed. We're really but we will

304
00:15:47,440 --> 00:15:50,560
Speaker 3: do everything from a Series A all the way to

305
00:15:50,600 --> 00:15:54,800
Speaker 3: a buyout. We have the capability to go across the continuum,

306
00:15:54,800 --> 00:15:56,920
Speaker 3: and I think that's important both ways, right, like if

307
00:15:56,960 --> 00:15:59,960
Speaker 3: you're a if you're a buyout investor. As an example,

308
00:16:00,560 --> 00:16:03,440
Speaker 3: particularly in a firm in a field like technology which

309
00:16:03,480 --> 00:16:06,720
Speaker 3: is changing quickly, not knowing what's going on at the

310
00:16:06,720 --> 00:16:10,120
Speaker 3: early stage what could be coming this disruptive is kind

311
00:16:10,120 --> 00:16:12,480
Speaker 3: of a risky way to be investing in more mature companies,

312
00:16:12,520 --> 00:16:16,600
Speaker 3: particularly in an AI world where that transformation is happening

313
00:16:16,640 --> 00:16:20,320
Speaker 3: a lot faster. And the flip side, you know, I

314
00:16:20,320 --> 00:16:24,280
Speaker 3: think on the early state side, understanding what does it

315
00:16:24,320 --> 00:16:26,640
Speaker 3: take for a company to actually be public, what does

316
00:16:26,640 --> 00:16:27,960
Speaker 3: it take for a company to actually be able to

317
00:16:28,000 --> 00:16:29,800
Speaker 3: raise the b's and c's and D rounds, and what

318
00:16:29,840 --> 00:16:32,120
Speaker 3: are the key metrics to make and having the network

319
00:16:32,120 --> 00:16:34,520
Speaker 3: and ecosystem to be able to help companies do that.

320
00:16:34,960 --> 00:16:38,120
Speaker 3: It's helpful to have your mid stage and growth stage

321
00:16:38,120 --> 00:16:40,280
Speaker 3: business too. So I think the ability for us to

322
00:16:40,280 --> 00:16:43,480
Speaker 3: be able to invest across that continuum really makes us

323
00:16:43,480 --> 00:16:46,120
Speaker 3: pretty unique relative to most other software investors out there.

324
00:16:47,120 --> 00:16:49,920
Speaker 3: The second thing is, you know the way we source,

325
00:16:50,080 --> 00:16:51,920
Speaker 3: the more firms are doing it now, which is you know,

326
00:16:51,960 --> 00:16:54,240
Speaker 3: we have over sixty people full time. That's all they

327
00:16:54,320 --> 00:16:57,520
Speaker 3: do is deal sourcing, and you know, think of it

328
00:16:57,520 --> 00:17:00,600
Speaker 3: as our outbound sales team, but it's a really smart

329
00:17:00,640 --> 00:17:02,840
Speaker 3: outpound sales team that are people who, when their success,

330
00:17:02,840 --> 00:17:07,040
Speaker 3: will end up being partners at Insight. And what we're

331
00:17:07,080 --> 00:17:10,439
Speaker 3: able to do is have tremendous market intelligence books. We're

332
00:17:10,480 --> 00:17:13,960
Speaker 3: talking to anywhere from twenty to thirty thousand companies a year, right,

333
00:17:14,320 --> 00:17:17,000
Speaker 3: obviously investing in a much more set of those. And

334
00:17:17,040 --> 00:17:19,879
Speaker 3: then the third thing is is are kind of a

335
00:17:19,960 --> 00:17:22,760
Speaker 3: value ad approach, right, because all investors like to say

336
00:17:22,800 --> 00:17:26,840
Speaker 3: they add value, it's hard to do. Very early on

337
00:17:27,720 --> 00:17:31,880
Speaker 3: in two thousand, created what we call Insight on Site.

338
00:17:31,920 --> 00:17:34,240
Speaker 3: And the reason it's called Insight on site is because

339
00:17:34,480 --> 00:17:37,199
Speaker 3: those team members are meant to be on site at

340
00:17:37,200 --> 00:17:40,040
Speaker 3: the company as opposed to in our office. Right, So

341
00:17:40,320 --> 00:17:42,960
Speaker 3: think of McKenzie or Baying. If you walk into the office,

342
00:17:43,000 --> 00:17:44,359
Speaker 3: you won't see a lot of those people in the

343
00:17:44,359 --> 00:17:46,280
Speaker 3: office because if they're doing their job, they're actually at

344
00:17:46,320 --> 00:17:50,040
Speaker 3: their clients. And our case, our clients are portfolio companies.

345
00:17:50,400 --> 00:17:53,040
Speaker 3: And what we've done is if you think about every

346
00:17:53,080 --> 00:17:57,520
Speaker 3: functional area of a software organization, whether that be sales, marketing, product,

347
00:17:57,560 --> 00:18:01,600
Speaker 3: customer introduction, strategy, and now a transformation, we have a

348
00:18:01,640 --> 00:18:05,359
Speaker 3: team for each one of those areas, and we have

349
00:18:05,400 --> 00:18:07,200
Speaker 3: a team for each one of those areas that's also

350
00:18:07,440 --> 00:18:09,600
Speaker 3: stage focused, right, So we have a team that works

351
00:18:09,600 --> 00:18:11,480
Speaker 3: with early stage companies, we have a team that works

352
00:18:11,480 --> 00:18:13,520
Speaker 3: with mid stage companies, we have a team that worked

353
00:18:13,520 --> 00:18:16,280
Speaker 3: with more mature companies, because the recruiting needs for a

354
00:18:16,280 --> 00:18:18,440
Speaker 3: company with five hundred million dollars revenue are very different

355
00:18:18,440 --> 00:18:20,280
Speaker 3: than the recruiting needs for a company with five million

356
00:18:20,320 --> 00:18:23,399
Speaker 3: dollars revenue. And that team is over one hundred and

357
00:18:23,440 --> 00:18:28,800
Speaker 3: twenty five people that's focused on really making sure that

358
00:18:28,880 --> 00:18:32,720
Speaker 3: the companies they're getting the benefit of not just anything

359
00:18:32,760 --> 00:18:36,359
Speaker 3: we know, best in class, thinking outside the firm, best

360
00:18:36,359 --> 00:18:41,440
Speaker 3: in class within the portfolio, and that those three things

361
00:18:41,440 --> 00:18:44,199
Speaker 3: together is really I think what allows us to have

362
00:18:44,880 --> 00:18:46,080
Speaker 3: a very successful strategy.

363
00:18:46,200 --> 00:18:50,400
Speaker 2: Huh. Really interesting. I was trying to conceptualize how Insight

364
00:18:51,200 --> 00:18:54,080
Speaker 2: is sort of a venture fund, sort of a pe shop.

365
00:18:55,000 --> 00:19:02,520
Speaker 2: Your explanation really explains why those titles and those descriptors

366
00:19:03,080 --> 00:19:06,719
Speaker 2: really only just describe part of what the thing is doing.

367
00:19:06,720 --> 00:19:09,439
Speaker 3: And I think things just overall, things are blurring, you

368
00:19:09,480 --> 00:19:10,960
Speaker 3: know in this world, Like you know, one of the

369
00:19:10,960 --> 00:19:14,560
Speaker 3: areas that we're very active in right now is something

370
00:19:14,600 --> 00:19:16,879
Speaker 3: that we call venture buyouts. And you'd say, well, okay,

371
00:19:17,000 --> 00:19:19,639
Speaker 3: like that seems like that's both and to some degree

372
00:19:19,640 --> 00:19:22,359
Speaker 3: it is. And what is it really, Well, what's the

373
00:19:22,359 --> 00:19:25,200
Speaker 3: biggest issue you hear right now in private equity? If

374
00:19:25,200 --> 00:19:26,959
Speaker 3: you were an interview at LP, they say, well, I'm

375
00:19:27,000 --> 00:19:29,080
Speaker 3: not getting enough money back, I don't have enough DPI

376
00:19:29,600 --> 00:19:32,360
Speaker 3: and so I'm over allocated. That's probably the number one

377
00:19:32,400 --> 00:19:35,520
Speaker 3: complaint that institutional investors have. Well, if you look in venture,

378
00:19:35,840 --> 00:19:39,399
Speaker 3: there's just a massive amount of funding of companies and

379
00:19:39,560 --> 00:19:41,720
Speaker 3: company creation and funding over the last so you have

380
00:19:41,760 --> 00:19:44,679
Speaker 3: thousands of companies out there. Many of them have not

381
00:19:44,800 --> 00:19:47,960
Speaker 3: reached a scale where they're ready to go public or

382
00:19:48,000 --> 00:19:50,320
Speaker 3: have a strategic really be focused on them right They

383
00:19:50,359 --> 00:19:53,600
Speaker 3: just don't have the scale yet. And what we're able

384
00:19:53,640 --> 00:19:55,280
Speaker 3: to do in those situations is find the ones that

385
00:19:55,320 --> 00:20:00,000
Speaker 3: are interesting companies, and we go to the shareholders and say, well,

386
00:20:00,040 --> 00:20:01,480
Speaker 3: buy seventy percent of the company. We will buy one

387
00:20:01,560 --> 00:20:03,560
Speaker 3: hundred percent of the company. You can either choose to

388
00:20:03,640 --> 00:20:05,560
Speaker 3: roll some of your investment if you think there's upside,

389
00:20:05,560 --> 00:20:07,879
Speaker 3: if not, we'll give you. We'll give you a return

390
00:20:08,520 --> 00:20:11,719
Speaker 3: whatever it is. And then we were able to take

391
00:20:11,760 --> 00:20:14,080
Speaker 3: control of these companies. What happens in a lot of

392
00:20:14,119 --> 00:20:17,760
Speaker 3: these venture companies is they have very diffuse cap tables. Right,

393
00:20:17,800 --> 00:20:20,920
Speaker 3: you have seven six, five different people, five different opinions.

394
00:20:20,960 --> 00:20:23,320
Speaker 3: It's actually hard for the CEO to get alignment with

395
00:20:23,400 --> 00:20:25,879
Speaker 3: their board on what the strategy should be. We can

396
00:20:25,920 --> 00:20:28,600
Speaker 3: create that alignment. So maybe he really want to he

397
00:20:28,800 --> 00:20:30,600
Speaker 3: or she wanted to execute an M and A strategy,

398
00:20:30,640 --> 00:20:32,600
Speaker 3: but only half the investors were willing to put up

399
00:20:32,600 --> 00:20:35,600
Speaker 3: more capital. We're able to in that case clean up

400
00:20:35,640 --> 00:20:39,640
Speaker 3: the cap table and then make whatever changes in strategy, team,

401
00:20:39,800 --> 00:20:42,159
Speaker 3: whatever it might be, that are necessary with a totally

402
00:20:42,200 --> 00:20:45,639
Speaker 3: aligned board. That's a strategy that touches both. It touches

403
00:20:45,720 --> 00:20:48,480
Speaker 3: some element to venture and it touches some element of

404
00:20:48,480 --> 00:20:49,160
Speaker 3: private equity.

405
00:20:50,000 --> 00:20:53,560
Speaker 2: Two of the people you work with, Ryan Ankle and

406
00:20:53,680 --> 00:20:58,280
Speaker 2: Richard Wells. As I'm doing my prep for this, anywhere

407
00:20:58,880 --> 00:21:01,399
Speaker 2: I searched for where as a service I seem to

408
00:21:01,840 --> 00:21:05,240
Speaker 2: come across Ryan Hinkel's name, tell us what it's like

409
00:21:05,600 --> 00:21:10,000
Speaker 2: working with those guys and working with the other founders

410
00:21:10,040 --> 00:21:12,119
Speaker 2: the two co founders, yeah, and others.

411
00:21:12,400 --> 00:21:15,679
Speaker 3: So you know, Mike Triplett and Jeff Lieberman, and we

412
00:21:15,760 --> 00:21:18,919
Speaker 3: have so many people who've kind of contributed to the

413
00:21:18,960 --> 00:21:24,480
Speaker 3: success of the firm. You know, Ryan actually joined Insight

414
00:21:24,560 --> 00:21:27,439
Speaker 3: as a summer intern right out of college, now on

415
00:21:27,520 --> 00:21:31,600
Speaker 3: the investment committee. Richard Wells joined us out of Harvard

416
00:21:31,640 --> 00:21:36,639
Speaker 3: Business School after a successful career TCD and some other firms,

417
00:21:37,000 --> 00:21:39,399
Speaker 3: and has been a huge driver of returns that some

418
00:21:39,600 --> 00:21:42,560
Speaker 3: great deals that have exited just this year. I think

419
00:21:42,600 --> 00:21:44,960
Speaker 3: that one of the things that we're most proud about

420
00:21:45,080 --> 00:21:47,560
Speaker 3: at Insight, and this is also I think very different

421
00:21:47,560 --> 00:21:50,560
Speaker 3: than a lot of firms out there, is that if

422
00:21:50,600 --> 00:21:53,920
Speaker 3: you look at the top four partners, the top six partners,

423
00:21:54,000 --> 00:21:56,679
Speaker 3: top eight partners, the vast majority of this people all

424
00:21:56,720 --> 00:22:00,200
Speaker 3: grew up with an Insight and we've really created a culture.

425
00:22:00,359 --> 00:22:03,399
Speaker 3: If you join Insight as an analyst, you can make

426
00:22:03,440 --> 00:22:07,399
Speaker 3: it to the top. And that's very different than a

427
00:22:07,400 --> 00:22:11,520
Speaker 3: lot of firms out there, and I think that's created

428
00:22:12,760 --> 00:22:16,159
Speaker 3: a very positive entrepreneurial culture where we give people a

429
00:22:16,160 --> 00:22:18,600
Speaker 3: lot of autonomy, We give people a lot of ability

430
00:22:18,640 --> 00:22:24,760
Speaker 3: to find new areas to invest in, and magic happens.

431
00:22:25,119 --> 00:22:28,080
Speaker 2: So let's talk a little bit about that magic. You've

432
00:22:28,119 --> 00:22:31,399
Speaker 2: made over one hundred and forty investments in various companies.

433
00:22:31,960 --> 00:22:34,920
Speaker 2: I'm assuming that you're doing this as part of a group,

434
00:22:34,960 --> 00:22:38,240
Speaker 2: as part of an investment committee. How does that work

435
00:22:38,880 --> 00:22:43,000
Speaker 2: if everybody has a slightly different expertise or focus. Take

436
00:22:43,080 --> 00:22:46,600
Speaker 2: us through the process of what companies get funded? How

437
00:22:46,600 --> 00:22:47,520
Speaker 2: does that process go?

438
00:22:48,400 --> 00:22:50,280
Speaker 3: Yeah, and look, first of all, that's the beauty of

439
00:22:50,840 --> 00:22:52,680
Speaker 3: I think our model too, which is why we might

440
00:22:52,760 --> 00:22:56,000
Speaker 3: all have slightly different focuses or areas. We're all just

441
00:22:56,080 --> 00:22:59,640
Speaker 3: investing in software. If you contrast that to firms where

442
00:22:59,680 --> 00:23:02,080
Speaker 3: somebody the biotech partner and someone as a software partner,

443
00:23:02,119 --> 00:23:06,120
Speaker 3: and someone is the industrial partner, that's much much harder

444
00:23:06,600 --> 00:23:11,000
Speaker 3: because you really don't have any sense of each other's businesses. Here,

445
00:23:11,480 --> 00:23:14,840
Speaker 3: the key metrics are common across all these things. There

446
00:23:14,920 --> 00:23:17,679
Speaker 3: might be some technical understanding around infrastructure, product or what

447
00:23:17,760 --> 00:23:20,560
Speaker 3: might be happening in a particular vertical that a partner

448
00:23:20,640 --> 00:23:22,720
Speaker 3: might have, but the key metrics are the same. And

449
00:23:22,760 --> 00:23:25,399
Speaker 3: so our process is that every deal, no matter how

450
00:23:25,440 --> 00:23:29,320
Speaker 3: small or how big, goes through the same investment committee process.

451
00:23:30,280 --> 00:23:33,119
Speaker 3: We meet once a week, kind of common like a

452
00:23:33,119 --> 00:23:36,639
Speaker 3: lot of other firms out there, and the team, whoever

453
00:23:36,680 --> 00:23:41,240
Speaker 3: the team is, presents the deal to the ICEE. We

454
00:23:41,320 --> 00:23:45,120
Speaker 3: debate it, we ask questions, we ask for follow up information,

455
00:23:45,720 --> 00:23:47,480
Speaker 3: and out of that either comes this is something we

456
00:23:47,520 --> 00:23:50,000
Speaker 3: want to pursue, we don't want to pursue. We only

457
00:23:50,000 --> 00:23:52,040
Speaker 3: want to pursue, but only at kind of this valuation.

458
00:23:53,080 --> 00:23:55,240
Speaker 3: And then the team then goes out and kind of

459
00:23:55,240 --> 00:23:57,600
Speaker 3: executes on that. And then if say we sign a

460
00:23:57,680 --> 00:24:02,080
Speaker 3: term sheet, they'll come back with a more detailed diligence

461
00:24:02,480 --> 00:24:06,199
Speaker 3: package that go through all the typical diligence things you'd assume.

462
00:24:07,320 --> 00:24:09,960
Speaker 3: That gets reviewed and discussed again. Sometimes there's follow up

463
00:24:10,040 --> 00:24:12,800
Speaker 3: questions that come out of that. Sometimes there's not has

464
00:24:12,800 --> 00:24:15,119
Speaker 3: to get through that second approval process, and then if

465
00:24:15,119 --> 00:24:17,720
Speaker 3: it gets through that approval process, then we would then fund.

466
00:24:18,200 --> 00:24:22,600
Speaker 3: But before anything even gets there, we have a number

467
00:24:22,600 --> 00:24:27,440
Speaker 3: of teams, their staff with these sourcing analysts, associates and

468
00:24:27,480 --> 00:24:30,800
Speaker 3: mid level people that really do the hard work before

469
00:24:30,800 --> 00:24:33,159
Speaker 3: something even gets the investment committe. So Ryan and Richard

470
00:24:33,359 --> 00:24:36,439
Speaker 3: both run a team, and you know, they each have

471
00:24:36,480 --> 00:24:38,760
Speaker 3: their slightly different focuses, but they each run a team

472
00:24:39,359 --> 00:24:41,480
Speaker 3: and they're meeting with their team on an even more

473
00:24:41,520 --> 00:24:45,720
Speaker 3: ongoing basis to kind of prioritize the deals that we

474
00:24:45,760 --> 00:24:48,040
Speaker 3: want to they want to pursue, and then if it

475
00:24:48,080 --> 00:24:50,720
Speaker 3: gets through their own team, then they would bring it

476
00:24:50,760 --> 00:24:52,600
Speaker 3: to the overall investment committee.

477
00:24:52,640 --> 00:24:57,920
Speaker 2: So I've heard some venture capitalists talk about valuation almost

478
00:24:57,960 --> 00:25:01,440
Speaker 2: as if it doesn't matter, which as the public market skuy,

479
00:25:02,000 --> 00:25:04,240
Speaker 2: I kind of shudder when I hear it. I think

480
00:25:04,240 --> 00:25:06,840
Speaker 2: it was Mark and Dreesen who once said, all right,

481
00:25:06,960 --> 00:25:10,760
Speaker 2: we were early state investors in Facebook. Had the valuation

482
00:25:10,920 --> 00:25:15,800
Speaker 2: been double, it practically wouldn't have affected our returns. My

483
00:25:15,880 --> 00:25:18,640
Speaker 2: immediate answer was, well, they would have been half if

484
00:25:18,680 --> 00:25:22,359
Speaker 2: the initial investment was double, but you know, one hundred

485
00:25:22,520 --> 00:25:26,480
Speaker 2: x point taken. How do you think about valuations, especially

486
00:25:27,160 --> 00:25:30,960
Speaker 2: when you're looking at early stage a ORB rounds, where

487
00:25:31,000 --> 00:25:35,120
Speaker 2: it kind of feels like total addressable market growth projections

488
00:25:35,560 --> 00:25:40,240
Speaker 2: I don't want to say fabricated, but they're squishy best estimates.

489
00:25:40,680 --> 00:25:44,000
Speaker 3: They're guesses. Yeah, okay, I mean look, but in this

490
00:25:44,200 --> 00:25:47,200
Speaker 3: early stage deal like it's a guess. I think the

491
00:25:47,240 --> 00:25:49,879
Speaker 3: person who wrote a checking Palenteer didn't know that Palenteer

492
00:25:49,920 --> 00:25:52,560
Speaker 3: was going to become what Palenteer became. But they saw

493
00:25:52,840 --> 00:25:56,240
Speaker 3: an entrepreneur with a vision with a potentially large market

494
00:25:56,320 --> 00:25:58,120
Speaker 3: and decided to make the bet that this person could

495
00:25:58,200 --> 00:26:01,720
Speaker 3: execute and turn it into that larger market. Right. Look,

496
00:26:01,760 --> 00:26:04,480
Speaker 3: I'm not gonna say that valuation doesn't matter, but I

497
00:26:04,520 --> 00:26:09,439
Speaker 3: think what you can say is that we have to.

498
00:26:09,880 --> 00:26:12,400
Speaker 3: It's a line that one of my partners uses that

499
00:26:12,640 --> 00:26:15,159
Speaker 3: we don't overpay companies just miss their numbers, which is

500
00:26:15,160 --> 00:26:17,159
Speaker 3: just I mean, it's said in jest, but really the

501
00:26:18,160 --> 00:26:22,240
Speaker 3: point is that generally, not always, but generally, the price

502
00:26:22,320 --> 00:26:24,959
Speaker 3: we paid if the company hit the numbers that we

503
00:26:25,040 --> 00:26:27,280
Speaker 3: thought they were going to hit, even if the price

504
00:26:27,320 --> 00:26:33,040
Speaker 3: seemed high on current revenue, this feels reasonable. So you

505
00:26:33,040 --> 00:26:37,280
Speaker 3: know companies that even recently AI companies that seemed expensive

506
00:26:37,280 --> 00:26:40,320
Speaker 3: six months ago don't look so expensive six months later,

507
00:26:40,600 --> 00:26:42,600
Speaker 3: just based on kind of how their run rate revenue

508
00:26:42,680 --> 00:26:45,320
Speaker 3: has changed. So the way we think about this is

509
00:26:45,359 --> 00:26:47,800
Speaker 3: we do care about valuation. We lose deals on valuation,

510
00:26:49,400 --> 00:26:51,760
Speaker 3: but that doesn't mean the deals that we win aren't

511
00:26:51,840 --> 00:26:54,960
Speaker 3: high absolute valuations. It's just how much conviction do we

512
00:26:55,040 --> 00:27:00,879
Speaker 3: have in the growth? Right, And this why these markets

513
00:27:00,880 --> 00:27:04,760
Speaker 3: are not efficient. You can have very high conviction on

514
00:27:05,000 --> 00:27:07,639
Speaker 3: XYZ company's growth, and I can have low conviction, and

515
00:27:08,480 --> 00:27:11,760
Speaker 3: one of us will likely be right. And if I

516
00:27:11,880 --> 00:27:13,960
Speaker 3: was right, and did it good for me? And if

517
00:27:14,040 --> 00:27:17,000
Speaker 3: I was writing, didn't do it? It just depends on

518
00:27:17,040 --> 00:27:19,200
Speaker 3: how you write. So I think the way we think

519
00:27:19,200 --> 00:27:25,000
Speaker 3: about it is we're all of these deals today. Certainly

520
00:27:25,040 --> 00:27:27,800
Speaker 3: AI deals on a multiple revenue basis are going to

521
00:27:27,800 --> 00:27:30,920
Speaker 3: feel expensive. Of course, you have to look at growth adjustment.

522
00:27:31,400 --> 00:27:33,600
Speaker 3: So even as a public market investor, you'd say that

523
00:27:33,640 --> 00:27:36,320
Speaker 3: a company that's growing at ten percent is going to

524
00:27:36,320 --> 00:27:38,159
Speaker 3: have a different valuation than a company that was going

525
00:27:38,200 --> 00:27:41,200
Speaker 3: to grow with thirty percent. Now, how do you even

526
00:27:41,240 --> 00:27:44,239
Speaker 3: start thinking about a company that's growing at one hundred percent? Right?

527
00:27:44,640 --> 00:27:46,439
Speaker 3: It's hard to think about and it's not hard to

528
00:27:46,440 --> 00:27:48,040
Speaker 3: think about it for a year. But if something you

529
00:27:48,040 --> 00:27:50,600
Speaker 3: can grow one hundred percent for three years and then

530
00:27:50,640 --> 00:27:53,040
Speaker 3: even if it de accelerates and compounds off three years

531
00:27:53,040 --> 00:27:56,000
Speaker 3: of one hundred percent growth, that's a pretty high multiple

532
00:27:56,080 --> 00:27:59,560
Speaker 3: that you can pay. So the way we really think

533
00:27:59,600 --> 00:28:02,760
Speaker 3: and talk about it is not valuation doesn't matter, but

534
00:28:02,800 --> 00:28:04,840
Speaker 3: we think about it in terms of if you're paying

535
00:28:04,840 --> 00:28:07,879
Speaker 3: a high multiple, then your conviction needs to be high

536
00:28:08,119 --> 00:28:10,160
Speaker 3: on the growth rate. Now, yeah, always gonna be right, right,

537
00:28:10,200 --> 00:28:12,919
Speaker 3: and that's part of the business. We just have to

538
00:28:12,920 --> 00:28:14,320
Speaker 3: be right enough.

539
00:28:15,200 --> 00:28:19,280
Speaker 2: And you mentioned software. The first thing that comes to

540
00:28:19,359 --> 00:28:23,760
Speaker 2: mind is Silicon Valley, San Francisco, the West Coast. Insight

541
00:28:23,840 --> 00:28:26,639
Speaker 2: Partners is New York City based. I know you have

542
00:28:26,720 --> 00:28:31,119
Speaker 2: offices around the world. Is there an advantage or disadvantage

543
00:28:31,160 --> 00:28:33,120
Speaker 2: to being based here in New York?

544
00:28:33,280 --> 00:28:35,520
Speaker 3: We think there's an advantage now, but maybe it's you know,

545
00:28:35,560 --> 00:28:38,360
Speaker 3: maybe we're just convincing ourselves that because we live here.

546
00:28:38,840 --> 00:28:42,920
Speaker 3: But you know, I think that not being in I mean,

547
00:28:42,960 --> 00:28:44,400
Speaker 3: I can tell you what the disadvantages are, but I

548
00:28:44,400 --> 00:28:46,880
Speaker 3: think the advantage is not being in the bubble. Like

549
00:28:46,880 --> 00:28:49,560
Speaker 3: we're not all having breakfast at Bucks and talking about

550
00:28:49,560 --> 00:28:53,320
Speaker 3: the same twenty deals. Now, maybe that's bad if those

551
00:28:53,440 --> 00:28:55,440
Speaker 3: twenty deals or the deals you have to be in,

552
00:28:55,920 --> 00:28:58,520
Speaker 3: but there's a tendency to have everybody kind of want

553
00:28:58,560 --> 00:29:00,840
Speaker 3: to do the same thing, and I think not being

554
00:29:00,880 --> 00:29:04,720
Speaker 3: in that every day, let's you step back more and

555
00:29:05,000 --> 00:29:07,000
Speaker 3: decide what you want to do as opposed to what

556
00:29:07,200 --> 00:29:10,000
Speaker 3: everybody else is doing. You know, I think there's a

557
00:29:10,000 --> 00:29:12,640
Speaker 3: disadvantage to, like, the strategic buyers are all out there,

558
00:29:13,080 --> 00:29:15,080
Speaker 3: you know, we're not in the same flow of those

559
00:29:15,480 --> 00:29:18,400
Speaker 3: companies sometimes as people might be seeing those people all

560
00:29:18,440 --> 00:29:22,560
Speaker 3: the time. But on balance, I think we've done okay

561
00:29:22,600 --> 00:29:24,760
Speaker 3: and we've managed to sell to a bunch of strategics

562
00:29:24,760 --> 00:29:28,360
Speaker 3: and so I don't think it's hurt us to be here.

563
00:29:28,640 --> 00:29:32,000
Speaker 2: And I mentioned you have offices around the world. You literally,

564
00:29:32,720 --> 00:29:35,520
Speaker 2: you know, it's not just New York, Silicon Valley, London,

565
00:29:35,640 --> 00:29:36,400
Speaker 2: you guys are.

566
00:29:36,480 --> 00:29:41,200
Speaker 3: Well, really it's really for presents. It's New York, it's

567
00:29:41,200 --> 00:29:44,040
Speaker 3: San Francisco, it's London, it's Israel. Those are really the

568
00:29:44,160 --> 00:29:44,840
Speaker 3: four places.

569
00:29:45,400 --> 00:29:48,240
Speaker 2: So how does being global help the firm? What do

570
00:29:48,280 --> 00:29:51,720
Speaker 2: you learn from having that sort of global perspective.

571
00:29:51,840 --> 00:29:56,600
Speaker 3: Well, I think we're pretty disciplined about how we've grown

572
00:29:56,640 --> 00:30:00,280
Speaker 3: and I'd be surprised if you see us have you know,

573
00:30:00,440 --> 00:30:03,120
Speaker 3: a lot more offices in five years. If you look

574
00:30:03,160 --> 00:30:07,000
Speaker 3: at take Israel with Jeff Morring really drove that strategy

575
00:30:07,040 --> 00:30:10,479
Speaker 3: for us to get into Israel. I think I might

576
00:30:10,560 --> 00:30:13,280
Speaker 3: get the numbers wrong slightly, but I think we had

577
00:30:14,080 --> 00:30:16,840
Speaker 3: sixty or seventy companies in the portfolio before we put

578
00:30:16,840 --> 00:30:19,760
Speaker 3: the first person on the ground, and at that point

579
00:30:20,040 --> 00:30:23,400
Speaker 3: there were six firms that had you know, five to

580
00:30:23,440 --> 00:30:26,520
Speaker 3: ten people there that had portfolios of five or ten, right,

581
00:30:26,800 --> 00:30:28,480
Speaker 3: Because I think the thing that we want to avoid

582
00:30:28,640 --> 00:30:31,000
Speaker 3: is if you put somebody on the ground before you

583
00:30:31,000 --> 00:30:33,880
Speaker 3: have a portfolio, then they need to rationalize their existence

584
00:30:33,920 --> 00:30:36,800
Speaker 3: by creating a portfolio. And maybe that's a good idea,

585
00:30:36,840 --> 00:30:39,680
Speaker 3: but maybe it's a horrible idea. And the bar by

586
00:30:39,720 --> 00:30:41,120
Speaker 3: having the bar that if you want to do a

587
00:30:41,160 --> 00:30:42,959
Speaker 3: deal in Israel or you want to do a deal

588
00:30:43,000 --> 00:30:44,640
Speaker 3: in India, you act to have to get out of

589
00:30:44,640 --> 00:30:48,040
Speaker 3: a plane and go ten thousand miles or fly you know,

590
00:30:48,080 --> 00:30:49,600
Speaker 3: twelve hours ought to be a really good deal. You

591
00:30:49,640 --> 00:30:51,680
Speaker 3: got to be really excited about, right, And so it

592
00:30:51,760 --> 00:30:54,840
Speaker 3: creates a natural like, no, I like this deal on

593
00:30:54,880 --> 00:30:58,280
Speaker 3: Long Island better, Okay, Well you spoke with your you know,

594
00:30:58,320 --> 00:31:01,040
Speaker 3: you spoke with and it probably should have a little

595
00:31:01,040 --> 00:31:03,800
Speaker 3: bit of a better return in order if it's that

596
00:31:03,880 --> 00:31:07,360
Speaker 3: far away, right. And so we've kind of weighted in

597
00:31:07,400 --> 00:31:11,240
Speaker 3: these places to have really conviction that that's going to

598
00:31:11,240 --> 00:31:13,160
Speaker 3: be a market because we have a lot of companies

599
00:31:13,160 --> 00:31:16,280
Speaker 3: in that market before we add presence there. So there's

600
00:31:16,320 --> 00:31:19,680
Speaker 3: plenty of places in the world where we have companies,

601
00:31:19,880 --> 00:31:22,719
Speaker 3: more companies than funds that are in that local market.

602
00:31:23,280 --> 00:31:28,120
Speaker 2: Really interesting coming up, we continue our conversation with Devin Perek,

603
00:31:28,680 --> 00:31:33,960
Speaker 2: managing director at Inside Partners, discussing the state of startup

604
00:31:34,000 --> 00:31:38,040
Speaker 2: investing today. I'm Barry Retults. You're listening to Masters in

605
00:31:38,120 --> 00:31:42,480
Speaker 2: Business on Bloomberg Radio. I'm Bury Results. You're listening to

606
00:31:42,560 --> 00:31:46,520
Speaker 2: Masters in Business on Bloomberg Radio. My extra special guest

607
00:31:46,560 --> 00:31:50,360
Speaker 2: this week is Devin Perrek, managing director at Inside Partners.

608
00:31:50,760 --> 00:31:54,920
Speaker 2: The firm runs over ninety billion dollars in venture capital

609
00:31:55,120 --> 00:31:59,280
Speaker 2: and various stages of private equity. So you guys have

610
00:31:59,320 --> 00:32:04,760
Speaker 2: a reputation for being software investors. Why have you focused

611
00:32:04,800 --> 00:32:09,000
Speaker 2: on that one space? And how many different sub sectors

612
00:32:09,560 --> 00:32:11,680
Speaker 2: are included under software?

613
00:32:12,240 --> 00:32:15,600
Speaker 3: O Look software, We've been doing software since nineteen ninety five,

614
00:32:16,760 --> 00:32:19,400
Speaker 3: and if you look since nineteen ninety five to today,

615
00:32:20,080 --> 00:32:22,760
Speaker 3: I think it. I might be wrong about this, and

616
00:32:22,800 --> 00:32:25,400
Speaker 3: maybe there's one other category for which this is true,

617
00:32:25,400 --> 00:32:27,440
Speaker 3: but I don't think since nineteen ninety five there's been

618
00:32:27,480 --> 00:32:31,920
Speaker 3: a single year where the software industry declined in aggregate revenue.

619
00:32:32,400 --> 00:32:37,320
Speaker 3: Through every recession, through every cycle, and as a percentage

620
00:32:37,520 --> 00:32:41,160
Speaker 3: of GDP, it just continues to increase. The software component

621
00:32:41,200 --> 00:32:43,440
Speaker 3: continues to increase. So you know, I think if you'd

622
00:32:43,440 --> 00:32:45,720
Speaker 3: asked a bunch of US ten years ago, we maybe thought,

623
00:32:45,760 --> 00:32:48,200
Speaker 3: oh maybe we're going to cap out on software, We're

624
00:32:48,200 --> 00:32:50,920
Speaker 3: going to have to go do something else. That really

625
00:32:50,960 --> 00:32:53,240
Speaker 3: hasn't been a problem. I don't foresee it being a problem.

626
00:32:53,440 --> 00:32:56,960
Speaker 3: So it's a massive industry who's had great growth, but

627
00:32:57,000 --> 00:33:00,000
Speaker 3: the projected growth over the next ten years is very strong.

628
00:33:00,520 --> 00:33:04,600
Speaker 3: So I think that we don't need a new category

629
00:33:04,640 --> 00:33:07,800
Speaker 3: to go after. We like this category. This category has

630
00:33:07,800 --> 00:33:10,360
Speaker 3: got amongst the highest growth rate of any category out there,

631
00:33:11,680 --> 00:33:14,520
Speaker 3: and it's really well downside protected too. If you were

632
00:33:14,520 --> 00:33:16,520
Speaker 3: to talk, if you had a lender on, they would

633
00:33:16,520 --> 00:33:18,400
Speaker 3: tell you that software is are lowest loss ratio.

634
00:33:19,160 --> 00:33:22,880
Speaker 2: What catches your attention first when you're looking at either

635
00:33:22,920 --> 00:33:28,160
Speaker 2: a startup in software or a reasonably developed company. Is

636
00:33:28,200 --> 00:33:31,240
Speaker 2: it the founders, Is it the technology? Is it a

637
00:33:31,240 --> 00:33:32,040
Speaker 2: combination of both?

638
00:33:32,120 --> 00:33:33,800
Speaker 3: Well, I think it depends on stage. Like you know,

639
00:33:33,840 --> 00:33:36,440
Speaker 3: I think in an early in an early stage company,

640
00:33:36,840 --> 00:33:39,920
Speaker 3: you know, founder and tech is really really important, right

641
00:33:40,000 --> 00:33:44,040
Speaker 3: and market Now, as you said earlier, you're making a

642
00:33:44,080 --> 00:33:47,080
Speaker 3: guess sometimes on a market at a very very early

643
00:33:47,120 --> 00:33:49,600
Speaker 3: at a series A stage. Now you're hopefully making an

644
00:33:49,720 --> 00:33:53,520
Speaker 3: educated guess based on lots of pattern recognition of companies

645
00:33:53,560 --> 00:33:55,960
Speaker 3: based on lots of data, and how big that market

646
00:33:56,040 --> 00:33:59,080
Speaker 3: is is measured in different ways. But it's a common

647
00:33:59,120 --> 00:34:02,440
Speaker 3: mistake to underestimated market, right. I mean, when we look back,

648
00:34:02,720 --> 00:34:04,440
Speaker 3: it's a little bit more of a consumer example. But

649
00:34:04,520 --> 00:34:07,520
Speaker 3: when we look back, I remember looking at Uber and

650
00:34:07,880 --> 00:34:10,319
Speaker 3: we convinced ourselves, how could you ever pay evaluation that's

651
00:34:10,360 --> 00:34:12,960
Speaker 3: higher than the total TAM, right, And the total TAM

652
00:34:13,000 --> 00:34:15,880
Speaker 3: was New York and San Francisco of black cars. Well,

653
00:34:16,000 --> 00:34:18,279
Speaker 3: it turns out that's not really the total TAM of

654
00:34:18,400 --> 00:34:21,719
Speaker 3: Uber today. I forget about food delivery and groceries. I

655
00:34:21,760 --> 00:34:24,480
Speaker 3: was just talking about cars. Yeah, it's just because they

656
00:34:24,480 --> 00:34:27,200
Speaker 3: went to uber X, and uber X totally changed the TAM.

657
00:34:27,600 --> 00:34:30,840
Speaker 3: So I think tams are not static, right, And I

658
00:34:30,880 --> 00:34:34,960
Speaker 3: think that's a very very hard thing to recognize that. Okay,

659
00:34:34,960 --> 00:34:37,120
Speaker 3: maybe they're going after a small problem today, but that

660
00:34:37,200 --> 00:34:39,480
Speaker 3: might be the trojan horse to get into it bigger

661
00:34:39,520 --> 00:34:44,040
Speaker 3: and bigger markets over time. Right. And that's where intuition

662
00:34:44,280 --> 00:34:46,799
Speaker 3: and pattern recognition and kind of seeing what a great

663
00:34:46,840 --> 00:34:48,560
Speaker 3: founder is, which is why I look early stage, I

664
00:34:48,560 --> 00:34:53,120
Speaker 3: think is much harder than growth stage or buyouts, where

665
00:34:53,160 --> 00:34:55,360
Speaker 3: you have lots of data and financial metrics that you

666
00:34:55,400 --> 00:34:56,399
Speaker 3: can kind of rely on.

667
00:34:57,040 --> 00:34:59,960
Speaker 2: I love the idea of the trojan horse. Somewhere along

668
00:35:00,080 --> 00:35:04,200
Speaker 2: in the lines, someone said you could practically ignore the

669
00:35:04,360 --> 00:35:08,560
Speaker 2: seed stage or early stage business model because there's always

670
00:35:08,560 --> 00:35:11,880
Speaker 2: going to be a pivot. The trojan horse are the founders?

671
00:35:12,400 --> 00:35:14,759
Speaker 2: How accurate is that that point of view?

672
00:35:14,840 --> 00:35:18,120
Speaker 3: Well, I mean I think it, like in everything, when

673
00:35:18,120 --> 00:35:20,080
Speaker 3: people make statements like that, they tend to focus on

674
00:35:20,120 --> 00:35:23,360
Speaker 3: the winners, right, so they'll look at XYZ company that

675
00:35:23,520 --> 00:35:28,360
Speaker 3: pivoted and say, oh, look, everybody can pivot. Well, everybody

676
00:35:28,400 --> 00:35:30,839
Speaker 3: doesn't pivot. And you do have a huge, very high

677
00:35:30,880 --> 00:35:35,120
Speaker 3: loss ratio at seed, early stage, and even series A,

678
00:35:35,280 --> 00:35:37,560
Speaker 3: and the strategy is different. Right, you have a power

679
00:35:37,640 --> 00:35:39,839
Speaker 3: law in series A. You have a paral law in seed,

680
00:35:39,840 --> 00:35:41,640
Speaker 3: and you have a para law even in buyout. It's

681
00:35:41,680 --> 00:35:45,280
Speaker 3: just a different power law. In buyout. You can basically

682
00:35:45,360 --> 00:35:47,600
Speaker 3: your power law is not a lot of losses. It's

683
00:35:47,880 --> 00:35:49,919
Speaker 3: you can have some one x's or one point five x's,

684
00:35:50,040 --> 00:35:51,800
Speaker 3: but you probably need a couple of four x or

685
00:35:51,800 --> 00:35:56,000
Speaker 3: five x's. In seed you probably need one hundred x

686
00:35:56,719 --> 00:35:58,480
Speaker 3: and you have a very high loss In Series A

687
00:35:58,800 --> 00:36:00,759
Speaker 3: you need a bunch of ten or fift twenty x's,

688
00:36:00,800 --> 00:36:04,400
Speaker 3: but you can still have losses. So depending on what stage,

689
00:36:04,680 --> 00:36:06,920
Speaker 3: there's this view that like parallel only applies to venture,

690
00:36:07,280 --> 00:36:10,880
Speaker 3: really applies to all stages. Is just what a loss is.

691
00:36:11,440 --> 00:36:13,720
Speaker 3: What a loss is is defined differently, right, A loss

692
00:36:13,719 --> 00:36:15,320
Speaker 3: in a buyout might be just a one x or

693
00:36:15,320 --> 00:36:17,360
Speaker 3: a point eight x. You can't really have a lot

694
00:36:17,400 --> 00:36:21,680
Speaker 3: of zero's in buyout, right, So I think the parallelaw

695
00:36:23,880 --> 00:36:25,920
Speaker 3: continuum is true across all these markets.

696
00:36:26,400 --> 00:36:30,879
Speaker 2: So AI is obviously a really big sector today. What

697
00:36:30,960 --> 00:36:35,000
Speaker 2: other sectors excite you the most or how much does

698
00:36:35,080 --> 00:36:40,040
Speaker 2: AI fit into just looking out there as game changing technologies.

699
00:36:40,800 --> 00:36:43,560
Speaker 3: Well, look, I think every firm, whether they're a venture

700
00:36:43,600 --> 00:36:47,080
Speaker 3: firm a buyout fund, doesn't really matter what type of

701
00:36:47,080 --> 00:36:49,239
Speaker 3: investing people are doing. I think it would be a

702
00:36:49,280 --> 00:36:53,360
Speaker 3: usual mistake to ignore AI. Right, even if you're not

703
00:36:53,440 --> 00:36:56,120
Speaker 3: investing quote unquote in an AI company, you better be

704
00:36:56,160 --> 00:36:58,600
Speaker 3: thinking about how AI is going to affect your business

705
00:36:58,600 --> 00:37:00,880
Speaker 3: model or how can it improve your business model? And

706
00:37:01,280 --> 00:37:04,640
Speaker 3: those who don't, even people in services businesses, like if

707
00:37:04,680 --> 00:37:07,160
Speaker 3: you're running law firm today, you're running an accounting firm today,

708
00:37:07,280 --> 00:37:10,319
Speaker 3: you really need to think about how is AI going

709
00:37:10,400 --> 00:37:13,520
Speaker 3: to affect my business. So of course in our case,

710
00:37:14,040 --> 00:37:16,359
Speaker 3: in our more mature companies, a lot of what we're

711
00:37:16,360 --> 00:37:19,800
Speaker 3: thinking about is how do we accelerate growth and revenue

712
00:37:19,840 --> 00:37:21,839
Speaker 3: through new AI products and how do we reduce cost

713
00:37:21,880 --> 00:37:25,800
Speaker 3: and increased margin through applying AI technology. In the companies

714
00:37:26,440 --> 00:37:29,480
Speaker 3: or earlier in mid stage companies are often AI native.

715
00:37:29,480 --> 00:37:31,960
Speaker 3: They're actually going after a new market, a legal vertical

716
00:37:32,000 --> 00:37:35,080
Speaker 3: or construction vertical with kind of a new AI focused product.

717
00:37:35,600 --> 00:37:37,720
Speaker 3: I mean, I think what's true is that every company

718
00:37:37,719 --> 00:37:40,120
Speaker 3: to some degree is an AI company. It doesn't mean

719
00:37:40,160 --> 00:37:44,400
Speaker 3: that they're nott Ai in their name, but every board

720
00:37:44,440 --> 00:37:48,840
Speaker 3: meeting that we go to at Insight, we're talking about AI.

721
00:37:48,960 --> 00:37:51,000
Speaker 3: And the irony is even the board meetings. I go

722
00:37:51,080 --> 00:37:55,440
Speaker 3: to an nyline own, we're talking about AI board meetings.

723
00:37:55,480 --> 00:37:58,399
Speaker 3: I go to a CFR, we're talking about AI. Because

724
00:37:58,400 --> 00:38:01,440
Speaker 3: if you're a medical if you're hospital today, you're thinking

725
00:38:01,440 --> 00:38:04,880
Speaker 3: about how do I have a better experience for my patient,

726
00:38:05,440 --> 00:38:08,799
Speaker 3: how do I think about increasing throughput? The average weight

727
00:38:08,880 --> 00:38:11,719
Speaker 3: for a neurologist today across the countries eight to nine

728
00:38:11,719 --> 00:38:15,239
Speaker 3: months to get an appointment. I imagine you're suffering from

729
00:38:15,400 --> 00:38:18,279
Speaker 3: like a real problem, and the doctor says, well, I'll

730
00:38:18,280 --> 00:38:21,880
Speaker 3: see you you know next year. Right, that's the average. Now,

731
00:38:22,239 --> 00:38:24,200
Speaker 3: what if we can kind of get AI to be

732
00:38:24,200 --> 00:38:27,600
Speaker 3: able to help assess these problems earlier, and all of

733
00:38:27,640 --> 00:38:30,960
Speaker 3: a sudden you take the data from the best institutions

734
00:38:31,640 --> 00:38:34,319
Speaker 3: and you make that available in an AI application. So

735
00:38:34,360 --> 00:38:38,200
Speaker 3: now people in Appalachia have access to the same level

736
00:38:38,239 --> 00:38:41,000
Speaker 3: of care as people who have the benefit of being

737
00:38:41,000 --> 00:38:44,000
Speaker 3: able to be near Mount Nyu link on our Mount Sinai. Right.

738
00:38:44,960 --> 00:38:48,760
Speaker 3: And so I'm going broader in my answer to your question,

739
00:38:49,560 --> 00:38:53,440
Speaker 3: which is I think AI is now affecting everything we do,

740
00:38:54,120 --> 00:38:56,359
Speaker 3: and so I think everything every company that we invest in,

741
00:38:56,560 --> 00:39:00,880
Speaker 3: we're talking about what's the impact or And then the

742
00:39:00,920 --> 00:39:04,680
Speaker 3: other thing we talked about is, like the other big

743
00:39:04,719 --> 00:39:08,319
Speaker 3: debate in kind of AI land is what will get

744
00:39:08,680 --> 00:39:11,480
Speaker 3: owned by the llms and what we'll get owned by

745
00:39:11,480 --> 00:39:15,399
Speaker 3: the application providers? Right? How much of this how much

746
00:39:15,440 --> 00:39:18,560
Speaker 3: of the value will accrue to the models they open

747
00:39:18,600 --> 00:39:20,600
Speaker 3: a eyes and anthropics, and how much of the value

748
00:39:20,640 --> 00:39:23,960
Speaker 3: will accrue to the applications. I don't think anybody can

749
00:39:24,280 --> 00:39:26,279
Speaker 3: answer that question. We don't know.

750
00:39:26,640 --> 00:39:31,000
Speaker 2: So I remember in the late nineties when the dot

751
00:39:31,040 --> 00:39:35,400
Speaker 2: com was just exploding, it kind of felt like a

752
00:39:35,480 --> 00:39:40,080
Speaker 2: handful of companies were sucking all the oxygen down the

753
00:39:40,160 --> 00:39:43,320
Speaker 2: room from everybody else. Is AI doing that? Like I

754
00:39:43,400 --> 00:39:48,520
Speaker 2: would imagine things like cybersecurity and fintech and other software

755
00:39:48,600 --> 00:39:54,000
Speaker 2: driven startups. Are they starving for capital or is there

756
00:39:54,120 --> 00:39:57,480
Speaker 2: just so much money out there that even AI can't

757
00:39:57,680 --> 00:39:58,479
Speaker 2: suck all the money?

758
00:39:59,160 --> 00:40:03,680
Speaker 3: There's a you know, there's a tremendous amount of capital

759
00:40:03,760 --> 00:40:06,480
Speaker 3: out there, and there are lots of companies outside of

760
00:40:06,520 --> 00:40:10,600
Speaker 3: the ones that everybody knows that are growing really really quickly,

761
00:40:11,440 --> 00:40:14,400
Speaker 3: often serving a vertical market. I mean, what's still true

762
00:40:15,520 --> 00:40:18,400
Speaker 3: is that if you have an application that is serving

763
00:40:18,440 --> 00:40:21,360
Speaker 3: a market where there's a lot of domain expertise or

764
00:40:21,480 --> 00:40:26,279
Speaker 3: data required, you still have a moat. And so I

765
00:40:26,280 --> 00:40:28,680
Speaker 3: think this, you know, because one of the big debates is, oh,

766
00:40:29,680 --> 00:40:32,400
Speaker 3: does AI mean that the software companies are going to

767
00:40:32,440 --> 00:40:35,000
Speaker 3: be dead? We don't believe that. What we do believe

768
00:40:35,040 --> 00:40:37,960
Speaker 3: is you have a very generic application that doesn't have

769
00:40:38,000 --> 00:40:42,000
Speaker 3: any vertical domain expertise, doesn't have any data mote, then

770
00:40:42,000 --> 00:40:45,200
Speaker 3: I think you're a significantly higher risk. But I think

771
00:40:45,239 --> 00:40:47,960
Speaker 3: there's lots of examples. We're seeing them, we're investing in them,

772
00:40:48,520 --> 00:40:53,960
Speaker 3: in specific healthcare applications and legal applications, construction industry where

773
00:40:54,280 --> 00:40:57,480
Speaker 3: you have companies that have true business process vertical expertise

774
00:40:57,560 --> 00:40:58,880
Speaker 3: couple with data modes.

775
00:41:00,040 --> 00:41:03,920
Speaker 2: Other spaces have you excited besides AI, which is obviously

776
00:41:03,960 --> 00:41:07,600
Speaker 2: going to have a giant, giant impact, what other areas

777
00:41:07,600 --> 00:41:08,520
Speaker 2: are really interesting?

778
00:41:08,600 --> 00:41:13,359
Speaker 3: I think, you know, cyber continues to be really important area.

779
00:41:13,360 --> 00:41:15,800
Speaker 3: And one could argue and we're just we have I

780
00:41:15,800 --> 00:41:17,759
Speaker 3: don't know if we might just be announcing it. So

781
00:41:17,760 --> 00:41:19,440
Speaker 3: I don't know whether we're now s yep, we're you know,

782
00:41:19,480 --> 00:41:22,240
Speaker 3: investing in something that's kind of related AI related security,

783
00:41:22,920 --> 00:41:25,279
Speaker 3: and so all every time you have these big new

784
00:41:25,680 --> 00:41:32,200
Speaker 3: platform shifts, you have infrastructure around that platform platform shift.

785
00:41:32,560 --> 00:41:35,719
Speaker 3: That's important, right, And so I think we're seeing a

786
00:41:35,719 --> 00:41:40,279
Speaker 3: lot of next generation infrastructure investments, cyber investments. There's a

787
00:41:40,320 --> 00:41:42,879
Speaker 3: lot of markets that we're seeing and I think what's

788
00:41:42,880 --> 00:41:46,120
Speaker 3: happening right now is if i'd answer this question, you know,

789
00:41:46,520 --> 00:41:49,360
Speaker 3: a year ago, I said, well, we're doing vertical applications,

790
00:41:49,400 --> 00:41:52,160
Speaker 3: we're doing these types of horizontal applications, and now it's

791
00:41:52,200 --> 00:41:55,760
Speaker 3: all getting bucketed into AI because it has an AI angle.

792
00:41:56,719 --> 00:42:01,200
Speaker 3: But there are subcategories you know, within within AI, there's

793
00:42:01,200 --> 00:42:06,000
Speaker 3: not like just one AI company out there. There's obviously

794
00:42:06,160 --> 00:42:08,520
Speaker 3: lots of companies, and it's just becoming that AI is

795
00:42:08,560 --> 00:42:12,399
Speaker 3: becoming almost like an operating system that all of these

796
00:42:12,440 --> 00:42:14,120
Speaker 3: new vertical applications are being built on.

797
00:42:14,840 --> 00:42:17,160
Speaker 2: I haven't heard you mentioned crypto. Is that a space

798
00:42:17,239 --> 00:42:20,400
Speaker 2: you guys explore or is that too specific?

799
00:42:20,840 --> 00:42:26,359
Speaker 3: We put in the past tense we explored and you know, decided, well,

800
00:42:26,440 --> 00:42:29,399
Speaker 3: one we didn't do that well with it. And two

801
00:42:30,600 --> 00:42:36,480
Speaker 3: the fundamental problem that like we've seen in it is

802
00:42:36,560 --> 00:42:38,400
Speaker 3: that when these companies were come in, we met with

803
00:42:38,480 --> 00:42:41,520
Speaker 3: hundreds of companies in crypto. When these companies would come

804
00:42:41,560 --> 00:42:43,799
Speaker 3: in and you'd say, okay, like tell me what it

805
00:42:43,880 --> 00:42:47,600
Speaker 3: is about your application that makes it better than if

806
00:42:47,640 --> 00:42:50,040
Speaker 3: it were just in a relational database, like a very

807
00:42:50,040 --> 00:42:53,160
Speaker 3: simple question. You'd kind of get back like all kinds

808
00:42:53,200 --> 00:42:55,799
Speaker 3: of technical answers and white papers, and I'm like, right,

809
00:42:55,840 --> 00:42:59,319
Speaker 3: but like, just as a user, what problem does this

810
00:42:59,400 --> 00:43:02,440
Speaker 3: solve that I can then I can't solve it right Generally,

811
00:43:03,040 --> 00:43:06,640
Speaker 3: we didn't just get a really good answer. Now I

812
00:43:06,719 --> 00:43:09,160
Speaker 3: don't wanna I don't want to say that there's not

813
00:43:09,200 --> 00:43:12,080
Speaker 3: gonna be any crypto applications that are going to be successful.

814
00:43:12,239 --> 00:43:14,400
Speaker 3: I'm sure there will be. I mean, obviously, if you

815
00:43:14,480 --> 00:43:17,160
Speaker 3: talk to COO Visa, you talk to the CEO MasterCard,

816
00:43:17,200 --> 00:43:19,280
Speaker 3: they'll talk to about stable coins and the impact stable

817
00:43:19,280 --> 00:43:23,400
Speaker 3: coins could have. Obviously an administration that's very pro crypto,

818
00:43:23,719 --> 00:43:26,720
Speaker 3: pro crypto regulatory, so I think you're gonna see money

819
00:43:26,719 --> 00:43:30,480
Speaker 3: being made in that category. We just I mean, I

820
00:43:30,520 --> 00:43:33,759
Speaker 3: guess we're used to trying to find applications where we see,

821
00:43:33,760 --> 00:43:37,400
Speaker 3: here's a clear business use, here's a clear payment for

822
00:43:37,440 --> 00:43:40,520
Speaker 3: that business use, and here's how they can scale. We

823
00:43:40,600 --> 00:43:45,400
Speaker 3: haven't really been able to decrypt that in crypto, but

824
00:43:45,480 --> 00:43:49,160
Speaker 3: I'm sure there are others out there who understand that better,

825
00:43:49,200 --> 00:43:50,759
Speaker 3: and I'm sure there'll be some winners, but we've just

826
00:43:50,840 --> 00:43:52,719
Speaker 3: chosen to not focus on it.

827
00:43:52,920 --> 00:43:55,680
Speaker 2: So let's talk about some winners. I see a run

828
00:43:55,760 --> 00:44:00,160
Speaker 2: of exits that Insight Partners is associated with. You're an

829
00:44:00,200 --> 00:44:04,960
Speaker 2: early investor in Twitter, which iPod, Buddy Media acquired by

830
00:44:05,040 --> 00:44:10,840
Speaker 2: Salesforce Investment, sold to Nasdaq, Ali Baba, JD dot Com,

831
00:44:10,960 --> 00:44:14,879
Speaker 2: Duck Creek, Appress, The list goes on and on. Tell

832
00:44:14,960 --> 00:44:18,640
Speaker 2: us about some of these exits. You guys really have

833
00:44:18,840 --> 00:44:21,080
Speaker 2: put together quite an impressive list.

834
00:44:21,239 --> 00:44:24,400
Speaker 3: Well, well, I'd rather talk about our exits from this year.

835
00:44:24,400 --> 00:44:26,080
Speaker 2: Okay, so keep it current.

836
00:44:26,160 --> 00:44:29,560
Speaker 3: Yeah, which, you know, so my partner Jeff horn letter

837
00:44:29,600 --> 00:44:33,520
Speaker 3: investment in Whiz, which sold to you know, Google, Well,

838
00:44:33,560 --> 00:44:35,680
Speaker 3: I should say I signed a definitive agreement to sell

839
00:44:35,760 --> 00:44:39,800
Speaker 3: to Google hasn't closed yet for thirty two billion dollars.

840
00:44:40,000 --> 00:44:44,920
Speaker 3: The largest venture backed acquisition by a strategic My partner

841
00:44:45,080 --> 00:44:50,120
Speaker 3: Richard Wells led an investment in a company called Central Reach,

842
00:44:51,120 --> 00:44:54,279
Speaker 3: which does software for autism clinics, and sold that for

843
00:44:54,400 --> 00:44:57,719
Speaker 3: just under two billion dollars to Roper Industries. And then

844
00:44:57,760 --> 00:45:01,160
Speaker 3: my partner Jeff Lieberman letter deal called dot Maddox, which

845
00:45:01,160 --> 00:45:04,760
Speaker 3: we sold the siemens for just over five billion dollars.

846
00:45:05,560 --> 00:45:08,000
Speaker 3: And you know, the interesting thing about both. The interesting

847
00:45:08,000 --> 00:45:11,640
Speaker 3: thing about those deals is one's a traditional or least,

848
00:45:11,760 --> 00:45:14,120
Speaker 3: so we did Whiz as a series I think B,

849
00:45:15,440 --> 00:45:18,560
Speaker 3: and then kind of continue to participate along the way. Both,

850
00:45:19,400 --> 00:45:27,200
Speaker 3: you know, Central Reach and Dotmatics were venture biots, but

851
00:45:27,480 --> 00:45:31,719
Speaker 3: the multiples on money were like venture multiples of money really, right,

852
00:45:31,800 --> 00:45:36,560
Speaker 3: So venture returns with buyout dollar deployment, it's a good combination. Yeah,

853
00:45:36,640 --> 00:45:38,920
Speaker 3: and so, and I think we've got We've got more

854
00:45:38,960 --> 00:45:41,560
Speaker 3: coming over the course of this year, so I think

855
00:45:41,560 --> 00:45:43,200
Speaker 3: we've had a really strong year. One of the things

856
00:45:43,200 --> 00:45:47,080
Speaker 3: that I think contributed to that is I think historically

857
00:45:47,840 --> 00:45:52,000
Speaker 3: we were not great on liquidity, and by that I

858
00:45:52,080 --> 00:45:54,640
Speaker 3: mean not that we didn't have good companies. We just

859
00:45:54,680 --> 00:45:57,440
Speaker 3: didn't focus a lot on liquidity. And as big LPs

860
00:45:57,480 --> 00:46:00,160
Speaker 3: and our funds, we're generally the gp is top. I

861
00:46:00,400 --> 00:46:02,640
Speaker 3: were close to Tide as the largest investor in the fund,

862
00:46:02,719 --> 00:46:06,160
Speaker 3: so we're pretty aligned with our investors. We kind of

863
00:46:06,160 --> 00:46:09,280
Speaker 3: were focused on multiple money and that's so focused on IRR.

864
00:46:09,320 --> 00:46:11,120
Speaker 3: I mean, within reason, we're focused on ir but it

865
00:46:11,160 --> 00:46:14,200
Speaker 3: wasn't what we and I think over the last ten

866
00:46:14,280 --> 00:46:16,759
Speaker 3: years fifteen years, you've seen a massive transition and the

867
00:46:16,760 --> 00:46:20,760
Speaker 3: institutional p base of a shift from WAKE to IRR.

868
00:46:21,080 --> 00:46:24,080
Speaker 2: So I want to stay there because it's kind of fascinating.

869
00:46:24,160 --> 00:46:26,920
Speaker 2: I had no idea because I don't play all that

870
00:46:27,040 --> 00:46:30,120
Speaker 2: much in the venture space or the private equity space,

871
00:46:30,239 --> 00:46:34,520
Speaker 2: that Hey, we have long standing liabilities that we eventually

872
00:46:34,520 --> 00:46:36,960
Speaker 2: want to meet and even though we knew this was

873
00:46:37,040 --> 00:46:40,360
Speaker 2: locked up for depending on the fund five seven, nine years,

874
00:46:40,840 --> 00:46:43,839
Speaker 2: we'd like to see some exits sooner than later. When

875
00:46:43,880 --> 00:46:45,960
Speaker 2: did this start happening? And what do you think is

876
00:46:46,040 --> 00:46:46,600
Speaker 2: driving this?

877
00:46:47,120 --> 00:46:49,320
Speaker 3: Well, I mean it's probably been happening for years, but

878
00:46:49,360 --> 00:46:50,719
Speaker 3: it's accelerated in the.

879
00:46:50,760 --> 00:46:52,600
Speaker 2: Last post pandemic.

880
00:46:52,680 --> 00:46:55,280
Speaker 3: Yeah, post two three years and we had the correction

881
00:46:55,360 --> 00:46:58,759
Speaker 3: and people felt over allocated and twenty one had this

882
00:46:58,880 --> 00:47:02,080
Speaker 3: huge peak investing and so now there's this big bubble

883
00:47:02,080 --> 00:47:04,799
Speaker 3: of investing but not enough liquidity coming back relative to

884
00:47:04,840 --> 00:47:07,560
Speaker 3: the deployment. In the last two to three years, it's

885
00:47:07,560 --> 00:47:12,840
Speaker 3: accelerated and so we you know, we we took that feedback. Seriously,

886
00:47:13,080 --> 00:47:15,000
Speaker 3: I don't think we're the only ones who got that feedback.

887
00:47:15,400 --> 00:47:19,480
Speaker 3: But we actually put a liquidity committee together. It's from

888
00:47:19,480 --> 00:47:23,839
Speaker 3: people across the firm, both both our financial function, our

889
00:47:23,840 --> 00:47:27,360
Speaker 3: investment team or operating team, and we now have quarterly

890
00:47:27,400 --> 00:47:30,799
Speaker 3: liquidity meetings where we target companies for liquidity. We kind

891
00:47:30,800 --> 00:47:33,160
Speaker 3: of talk about what the IR is from here and

892
00:47:33,200 --> 00:47:35,680
Speaker 3: I think the and that was set set up about

893
00:47:35,680 --> 00:47:38,759
Speaker 3: eighteen months ago. But I think a result of that

894
00:47:39,880 --> 00:47:41,520
Speaker 3: is you know, I don't want to say it's a

895
00:47:41,560 --> 00:47:44,200
Speaker 3: direct result because you can't press a button, but a

896
00:47:44,280 --> 00:47:47,680
Speaker 3: focus on it, everyone talking about it, everybody feeling like

897
00:47:47,719 --> 00:47:50,399
Speaker 3: they have accountability to that process. I think it's led

898
00:47:50,440 --> 00:47:52,920
Speaker 3: to a lot more liquidity over the last So I

899
00:47:52,920 --> 00:47:56,280
Speaker 3: think we've gotten an ROI on really putting focus against

900
00:47:56,280 --> 00:47:59,040
Speaker 3: it really and I think you know, our LPs gave

901
00:47:59,120 --> 00:48:02,680
Speaker 3: us feedback on it. You know, I think we look,

902
00:48:02,760 --> 00:48:04,960
Speaker 3: we thought about it. We said, yeah, it's fair feedback.

903
00:48:05,360 --> 00:48:07,040
Speaker 3: Let's make a change, let's make an adjustment.

904
00:48:07,239 --> 00:48:09,719
Speaker 2: So so you mentioned the boom in twenty one and

905
00:48:09,760 --> 00:48:13,000
Speaker 2: then the pullback in twenty two. You start in the

906
00:48:13,000 --> 00:48:17,600
Speaker 2: mid nineties, You've lived through numerous boom and bus cycles.

907
00:48:17,640 --> 00:48:21,080
Speaker 2: What what's your big takeaway from from those experiences.

908
00:48:21,200 --> 00:48:25,120
Speaker 3: Well, I think when you're living in the depth of it,

909
00:48:25,120 --> 00:48:28,319
Speaker 3: it feels like it's never going to end, and it

910
00:48:28,400 --> 00:48:32,279
Speaker 3: always ends and this too shell pass, this too shall pass. Uh,

911
00:48:32,520 --> 00:48:35,799
Speaker 3: And I think that's it's a hard it's a hard

912
00:48:35,880 --> 00:48:38,920
Speaker 3: lesson because it's listen, the thing that's still the hardest

913
00:48:38,960 --> 00:48:43,120
Speaker 3: to do is, you know Warren Buffett's investment. Everybody's scared,

914
00:48:43,280 --> 00:48:46,160
Speaker 3: and you you get yourself ready and you've got your

915
00:48:46,400 --> 00:48:48,759
Speaker 3: you know, I'm going to put move X dollars to

916
00:48:48,800 --> 00:48:51,560
Speaker 3: the Vanguard Index fund and then you don't do it.

917
00:48:51,600 --> 00:48:53,440
Speaker 3: Why because you don't think it's ever going to pass, right,

918
00:48:53,440 --> 00:48:54,759
Speaker 3: because if you thought you were going to pass, of

919
00:48:54,800 --> 00:48:59,360
Speaker 3: course you do it. And human psychology is really really

920
00:48:59,400 --> 00:49:02,680
Speaker 3: hard to change. And I'm including myself in that definition.

921
00:49:02,880 --> 00:49:06,240
Speaker 2: It's so difficult to fight the crowd when everybody's running

922
00:49:06,239 --> 00:49:09,439
Speaker 2: for the exit. You have to be built a certain way.

923
00:49:09,520 --> 00:49:11,560
Speaker 3: I still remember when the market two thousand and eight,

924
00:49:11,680 --> 00:49:15,480
Speaker 3: the market was, you know, the really crashing, and I

925
00:49:15,480 --> 00:49:19,239
Speaker 3: remember having a conversation with somebody who know the market,

926
00:49:19,280 --> 00:49:22,040
Speaker 3: is really well well known person. He said, yeah, gee,

927
00:49:22,160 --> 00:49:24,520
Speaker 3: can't roll their commercial paper right, And I was like,

928
00:49:24,600 --> 00:49:25,319
Speaker 3: holy crap.

929
00:49:25,400 --> 00:49:28,799
Speaker 2: I was after Aig and Lehman and I remember.

930
00:49:28,600 --> 00:49:31,239
Speaker 3: Eight and I remember it was like a Friday and

931
00:49:31,280 --> 00:49:33,160
Speaker 3: it was a long week, and I call my wife

932
00:49:33,200 --> 00:49:35,160
Speaker 3: and I'm like, you know, honey, let's just like go

933
00:49:35,239 --> 00:49:36,880
Speaker 3: out for dinner. And she was like, let's stay in

934
00:49:36,920 --> 00:49:38,839
Speaker 3: and were having these like five minutes back and forth.

935
00:49:38,880 --> 00:49:41,719
Speaker 3: I'm like, like, why are we talking about this? And

936
00:49:41,760 --> 00:49:43,479
Speaker 3: she was like, well, I thought maybe she'd save some money.

937
00:49:43,480 --> 00:49:45,880
Speaker 3: I'm like, it's not that bad, Like we could go

938
00:49:45,880 --> 00:49:46,480
Speaker 3: back to dinner.

939
00:49:46,920 --> 00:49:51,160
Speaker 2: Well. But Ben Bernanke, former Chairman of the Federal Reserve,

940
00:49:51,239 --> 00:49:54,480
Speaker 2: famously sent his wife out to the ATM to get

941
00:49:54,520 --> 00:49:59,040
Speaker 2: cash in case the system went bad, if he was terrified.

942
00:49:59,360 --> 00:50:03,520
Speaker 2: It just showed you human nature is we're always going

943
00:50:03,560 --> 00:50:03,719
Speaker 2: to be.

944
00:50:03,960 --> 00:50:06,040
Speaker 3: I think that the thing. So I don't know that

945
00:50:06,080 --> 00:50:08,360
Speaker 3: you could ever teach people to like go move money.

946
00:50:08,880 --> 00:50:13,239
Speaker 3: But I think the hard part is really besides maybe

947
00:50:13,239 --> 00:50:15,160
Speaker 3: not making as much money as you could make, the

948
00:50:15,160 --> 00:50:17,799
Speaker 3: heart part is just feeling like it's never gonna end right.

949
00:50:18,239 --> 00:50:20,600
Speaker 3: And now, having been through this as many times as

950
00:50:20,800 --> 00:50:23,480
Speaker 3: you know I have and my partners have, you know,

951
00:50:23,520 --> 00:50:27,200
Speaker 3: I think it's easier to recognize that no, there's there's

952
00:50:27,280 --> 00:50:28,240
Speaker 3: light at the end of the tunnel.

953
00:50:29,440 --> 00:50:32,440
Speaker 2: Makes perfect sense. Let me throw you a curveball question

954
00:50:32,800 --> 00:50:36,520
Speaker 2: before we jump to our favorite questions. So we talked

955
00:50:36,520 --> 00:50:40,000
Speaker 2: about AI, and we've talked about cycles. What do you

956
00:50:40,040 --> 00:50:45,160
Speaker 2: think investors in this space, either technology, your startup or

957
00:50:45,400 --> 00:50:48,359
Speaker 2: M and A or ventures are not really talking about

958
00:50:48,440 --> 00:50:51,600
Speaker 2: or thinking about, but perhaps should be what what's the

959
00:50:51,640 --> 00:50:58,239
Speaker 2: most important topic asset geography policy that's getting overlooked, but

960
00:50:58,360 --> 00:51:00,080
Speaker 2: shouldn't I think people.

961
00:51:00,080 --> 00:51:02,120
Speaker 3: Still as much as we talk about it, I don't

962
00:51:02,120 --> 00:51:05,880
Speaker 3: think people. I think people still underpriced what happens if

963
00:51:05,920 --> 00:51:09,680
Speaker 3: there's a real cyber risk. We think about cyber as, oh,

964
00:51:09,800 --> 00:51:12,520
Speaker 3: my city bank account got hacked. We think about cyber

965
00:51:12,560 --> 00:51:14,799
Speaker 3: as you know, I got a phishing email work. By

966
00:51:14,800 --> 00:51:16,319
Speaker 3: the way, all those things are bad and bad things

967
00:51:16,320 --> 00:51:18,319
Speaker 3: can happen out of them, and you know, everyone has

968
00:51:18,360 --> 00:51:19,680
Speaker 3: probably dealt with some version.

969
00:51:19,760 --> 00:51:22,200
Speaker 2: I mean, I'm more concerned about someone taking control of

970
00:51:22,239 --> 00:51:23,520
Speaker 2: the electrical grid.

971
00:51:24,000 --> 00:51:26,439
Speaker 3: I think we still I mean, I think I don't

972
00:51:26,440 --> 00:51:28,160
Speaker 3: want to make it sound like the government doesn't think

973
00:51:28,200 --> 00:51:30,799
Speaker 3: about it. I think they do, but I think it's

974
00:51:30,880 --> 00:51:35,360
Speaker 3: just people. I don't think we realize like the level

975
00:51:35,400 --> 00:51:39,000
Speaker 3: of risk if physical infrastructure were kind of taken over,

976
00:51:39,280 --> 00:51:41,239
Speaker 3: and there have been examples of it.

977
00:51:41,200 --> 00:51:44,680
Speaker 2: Happened, like physical infrastructure like the electrical grid is something

978
00:51:44,719 --> 00:51:46,400
Speaker 2: most specific water.

979
00:51:46,400 --> 00:51:51,879
Speaker 3: Water purification plants, electrical plants, I mean, hospital systems going down.

980
00:51:52,080 --> 00:51:53,920
Speaker 2: Right, Well, we've seen we've seen in a lot of

981
00:51:53,960 --> 00:51:54,960
Speaker 2: ransomware with that.

982
00:51:55,040 --> 00:51:58,960
Speaker 3: We've seen that in individual institutions, right, We've not seen

983
00:51:59,000 --> 00:52:04,000
Speaker 3: it system systemically, right, And you know, that's a that's

984
00:52:04,040 --> 00:52:07,640
Speaker 3: a pretty that's a that's a pretty pretty terrified that's

985
00:52:07,640 --> 00:52:10,400
Speaker 3: a pretty terrifying risk. Now I'm not saying I mean,

986
00:52:10,400 --> 00:52:12,719
Speaker 3: I'm answering your question as to something that I worry

987
00:52:12,760 --> 00:52:15,400
Speaker 3: about that maybe we don't worry about enough. I'm not

988
00:52:15,440 --> 00:52:17,360
Speaker 3: necessarily sure. It's like, I'm not how to price that

989
00:52:17,400 --> 00:52:20,839
Speaker 3: into the market. It's not really a market answer. It's

990
00:52:20,920 --> 00:52:25,600
Speaker 3: just something that I think, like it's it's an asymmetric risk.

991
00:52:25,719 --> 00:52:27,440
Speaker 2: No, that's the right. So I'm not looking for a

992
00:52:27,520 --> 00:52:32,080
Speaker 2: market you know, asymmetrical dollar. Bet You're raising an issue

993
00:52:32,120 --> 00:52:34,040
Speaker 2: that perhaps we're not paying enough attention to.

994
00:52:34,520 --> 00:52:37,640
Speaker 3: I think, as the average the average investor of the

995
00:52:37,680 --> 00:52:40,680
Speaker 3: average person, I don't think I think that risk is

996
00:52:40,680 --> 00:52:41,799
Speaker 3: way bigger than we think it is.

997
00:52:42,000 --> 00:52:42,160
Speaker 1: Huh.

998
00:52:42,239 --> 00:52:43,680
Speaker 3: And if you talk to people in government, they would

999
00:52:43,680 --> 00:52:44,920
Speaker 3: probably they would agree with that.

1000
00:52:45,480 --> 00:52:47,680
Speaker 2: All right, so we only have a certain amount of time.

1001
00:52:47,760 --> 00:52:50,600
Speaker 2: Let's let's jump to our favorite questions we ask all

1002
00:52:50,640 --> 00:52:53,799
Speaker 2: of our guests, starting with who were your mentors who

1003
00:52:53,880 --> 00:52:55,560
Speaker 2: helped shape your career?

1004
00:52:56,880 --> 00:52:59,200
Speaker 3: Well, you know, I think to a few different mentors.

1005
00:52:59,760 --> 00:53:03,280
Speaker 3: I I was in elementary school a pretty indifferent student,

1006
00:53:03,960 --> 00:53:06,440
Speaker 3: to the point where, you know, I had Indian parents

1007
00:53:06,480 --> 00:53:08,239
Speaker 3: who are like, you're supposed to have good grades, and

1008
00:53:09,120 --> 00:53:11,880
Speaker 3: you know, I didn't have bad grades, but like, I

1009
00:53:11,920 --> 00:53:13,960
Speaker 3: was kind of an indifferent student, didn't really focus a

1010
00:53:13,960 --> 00:53:15,600
Speaker 3: lot on school. I had a teacher in third grade

1011
00:53:15,640 --> 00:53:17,359
Speaker 3: who said you shouldn't spend more than thirty or forty

1012
00:53:17,360 --> 00:53:19,080
Speaker 3: five minutes on your homework. I'd go home, look at

1013
00:53:19,080 --> 00:53:22,640
Speaker 3: the clock, forty five minutes, close my book. And then

1014
00:53:22,680 --> 00:53:26,440
Speaker 3: I had a teacher in sixth grade, mister Brown. I'll

1015
00:53:26,440 --> 00:53:30,200
Speaker 3: never forget mister Brown, who, for whatever reason, and I

1016
00:53:30,320 --> 00:53:34,120
Speaker 3: still can't tell you why, saw some potential, you know,

1017
00:53:34,320 --> 00:53:36,920
Speaker 3: saw something in me that maybe other people didn't see,

1018
00:53:37,440 --> 00:53:39,239
Speaker 3: and all of a sudden, I went from like a

1019
00:53:39,280 --> 00:53:42,359
Speaker 3: indifferent student to like a straight a student. And it

1020
00:53:42,440 --> 00:53:45,680
Speaker 3: was that year he took interest in me. He would say, hey, look,

1021
00:53:45,680 --> 00:53:47,680
Speaker 3: you're really good, right, you should focus more on these things.

1022
00:53:47,680 --> 00:53:51,839
Speaker 3: And so for me sixth grade, mister Brown very transformational

1023
00:53:52,160 --> 00:53:55,880
Speaker 3: mentor in a way, because he made me believe that

1024
00:53:55,920 --> 00:54:00,000
Speaker 3: I had something that I didn't really think I had.

1025
00:54:01,160 --> 00:54:05,040
Speaker 3: And then my dad gave me three important things that

1026
00:54:05,080 --> 00:54:07,880
Speaker 3: he told me was one of them. Is kind of funny.

1027
00:54:08,840 --> 00:54:11,040
Speaker 3: He's like, you really need to learn how to You

1028
00:54:11,080 --> 00:54:13,520
Speaker 3: need to be able to speak well, you need to

1029
00:54:13,520 --> 00:54:15,640
Speaker 3: be able to read well. And he's like, if you're

1030
00:54:15,640 --> 00:54:17,399
Speaker 3: living in this country, you should know how to play

1031
00:54:17,440 --> 00:54:20,839
Speaker 3: a sport, right, and so he The way he tried

1032
00:54:20,880 --> 00:54:24,040
Speaker 3: to implement those is he maybe take a speed reading

1033
00:54:24,040 --> 00:54:25,160
Speaker 3: class in elementary school.

1034
00:54:26,200 --> 00:54:30,360
Speaker 2: Was that useful? I speed read, you do no loss

1035
00:54:30,360 --> 00:54:31,960
Speaker 2: of comprehension.

1036
00:54:31,239 --> 00:54:34,560
Speaker 3: No loss of comprehension. He maybe take a public speaking

1037
00:54:34,560 --> 00:54:36,360
Speaker 3: class with college students when I was at high school

1038
00:54:37,160 --> 00:54:40,839
Speaker 3: and I was so scared of public speaking. I never

1039
00:54:40,920 --> 00:54:43,799
Speaker 3: could imagine then that i'd be doing a you know,

1040
00:54:43,920 --> 00:54:47,960
Speaker 3: a podcast. And he didn't. He didn't succeed on sports,

1041
00:54:47,960 --> 00:54:51,000
Speaker 3: but his idea was he was like, you know, you

1042
00:54:51,000 --> 00:54:52,680
Speaker 3: should you should learn how to play golf, you know,

1043
00:54:52,719 --> 00:54:55,200
Speaker 3: like that'd be a good thing to know. And living

1044
00:54:55,200 --> 00:55:01,719
Speaker 3: state school, Well, I I play golf horrifically. But the

1045
00:55:02,360 --> 00:55:04,399
Speaker 3: but in high school you could join the golf team.

1046
00:55:04,600 --> 00:55:06,279
Speaker 3: It was a no cut team. That doesn't mean you

1047
00:55:06,320 --> 00:55:06,640
Speaker 3: were going to.

1048
00:55:06,640 --> 00:55:08,719
Speaker 2: Get to play but varsity letter.

1049
00:55:08,760 --> 00:55:11,080
Speaker 3: But you got to you got to learn. And I

1050
00:55:11,160 --> 00:55:13,280
Speaker 3: just said, now I'm not doing that. So I got.

1051
00:55:13,440 --> 00:55:16,040
Speaker 3: I got two out of the three. But I think

1052
00:55:16,040 --> 00:55:19,120
Speaker 3: those two out of the three have been really really important.

1053
00:55:19,400 --> 00:55:23,200
Speaker 3: I know that a very very positive impact on my life.

1054
00:55:23,239 --> 00:55:24,520
Speaker 3: And of course along the way, there've been lots of

1055
00:55:24,560 --> 00:55:27,080
Speaker 3: people at all the places I've worked that have been

1056
00:55:27,360 --> 00:55:28,399
Speaker 3: mentors as well.

1057
00:55:28,680 --> 00:55:32,680
Speaker 2: Huh, very very interesting. Coming up, we continue our conversation

1058
00:55:32,800 --> 00:55:38,480
Speaker 2: with Devin Perek, Managing director at Inside Partners. I'm Barry Dults.

1059
00:55:38,520 --> 00:55:42,520
Speaker 2: You're listening to Masters in Business on Bloomberg Radio. I'm

1060
00:55:42,560 --> 00:55:46,240
Speaker 2: Bury Redults. You're listening to Masters in Business on Bloomberg Radio.

1061
00:55:46,640 --> 00:55:50,360
Speaker 2: My extra special guest this week is Devin Perrek, Managing

1062
00:55:50,400 --> 00:55:55,960
Speaker 2: director at Inside Partners. Let's talk about what's keeping you

1063
00:55:56,120 --> 00:55:59,560
Speaker 2: entertained these days. What are you watching or listening, streaming

1064
00:55:59,600 --> 00:56:00,800
Speaker 2: podcast anything along.

1065
00:56:00,680 --> 00:56:03,279
Speaker 3: Those This week could do it a whole We could

1066
00:56:03,280 --> 00:56:06,440
Speaker 3: do a podcast on the podcast. But my wife and

1067
00:56:06,480 --> 00:56:09,239
Speaker 3: I just finished watching Friends and Neighbors with John so Good.

1068
00:56:09,440 --> 00:56:12,120
Speaker 3: I thought it was great. I really enjoy that's just

1069
00:56:12,200 --> 00:56:15,759
Speaker 3: pure kind of entertainment. On the podcast side, you know,

1070
00:56:16,040 --> 00:56:18,839
Speaker 3: I just like I speed read. I can only listen

1071
00:56:18,920 --> 00:56:20,640
Speaker 3: to podcasts if I speed listen. So I listened to

1072
00:56:20,680 --> 00:56:23,920
Speaker 3: all of these at two point four acts, which drives

1073
00:56:23,920 --> 00:56:26,560
Speaker 3: my wife bananas because I'll get in the car and

1074
00:56:26,600 --> 00:56:28,400
Speaker 3: you know, I'm listening to something. It goes to the

1075
00:56:28,840 --> 00:56:31,360
Speaker 3: you know, the Apple thing, and She's like, we turned

1076
00:56:31,360 --> 00:56:37,640
Speaker 3: this off. But you know, there's a bunch interestingly in reading.

1077
00:56:37,760 --> 00:56:39,640
Speaker 3: I tend not to read a lot of business ebooks,

1078
00:56:39,680 --> 00:56:42,880
Speaker 3: but in podcasts I do listen to that. So but

1079
00:56:42,920 --> 00:56:44,799
Speaker 3: the ones I listened to I listened to Acquired, I

1080
00:56:44,840 --> 00:56:48,279
Speaker 3: listened to Business Breakdowns. I listened to Nikolai Tangens where

1081
00:56:48,280 --> 00:56:51,680
Speaker 3: he interviews the CEOs. I listened to invest Like the Best.

1082
00:56:52,239 --> 00:56:56,920
Speaker 3: I listened to You. I listened to Lex Friedman, and

1083
00:56:56,960 --> 00:56:58,480
Speaker 3: then I'm at the ft.

1084
00:56:59,280 --> 00:56:59,360
Speaker 2: Uh.

1085
00:56:59,560 --> 00:57:03,200
Speaker 3: Well, let's Freeman's got his own He's a affiliate with

1086
00:57:03,280 --> 00:57:05,239
Speaker 3: MIT in some way. He's had his own podcast. He

1087
00:57:05,280 --> 00:57:09,160
Speaker 3: gets really really interesting people to come on. I'm involved

1088
00:57:09,200 --> 00:57:12,000
Speaker 3: in Carnegie and CFR, so they both have a podcast.

1089
00:57:12,080 --> 00:57:14,600
Speaker 3: One's called Grant Tamasha, which is on India, which is

1090
00:57:14,760 --> 00:57:16,920
Speaker 3: just a policy area I'm interested in. Why it Matters

1091
00:57:16,960 --> 00:57:20,440
Speaker 3: is CFR's podcast. So I've got a driving to the

1092
00:57:20,440 --> 00:57:23,440
Speaker 3: Hampton's easy because it I can. I have hours and

1093
00:57:23,440 --> 00:57:27,400
Speaker 3: hours of kind of content really interesting.

1094
00:57:27,480 --> 00:57:29,960
Speaker 2: Let's talk about books. Are what are some of your favorites?

1095
00:57:30,000 --> 00:57:31,360
Speaker 2: What are you reading currently? Well?

1096
00:57:31,440 --> 00:57:35,640
Speaker 3: I read a lot, and you know, I think two

1097
00:57:35,720 --> 00:57:39,400
Speaker 3: books that I just gave both my one kid just

1098
00:57:39,440 --> 00:57:42,840
Speaker 3: graduated from college and one is two years out of college,

1099
00:57:42,880 --> 00:57:44,439
Speaker 3: three years out of college. I gave both of them.

1100
00:57:44,560 --> 00:57:46,360
Speaker 3: I don't know if they've both read both, but I

1101
00:57:46,400 --> 00:57:49,680
Speaker 3: gave them both books to read. One is Psychology of

1102
00:57:49,680 --> 00:57:52,320
Speaker 3: Money by Morgan House. Sure, I thought that was a

1103
00:57:52,360 --> 00:57:53,920
Speaker 3: great book. I wish I read that when I was

1104
00:57:54,280 --> 00:57:56,840
Speaker 3: twenty one, but I still felt like it was valuable.

1105
00:57:58,080 --> 00:58:00,640
Speaker 3: The other is called Five Types of Health by ce

1106
00:58:00,680 --> 00:58:02,880
Speaker 3: Heel Bloom. Sure, I thought that was a great book.

1107
00:58:03,480 --> 00:58:06,800
Speaker 3: And those are more I would put those as entertainment,

1108
00:58:07,000 --> 00:58:10,200
Speaker 3: but I found those if you read those books and

1109
00:58:10,240 --> 00:58:12,440
Speaker 3: you kind of try to apply them to life. I

1110
00:58:12,480 --> 00:58:16,080
Speaker 3: thought both of those were really useful then. And then

1111
00:58:16,120 --> 00:58:19,440
Speaker 3: a lot of what I read is around topics that

1112
00:58:20,120 --> 00:58:24,680
Speaker 3: like are around our philanthropy. Right. So you know one

1113
00:58:24,760 --> 00:58:26,920
Speaker 3: book I read, which is this is not an upper

1114
00:58:27,920 --> 00:58:31,720
Speaker 3: It's a book called Anatomy of an Epidemic by Robert Whitaker,

1115
00:58:31,760 --> 00:58:35,080
Speaker 3: which is about the use of psychiatric drugs in this country.

1116
00:58:36,200 --> 00:58:39,480
Speaker 3: And this is not an uplifting book. Of course, there's

1117
00:58:39,480 --> 00:58:42,240
Speaker 3: an epidemic of anxiety and depressed.

1118
00:58:41,920 --> 00:58:45,960
Speaker 2: To say anything about American healthcare or psychology.

1119
00:58:45,360 --> 00:58:48,560
Speaker 3: But it motivated. It motivated. So one of the areas

1120
00:58:48,600 --> 00:58:53,240
Speaker 3: that were philanthropically investing in is next generation ways of

1121
00:58:53,240 --> 00:58:56,960
Speaker 3: dealing with psychiatric conditions, and that book kind of was

1122
00:58:57,000 --> 00:58:59,880
Speaker 3: the starting point, you know, of that. And then the

1123
00:59:00,200 --> 00:59:03,920
Speaker 3: really depressing book I'm reading right now is it's a

1124
00:59:03,920 --> 00:59:08,280
Speaker 3: new book. It's called Nuclear War by Annie Jacobson, and

1125
00:59:08,320 --> 00:59:11,320
Speaker 3: it's you talked about what are these theories, what are

1126
00:59:11,320 --> 00:59:15,360
Speaker 3: the scenarios out there that you know we're underpricing, and

1127
00:59:15,800 --> 00:59:17,600
Speaker 3: you know, I just felt with what happened over the

1128
00:59:17,680 --> 00:59:21,000
Speaker 3: last two years. You know, I think we all, you know,

1129
00:59:21,000 --> 00:59:23,400
Speaker 3: we used to have fallout shelters and it would just

1130
00:59:23,400 --> 00:59:25,960
Speaker 3: like a nuclear war, that's that's done. There's like, there's

1131
00:59:25,960 --> 00:59:29,040
Speaker 3: no risk of that. And I think the last couple

1132
00:59:29,080 --> 00:59:31,760
Speaker 3: of years just reminded me that, like, nah, it's not done.

1133
00:59:31,880 --> 00:59:34,400
Speaker 3: Like now, it's not a high probability maybe, but it's

1134
00:59:34,440 --> 00:59:37,640
Speaker 3: not done. And what this book does is it actually

1135
00:59:38,200 --> 00:59:42,240
Speaker 3: starts at time zero, a nuclear bomb drops. What actually happens, right,

1136
00:59:42,440 --> 00:59:46,000
Speaker 3: what is the defense mechanism that the offensive person uses,

1137
00:59:46,080 --> 00:59:48,800
Speaker 3: what's the defensive mechanism that the other country uses? What

1138
00:59:49,080 --> 00:59:51,040
Speaker 3: happens from I mean, and it goes into it in

1139
00:59:51,360 --> 00:59:54,360
Speaker 3: not very uplifting detail, and it was just a good

1140
00:59:54,440 --> 00:59:57,640
Speaker 3: reminder that you have this thing out there that still

1141
00:59:57,680 --> 01:00:03,600
Speaker 3: has the chance to obliterate the world as we know it, right,

1142
01:00:03,680 --> 01:00:06,240
Speaker 3: And it's not a zero percent probability. It's a low probability.

1143
01:00:06,240 --> 01:00:08,200
Speaker 3: But I think it is important to understand tail cases.

1144
01:00:08,720 --> 01:00:10,600
Speaker 2: Yeah, to say, to say that at least.

1145
01:00:10,400 --> 01:00:12,360
Speaker 3: We're ending on a very depressing note, so we might

1146
01:00:12,400 --> 01:00:14,320
Speaker 3: want to start you might want to end on something

1147
01:00:14,360 --> 01:00:15,200
Speaker 3: more more fun.

1148
01:00:15,560 --> 01:00:19,560
Speaker 2: No, it's listen, you know sometimes you mentioned so to

1149
01:00:20,200 --> 01:00:22,800
Speaker 2: make this positive. You mentioned Saheel Bloom. I had him

1150
01:00:22,840 --> 01:00:25,560
Speaker 2: as a guest on the podcast. You mentioned Morgan Housell.

1151
01:00:25,600 --> 01:00:28,400
Speaker 2: I've had him several times. He wrote the forward to

1152
01:00:28,440 --> 01:00:33,720
Speaker 2: my book. Both those guys younger, all their work is

1153
01:00:33,920 --> 01:00:35,480
Speaker 2: much more uplifting, much.

1154
01:00:37,520 --> 01:00:39,200
Speaker 3: Age thing. Yeah I should have I should have. I

1155
01:00:39,240 --> 01:00:40,120
Speaker 3: should have ended with that.

1156
01:00:40,200 --> 01:00:43,400
Speaker 2: But no, it's it's absolutely fine. Listen. Sometimes you gotta,

1157
01:00:44,080 --> 01:00:46,240
Speaker 2: you know, you gotta shake people up and say, hey,

1158
01:00:46,280 --> 01:00:48,720
Speaker 2: this is a real risk, and you know, non zero

1159
01:00:48,880 --> 01:00:53,800
Speaker 2: is a pretty significant risk when the outcome is so catastrophic.

1160
01:00:53,840 --> 01:00:54,200
Speaker 3: Correct.

1161
01:00:54,840 --> 01:00:58,360
Speaker 2: So, final two questions, What sort of advice would you

1162
01:00:58,360 --> 01:01:01,280
Speaker 2: give to a recent college grad interested in a career

1163
01:01:01,480 --> 01:01:05,400
Speaker 2: in either startups, venture capital, or private equity.

1164
01:01:06,040 --> 01:01:10,320
Speaker 3: Yeah, so I think that keep your intellectual curiosity broad

1165
01:01:11,080 --> 01:01:13,960
Speaker 3: And I was just speaking to our summer interns a

1166
01:01:14,000 --> 01:01:17,040
Speaker 3: month ago and somebody asked me, like, what's your advice?

1167
01:01:17,520 --> 01:01:20,240
Speaker 3: And I think the mistake a lot of people make

1168
01:01:20,480 --> 01:01:21,760
Speaker 3: is they decide, Okay, I want to be a venture

1169
01:01:21,760 --> 01:01:23,520
Speaker 3: capitalis so all I'm gonna do is read tech Crunch

1170
01:01:23,600 --> 01:01:26,520
Speaker 3: and listen to tech podcasts. And this just make you

1171
01:01:26,560 --> 01:01:30,720
Speaker 3: a very interesting person. And you know, I've probably had

1172
01:01:30,880 --> 01:01:35,280
Speaker 3: more dinners or one deals because we found a common

1173
01:01:35,280 --> 01:01:37,480
Speaker 3: interest in art or a common interest in Why it

1174
01:01:37,520 --> 01:01:39,800
Speaker 3: doesn't I'm using the things I happen to be interested in,

1175
01:01:39,800 --> 01:01:42,400
Speaker 3: But it doesn't have to be those things, right, And

1176
01:01:42,880 --> 01:01:46,360
Speaker 3: you know, everyone has intellectual interests outside of the thing

1177
01:01:46,400 --> 01:01:48,960
Speaker 3: that they want to do, and I would encourage them

1178
01:01:49,000 --> 01:01:51,640
Speaker 3: to like pursue those and pursue those with passion, because

1179
01:01:51,680 --> 01:01:53,880
Speaker 3: it's going to make you a way more interesting, well

1180
01:01:53,960 --> 01:01:57,840
Speaker 3: rounded person, and don't just be so micro focused on

1181
01:01:57,920 --> 01:02:00,080
Speaker 3: that thing. And I just think it makes you that

1182
01:02:00,200 --> 01:02:02,840
Speaker 3: or investor, makes you a better person, makes you more interesting.

1183
01:02:03,720 --> 01:02:06,720
Speaker 3: So that's one two. In a world where we start

1184
01:02:06,760 --> 01:02:10,360
Speaker 3: getting people to do, you know, varsity soccer when they're three,

1185
01:02:13,960 --> 01:02:17,960
Speaker 3: allow a little serendipity in your life, right, I wouldn't

1186
01:02:17,960 --> 01:02:20,080
Speaker 3: have ended up doing what I was doing if I

1187
01:02:20,160 --> 01:02:24,840
Speaker 3: just followed the plan and you know something's interesting, try

1188
01:02:24,880 --> 01:02:26,840
Speaker 3: it and it turns out you might like it. Now

1189
01:02:26,880 --> 01:02:28,200
Speaker 3: you might not like it and go back to your

1190
01:02:28,240 --> 01:02:31,480
Speaker 3: original plan. But we've forgotten serendipity. It's why I still

1191
01:02:31,520 --> 01:02:35,280
Speaker 3: subscribe to paper newspapers because I'm probably the only person

1192
01:02:35,280 --> 01:02:37,200
Speaker 3: in my building that might still get to paper newspapers,

1193
01:02:37,240 --> 01:02:40,160
Speaker 3: but because they're serendipity. When you're flipping through the newspaper,

1194
01:02:40,240 --> 01:02:42,960
Speaker 3: it's the article that you weren't looking for is where

1195
01:02:42,960 --> 01:02:43,600
Speaker 3: you learn something.

1196
01:02:43,640 --> 01:02:48,480
Speaker 2: Guess don't have that same discovery. And I am very

1197
01:02:48,520 --> 01:02:51,200
Speaker 2: aggressive looking for interesting things.

1198
01:02:51,040 --> 01:02:52,640
Speaker 3: Too, And I think you don't get that.

1199
01:02:52,760 --> 01:02:53,480
Speaker 2: You really don't.

1200
01:02:53,800 --> 01:02:55,920
Speaker 3: Economists is a great example. If you just get the

1201
01:02:56,120 --> 01:02:58,240
Speaker 3: Digital Economist and you just see the article in AI,

1202
01:02:58,280 --> 01:03:01,200
Speaker 3: I'm gonna read that. Guess what, I probably already know that, right,

1203
01:03:01,280 --> 01:03:03,640
Speaker 3: I'm reinforcing dollars that I have. Maybe I learned one

1204
01:03:03,640 --> 01:03:06,480
Speaker 3: tidbit that I didn't know. It's when you open it

1205
01:03:06,560 --> 01:03:10,080
Speaker 3: up and oh, there's this interesting article about nuclear that

1206
01:03:10,120 --> 01:03:12,080
Speaker 3: I don't know anything about, and I read it. Oh wow,

1207
01:03:12,120 --> 01:03:13,760
Speaker 3: this is maybe this is this is a this is

1208
01:03:13,760 --> 01:03:15,480
Speaker 3: a real tail risk. Maybe I should understand this.

1209
01:03:15,520 --> 01:03:18,560
Speaker 2: I will give you the one exception to this is

1210
01:03:19,000 --> 01:03:21,480
Speaker 2: The Times doesn't do this well, but the Wall Street

1211
01:03:21,560 --> 01:03:24,560
Speaker 2: Journal does. So you can go to the digital edition

1212
01:03:25,160 --> 01:03:29,600
Speaker 2: of the WALLSTREETWSJ dot com, but you could also click

1213
01:03:29,760 --> 01:03:33,800
Speaker 2: in today's paper and you get the breakdown by sections.

1214
01:03:33,400 --> 01:03:35,400
Speaker 3: And then you invest, and then you can kind of

1215
01:03:35,400 --> 01:03:36,919
Speaker 3: click and as you scroll.

1216
01:03:36,640 --> 01:03:39,560
Speaker 2: Through it, it's the equivalent of flipping the newspaper page

1217
01:03:39,840 --> 01:03:41,800
Speaker 2: where you get those oh I never would have.

1218
01:03:41,960 --> 01:03:43,960
Speaker 3: People always laugh. I show up on a news I'll

1219
01:03:43,960 --> 01:03:46,360
Speaker 3: show up on a plane and I've got my newspapers

1220
01:03:46,400 --> 01:03:48,360
Speaker 3: and they're like looking at me, like I'm like a Martian,

1221
01:03:48,400 --> 01:03:50,640
Speaker 3: you know, And I'm like, no, there's a reason.

1222
01:03:50,520 --> 01:03:54,200
Speaker 2: No, absolutely. And our final question, what do you know

1223
01:03:54,240 --> 01:03:57,240
Speaker 2: about the world of investing today that would have been

1224
01:03:57,240 --> 01:04:00,240
Speaker 2: helpful to know back in nineteen ninety five when you

1225
01:04:00,240 --> 01:04:01,320
Speaker 2: were first getting started?

1226
01:04:01,960 --> 01:04:04,240
Speaker 3: Well, well, I think a really important one. It applies

1227
01:04:04,280 --> 01:04:06,640
Speaker 3: to investing, but I also think it applies to life

1228
01:04:07,200 --> 01:04:11,160
Speaker 3: is oftentimes people don't trust their instinct because they don't

1229
01:04:11,160 --> 01:04:14,160
Speaker 3: think their instinct is a real thing. They think their

1230
01:04:14,200 --> 01:04:16,680
Speaker 3: instinct the gut. They have these words that people use,

1231
01:04:17,080 --> 01:04:19,560
Speaker 3: but The reality is it's micro slicing a lot of

1232
01:04:19,640 --> 01:04:22,000
Speaker 3: data that you've experienced over your life. Now, maybe at

1233
01:04:22,040 --> 01:04:24,560
Speaker 3: twenty one, your gut's not worth a lot. Okay, it's

1234
01:04:24,640 --> 01:04:27,400
Speaker 3: probably worth a lot in certain things, maybe some human

1235
01:04:27,400 --> 01:04:29,439
Speaker 3: interactions and things like that's probably not worth a lot

1236
01:04:29,440 --> 01:04:31,760
Speaker 3: in investing because you just don't have a database. But

1237
01:04:31,880 --> 01:04:34,360
Speaker 3: even at my age, you don't like you have this

1238
01:04:34,480 --> 01:04:37,040
Speaker 3: inclination to not trust your gut, Like there's something about

1239
01:04:37,040 --> 01:04:39,680
Speaker 3: this deal that just doesn't make sense. But oh, but

1240
01:04:39,760 --> 01:04:42,080
Speaker 3: the revenue looks good and the margins look good, and

1241
01:04:42,120 --> 01:04:45,360
Speaker 3: so I'll just overlook my gut. And I've just generally

1242
01:04:45,400 --> 01:04:49,560
Speaker 3: when I've overlooked my gut, it's not been. It's not been.

1243
01:04:49,560 --> 01:04:50,920
Speaker 3: It's it's not been a good thing.

1244
01:04:51,160 --> 01:04:55,880
Speaker 2: You mentioned pattern recognition earlier. Your intuition improves as.

1245
01:04:55,720 --> 01:04:57,600
Speaker 3: You get more experience, you get more experienced in.

1246
01:04:57,600 --> 01:05:01,840
Speaker 2: Wor now blank is perhaps overstates the case, but there's

1247
01:05:01,880 --> 01:05:02,320
Speaker 2: a lot, but.

1248
01:05:02,600 --> 01:05:04,880
Speaker 3: It's but it's I agree. I've read the book and

1249
01:05:04,920 --> 01:05:08,000
Speaker 3: I think it overstates it. But there's a there's something

1250
01:05:08,000 --> 01:05:11,000
Speaker 3: to start from there, you know, at the core. And

1251
01:05:11,040 --> 01:05:12,360
Speaker 3: then the second one is I think what we talked

1252
01:05:12,360 --> 01:05:16,640
Speaker 3: about earlier at times come. Bad times will invariably come,

1253
01:05:16,800 --> 01:05:19,400
Speaker 3: and good times will invariably follow, and you just have

1254
01:05:19,440 --> 01:05:21,160
Speaker 3: to have confidence that both are going to be there

1255
01:05:21,640 --> 01:05:22,760
Speaker 3: and that you're gonna learn from both.

1256
01:05:23,480 --> 01:05:26,560
Speaker 2: Devin, this has been absolutely fascinating. Thank you for being

1257
01:05:26,600 --> 01:05:30,360
Speaker 2: so generous with your time. We have been speaking with

1258
01:05:30,440 --> 01:05:35,760
Speaker 2: Devin PreK, Managing director at Inside Partners. If you enjoy

1259
01:05:35,840 --> 01:05:38,840
Speaker 2: this conversation, well check out any of the five hundred

1260
01:05:38,840 --> 01:05:42,720
Speaker 2: and fifty we've done over the past eleven years. You

1261
01:05:42,760 --> 01:05:47,360
Speaker 2: can find those at Bloomberg iTunes, Spotify, YouTube, wherever you

1262
01:05:47,480 --> 01:05:50,800
Speaker 2: find your favorite podcast. Be sure to check out my

1263
01:05:50,880 --> 01:05:55,640
Speaker 2: new book, How Not to Invest The Ideas, numbers and

1264
01:05:55,720 --> 01:05:59,520
Speaker 2: behaviors that destroy wealth and how to avoid them How

1265
01:05:59,560 --> 01:06:02,880
Speaker 2: Not to Best wherever you find your favorite books. I

1266
01:06:02,920 --> 01:06:04,959
Speaker 2: would be remiss if I did not thank the Crack

1267
01:06:05,040 --> 01:06:09,800
Speaker 2: team that helps put these conversations together each week. Alexis

1268
01:06:09,920 --> 01:06:14,480
Speaker 2: Noriega is my video engineer. Anna Luke is my producer.

1269
01:06:14,760 --> 01:06:17,800
Speaker 2: Sage Bauman is the head of podcast That Bloomberg. Sean

1270
01:06:17,920 --> 01:06:22,120
Speaker 2: Russo is my researcher. I'm Barry Ridhelts. You've been listening

1271
01:06:22,200 --> 01:06:25,720
Speaker 2: to Masters in Business on Bloomberg Radio.