1
00:00:02,600 --> 00:00:11,840
Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. This is Master's in

2
00:00:11,920 --> 00:00:15,520
Speaker 1: Business with Barry Ridholts on Bloomberg.

3
00:00:15,120 --> 00:00:19,880
Speaker 2: Radio this week on the podcast, Boy Do I have

4
00:00:19,960 --> 00:00:21,160
Speaker 2: an extra special guest.

5
00:00:21,680 --> 00:00:23,640
Speaker 1: I know Jim O'Shaughnessy.

6
00:00:23,079 --> 00:00:26,320
Speaker 2: For I don't know, maybe twenty plus years something like that.

7
00:00:26,640 --> 00:00:30,240
Speaker 2: We actually first met in the green room at CMBC

8
00:00:30,800 --> 00:00:35,599
Speaker 2: like early two thousands and found we shared some similar

9
00:00:35,840 --> 00:00:39,760
Speaker 2: likes and philosophies. And I've been a fan of his

10
00:00:39,800 --> 00:00:42,519
Speaker 2: book What Works on Wall Street pretty much from when

11
00:00:42,560 --> 00:00:45,879
Speaker 2: it came out. This is a fascinating conversation about a

12
00:00:45,880 --> 00:00:52,840
Speaker 2: person who has worked through multiple locales and seats in finance,

13
00:00:52,960 --> 00:00:57,400
Speaker 2: not just running a systematic investing at bear Stearn's, but

14
00:00:57,960 --> 00:01:05,240
Speaker 2: creating O'Shaughnessy asset management, creating a unique custom index product

15
00:01:05,280 --> 00:01:09,640
Speaker 2: that ended up attracting the attention of Franklin Templeton, who

16
00:01:09,720 --> 00:01:13,480
Speaker 2: paid some undisclosed and ungodly amount of money for the

17
00:01:13,520 --> 00:01:17,960
Speaker 2: whole firm, and now in a later phase of his

18
00:01:18,120 --> 00:01:23,200
Speaker 2: career doing O'shaughnessee ventures and the O'shaughnessee Fellowship. I first

19
00:01:23,560 --> 00:01:26,320
Speaker 2: know him from really the first pant book What Works

20
00:01:26,360 --> 00:01:29,480
Speaker 2: on Wall Street? That was a half a century of

21
00:01:29,560 --> 00:01:32,560
Speaker 2: data analysis. Really was never accessible to.

22
00:01:32,520 --> 00:01:33,360
Speaker 1: The public before.

23
00:01:33,920 --> 00:01:36,400
Speaker 2: I found the conversation to be fascinating and I think

24
00:01:36,440 --> 00:01:39,800
Speaker 2: you will also. And at this point I am obligated

25
00:01:39,840 --> 00:01:43,640
Speaker 2: to do a disclosure. My firm Retults Wealth Management has

26
00:01:43,640 --> 00:01:48,240
Speaker 2: been working with O'Shaughnessey on their direct Index platform. Really

27
00:01:48,280 --> 00:01:50,480
Speaker 2: we were one of the first beta testers. We now

28
00:01:50,520 --> 00:01:54,000
Speaker 2: have over a billion dollars on that platform, maybe coming

29
00:01:54,120 --> 00:01:58,280
Speaker 2: even closer to another big round number with no further ado.

30
00:01:58,640 --> 00:02:03,120
Speaker 2: My discussion with O'Shaughnessy ventures Jim O'Shaughnessy.

31
00:02:03,880 --> 00:02:07,600
Speaker 1: It's great to see you, Berry and congratulations. Wow, that's a.

32
00:02:07,760 --> 00:02:11,200
Speaker 2: Well congratulations to you. I'm still My firm just had

33
00:02:11,200 --> 00:02:15,560
Speaker 2: its tenth anniversary. You guys, anytime I see the phrase

34
00:02:15,960 --> 00:02:19,880
Speaker 2: for an undisclosed amount, my brain automatically says.

35
00:02:19,680 --> 00:02:21,280
Speaker 1: Wow, that has to be a lot of money. If

36
00:02:21,280 --> 00:02:21,880
Speaker 1: it's if.

37
00:02:21,760 --> 00:02:25,640
Speaker 2: They're not disclosing it, it's material, but undisclosed, that's a

38
00:02:25,639 --> 00:02:26,200
Speaker 2: lot of casts.

39
00:02:26,560 --> 00:02:29,400
Speaker 1: Or it could be like trading places and the normal

40
00:02:29,440 --> 00:02:30,600
Speaker 1: bet of a dollar.

41
00:02:32,520 --> 00:02:36,200
Speaker 2: The usual bet Mortimer one dollar. So we know each

42
00:02:36,280 --> 00:02:40,040
Speaker 2: other from way back when you first came into my

43
00:02:40,200 --> 00:02:43,480
Speaker 2: orbit from the book What Works on Wall Street. I

44
00:02:43,560 --> 00:02:45,360
Speaker 2: read it from cover to cover. I was on a

45
00:02:45,440 --> 00:02:48,720
Speaker 2: trading desk when that came out, and I'm like, huh,

46
00:02:48,760 --> 00:02:52,000
Speaker 2: so there's some science and math behind this. It's not

47
00:02:52,160 --> 00:02:55,839
Speaker 2: just rumors and whatever happens to cross TV that day.

48
00:02:56,360 --> 00:02:57,120
Speaker 1: I'm intrigued.

49
00:02:57,600 --> 00:03:00,920
Speaker 2: Before we get there, let's talk a little bit about

50
00:03:01,000 --> 00:03:04,280
Speaker 2: what you were doing prior. Tell us about the early

51
00:03:04,360 --> 00:03:05,280
Speaker 2: Jim O'Shaughnessy.

52
00:03:05,800 --> 00:03:10,720
Speaker 1: Well, I was always fascinated about the markets in general,

53
00:03:11,320 --> 00:03:17,480
Speaker 1: which stemmed from a very angry conversation between my uncle

54
00:03:17,560 --> 00:03:22,480
Speaker 1: and father about IBM. And I had just been allowed

55
00:03:22,480 --> 00:03:25,480
Speaker 1: to go to the adult table, and I was sitting

56
00:03:25,560 --> 00:03:28,800
Speaker 1: next to my dad and he and my uncle John

57
00:03:28,880 --> 00:03:32,400
Speaker 1: were going hammer and tong about whether IBM was a

58
00:03:32,440 --> 00:03:35,080
Speaker 1: good company or not. And I was listening, and it

59
00:03:35,160 --> 00:03:38,760
Speaker 1: was all about the chairman. It was all about, you know,

60
00:03:39,120 --> 00:03:42,600
Speaker 1: things that I looked at as kind of soft intelligence,

61
00:03:42,720 --> 00:03:47,040
Speaker 1: squishy squishy, and so I just thought I asked at

62
00:03:47,080 --> 00:03:50,280
Speaker 1: the dinner, I said, well, would it make more sense

63
00:03:50,360 --> 00:03:52,840
Speaker 1: to like look at how much money they're making and

64
00:03:52,880 --> 00:03:56,000
Speaker 1: what their earnings are and how much you have to

65
00:03:56,040 --> 00:03:59,160
Speaker 1: pay for that? And they both just literally glared at me.

66
00:04:00,600 --> 00:04:05,040
Speaker 1: That's hilarious kids. They don't know anything exactly exactly. It's

67
00:04:05,080 --> 00:04:07,440
Speaker 1: the chairman. How tall is he? I like to cut

68
00:04:07,520 --> 00:04:11,600
Speaker 1: his gips. It's almost as if you were there that

69
00:04:11,680 --> 00:04:15,880
Speaker 1: bug got implanted, that mind worm got implanted in my brain.

70
00:04:16,200 --> 00:04:19,120
Speaker 1: How old were you when that? I was seventeen. Oh

71
00:04:19,200 --> 00:04:21,600
Speaker 1: so you're just going into college? Yeah?

72
00:04:21,600 --> 00:04:24,920
Speaker 2: Absolutely, And you were a Minnesota kid, Is that right?

73
00:04:24,960 --> 00:04:29,040
Speaker 2: I grew up in Saint Paul, Minnesota and beautiful country.

74
00:04:29,279 --> 00:04:32,119
Speaker 2: Certainly in the summer anyway, gorgeous. The winter's tough.

75
00:04:32,240 --> 00:04:36,560
Speaker 1: Yeah. Yeah. Well, if this were the old USSR, that

76
00:04:36,720 --> 00:04:41,599
Speaker 1: is where all the political prisoners would be of Minnesota.

77
00:04:42,400 --> 00:04:46,640
Speaker 1: But so I started doing research on essentially the Dow

78
00:04:46,760 --> 00:04:51,559
Speaker 1: thirty because it was manageable, thirty stocks I could list

79
00:04:51,680 --> 00:04:55,040
Speaker 1: by hand showing how old I am, because you literally

80
00:04:55,200 --> 00:04:57,880
Speaker 1: there were no computers that we could use at the time.

81
00:04:59,160 --> 00:05:02,640
Speaker 1: Simple things like like what's the price, what's the dividend,

82
00:05:02,760 --> 00:05:06,400
Speaker 1: what's the price to earnings, book value, etc. And I

83
00:05:06,560 --> 00:05:12,040
Speaker 1: found a definite trend, right. I found that buying the

84
00:05:12,160 --> 00:05:15,279
Speaker 1: ten stocks and the diw with the lowest pees from

85
00:05:15,400 --> 00:05:19,320
Speaker 1: nineteen like thirty five. I think I started through when

86
00:05:19,360 --> 00:05:21,320
Speaker 1: I was doing it, and this would have been about

87
00:05:21,400 --> 00:05:28,599
Speaker 1: nineteen eighty, absolutely decimated the ten highest PE stocks. So wow,

88
00:05:28,760 --> 00:05:31,920
Speaker 1: I love this. In the meantime, I had computers, and

89
00:05:32,120 --> 00:05:35,080
Speaker 1: the only reason I actually got to write What Works

90
00:05:35,120 --> 00:05:39,640
Speaker 1: on Wall Street was because Ben Graham didn't have computers.

91
00:05:40,279 --> 00:05:42,839
Speaker 1: If he had had them, I would have had no

92
00:05:43,000 --> 00:05:45,440
Speaker 1: chance because he would have done it. Basically, what I

93
00:05:45,480 --> 00:05:49,239
Speaker 1: wanted to see was, is there any rhyme or reason

94
00:05:49,400 --> 00:05:52,120
Speaker 1: to all of these reasons people say they like or

95
00:05:52,160 --> 00:05:55,400
Speaker 1: hate a stock? Right? Where is the proof? Where is

96
00:05:55,480 --> 00:05:59,560
Speaker 1: the empirical evidence that say, buying the low PE stocks

97
00:05:59,560 --> 00:06:03,120
Speaker 1: from the die works very well over many market cycles.

98
00:06:03,760 --> 00:06:06,960
Speaker 1: So I wrote a first book called invest Like the Best,

99
00:06:07,520 --> 00:06:10,560
Speaker 1: in which I basically showed you how you could clone

100
00:06:10,600 --> 00:06:15,080
Speaker 1: your favorite portfolio manager by taking his or her stocks,

101
00:06:15,440 --> 00:06:19,599
Speaker 1: putting them on a big database like Compustat, seeing how

102
00:06:19,640 --> 00:06:23,360
Speaker 1: they differed from the overall market, and then using those

103
00:06:23,480 --> 00:06:28,280
Speaker 1: as factor screens to get down to a portfolio that looked, acted,

104
00:06:28,440 --> 00:06:32,080
Speaker 1: and most importantly, performed like your favorite manager.

105
00:06:32,279 --> 00:06:37,080
Speaker 2: Now, the average investor typically didn't have access to Compustat,

106
00:06:37,200 --> 00:06:40,640
Speaker 2: to big data, to big computers, and so they relied

107
00:06:40,839 --> 00:06:42,039
Speaker 2: on you who did.

108
00:06:42,640 --> 00:06:45,000
Speaker 1: And if I recall what Works on Wall Street.

109
00:06:45,480 --> 00:06:48,239
Speaker 2: You back tested like half a century worth of data

110
00:06:48,279 --> 00:06:50,920
Speaker 2: something like that, and it was the full market, not

111
00:06:51,120 --> 00:06:52,880
Speaker 2: just the thirty dove stocks.

112
00:06:53,080 --> 00:06:56,919
Speaker 1: Yeah. Absolutely. And also not just the full market, it

113
00:06:57,040 --> 00:07:01,640
Speaker 1: was also any company that had been but went bankrupt

114
00:07:01,720 --> 00:07:07,040
Speaker 1: or got taken over, the very very needed research database

115
00:07:07,160 --> 00:07:13,440
Speaker 1: on compustat. So no survivorship bias none, You back that out, Yeah, great, Yeah,

116
00:07:13,480 --> 00:07:19,040
Speaker 1: because some of the early academic studies were they had

117
00:07:19,120 --> 00:07:24,800
Speaker 1: a lot of survivorship bias. They didn't properly lag for

118
00:07:25,000 --> 00:07:29,280
Speaker 1: when you actually knew a number, so they just assumed, right, well,

119
00:07:29,320 --> 00:07:32,680
Speaker 1: there's the number on March thirty first, I'm going to

120
00:07:32,800 --> 00:07:35,920
Speaker 1: use that number. Well, you didn't really know that for

121
00:07:36,080 --> 00:07:40,160
Speaker 1: most of history until maybe May or June. Really interesting,

122
00:07:40,240 --> 00:07:44,120
Speaker 1: so you run these numbers. What sort of strategies do

123
00:07:44,200 --> 00:07:48,760
Speaker 1: you find perform best. Well, we found that on the

124
00:07:49,040 --> 00:07:55,040
Speaker 1: value side, smaller value stocks that had some catalysts and

125
00:07:55,120 --> 00:07:58,640
Speaker 1: had turned a corner and their prices had started to

126
00:07:58,640 --> 00:08:02,800
Speaker 1: go up. A beautiful strategy.

127
00:08:02,320 --> 00:08:05,560
Speaker 2: Small cap value with a touch of momentum.

128
00:08:05,800 --> 00:08:10,200
Speaker 1: Yes, okay. On the growth side, we found momentum works

129
00:08:10,600 --> 00:08:15,480
Speaker 1: really really well. As we continued the research, we found okay,

130
00:08:15,520 --> 00:08:19,520
Speaker 1: there's all sorts of caveats. So, for example, we learned

131
00:08:20,120 --> 00:08:23,480
Speaker 1: after a severe bear market i e. One in which

132
00:08:23,560 --> 00:08:26,960
Speaker 1: the market had declined by forty or more percent, not

133
00:08:27,000 --> 00:08:29,240
Speaker 1: a lot of those, not a lot, thank god, but

134
00:08:30,400 --> 00:08:35,600
Speaker 1: momentum inverted, and the stocks with the worst six or

135
00:08:35,640 --> 00:08:40,440
Speaker 1: twelve month momentum actually did vastly better than the ones

136
00:08:40,559 --> 00:08:42,440
Speaker 1: with the best. And if you think about it even

137
00:08:42,440 --> 00:08:45,760
Speaker 1: for a minute, it makes sense, right deepest value. But

138
00:08:45,960 --> 00:08:49,120
Speaker 1: what happened was a lot of really great stocks during

139
00:08:49,160 --> 00:08:54,199
Speaker 1: the bear market got pushed way low in price, and

140
00:08:54,320 --> 00:08:59,080
Speaker 1: so people when the market was recovering jumped on those stocks.

141
00:08:59,160 --> 00:09:01,199
Speaker 1: They were like, I can't believe I'm getting you know,

142
00:09:01,280 --> 00:09:05,160
Speaker 1: these earnings at six times earnings for an IBM or

143
00:09:05,240 --> 00:09:07,840
Speaker 1: a you know Qualcom, Right, that's the baby with the

144
00:09:07,880 --> 00:09:13,240
Speaker 1: bathwater strategy exactly. And so but we found, you know,

145
00:09:13,520 --> 00:09:17,640
Speaker 1: that value actually works now. It hasn't for a long time.

146
00:09:18,240 --> 00:09:23,360
Speaker 1: But we also found that large stocks with high shareholder

147
00:09:23,520 --> 00:09:29,240
Speaker 1: yield i e. Dividend yield plus buyback yield was an

148
00:09:29,440 --> 00:09:35,400
Speaker 1: excellent way to identify big stocks that are obviously much

149
00:09:35,440 --> 00:09:39,920
Speaker 1: more conservative than the smaller fry in the small cap world.

150
00:09:40,720 --> 00:09:44,200
Speaker 2: Interesting, So let's talk a little bit about your work

151
00:09:44,240 --> 00:09:46,480
Speaker 2: at bear Stearns. Really, where I first met you in

152
00:09:46,520 --> 00:09:50,800
Speaker 2: the two thousands, you were head of systematic Equity at

153
00:09:50,800 --> 00:09:54,800
Speaker 2: bear Stearn's asset management. I'm assuming you were applying a

154
00:09:54,800 --> 00:09:56,880
Speaker 2: lot of the lessons you learned in what works on

155
00:09:56,920 --> 00:10:01,520
Speaker 2: Wall Street to the Bear institutional and tel investing strategies.

156
00:10:01,600 --> 00:10:06,320
Speaker 1: Absolutely, and you know, let me just say Bear was

157
00:10:06,960 --> 00:10:10,400
Speaker 1: really a great company, very unfortunate what happened to it

158
00:10:10,520 --> 00:10:14,600
Speaker 1: during the financial crisis. But the reason I love Bear is,

159
00:10:14,960 --> 00:10:18,160
Speaker 1: you know, a lot of big banks talk about being entrepreneurial.

160
00:10:18,559 --> 00:10:23,080
Speaker 1: Bear Stearns really was. And essentially, if you were doing

161
00:10:23,120 --> 00:10:26,520
Speaker 1: your thing and playing by the rules and doing well,

162
00:10:26,679 --> 00:10:30,480
Speaker 1: they let you alone. Which was pretty important for me

163
00:10:30,720 --> 00:10:34,040
Speaker 1: because when I got there, it was right after the

164
00:10:34,240 --> 00:10:38,400
Speaker 1: dot bomb, and a lot of the brokers had done

165
00:10:38,640 --> 00:10:41,160
Speaker 1: pretty poorly because they were in a lot of those names.

166
00:10:41,760 --> 00:10:45,640
Speaker 1: And so I convinced Steve Dantis, who was then head

167
00:10:45,840 --> 00:10:50,079
Speaker 1: of Private Client Services, that wouldn't it be better if

168
00:10:50,120 --> 00:10:55,840
Speaker 1: we did a packaged portfolio, a separately managed to count

169
00:10:56,200 --> 00:10:59,319
Speaker 1: and we offered at one time I think we were

170
00:10:59,440 --> 00:11:03,920
Speaker 1: all the way up to the brokers so that they

171
00:11:04,040 --> 00:11:10,120
Speaker 1: could use a more systematic time tested way of investing

172
00:11:10,160 --> 00:11:11,440
Speaker 1: for their clients.

173
00:11:11,160 --> 00:11:14,960
Speaker 2: Bringing a little discipline into what had been, at least

174
00:11:14,960 --> 00:11:18,199
Speaker 2: in the nineties very much a cowboy type of environment.

175
00:11:18,840 --> 00:11:21,480
Speaker 2: And I'm not just for fron of Bear. The entire

176
00:11:21,920 --> 00:11:25,480
Speaker 2: retail stock brokerage was wild totally.

177
00:11:25,720 --> 00:11:29,200
Speaker 1: He was very open to it. We ended up putting

178
00:11:29,240 --> 00:11:33,840
Speaker 1: together a separately managed to count platform that they brokers embraced.

179
00:11:34,080 --> 00:11:37,679
Speaker 1: They loved it because literally they did what they did well,

180
00:11:37,760 --> 00:11:41,440
Speaker 1: which was calm the client during bad times, try to

181
00:11:41,559 --> 00:11:44,640
Speaker 1: keep them from getting too excited during great times. But

182
00:11:44,679 --> 00:11:47,960
Speaker 1: they also loved the idea that it had a very

183
00:11:48,160 --> 00:11:52,760
Speaker 1: explicit explanation for why they were putting that client in

184
00:11:52,840 --> 00:11:56,400
Speaker 1: that portfolio. So that was a lot of fun. By

185
00:11:56,440 --> 00:12:01,320
Speaker 1: the time I left Bear, my group controlled about seventy

186
00:12:01,360 --> 00:12:04,920
Speaker 1: percent of Bear Stearn's asset management long only, and that

187
00:12:05,080 --> 00:12:07,240
Speaker 1: was a lot of money, wasn't it. It was It

188
00:12:07,280 --> 00:12:09,480
Speaker 1: was about fourteen billion dollars.

189
00:12:09,520 --> 00:12:13,040
Speaker 2: Okay, so you mentioned you left Bear. Let's put a

190
00:12:13,040 --> 00:12:17,000
Speaker 2: little flesh on those bones. Your timing was perfect.

191
00:12:17,040 --> 00:12:20,000
Speaker 1: You exit Bear in two thousand and seven, Is that right?

192
00:12:20,840 --> 00:12:25,840
Speaker 2: To set up O'Shaughnessy's asset management was the thinking, Hey,

193
00:12:25,880 --> 00:12:28,079
Speaker 2: I want to do this out on my own shop,

194
00:12:28,640 --> 00:12:33,120
Speaker 2: or were you sniffing something out in O seven that's like, hey,

195
00:12:33,160 --> 00:12:36,960
Speaker 2: maybe I don't want to be attached to a giant

196
00:12:37,000 --> 00:12:38,679
Speaker 2: ocean liner taking on water.

197
00:12:39,040 --> 00:12:42,120
Speaker 1: You know, that's funny. I spent the next two years

198
00:12:42,240 --> 00:12:46,920
Speaker 1: after that trying to convince reporters that I really didn't

199
00:12:46,960 --> 00:12:52,000
Speaker 1: know anything. Why I left Bear was because I felt

200
00:12:52,000 --> 00:12:54,480
Speaker 1: that I really wanted to be on my own again.

201
00:12:55,160 --> 00:12:58,400
Speaker 1: I really wanted to be able to just talk about

202
00:12:58,480 --> 00:13:02,360
Speaker 1: quantitative investing. Bear was a boutique, so there were a

203
00:13:02,360 --> 00:13:06,360
Speaker 1: lot of different managers. Liked them all. I thought they

204
00:13:06,400 --> 00:13:10,160
Speaker 1: all were great, but I really really wanted to focus

205
00:13:10,640 --> 00:13:15,280
Speaker 1: just exclusively on quant And secondly, we had upgraded a

206
00:13:15,320 --> 00:13:20,360
Speaker 1: lot of our systems to the idea that would become canvas, right,

207
00:13:20,440 --> 00:13:23,920
Speaker 1: because remember Netfolio was our first try at that. That

208
00:13:24,120 --> 00:13:27,760
Speaker 1: was nineties or ninety nine. Yeah, really, well, of course,

209
00:13:28,200 --> 00:13:31,840
Speaker 1: you know the really funny story here is in April

210
00:13:31,960 --> 00:13:35,880
Speaker 1: of nineteen ninety nine, I wrote a piece called the

211
00:13:36,000 --> 00:13:40,120
Speaker 1: Internet Contrarian, and in that piece I said, eighty five

212
00:13:40,160 --> 00:13:44,000
Speaker 1: percent of the companies currently extant in the Internet space

213
00:13:44,240 --> 00:13:46,440
Speaker 1: are going to be carried out of the market. Feet First,

214
00:13:47,480 --> 00:13:51,079
Speaker 1: I've never seen a bubble like this in my history

215
00:13:51,120 --> 00:13:54,360
Speaker 1: of investing. And what did I do next? Berry? I

216
00:13:54,520 --> 00:13:56,120
Speaker 1: started an Internet company.

217
00:13:57,440 --> 00:14:00,120
Speaker 2: Well just because the stocks or a bubble, does I

218
00:14:00,160 --> 00:14:02,480
Speaker 2: mean this internet thing isn't going to catch on?

219
00:14:02,720 --> 00:14:05,640
Speaker 1: That's true, right, it's there are you know?

220
00:14:05,679 --> 00:14:09,480
Speaker 2: It's funny we forget in the thirties, forties, fifties there

221
00:14:09,520 --> 00:14:13,600
Speaker 2: was only ma bel Every company used telephones. Yep, the

222
00:14:13,640 --> 00:14:18,160
Speaker 2: way we describe internet companies, if you use the Internet

223
00:14:18,200 --> 00:14:22,360
Speaker 2: as a core part of your platform. There's difference between

224
00:14:22,400 --> 00:14:23,160
Speaker 2: the dot.

225
00:14:22,880 --> 00:14:24,800
Speaker 1: Coms and the nineties and people.

226
00:14:24,480 --> 00:14:29,480
Speaker 2: Who have just really integrated the technology into their business. Right,

227
00:14:29,960 --> 00:14:33,560
Speaker 2: So I think Netfolio is not a dot com but

228
00:14:33,800 --> 00:14:36,880
Speaker 2: a comm that used the net as a way to

229
00:14:36,920 --> 00:14:39,560
Speaker 2: reach more people and give them access to data.

230
00:14:39,680 --> 00:14:42,440
Speaker 1: Well, it's really funny because I made a couple well

231
00:14:42,440 --> 00:14:44,840
Speaker 1: I made more than a couple of mistakes, but one

232
00:14:44,880 --> 00:14:49,360
Speaker 1: of the big ones I made was we designed Netfolio

233
00:14:49,440 --> 00:14:53,600
Speaker 1: as a B two C company, so we called we

234
00:14:53,600 --> 00:14:56,800
Speaker 1: were taking on at the time mutual funds, which were dominant.

235
00:14:56,800 --> 00:14:59,520
Speaker 1: We didn't have ETFs while we had them, but they

236
00:14:59,520 --> 00:15:02,400
Speaker 1: were there very early day, very very early days.

237
00:15:03,480 --> 00:15:07,000
Speaker 2: And so what did the spiders just turned twenty five recently, Yeah,

238
00:15:07,000 --> 00:15:09,680
Speaker 2: I think of something like that. Yeah, So ninety nine

239
00:15:09,840 --> 00:15:13,280
Speaker 2: is like it was really the beginning.

240
00:15:12,880 --> 00:15:16,600
Speaker 1: Oh totally. And basically the idea was it was the

241
00:15:16,640 --> 00:15:22,200
Speaker 1: first online investment advisor. And the reason that we thought

242
00:15:22,240 --> 00:15:27,400
Speaker 1: it would work so well was personalization, tax management, all

243
00:15:27,440 --> 00:15:30,320
Speaker 1: of those things. So, for example, we would they were

244
00:15:30,360 --> 00:15:33,080
Speaker 1: all run by quant models that we had developed, right,

245
00:15:35,080 --> 00:15:37,720
Speaker 1: but it gave the user the ability to say, let's

246
00:15:37,720 --> 00:15:41,200
Speaker 1: say they're anti smoking, right, and Philip Morris is one

247
00:15:41,240 --> 00:15:44,560
Speaker 1: of these selections they could just check nope, don't want it.

248
00:15:45,120 --> 00:15:48,640
Speaker 1: Up comes the next stock that meets the criteria, and

249
00:15:48,720 --> 00:15:51,560
Speaker 1: so it had a lot of really great features, but

250
00:15:51,920 --> 00:15:54,080
Speaker 1: the tech was not quite there yet.

251
00:15:54,120 --> 00:15:57,800
Speaker 2: You were twenty years ahead of where you would end

252
00:15:57,920 --> 00:16:01,160
Speaker 2: up in the late tens, right.

253
00:16:01,360 --> 00:16:01,440
Speaker 1: I.

254
00:16:03,000 --> 00:16:03,120
Speaker 2: Was.

255
00:16:03,760 --> 00:16:07,080
Speaker 1: I really do have to give my son Patrick the

256
00:16:07,280 --> 00:16:12,480
Speaker 1: credit for resurrecting the idea because when we were at OSAM,

257
00:16:13,080 --> 00:16:16,960
Speaker 1: I said, listen, we left Bear right into the Great

258
00:16:17,000 --> 00:16:20,200
Speaker 1: Financial Crisis, and I put the team together and I'm like,

259
00:16:20,560 --> 00:16:22,280
Speaker 1: I don't think that we're going to be able to

260
00:16:22,280 --> 00:16:26,680
Speaker 1: sell many long only portfolios after the market has collapsed

261
00:16:26,680 --> 00:16:31,560
Speaker 1: by nearly fifty percent, So let's spend our time developing

262
00:16:32,000 --> 00:16:36,400
Speaker 1: internal technology that works the way we work. The office

263
00:16:36,440 --> 00:16:40,040
Speaker 1: shelf stuff really wasn't cutting it, and so the project

264
00:16:40,080 --> 00:16:44,200
Speaker 1: to get there was multi year and Patrick oversaw that,

265
00:16:44,400 --> 00:16:46,680
Speaker 1: and then he walked into my office one day and

266
00:16:46,720 --> 00:16:49,960
Speaker 1: he goes, you know, Dad, we've been using the desk

267
00:16:50,000 --> 00:16:52,800
Speaker 1: star to kill a mouse. And I'm like, okay, I

268
00:16:52,960 --> 00:16:55,840
Speaker 1: like the metaphor, but what do you mean? And he

269
00:16:56,000 --> 00:16:59,840
Speaker 1: started talking about AWS, talking about netfolio and he's like,

270
00:17:00,320 --> 00:17:05,360
Speaker 1: we have the perfect tech now that our clients results

271
00:17:05,440 --> 00:17:09,280
Speaker 1: being one of them, could use and I'm like, brilliant,

272
00:17:09,440 --> 00:17:10,199
Speaker 1: let's go with it.

273
00:17:10,800 --> 00:17:13,480
Speaker 2: So we're going to talk a little more about Canvas,

274
00:17:13,640 --> 00:17:16,800
Speaker 2: but I want to stay with the launch of O

275
00:17:16,960 --> 00:17:20,399
Speaker 2: SM and O seven. So A, you don't need to

276
00:17:20,480 --> 00:17:23,000
Speaker 2: disclose this, but I'm going to assume you had a

277
00:17:23,000 --> 00:17:25,200
Speaker 2: lot of bear Stern stock options that you had a

278
00:17:25,320 --> 00:17:28,840
Speaker 2: vest on your exit, so you probably had a pretty

279
00:17:28,840 --> 00:17:33,840
Speaker 2: good sale, pretty good print on those when you first

280
00:17:33,920 --> 00:17:39,040
Speaker 2: set up O'Shaughnessy, you're running your traditional models, things like

281
00:17:39,280 --> 00:17:43,320
Speaker 2: Cornerstone value and Cornerstone growth, and I'm a big fan

282
00:17:43,520 --> 00:17:49,800
Speaker 2: of your microcap sleeve, which really operates parallel to venture

283
00:17:49,840 --> 00:17:53,000
Speaker 2: capital returns, only using public stocks.

284
00:17:53,200 --> 00:17:55,159
Speaker 1: Am I getting that more or less right? Actually we

285
00:17:55,760 --> 00:17:59,040
Speaker 1: use that also. Yeah, we wrote a paper saying that

286
00:17:59,080 --> 00:18:01,919
Speaker 1: it was the poor man's way to get exposure to

287
00:18:01,960 --> 00:18:05,840
Speaker 1: private equity. Private equity or venture capital are both both

288
00:18:05,920 --> 00:18:10,840
Speaker 1: really private equity closer because the the microcap I love

289
00:18:11,119 --> 00:18:14,639
Speaker 1: microcap investing. The only real reason that we offered that

290
00:18:14,840 --> 00:18:17,600
Speaker 1: was because I loved it so much. Well, and the

291
00:18:17,720 --> 00:18:22,640
Speaker 1: data backs it up, Oh, totally, totally it is. Microcap

292
00:18:23,000 --> 00:18:27,760
Speaker 1: is an amazing place if you've got the right tools

293
00:18:27,800 --> 00:18:31,920
Speaker 1: to sort through the thousands of names in the microcap universe,

294
00:18:32,040 --> 00:18:36,479
Speaker 1: because you would not want to buy an index of

295
00:18:36,600 --> 00:18:40,960
Speaker 1: microcap stocks. For the most part, there are microcaps because

296
00:18:41,080 --> 00:18:46,040
Speaker 1: they kind of suck. However, there are so many diamonds

297
00:18:46,080 --> 00:18:50,040
Speaker 1: in the rough in microcap that if you have a strategy,

298
00:18:50,440 --> 00:18:54,480
Speaker 1: like a quant strategy that can sort through these thousands

299
00:18:54,520 --> 00:18:58,439
Speaker 1: of names, you can do extraordinarily well. I love the

300
00:18:58,680 --> 00:18:59,879
Speaker 1: strategy and I know.

301
00:19:00,000 --> 00:19:04,040
Speaker 2: Oh the OSAM microcap sleeve is what I call. It

302
00:19:04,080 --> 00:19:08,120
Speaker 2: has just really shot the lights out, especially last year

303
00:19:08,160 --> 00:19:09,440
Speaker 2: when the market was having.

304
00:19:09,240 --> 00:19:11,959
Speaker 1: A pretty good year. They did pretty well, didn't they

305
00:19:12,040 --> 00:19:14,800
Speaker 1: They did? They did. Now, remember you introduced me as

306
00:19:14,880 --> 00:19:17,919
Speaker 1: chairman of o SAM, I'm no longer no longer. Yeah,

307
00:19:17,960 --> 00:19:23,600
Speaker 1: they let me retire and actually Patrick is now chairman

308
00:19:23,680 --> 00:19:28,000
Speaker 1: emeritus over at OSAM. Let's talk a little bit about Canvas,

309
00:19:28,040 --> 00:19:29,840
Speaker 1: and again full disclosure, we're a client.

310
00:19:30,200 --> 00:19:32,480
Speaker 2: We were a beta test. Do we love the product?

311
00:19:32,800 --> 00:19:36,119
Speaker 2: Which is kind of ironic because I used to hate

312
00:19:36,160 --> 00:19:41,280
Speaker 2: direct indexing every time I would demo or see a product.

313
00:19:41,880 --> 00:19:45,280
Speaker 2: It was clunky, it was cludgy. You would get these

314
00:19:45,320 --> 00:19:48,199
Speaker 2: statements that were like hundreds of pages long.

315
00:19:48,680 --> 00:19:50,240
Speaker 1: You guys kind.

316
00:19:49,880 --> 00:19:53,919
Speaker 2: Of figured out the secret sauce for how do we

317
00:19:53,960 --> 00:19:58,320
Speaker 2: make this clean, usable and easier to understand. Tell us

318
00:19:58,320 --> 00:20:02,040
Speaker 2: a little bit about the genesis of Canvas.

319
00:20:02,440 --> 00:20:06,360
Speaker 1: Well, first of all, we call it custom indexing as

320
00:20:06,359 --> 00:20:10,159
Speaker 1: opposed to direct and the reason I make that distinction

321
00:20:10,600 --> 00:20:14,840
Speaker 1: is because, as you point out, the direct indexing products

322
00:20:14,880 --> 00:20:19,240
Speaker 1: of that time were clunky, they were difficult. You got

323
00:20:19,560 --> 00:20:23,159
Speaker 1: reams and reams of paper reports, and they were really

324
00:20:23,280 --> 00:20:28,359
Speaker 1: only focusing on tax benefits. Right. What we wanted to

325
00:20:28,440 --> 00:20:32,600
Speaker 1: do with Canvas, which is custom indexing, is, as the

326
00:20:32,680 --> 00:20:38,480
Speaker 1: name implies, give you, as the advisor, full control over

327
00:20:38,800 --> 00:20:42,280
Speaker 1: what your client portfolio wanted to look like. You got

328
00:20:42,320 --> 00:20:47,000
Speaker 1: the advantages of tax harvesting. You got the advantages of

329
00:20:47,080 --> 00:20:51,560
Speaker 1: being able to mix indexes in with active strategies, but

330
00:20:51,920 --> 00:20:55,800
Speaker 1: you could also do a social investing fund if you

331
00:20:55,880 --> 00:20:58,399
Speaker 1: want it. But the way we did it was we

332
00:20:58,480 --> 00:21:02,399
Speaker 1: didn't presume what your client was going to think of

333
00:21:02,640 --> 00:21:06,960
Speaker 1: as good social investing. So often when you see some

334
00:21:07,119 --> 00:21:11,720
Speaker 1: of the ESG portfolios, they've been predetermined as to what

335
00:21:11,960 --> 00:21:15,360
Speaker 1: is going to be included. We give you the tools

336
00:21:15,800 --> 00:21:19,080
Speaker 1: to turn a dial up or down on whatever you want.

337
00:21:19,160 --> 00:21:22,240
Speaker 1: I think last I looked, there were over fifty eight

338
00:21:22,560 --> 00:21:25,320
Speaker 1: separate things that you could find tune around on the

339
00:21:25,359 --> 00:21:28,560
Speaker 1: idea of ESG. We wanted to give the tools to

340
00:21:28,680 --> 00:21:33,119
Speaker 1: you because you knew your client vastly better than we did,

341
00:21:33,680 --> 00:21:36,920
Speaker 1: and we thought, let's try. As you mentioned you were

342
00:21:36,920 --> 00:21:39,240
Speaker 1: one of the beta testers. That was actually one of

343
00:21:39,280 --> 00:21:43,000
Speaker 1: the smartest things we did, I think, because we had

344
00:21:43,119 --> 00:21:45,639
Speaker 1: really good advice from a lot of people that we

345
00:21:45,720 --> 00:21:49,960
Speaker 1: knew in both venture and other places. The first thing

346
00:21:50,040 --> 00:21:52,720
Speaker 1: that many of them said to us was do not

347
00:21:53,240 --> 00:21:57,960
Speaker 1: try to go big with this. Originally, find advisors who

348
00:21:58,000 --> 00:22:01,600
Speaker 1: you trust who will give you you real feedback. In

349
00:22:01,640 --> 00:22:04,200
Speaker 1: other words, they won't shine you on if they didn't

350
00:22:04,320 --> 00:22:06,440
Speaker 1: like you. Guys were very good at times.

351
00:22:06,480 --> 00:22:09,440
Speaker 2: And Michael batt nicking my office, one of my partners,

352
00:22:09,960 --> 00:22:12,439
Speaker 2: who was over the moon when he first saw this.

353
00:22:12,840 --> 00:22:15,639
Speaker 2: Every time another product came in, it would take me

354
00:22:15,720 --> 00:22:18,920
Speaker 2: thirty seconds to poke holes in it. And he came

355
00:22:19,400 --> 00:22:22,119
Speaker 2: breathless into my office, Dude, you got to see this.

356
00:22:22,680 --> 00:22:26,080
Speaker 2: And I'm like, yeah, yeah, okay, another garbage right, tee

357
00:22:26,080 --> 00:22:28,320
Speaker 2: it up. And it took about thirty seconds to go,

358
00:22:28,440 --> 00:22:30,520
Speaker 2: Oh my god, how do we get a piece of this?

359
00:22:30,520 --> 00:22:31,480
Speaker 1: This is fantastic.

360
00:22:31,960 --> 00:22:35,840
Speaker 2: The interface, the design, all of the bullet points that

361
00:22:36,000 --> 00:22:39,840
Speaker 2: all the boxes checked were great. Let's stick with what

362
00:22:39,880 --> 00:22:45,359
Speaker 2: we no longer call esg and Meyer Statman famously called

363
00:22:45,440 --> 00:22:49,879
Speaker 2: values based investing. Some people have called it woke investing,

364
00:22:49,920 --> 00:22:53,760
Speaker 2: but that's really the wrong phrase. I'm fascinated, for example,

365
00:22:54,359 --> 00:22:58,800
Speaker 2: by the Catholic bishops whose endowment says, look, we don't

366
00:22:58,800 --> 00:23:01,720
Speaker 2: want any aboard of any drugs that do that. We

367
00:23:01,720 --> 00:23:05,520
Speaker 2: can't invest in those companies. We can invest in hospital

368
00:23:05,600 --> 00:23:09,600
Speaker 2: chains that perform these sort of surgeries, or insurers. You

369
00:23:09,720 --> 00:23:13,720
Speaker 2: have the ability to say, whatever your personal preferences are,

370
00:23:14,240 --> 00:23:17,560
Speaker 2: you could just tune those out of pick an index,

371
00:23:17,600 --> 00:23:20,760
Speaker 2: the S and P five hundred the Vanguard Total Market.

372
00:23:20,800 --> 00:23:23,879
Speaker 2: You could say, I don't want X or Y or Z,

373
00:23:24,280 --> 00:23:27,080
Speaker 2: and how it comes tell us a little bit about that.

374
00:23:27,520 --> 00:23:31,480
Speaker 1: I felt that that was really really important because everybody

375
00:23:31,560 --> 00:23:34,840
Speaker 1: has different ideas. As you point out, the Catholic bishops

376
00:23:35,320 --> 00:23:39,440
Speaker 1: wanted to exclude certain things. Others might want to include

377
00:23:39,520 --> 00:23:43,000
Speaker 1: certain things. Actually felt it would be very arrogant of

378
00:23:43,119 --> 00:23:48,520
Speaker 1: us to determine what good social investing was because we

379
00:23:48,600 --> 00:23:53,080
Speaker 1: had managed money for a variety of religious institutions, and

380
00:23:53,240 --> 00:23:57,200
Speaker 1: guess what, they all have different takes on what they

381
00:23:57,240 --> 00:24:00,240
Speaker 1: want to see. We did one where, for example, well

382
00:24:00,359 --> 00:24:03,880
Speaker 1: you couldn't buy any company that did anything with animals

383
00:24:03,960 --> 00:24:07,480
Speaker 1: with eyes. That was an interesting one. But then on

384
00:24:07,520 --> 00:24:10,479
Speaker 1: the other hand, we had a client who wanted to

385
00:24:10,520 --> 00:24:15,000
Speaker 1: see more female board members and females in the C suite,

386
00:24:15,040 --> 00:24:16,399
Speaker 1: and you could you could screen for that.

387
00:24:16,520 --> 00:24:18,480
Speaker 2: You can screen and there's a bunch of research that

388
00:24:18,560 --> 00:24:21,000
Speaker 2: shows those companies. Now you don't know if it's posative

389
00:24:21,720 --> 00:24:25,280
Speaker 2: or just merely correlated, but those companies tend to outperform.

390
00:24:25,600 --> 00:24:30,600
Speaker 2: The request we probably hear the most is no gun stocks,

391
00:24:30,640 --> 00:24:31,800
Speaker 2: no tobacco stocks.

392
00:24:31,960 --> 00:24:36,680
Speaker 1: Yeah, kind of interesting. Yeah, the tobacco guns, those are

393
00:24:36,840 --> 00:24:41,959
Speaker 1: pretty large groups where majority of investors want nothing to

394
00:24:41,960 --> 00:24:44,760
Speaker 1: do with them. But the other thing that's cool about

395
00:24:44,840 --> 00:24:49,520
Speaker 1: our dials on canvas you Let's say that Ritholtz has

396
00:24:49,800 --> 00:24:53,280
Speaker 1: a wild eyed libertarian walk in who happens to have

397
00:24:53,320 --> 00:24:56,560
Speaker 1: a billion dollars and he says, you know what I

398
00:24:56,720 --> 00:25:00,159
Speaker 1: want the gun manufacturers. I want I'm a big like

399
00:25:00,160 --> 00:25:04,000
Speaker 1: an amendment guy, right, Or I want the pharmaceuticals, or

400
00:25:04,040 --> 00:25:06,920
Speaker 1: I mean the sinstock, I mean gambling and alcohol. Well,

401
00:25:07,040 --> 00:25:09,680
Speaker 1: and you know the joke there was that my first company,

402
00:25:09,720 --> 00:25:13,800
Speaker 1: O'Shaughnessy Capital Management, we used to keep a joke portfolio

403
00:25:13,960 --> 00:25:16,240
Speaker 1: which was called the Eat, Drink and Be Merry for

404
00:25:16,359 --> 00:25:20,240
Speaker 1: tomorrow you die Berry. It killed me sure.

405
00:25:20,800 --> 00:25:24,000
Speaker 2: So what ends up happening very often is when there's

406
00:25:24,080 --> 00:25:28,920
Speaker 2: a non financial reason for kicking a stock out out

407
00:25:28,960 --> 00:25:33,800
Speaker 2: of a lot of portfolios. Eventually a company with still

408
00:25:33,840 --> 00:25:37,320
Speaker 2: having decent financial prospects, it becomes cheap.

409
00:25:37,680 --> 00:25:43,000
Speaker 1: Yep. Absolutely, But the thing with the social style investing,

410
00:25:43,280 --> 00:25:47,160
Speaker 1: we wanted you to be able to reflect your client's

411
00:25:47,280 --> 00:25:51,600
Speaker 1: unique needs. And there really wasn't anything like that. I

412
00:25:51,600 --> 00:25:54,240
Speaker 1: don't know if there is now, but I haven't seen

413
00:25:54,280 --> 00:25:55,160
Speaker 1: anything like that.

414
00:25:55,320 --> 00:25:59,480
Speaker 2: Well, certainly not to this degree of granularity. By the way,

415
00:25:59,520 --> 00:26:06,040
Speaker 2: when we first we're beta testing Canvas. Internally, my view was, Hey,

416
00:26:06,240 --> 00:26:08,639
Speaker 2: people are going to want to use this for value

417
00:26:08,640 --> 00:26:12,880
Speaker 2: based investing. Then they're going to want to deconcentrate. If

418
00:26:12,880 --> 00:26:15,159
Speaker 2: I work for Google, do I really need all this

419
00:26:15,359 --> 00:26:18,080
Speaker 2: tech exposure My income is coming from there, Let me

420
00:26:18,160 --> 00:26:21,399
Speaker 2: diversify that way. And then tax loss harvesting was going

421
00:26:21,480 --> 00:26:24,680
Speaker 2: to bring up the rear. I had it exactly backwards,

422
00:26:25,200 --> 00:26:28,160
Speaker 2: in large part because I don't know, maybe a year

423
00:26:28,200 --> 00:26:32,399
Speaker 2: into it we had the COVID crash market falls thirty

424
00:26:32,440 --> 00:26:36,680
Speaker 2: four percent and coincidentally bottoms just near the end of

425
00:26:36,720 --> 00:26:41,720
Speaker 2: the quarter. That rebalance. You know, typical tax loss harvesting

426
00:26:41,920 --> 00:26:46,040
Speaker 2: own a dozen mutual funds. You pick up ten twenty

427
00:26:46,119 --> 00:26:50,399
Speaker 2: basis points against the portfolio of losses to offset gains.

428
00:26:51,119 --> 00:26:53,720
Speaker 2: The hope with this was it would be fifty sixty.

429
00:26:54,400 --> 00:26:57,359
Speaker 2: We had clients getting two hundred, three hundred, four hundred

430
00:26:57,359 --> 00:27:00,600
Speaker 2: basis points. And I've talked to some of your staff

431
00:27:01,000 --> 00:27:04,440
Speaker 2: or former staff, and they've told us some unique use

432
00:27:04,480 --> 00:27:08,520
Speaker 2: cases where the numbers are bonkers. First off, explain to

433
00:27:09,160 --> 00:27:11,679
Speaker 2: the audience who may not be familiar with this what

434
00:27:11,920 --> 00:27:13,200
Speaker 2: is tax loss harvesting.

435
00:27:13,440 --> 00:27:17,200
Speaker 1: So essentially what it does is we had to build

436
00:27:17,280 --> 00:27:22,720
Speaker 1: a non trivial algorithm that could monitor every portfolio we

437
00:27:22,720 --> 00:27:26,679
Speaker 1: were managing on behalf of clients and as you know,

438
00:27:27,119 --> 00:27:31,080
Speaker 1: they can go all the way up get maximized tax

439
00:27:31,160 --> 00:27:34,960
Speaker 1: losses or all the way down don't worry about them. So,

440
00:27:35,040 --> 00:27:37,240
Speaker 1: for example, you wouldn't care about it in an IRA

441
00:27:37,600 --> 00:27:43,920
Speaker 1: right right. But the purpose was that we found through

442
00:27:43,920 --> 00:27:48,480
Speaker 1: our research that a tremendous amount of alpha was being

443
00:27:48,600 --> 00:27:51,200
Speaker 1: left on the table, and that was the alpha from

444
00:27:51,359 --> 00:27:55,040
Speaker 1: tax lost harvesting. When you're in a market like the

445
00:27:55,080 --> 00:27:58,159
Speaker 1: market we had when we went into COVID and the

446
00:27:58,200 --> 00:28:02,919
Speaker 1: bear market ensued, and under other circumstances, well kind of

447
00:28:02,960 --> 00:28:06,520
Speaker 1: you're out of luck. But in this particular case, that

448
00:28:06,720 --> 00:28:12,280
Speaker 1: creates the kick in for harvesting the losses, reducing the

449
00:28:12,359 --> 00:28:16,680
Speaker 1: overall tax needs for the portfolio, and you could really

450
00:28:16,720 --> 00:28:19,440
Speaker 1: look at that as that's money in your pocket. By

451
00:28:19,480 --> 00:28:23,680
Speaker 1: the way, we had the benefits completely backward too. A

452
00:28:23,800 --> 00:28:26,600
Speaker 1: tax loss harvesting was at the bottom of our list

453
00:28:26,800 --> 00:28:27,440
Speaker 1: as well.

454
00:28:27,480 --> 00:28:31,200
Speaker 2: It's arcane and technical and you don't really think about it,

455
00:28:31,640 --> 00:28:34,640
Speaker 2: but we have clients who were either you know startup

456
00:28:34,720 --> 00:28:38,920
Speaker 2: founders that cashed out, or they inherited or or just

457
00:28:39,080 --> 00:28:42,400
Speaker 2: owned stock with a very low cost basis. You know,

458
00:28:42,440 --> 00:28:44,760
Speaker 2: it's always funny when you see a five million dollar

459
00:28:44,800 --> 00:28:49,680
Speaker 2: portfolio and some stock has blown up where it's eighty

460
00:28:49,720 --> 00:28:52,880
Speaker 2: percent of the holdings. Hey, if you have five million

461
00:28:52,960 --> 00:28:56,240
Speaker 2: dollars and four million of it is Apple or Amazon

462
00:28:56,360 --> 00:28:59,760
Speaker 2: or some combination of big stocks, that's a lot of

463
00:29:00,040 --> 00:29:04,160
Speaker 2: angle stock risk. And to a man, every person says, hey,

464
00:29:05,200 --> 00:29:08,239
Speaker 2: you should diversify. The answer as always, I'm gonna get

465
00:29:08,320 --> 00:29:11,280
Speaker 2: killed in capital gains taxes. This worked out to be

466
00:29:11,320 --> 00:29:14,280
Speaker 2: a really good way to say we're gonna work out

467
00:29:14,360 --> 00:29:17,600
Speaker 2: of your concentrated position over three, four or five years,

468
00:29:18,080 --> 00:29:21,080
Speaker 2: and then twenty twenty comes along and what should have

469
00:29:21,120 --> 00:29:24,640
Speaker 2: been a five year process took half as long because

470
00:29:24,960 --> 00:29:28,000
Speaker 2: you had so many losses. So for those people who

471
00:29:28,240 --> 00:29:30,480
Speaker 2: may not be familiar with this, let's say you own

472
00:29:30,600 --> 00:29:34,280
Speaker 2: ten mutual funds, right and some are up, one or

473
00:29:34,320 --> 00:29:37,240
Speaker 2: two are down. You sell the ones that are down,

474
00:29:37,640 --> 00:29:40,440
Speaker 2: you replace it with something very similar. Hey, now I

475
00:29:40,440 --> 00:29:42,560
Speaker 2: got a little bit of loss even and my portfolio

476
00:29:42,600 --> 00:29:45,520
Speaker 2: looks the same, but I have an actual realized loss

477
00:29:45,520 --> 00:29:48,600
Speaker 2: that I could use to offset my real gains. But

478
00:29:48,720 --> 00:29:51,440
Speaker 2: those losses are three five ten percent.

479
00:29:51,480 --> 00:29:52,000
Speaker 1: They're nothing.

480
00:29:52,640 --> 00:29:55,480
Speaker 2: On the other hand, if you have a direct index

481
00:29:55,600 --> 00:29:59,840
Speaker 2: or a custom index that has a couple of hundred stocks, well,

482
00:30:00,120 --> 00:30:03,680
Speaker 2: the worst stocks in those portfolios, they're not down three

483
00:30:03,760 --> 00:30:08,200
Speaker 2: four five percent, they're down forty sixty seventy five percent.

484
00:30:08,760 --> 00:30:11,360
Speaker 2: You sell the ones that are down, you replace them.

485
00:30:11,440 --> 00:30:13,440
Speaker 2: And this is one of the things I like about canvas.

486
00:30:13,960 --> 00:30:18,160
Speaker 2: You identify the replacement stocks that are is it fair

487
00:30:18,200 --> 00:30:19,880
Speaker 2: to say mathematically similar?

488
00:30:19,960 --> 00:30:24,560
Speaker 1: They look, well, they come from the same strategy, so yeah,

489
00:30:24,640 --> 00:30:28,120
Speaker 1: you could say they were mathematically similar. So the overall

490
00:30:28,160 --> 00:30:33,200
Speaker 1: portfolio more or less retains the same characteristics. You're just

491
00:30:33,320 --> 00:30:38,240
Speaker 1: realizing losses deep losses on some stocks and replacing them

492
00:30:38,280 --> 00:30:42,680
Speaker 1: with something relatively similar exactly. And you know, we're just

493
00:30:42,920 --> 00:30:47,560
Speaker 1: basically making math work for us. And because the entire

494
00:30:47,680 --> 00:30:53,440
Speaker 1: thing is operated within the canvas architecture. After getting the algorithm,

495
00:30:53,440 --> 00:30:56,320
Speaker 1: which was non trivial what do you mean by non

496
00:30:56,360 --> 00:30:58,560
Speaker 1: trivial outbum, it took a hell of a lot of work,

497
00:30:58,640 --> 00:31:03,280
Speaker 1: okay to be able to make that function properly, And

498
00:31:03,880 --> 00:31:07,800
Speaker 1: as we worked with firms like yours, it became very

499
00:31:07,920 --> 00:31:09,920
Speaker 1: very clear to us that that was going to be

500
00:31:09,960 --> 00:31:14,920
Speaker 1: a big deal in canvas, So we wanted that algorithm

501
00:31:14,960 --> 00:31:18,480
Speaker 1: to work perfectly. But as you also note, we wanted

502
00:31:18,640 --> 00:31:21,720
Speaker 1: the nearest neighbor, if you will, that would replace that

503
00:31:21,880 --> 00:31:27,680
Speaker 1: stock to not affect the overall metrics of your portfolio.

504
00:31:27,800 --> 00:31:31,040
Speaker 1: So it's going to look, act and perform very much

505
00:31:31,360 --> 00:31:35,360
Speaker 1: like the earlier portfolio, but you've already taken that wonderful

506
00:31:35,440 --> 00:31:39,880
Speaker 1: tex loss so that you can offset the gains from elsewhere.

507
00:31:40,480 --> 00:31:43,600
Speaker 1: The other use case that we thought would be number

508
00:31:43,640 --> 00:31:48,240
Speaker 1: one was, you know you have a concentrated position. Let's

509
00:31:48,280 --> 00:31:53,720
Speaker 1: say Google right, don't give me any tech exposure, or

510
00:31:53,760 --> 00:31:57,239
Speaker 1: give me tech exposure only in this tech which is

511
00:31:57,400 --> 00:32:01,160
Speaker 1: like hardware for example, right that I can do and

512
00:32:01,320 --> 00:32:04,719
Speaker 1: that type of use case would work hand in hand

513
00:32:05,160 --> 00:32:09,200
Speaker 1: with the tax loss, making it a much much more efficient,

514
00:32:09,680 --> 00:32:13,960
Speaker 1: more money in the investors pocket. In terms of final

515
00:32:14,080 --> 00:32:16,280
Speaker 1: outcomes with the portfolios.

516
00:32:15,720 --> 00:32:19,720
Speaker 2: What was the uptake on that approach? People enthusiastic about.

517
00:32:19,480 --> 00:32:24,200
Speaker 1: It, they were, but they were not nearly as enthusiastic

518
00:32:24,360 --> 00:32:28,120
Speaker 1: as we anticipated they would be. There were a few

519
00:32:28,200 --> 00:32:32,760
Speaker 1: advisors that we were working with who worked specifically with

520
00:32:33,040 --> 00:32:37,240
Speaker 1: founders and early employees who had a lot of options

521
00:32:37,280 --> 00:32:41,600
Speaker 1: in that particular and usually tech, but we also did

522
00:32:41,640 --> 00:32:44,800
Speaker 1: work and do work with a lot of people who

523
00:32:45,000 --> 00:32:50,520
Speaker 1: just amassed through employment a huge position in their particular company,

524
00:32:51,040 --> 00:32:54,320
Speaker 1: and they wanted to have the rest of the portfolio

525
00:32:54,520 --> 00:32:59,200
Speaker 1: be built to complement and offset, if you will, any

526
00:32:59,400 --> 00:33:02,600
Speaker 1: further invent's over there. So it's worked actually quite nicely.

527
00:33:03,600 --> 00:33:07,120
Speaker 2: And then in twenty twenty one, Franklin Templeton comes knocking

528
00:33:07,240 --> 00:33:11,520
Speaker 2: at the door. They're an investment giant with a trillion

529
00:33:11,520 --> 00:33:15,720
Speaker 2: plus dollars on their books and they've been pretty acquisitive

530
00:33:15,840 --> 00:33:18,840
Speaker 2: over the past few years. Tell us a little bit

531
00:33:18,840 --> 00:33:23,240
Speaker 2: about how that transaction began. If I recall correctly, you

532
00:33:23,320 --> 00:33:25,480
Speaker 2: guys weren't out shopping the firm to.

533
00:33:25,440 --> 00:33:28,760
Speaker 1: Be sold, were you not? At all? We were. It's

534
00:33:28,800 --> 00:33:31,360
Speaker 1: a funny story. We almost got kind of a cold

535
00:33:31,400 --> 00:33:35,440
Speaker 1: call from a gentleman at Franklin Templeton. I was sort

536
00:33:35,440 --> 00:33:38,880
Speaker 1: of like, give it to Chris Lovelace or you know

537
00:33:38,880 --> 00:33:42,880
Speaker 1: who's the president of the firm, And ultimately Patrick spoke

538
00:33:42,920 --> 00:33:45,600
Speaker 1: with him and came into my office and he's like, hey,

539
00:33:46,200 --> 00:33:50,920
Speaker 1: Franklin Templeton is really interested in canvas. I'm like, okay,

540
00:33:51,320 --> 00:33:54,960
Speaker 1: they want to use it. No, no, they want to

541
00:33:55,000 --> 00:33:58,840
Speaker 1: buy it. And I'm like, okay, well, let's do a

542
00:33:59,040 --> 00:34:02,680
Speaker 1: due diligence on Franklin Templeton. They're massive, as you know,

543
00:34:02,840 --> 00:34:06,000
Speaker 1: I think trillion and a half in assets under management,

544
00:34:06,400 --> 00:34:09,960
Speaker 1: and we were really having great results, as you know,

545
00:34:10,320 --> 00:34:13,680
Speaker 1: with Canvas on our own. We thought about it for

546
00:34:13,719 --> 00:34:17,880
Speaker 1: a long time, and you know, we really wanted custom

547
00:34:17,920 --> 00:34:21,839
Speaker 1: indexing to be a new category of asset management, and

548
00:34:22,080 --> 00:34:24,719
Speaker 1: we felt really proud about that because it isn't too

549
00:34:24,760 --> 00:34:27,359
Speaker 1: often that you're able to invent kind of a new

550
00:34:27,440 --> 00:34:32,200
Speaker 1: category of investing. And as we chatted about it and

551
00:34:32,239 --> 00:34:35,239
Speaker 1: talked it out, we're like, you know, we're at an

552
00:34:35,239 --> 00:34:40,600
Speaker 1: inflection point here. We are relatively small boutique, even though

553
00:34:40,640 --> 00:34:45,680
Speaker 1: this is working really really well. If we want custom indexing,

554
00:34:45,880 --> 00:34:50,440
Speaker 1: custom portfolio creation to really make the big time, it

555
00:34:50,680 --> 00:34:56,040
Speaker 1: probably makes sense for a much larger asset manager with

556
00:34:56,239 --> 00:35:00,480
Speaker 1: all sorts of advantages that we did not have to

557
00:35:00,920 --> 00:35:04,520
Speaker 1: take it and run with it. So we let that

558
00:35:04,640 --> 00:35:07,160
Speaker 1: be our guide, and after doing quite a bit of

559
00:35:07,280 --> 00:35:10,200
Speaker 1: due diligence on the people at Franklin, we were like, okay,

560
00:35:10,280 --> 00:35:13,120
Speaker 1: let's negotiate about selling the firm to them.

561
00:35:13,320 --> 00:35:18,000
Speaker 2: Talk about good timing. Morgan Stanley bought one of your

562
00:35:18,120 --> 00:35:23,040
Speaker 2: competitors in that space. Vanguard rolled out their own product,

563
00:35:23,040 --> 00:35:26,560
Speaker 2: which quickly amassed you know, billions and billions of dollars

564
00:35:26,560 --> 00:35:31,440
Speaker 2: on it. So this has worked its way into the mainstream,

565
00:35:31,520 --> 00:35:35,120
Speaker 2: even though it's still relatively I don't want to call

566
00:35:35,160 --> 00:35:37,440
Speaker 2: it a niche product because it's bigger than that.

567
00:35:38,040 --> 00:35:43,160
Speaker 1: But it's not ETFs. It's not giant yet, but it's

568
00:35:43,200 --> 00:35:45,920
Speaker 1: still growing at a pretty rapid clip, isn't it Totally?

569
00:35:46,080 --> 00:35:50,879
Speaker 1: And I think that ultimately we might look back ten

570
00:35:50,960 --> 00:35:54,759
Speaker 1: years from now and have the thought, can you imagine

571
00:35:54,760 --> 00:35:59,360
Speaker 1: that people just bought packaged products? I mean, like, my god,

572
00:36:00,360 --> 00:36:04,440
Speaker 1: no tax advantage, none of the customization, none of the

573
00:36:04,480 --> 00:36:09,799
Speaker 1: immunization for concentrated positions that I have. And so we

574
00:36:10,000 --> 00:36:15,439
Speaker 1: definitely think that this is a way of investing that well,

575
00:36:15,520 --> 00:36:21,520
Speaker 1: you know, once a client sees their portfolio under canvas

576
00:36:21,680 --> 00:36:26,719
Speaker 1: and with the customization, it's really really hard to go

577
00:36:26,840 --> 00:36:29,200
Speaker 1: back to thinking, ah, you know what, I think I'll

578
00:36:29,239 --> 00:36:32,600
Speaker 1: just go with five mutual funds or five ETFs. I

579
00:36:32,600 --> 00:36:35,319
Speaker 1: don't really care about much of the other I think

580
00:36:35,400 --> 00:36:39,560
Speaker 1: that you know, these things take time. But I mean again,

581
00:36:39,640 --> 00:36:43,400
Speaker 1: your firm is a classic example here. You were able

582
00:36:43,520 --> 00:36:47,839
Speaker 1: to use custom in a way that was good for

583
00:36:47,920 --> 00:36:51,520
Speaker 1: your firm, good for your clients. And you know, the

584
00:36:51,600 --> 00:36:55,040
Speaker 1: clients that we speak with love it. You know, they

585
00:36:55,080 --> 00:36:55,560
Speaker 1: all love it.

586
00:36:55,880 --> 00:36:59,920
Speaker 2: That's been our experience. It's really Mark Andriesen's software is

587
00:37:00,280 --> 00:37:04,600
Speaker 2: the world writ large. Because there are two aspects to this,

588
00:37:04,680 --> 00:37:07,440
Speaker 2: and I'm going to circle back to the database part

589
00:37:07,480 --> 00:37:10,520
Speaker 2: of it in a bit. But the front end, the

590
00:37:10,640 --> 00:37:15,840
Speaker 2: user interface and the software that allows a very simple

591
00:37:16,280 --> 00:37:20,399
Speaker 2: set of choices and that you could go increasingly down

592
00:37:20,480 --> 00:37:22,479
Speaker 2: the rabbit hole and find more and more and more

593
00:37:22,640 --> 00:37:26,759
Speaker 2: issues certainly is a big factor a lot of what

594
00:37:27,040 --> 00:37:33,080
Speaker 2: is done. The technology just wasn't quite mature enough fifteen

595
00:37:33,200 --> 00:37:36,879
Speaker 2: twenty years beforehand. And when you look at it, it's

596
00:37:36,920 --> 00:37:39,680
Speaker 2: just well, this is just software. It's just a user

597
00:37:39,719 --> 00:37:43,319
Speaker 2: interface and a way of organizing it. But now let's

598
00:37:43,360 --> 00:37:47,280
Speaker 2: circle back to the database, which I recall you saying

599
00:37:47,800 --> 00:37:50,560
Speaker 2: was the secret sauce. Tell us a little bit about

600
00:37:50,600 --> 00:37:54,400
Speaker 2: the database that you've been working on for a quarter century.

601
00:37:54,560 --> 00:37:56,920
Speaker 2: That drives canvas.

602
00:37:57,280 --> 00:38:02,840
Speaker 1: So we use the copystat universe. They cover virtually every

603
00:38:02,920 --> 00:38:07,480
Speaker 1: company that trades both here on American exchanges and elsewhere,

604
00:38:08,960 --> 00:38:12,560
Speaker 1: and it is kind of the gold standard really in

605
00:38:12,640 --> 00:38:14,000
Speaker 1: terms of databases.

606
00:38:14,200 --> 00:38:17,399
Speaker 2: How does it compare to something like CRISPER or some

607
00:38:17,480 --> 00:38:18,160
Speaker 2: of the other.

608
00:38:18,160 --> 00:38:20,920
Speaker 1: Well, so, CRISP. It comes to us from the University

609
00:38:20,960 --> 00:38:25,480
Speaker 1: of Chicago Center for Research and Security Pricing. The downside

610
00:38:25,520 --> 00:38:29,040
Speaker 1: of CRISP is it's, first off, I love chris We

611
00:38:29,200 --> 00:38:32,240
Speaker 1: used it in the most recent edition of What Works,

612
00:38:32,640 --> 00:38:37,839
Speaker 1: but it doesn't have enough of the fundamental factors attached

613
00:38:37,880 --> 00:38:41,360
Speaker 1: to it. In other words, it's mostly price history rice history.

614
00:38:41,719 --> 00:38:48,240
Speaker 1: And it also tries and generally succeeds to include all

615
00:38:48,400 --> 00:38:51,560
Speaker 1: of the names that might have been around trading on

616
00:38:51,600 --> 00:38:54,680
Speaker 1: the AMEX or the New York Stock Exchange or NASDAC.

617
00:38:55,080 --> 00:38:56,960
Speaker 1: But the challenge is a guy by the name of

618
00:38:57,120 --> 00:39:01,480
Speaker 1: Macquarie wrote a really compelling paper talking about how a

619
00:39:01,520 --> 00:39:05,400
Speaker 1: lot of the historical data not compustat, but further back

620
00:39:05,560 --> 00:39:09,080
Speaker 1: right in the twenties and thirties, come from the papers. Yeah,

621
00:39:09,200 --> 00:39:16,279
Speaker 1: and also wasn't nearly as thorough as say the compustat is.

622
00:39:16,760 --> 00:39:18,960
Speaker 1: In fact, one of the things that we were doing

623
00:39:19,320 --> 00:39:25,160
Speaker 1: before Franklin Templeton approached us is we were literally digitizing

624
00:39:25,280 --> 00:39:29,200
Speaker 1: old Moody's manuals. They go back to nineteen hundred, and

625
00:39:29,280 --> 00:39:33,080
Speaker 1: what we wanted to do was marry into the CRISP

626
00:39:33,200 --> 00:39:36,960
Speaker 1: data all of the fundamental factors that would have given

627
00:39:37,040 --> 00:39:41,320
Speaker 1: us the ability to run a nineteen hundred through nineteen

628
00:39:41,600 --> 00:39:46,760
Speaker 1: fifty five when Compustat begins test We ran some test runs.

629
00:39:46,760 --> 00:39:49,200
Speaker 1: We did price to book, and we did a couple others.

630
00:39:49,719 --> 00:39:53,600
Speaker 1: And what we were finding and won't surprise you, generally speaking,

631
00:39:54,000 --> 00:39:57,200
Speaker 1: same kind of results, right with the exceptional price to book,

632
00:39:57,640 --> 00:40:01,319
Speaker 1: we actually took price to book of our composites. You

633
00:40:01,360 --> 00:40:04,680
Speaker 1: know how we have the composits for value and momentum

634
00:40:04,680 --> 00:40:07,120
Speaker 1: and all of those things, and we took price to

635
00:40:07,120 --> 00:40:10,000
Speaker 1: book out because of the research that we did that

636
00:40:10,080 --> 00:40:13,600
Speaker 1: covered the great depression from the thirties. You know, and

637
00:40:13,640 --> 00:40:16,640
Speaker 1: I know if you've taken any finance courses, price to

638
00:40:16,680 --> 00:40:20,920
Speaker 1: book previously had been used as a proxy for likelihood

639
00:40:20,920 --> 00:40:25,600
Speaker 1: of bankruptcy. Right, Well, guess what during the thirties, a

640
00:40:25,600 --> 00:40:28,799
Speaker 1: lot of those low price to book companies went bankrupt. Well,

641
00:40:28,800 --> 00:40:32,279
Speaker 1: when your book value collapses, exactly, it's the book isn't

642
00:40:32,520 --> 00:40:37,040
Speaker 1: much value exactly exactly. So we did find some learnings

643
00:40:37,719 --> 00:40:41,919
Speaker 1: where we jiggered with the composites that we use. That's

644
00:40:41,920 --> 00:40:45,520
Speaker 1: another thing we do. We don't use a single factor.

645
00:40:45,560 --> 00:40:47,719
Speaker 1: In my first version of What Works on Wall Street,

646
00:40:47,800 --> 00:40:50,719
Speaker 1: we would sort down for the final portfolio on a

647
00:40:50,760 --> 00:40:54,359
Speaker 1: single factor, and we found that that wasn't nearly as

648
00:40:54,560 --> 00:40:59,080
Speaker 1: effective as a composite of factors. Again, a lot of

649
00:40:59,120 --> 00:41:01,279
Speaker 1: people the old joke about quants, right, what do you

650
00:41:01,360 --> 00:41:04,320
Speaker 1: guys do golf all day? You know you're just running

651
00:41:04,320 --> 00:41:07,560
Speaker 1: your models. Well, we don't golf all day. But what

652
00:41:07,640 --> 00:41:11,960
Speaker 1: we do do all day is research the underlying models.

653
00:41:12,400 --> 00:41:16,560
Speaker 1: What we're always trying to do is improve them. But

654
00:41:16,840 --> 00:41:23,000
Speaker 1: it's evolutionary, not revolutionary. Listen, the foundations are very, very similar.

655
00:41:23,120 --> 00:41:25,239
Speaker 1: By the way. They make a lot of sense too.

656
00:41:25,400 --> 00:41:29,279
Speaker 1: I say, if we changed it and walked out onto

657
00:41:29,360 --> 00:41:32,359
Speaker 1: Lexington Avenue here and we found a food truck, right,

658
00:41:32,960 --> 00:41:35,959
Speaker 1: and we went up and long line. Everything looks good.

659
00:41:36,040 --> 00:41:38,799
Speaker 1: And we talked to the owner and we said, how

660
00:41:38,800 --> 00:41:41,279
Speaker 1: about you clearing a year And he says, well, I'm

661
00:41:41,280 --> 00:41:44,320
Speaker 1: clearing one hundred thousand. And we're like, well, would you

662
00:41:44,360 --> 00:41:46,920
Speaker 1: take a buy offer from us? And he goes, yeah,

663
00:41:47,000 --> 00:41:49,480
Speaker 1: you can buy it for ten million. You and I

664
00:41:49,520 --> 00:41:51,759
Speaker 1: are going to go get out of here. There's no

665
00:41:51,840 --> 00:41:54,759
Speaker 1: way we're going to buy this right, Well, change it

666
00:41:54,800 --> 00:41:57,800
Speaker 1: to a stock ticker. There's a lot of stocks trading

667
00:41:58,080 --> 00:42:01,319
Speaker 1: at that kind of multiple, and so when you look

668
00:42:01,320 --> 00:42:06,600
Speaker 1: at the underlying strategies, they make intuitive economic sense, and

669
00:42:06,719 --> 00:42:11,239
Speaker 1: so the data set that you're using becomes of paramount importance.

670
00:42:11,640 --> 00:42:14,640
Speaker 1: The other thing I found was that, and this one

671
00:42:14,760 --> 00:42:17,520
Speaker 1: disturbed me a little. I haven't looked at this recently,

672
00:42:17,640 --> 00:42:20,480
Speaker 1: but when I was doing it several years ago, you

673
00:42:20,560 --> 00:42:24,319
Speaker 1: could get really different numbers if you went to Bloomberg,

674
00:42:24,800 --> 00:42:27,040
Speaker 1: or if you went to Reuter's, or if you went

675
00:42:27,080 --> 00:42:31,920
Speaker 1: to Dow Jones or any other innumerable providers of data.

676
00:42:32,360 --> 00:42:35,480
Speaker 1: And so that was another huge project for us, and

677
00:42:35,719 --> 00:42:39,000
Speaker 1: also part of the data set that we're talking about.

678
00:42:39,400 --> 00:42:41,840
Speaker 1: One of the other things that I was widely hated

679
00:42:41,880 --> 00:42:45,839
Speaker 1: for by my research team was we went on a

680
00:42:46,040 --> 00:42:51,200
Speaker 1: multi year data cleansing exercise because we found that a

681
00:42:51,239 --> 00:42:53,680
Speaker 1: lot of it had a lot of hair on it.

682
00:42:54,239 --> 00:42:58,080
Speaker 1: And so I'd made no friends on the research desk

683
00:42:58,239 --> 00:43:02,280
Speaker 1: when I said, listen, we've got to get this pristine.

684
00:43:02,960 --> 00:43:07,000
Speaker 1: And so our data cleansing of the universe also is

685
00:43:07,080 --> 00:43:13,319
Speaker 1: another real important distinction between just generally available data and

686
00:43:13,440 --> 00:43:14,640
Speaker 1: that which we are using.

687
00:43:14,719 --> 00:43:18,480
Speaker 2: Huh, really really interesting. Let's stay with price to book

688
00:43:18,920 --> 00:43:21,719
Speaker 2: because I want to ask your opinion on something, and

689
00:43:21,760 --> 00:43:25,040
Speaker 2: you're the perfect quant to bring this up to. Which

690
00:43:25,120 --> 00:43:27,720
Speaker 2: is all right? So we're talking about price to book.

691
00:43:28,360 --> 00:43:32,520
Speaker 2: Back in the day when manufacturing required a lot of

692
00:43:33,160 --> 00:43:36,640
Speaker 2: men and material and capital, and you had big factories

693
00:43:36,719 --> 00:43:40,520
Speaker 2: and railroads were laying thousands of miles of steel, and

694
00:43:41,080 --> 00:43:43,960
Speaker 2: you know, you were building these forges and foundries to

695
00:43:44,040 --> 00:43:50,040
Speaker 2: make cars. The modern era, especially with technology, there are

696
00:43:50,080 --> 00:43:53,000
Speaker 2: a lot of intangibles that don't seem to find their

697
00:43:53,040 --> 00:43:57,040
Speaker 2: way to book value. Things like patents and copyrights and

698
00:43:57,120 --> 00:44:02,319
Speaker 2: algorithms and processes that are prepared rietary that really are

699
00:44:02,360 --> 00:44:07,160
Speaker 2: the whole value of the company, but somehow never show

700
00:44:07,239 --> 00:44:10,680
Speaker 2: up in metrics like price to book, which has led

701
00:44:11,040 --> 00:44:14,640
Speaker 2: to some people and I'm not positive who to name.

702
00:44:14,760 --> 00:44:17,920
Speaker 2: I don't want to mischaracterize anybody, but some folks have

703
00:44:18,000 --> 00:44:24,000
Speaker 2: said we're mispricing companies that operate in the tech space

704
00:44:24,320 --> 00:44:27,040
Speaker 2: because we're not giving them the appropriate credit for all

705
00:44:27,080 --> 00:44:31,160
Speaker 2: of this intellectual property. Is that an overstatement or is

706
00:44:31,160 --> 00:44:32,120
Speaker 2: there some truth there?

707
00:44:32,560 --> 00:44:35,680
Speaker 1: I think there's more than some truth to that. We

708
00:44:36,000 --> 00:44:39,520
Speaker 1: published a papers called the Veiled Value, and it looked

709
00:44:39,560 --> 00:44:43,640
Speaker 1: at the idea that brand value, that all of the

710
00:44:43,680 --> 00:44:47,920
Speaker 1: items that you just delineated, we're not being captured in

711
00:44:48,160 --> 00:44:54,239
Speaker 1: Trademark's research and development straight across the board. When we

712
00:44:54,400 --> 00:44:57,600
Speaker 1: took a look at that, we found that you could

713
00:44:57,640 --> 00:45:01,320
Speaker 1: figure out a way to price that into the model.

714
00:45:01,800 --> 00:45:05,480
Speaker 1: So you are absolutely right. This is one of my bugaboos,

715
00:45:05,520 --> 00:45:09,440
Speaker 1: things like GDP, all of the metrics that we continue

716
00:45:09,480 --> 00:45:14,040
Speaker 1: to report and get obsessed about. Basically they've lost a

717
00:45:14,080 --> 00:45:17,440
Speaker 1: lot of their meaning because they were designed for the

718
00:45:17,480 --> 00:45:21,960
Speaker 1: world you just articulated. They were designed for manufacturing. They

719
00:45:21,960 --> 00:45:25,960
Speaker 1: were designed for physical things, and we moved off that

720
00:45:26,239 --> 00:45:29,640
Speaker 1: for many many decades. Atoms to bitts was a big transition,

721
00:45:29,880 --> 00:45:34,680
Speaker 1: huge transition, and so we think that we another aspect

722
00:45:34,760 --> 00:45:38,160
Speaker 1: of research right when when we got the idea, you know,

723
00:45:38,239 --> 00:45:42,239
Speaker 1: we think we're missing something here. That's what resulted in

724
00:45:42,280 --> 00:45:46,680
Speaker 1: the paper about brand value and goodwill and all those

725
00:45:46,719 --> 00:45:50,439
Speaker 1: things not being taken into account by investors at all.

726
00:45:50,960 --> 00:45:54,080
Speaker 1: And so we found ways we could do that with

727
00:45:54,200 --> 00:46:00,520
Speaker 1: factors and improved the efficacy of the underlying modelsificantly.

728
00:46:00,760 --> 00:46:03,719
Speaker 2: I think one of the greatest quotes ever issued by

729
00:46:03,760 --> 00:46:08,480
Speaker 2: a statistics professor is George Bachs All models are wrong,

730
00:46:08,640 --> 00:46:09,040
Speaker 2: but some.

731
00:46:09,000 --> 00:46:12,800
Speaker 1: Are useful, exactly. I quote him all the time because

732
00:46:12,880 --> 00:46:17,279
Speaker 1: he's absolutely right. The idea that you're going to get

733
00:46:17,320 --> 00:46:21,520
Speaker 1: anything to perfection is a fool's errand right. I have

734
00:46:21,600 --> 00:46:25,120
Speaker 1: a writer that we're working with under O'Shaughnessy Adventures, one

735
00:46:25,200 --> 00:46:28,759
Speaker 1: of our new verticals, which is Infinite Books, and he's

736
00:46:28,800 --> 00:46:32,280
Speaker 1: got a great quote, which is perfection is a one

737
00:46:32,360 --> 00:46:33,960
Speaker 1: hundred percent tax.

738
00:46:34,520 --> 00:46:39,320
Speaker 2: Really interesting. Let's talk a little about O'Shaughnessy Adventures starting

739
00:46:39,360 --> 00:46:43,640
Speaker 2: with your mission statement. Osv's mission is to fuel creators

740
00:46:43,680 --> 00:46:48,800
Speaker 2: in the worlds of art, science and technology with the advice,

741
00:46:49,000 --> 00:46:53,240
Speaker 2: data and resources they need to stay focused and get

742
00:46:53,320 --> 00:46:58,040
Speaker 2: great ideas out of their heads, off of their whiteboards,

743
00:46:58,480 --> 00:47:02,080
Speaker 2: and out into the world discuss I had.

744
00:47:01,960 --> 00:47:06,680
Speaker 1: A thesis that started to develop around twenty seventeen twenty

745
00:47:06,800 --> 00:47:11,160
Speaker 1: eighteen as I watched old playbooks that used to work

746
00:47:11,239 --> 00:47:15,520
Speaker 1: beautifully stop working, and so I came up with this

747
00:47:15,719 --> 00:47:20,120
Speaker 1: idea that we were in a great reshuffle where all

748
00:47:20,160 --> 00:47:24,640
Speaker 1: of the old models were collapsing and people were kind

749
00:47:24,680 --> 00:47:28,239
Speaker 1: of freaked out. They were like, this has worked for decades,

750
00:47:28,760 --> 00:47:31,680
Speaker 1: Why doesn't it work anymore? And I think that one

751
00:47:31,760 --> 00:47:36,239
Speaker 1: of the reasons it didn't work anymore was because the tools,

752
00:47:36,320 --> 00:47:40,799
Speaker 1: the tech tools, and the platforms and the Internet and

753
00:47:40,960 --> 00:47:46,719
Speaker 1: all of that put together allowed for much more innovative

754
00:47:47,040 --> 00:47:52,200
Speaker 1: business models in a variety of industries. Right, So if

755
00:47:52,200 --> 00:47:55,879
Speaker 1: you look at the verticals of O'shaughness adventures, you'll see

756
00:47:55,920 --> 00:47:58,959
Speaker 1: what we think. Right, So we have what we call

757
00:47:59,480 --> 00:48:03,880
Speaker 1: infinite adventures. That's venture capital. But I love in the

758
00:48:03,920 --> 00:48:07,320
Speaker 1: old days they used to call venture capital adventure capital.

759
00:48:07,400 --> 00:48:12,200
Speaker 1: And the one I really loved liberation capital. Uh, well,

760
00:48:12,200 --> 00:48:14,560
Speaker 1: to find that what is what is liberation? And I've

761
00:48:14,600 --> 00:48:16,920
Speaker 1: heard the phrase, yeah in the old days, the so

762
00:48:17,000 --> 00:48:20,160
Speaker 1: called hateful eight that wanted to leave shockly.

763
00:48:20,000 --> 00:48:23,759
Speaker 2: Right, the early days of semiconductors and the pre in

764
00:48:24,000 --> 00:48:25,760
Speaker 2: Fairchild semi conductor.

765
00:48:25,400 --> 00:48:31,560
Speaker 1: Exactly exactly right, good call. And back then, the idea

766
00:48:31,719 --> 00:48:35,480
Speaker 1: that a group of engineers or even you know, regular

767
00:48:35,560 --> 00:48:39,840
Speaker 1: business people would leave a big company that was well

768
00:48:39,920 --> 00:48:43,040
Speaker 1: funded by a bank or a series of other investors

769
00:48:43,360 --> 00:48:47,839
Speaker 1: was almost unthinkable. And so what came to be known

770
00:48:47,880 --> 00:48:52,040
Speaker 1: as the hateful eight who created Fairchild got pitched by

771
00:48:52,200 --> 00:48:56,360
Speaker 1: a variety of investors, external investors saying why don't you

772
00:48:56,400 --> 00:48:59,560
Speaker 1: guys to start your own company. He finally talked them

773
00:48:59,600 --> 00:49:02,680
Speaker 1: into it, and that's when we use the term this

774
00:49:02,920 --> 00:49:08,040
Speaker 1: is your liberation capital, where you can focus on just

775
00:49:08,200 --> 00:49:12,279
Speaker 1: what you want to focus on, making better semiconductors. You

776
00:49:12,280 --> 00:49:14,400
Speaker 1: don't have to play any of the politics of the

777
00:49:14,440 --> 00:49:17,239
Speaker 1: big company, you don't have to answer to people who

778
00:49:17,280 --> 00:49:20,920
Speaker 1: don't really understand what you're doing. Right, the people in

779
00:49:20,960 --> 00:49:23,480
Speaker 1: New York that might have owned it or financed it

780
00:49:23,640 --> 00:49:27,840
Speaker 1: had very little understanding of what semiconductors were all about

781
00:49:27,840 --> 00:49:31,279
Speaker 1: in the fifties and sixties, and so I like that

782
00:49:31,520 --> 00:49:33,000
Speaker 1: part very very much.

783
00:49:33,040 --> 00:49:36,960
Speaker 2: That's the genesis of Intel right, as well as the

784
00:49:37,000 --> 00:49:40,680
Speaker 2: whole run of other semiconductors, can trace its roots back

785
00:49:40,719 --> 00:49:43,200
Speaker 2: to fair Child right exactly.

786
00:49:43,800 --> 00:49:48,080
Speaker 1: And so there we're looking for companies that we think

787
00:49:48,239 --> 00:49:55,319
Speaker 1: will expand the opportunity set for very clever entrepreneurs and creators.

788
00:49:56,080 --> 00:50:00,200
Speaker 1: Another vertical is infinite films. Why that, well, I think

789
00:50:00,239 --> 00:50:05,920
Speaker 1: we're approaching a period where you can make films, documentaries,

790
00:50:06,040 --> 00:50:09,959
Speaker 1: You can use AI to augment your filmmaking in such

791
00:50:10,000 --> 00:50:13,200
Speaker 1: a way that the people who couldn't make movies in

792
00:50:13,239 --> 00:50:15,359
Speaker 1: the past are going to be able to make them

793
00:50:15,360 --> 00:50:18,120
Speaker 1: in the future. You could legitimately make.

794
00:50:17,920 --> 00:50:20,680
Speaker 2: A film with an iPhone. Now that you can you

795
00:50:20,680 --> 00:50:24,120
Speaker 2: couldn't do even five years ago. Is kind of on

796
00:50:24,160 --> 00:50:24,680
Speaker 2: the border.

797
00:50:24,800 --> 00:50:28,759
Speaker 1: Barry. Some of the things that I've seen as submissions

798
00:50:28,800 --> 00:50:35,479
Speaker 1: to infinite films, Oh my god, Like literally, I'm sixty three.

799
00:50:35,840 --> 00:50:39,000
Speaker 1: If I had seen that as a trailer for a

800
00:50:39,040 --> 00:50:42,000
Speaker 1: movie at a movie theater like ten years ago, I

801
00:50:42,000 --> 00:50:45,120
Speaker 1: would have thought, Wow, this is amazing, this is cool.

802
00:50:45,200 --> 00:50:47,440
Speaker 1: And then the guy at the bottom says, by the

803
00:50:47,440 --> 00:50:50,080
Speaker 1: way I made this on my iPhone, that's crazy. That

804
00:50:50,160 --> 00:50:54,600
Speaker 1: really is great, And so that Unlock's tremendous talent that

805
00:50:54,840 --> 00:51:00,800
Speaker 1: never had access to the Hollywood infrastructure. So our thesis

806
00:51:00,920 --> 00:51:04,319
Speaker 1: is there are tons of really creative people out there

807
00:51:04,719 --> 00:51:09,280
Speaker 1: who now have the tools to make great movies. Another

808
00:51:09,320 --> 00:51:12,239
Speaker 1: thing I wanted to do was where are the Rudies

809
00:51:12,600 --> 00:51:15,960
Speaker 1: of movies today? Now? Rudy's, of course, is about the

810
00:51:16,040 --> 00:51:18,399
Speaker 1: kid who goes to Notre Dame and he's five foot

811
00:51:18,480 --> 00:51:21,959
Speaker 1: nothing and weighs a buck nothing and he gets on

812
00:51:22,040 --> 00:51:25,399
Speaker 1: the team, the Notre Dame team. Why was that such

813
00:51:25,400 --> 00:51:30,520
Speaker 1: a great movie? Because it's incredibly inspirational, It gives the

814
00:51:30,600 --> 00:51:33,040
Speaker 1: viewer like, you know what, I can take a shot

815
00:51:33,080 --> 00:51:35,560
Speaker 1: at it, I can do it. Hollywood seems to have

816
00:51:35,640 --> 00:51:38,320
Speaker 1: completely forgotten about making these types of movies.

817
00:51:39,040 --> 00:51:42,640
Speaker 2: And just for people who might not remember the movie Rudy,

818
00:51:43,080 --> 00:51:47,000
Speaker 2: it's the story that drives the whole thing and the characters.

819
00:51:47,480 --> 00:51:50,640
Speaker 2: There's not a whole lot of expensive special effects or

820
00:51:51,200 --> 00:51:54,040
Speaker 2: you know, they're not flying out to Nepal. It's all

821
00:51:54,120 --> 00:51:58,520
Speaker 2: done pretty much on the cheap. And that's the area

822
00:51:58,719 --> 00:52:03,920
Speaker 2: of film you're looking to explore, or narrative driven accessible story.

823
00:52:04,120 --> 00:52:09,960
Speaker 1: Narrative driven accessible stories that we're also changing the underlying

824
00:52:10,040 --> 00:52:14,360
Speaker 1: economics on. So here's how we're gonna do that. Everyone

825
00:52:14,440 --> 00:52:17,239
Speaker 1: who comes and works on one of our films is

826
00:52:17,280 --> 00:52:21,200
Speaker 1: going to own a piece of that film and back

827
00:52:21,320 --> 00:52:25,160
Speaker 1: end points, back end points. But for everybody, we're not

828
00:52:25,200 --> 00:52:29,560
Speaker 1: going to use Hollywood accounting. Our accounting is very, very straightforward.

829
00:52:30,040 --> 00:52:32,920
Speaker 1: Here's what it cost us to make it. What happens

830
00:52:32,960 --> 00:52:36,960
Speaker 1: after we recover those costs, You own x percent. If

831
00:52:37,000 --> 00:52:40,160
Speaker 1: we manage to sell it or generate revenue from it

832
00:52:40,320 --> 00:52:43,359
Speaker 1: through the multiple platforms you can put it out on,

833
00:52:43,800 --> 00:52:46,680
Speaker 1: you're going to benefit from that. The other thing that

834
00:52:46,719 --> 00:52:48,799
Speaker 1: we're going to do is we're going to give young

835
00:52:48,880 --> 00:52:52,640
Speaker 1: people a shot right now, if you want to try

836
00:52:52,719 --> 00:52:55,720
Speaker 1: to beat Let's say you graduate from NYU Film School

837
00:52:56,120 --> 00:52:58,160
Speaker 1: and you decide you're gonna go out to Hollywood and

838
00:52:58,200 --> 00:53:00,440
Speaker 1: you're going to pitch all of these student video its,

839
00:53:00,480 --> 00:53:03,879
Speaker 1: say you want to get good luck, because it ain't

840
00:53:03,960 --> 00:53:07,920
Speaker 1: gonna happen. Right There is almost a guild like system

841
00:53:08,080 --> 00:53:11,440
Speaker 1: out in Hollywood where you know, it's kind of the

842
00:53:11,840 --> 00:53:14,760
Speaker 1: idea that, yeah, I want to get in the Screen

843
00:53:14,800 --> 00:53:17,520
Speaker 1: Actors Guild. How do I do that? Well, to get

844
00:53:17,560 --> 00:53:19,520
Speaker 1: in the Screen Actors Guild, you have to be in

845
00:53:19,560 --> 00:53:22,000
Speaker 1: three movies. Well, wait a minute, how do I get

846
00:53:22,040 --> 00:53:24,360
Speaker 1: in the movie if I'm not in the Screen Actors Guild.

847
00:53:24,800 --> 00:53:28,400
Speaker 1: So there are a lot of really old fashioned rules.

848
00:53:28,400 --> 00:53:31,520
Speaker 1: And it's not just Hollywood, by the way, it's much

849
00:53:31,520 --> 00:53:34,960
Speaker 1: of media. It's much of all of the things that

850
00:53:35,000 --> 00:53:39,440
Speaker 1: we consume every day. And so basically what I did

851
00:53:39,640 --> 00:53:43,759
Speaker 1: was say, what industries that I find fascinating that I'm

852
00:53:43,840 --> 00:53:49,040
Speaker 1: interested in have the greatest arbitrage ability. Huh.

853
00:53:49,080 --> 00:53:51,719
Speaker 2: I love that concept. And you know it's funny you

854
00:53:51,760 --> 00:53:57,719
Speaker 2: mentioned films because that dynamic tension of indie films. Look

855
00:53:57,760 --> 00:54:00,960
Speaker 2: at how great A twenty four has been doing amazing

856
00:54:01,640 --> 00:54:06,200
Speaker 2: as a as an independent studio. The timing is really good,

857
00:54:06,280 --> 00:54:10,280
Speaker 2: and the technology tools, the ability to film on a phone,

858
00:54:10,840 --> 00:54:14,840
Speaker 2: edit on your laptop and then distribute it by uploading

859
00:54:14,840 --> 00:54:16,280
Speaker 2: to YouTube or wherever.

860
00:54:17,000 --> 00:54:21,160
Speaker 1: Barry, that's the key. There's always cultural lag, right, you

861
00:54:21,239 --> 00:54:26,480
Speaker 1: know the S curve tech adoption, right, it's real, And

862
00:54:26,880 --> 00:54:31,520
Speaker 1: let's change industries and let's look at publishing. Right, So

863
00:54:31,680 --> 00:54:36,640
Speaker 1: we are launching Infinite Books. Why well, because the current

864
00:54:36,760 --> 00:54:41,600
Speaker 1: publishing industry is still playing under nineteen twenty rules, not

865
00:54:41,840 --> 00:54:47,080
Speaker 1: twenty twenty rules. We no longer have to have minuscule

866
00:54:47,280 --> 00:54:51,000
Speaker 1: amounts going to the author. We can, because of the tech,

867
00:54:51,120 --> 00:54:54,440
Speaker 1: because of our ability to produce that book, give the

868
00:54:54,560 --> 00:54:59,000
Speaker 1: author much more of the upside. So, for example, we're

869
00:54:59,040 --> 00:55:02,239
Speaker 1: going to give any where between depending on what the

870
00:55:02,280 --> 00:55:05,120
Speaker 1: author wants us to do for them. It's going to

871
00:55:05,239 --> 00:55:08,319
Speaker 1: always be above fifty percent. Mostly it's going to be

872
00:55:08,440 --> 00:55:12,600
Speaker 1: seventy percent. But that's just the start. Imagine Berry, you

873
00:55:12,640 --> 00:55:15,319
Speaker 1: write a book, you bring it to Infinite Books, and

874
00:55:15,360 --> 00:55:18,400
Speaker 1: I say, hey, Barry, what other languages do you want

875
00:55:18,400 --> 00:55:22,239
Speaker 1: this published in? And You're like, I don't know, maybe Spanish,

876
00:55:22,360 --> 00:55:27,360
Speaker 1: maybe French. Maybe done. Because of AI, we can translate

877
00:55:27,400 --> 00:55:30,920
Speaker 1: the entire book and have it available for the French

878
00:55:31,000 --> 00:55:34,719
Speaker 1: or Spanish speaking markets. Even better, let's say you want

879
00:55:34,719 --> 00:55:36,560
Speaker 1: to do an audiobook and you want to read it

880
00:55:36,560 --> 00:55:39,680
Speaker 1: because you've got a great voice. I say, Berry, do

881
00:55:39,800 --> 00:55:44,080
Speaker 1: a minute on this for me, say Express Surprise or

882
00:55:44,160 --> 00:55:48,960
Speaker 1: Anger or whatever. It will model your voice and you

883
00:55:49,040 --> 00:55:52,439
Speaker 1: can read your book on all the audiobooks. But what's

884
00:55:52,560 --> 00:55:57,760
Speaker 1: really cool is we can translate your voice into French,

885
00:55:58,280 --> 00:56:03,040
Speaker 1: into Spanish, in to Russian, into anything. Wow. And so

886
00:56:03,680 --> 00:56:08,040
Speaker 1: all of these tech advantages are being left just lying

887
00:56:08,080 --> 00:56:11,279
Speaker 1: around on the floor, and we think that's crazy.

888
00:56:11,600 --> 00:56:14,840
Speaker 2: We're still early days of the transition, oh very early,

889
00:56:15,000 --> 00:56:20,280
Speaker 2: to technology, to AI, to all these changes in platforms.

890
00:56:20,640 --> 00:56:25,640
Speaker 2: It's amazing how slowly it takes place. I think our

891
00:56:26,080 --> 00:56:31,799
Speaker 2: mutual friend Morgan Housel described how long it took from

892
00:56:32,200 --> 00:56:35,719
Speaker 2: the Wright Brothers doing the first test flight in Kitty

893
00:56:35,760 --> 00:56:38,600
Speaker 2: Hawk before it even made its way into newspapers.

894
00:56:38,800 --> 00:56:43,000
Speaker 1: Exactly takes forever, and it does. And this lag, even

895
00:56:43,160 --> 00:56:48,160
Speaker 1: in our twenty four to seven always online environment remains right.

896
00:56:48,400 --> 00:56:51,360
Speaker 1: It's like, if you think about it, it makes tons of sense.

897
00:56:51,760 --> 00:56:55,480
Speaker 1: People are habitual, right, they get into habits, they do

898
00:56:55,600 --> 00:56:58,960
Speaker 1: all of these things. Now, I think that the pandemic

899
00:56:59,760 --> 00:57:04,440
Speaker 1: really sped up a lot of these trends, things like

900
00:57:04,800 --> 00:57:09,200
Speaker 1: work from Anywhere. O'shaughness Adventures is a work from anywhere enterprise.

901
00:57:09,600 --> 00:57:14,680
Speaker 1: We have people in Singapore, India, UK, all over the

902
00:57:14,719 --> 00:57:20,080
Speaker 1: world because we can, and the idea that we have

903
00:57:20,200 --> 00:57:24,280
Speaker 1: to have a traditional office, the idea that we have

904
00:57:24,400 --> 00:57:27,439
Speaker 1: to do any of those traditional things goes right out

905
00:57:27,440 --> 00:57:32,600
Speaker 1: the window. It becomes a much less costly enterprise when

906
00:57:32,640 --> 00:57:35,520
Speaker 1: you can do it this way. But we back to

907
00:57:35,760 --> 00:57:39,720
Speaker 1: infinite books like we also are going to at the

908
00:57:39,840 --> 00:57:42,960
Speaker 1: author's decision. Right, We're not going to force anything on

909
00:57:43,000 --> 00:57:47,800
Speaker 1: our authors. But if the author wants an AI agent

910
00:57:48,320 --> 00:57:51,360
Speaker 1: to Let's say, for example, your new book, Let's say

911
00:57:51,400 --> 00:57:55,760
Speaker 1: if it were an Infinite Books publication and you noted

912
00:57:55,880 --> 00:58:01,200
Speaker 1: that it quadrupled sales in Omaha, Nebraska, how about having

913
00:58:01,200 --> 00:58:05,240
Speaker 1: an AI agent find out what podcasts in Omaha are

914
00:58:05,320 --> 00:58:08,520
Speaker 1: interested in the subject Berry's written about. How about sending

915
00:58:08,600 --> 00:58:10,960
Speaker 1: them a query letter. How about setting them a clip

916
00:58:11,000 --> 00:58:13,840
Speaker 1: from the book and saying you really ought to have

917
00:58:13,960 --> 00:58:17,480
Speaker 1: him on your show or podcast or write about them.

918
00:58:17,520 --> 00:58:21,520
Speaker 1: In your substack. All of the tools that are available

919
00:58:21,560 --> 00:58:26,680
Speaker 1: to us work today, and people aren't using them, and

920
00:58:26,760 --> 00:58:30,840
Speaker 1: so we suspect that this is going to really I

921
00:58:30,880 --> 00:58:33,720
Speaker 1: hate the word revolutionize because that's, you know, come on,

922
00:58:34,640 --> 00:58:39,080
Speaker 1: but it's certainly going to accelerate. That's a better ride.

923
00:58:39,600 --> 00:58:43,160
Speaker 2: So I want to talk about another aspect of O'shaughnessee ventures,

924
00:58:43,680 --> 00:58:47,120
Speaker 2: which is the fellowship program, which I find to be

925
00:58:47,880 --> 00:58:51,600
Speaker 2: absolutely fascinating. How does this work tell us a little

926
00:58:51,600 --> 00:58:53,240
Speaker 2: bit about the O'shaughnessee Fellowship.

927
00:58:53,480 --> 00:58:56,960
Speaker 1: For most of history, a genius could be born, live

928
00:58:57,080 --> 00:59:00,439
Speaker 1: and die without even knowing they were a genius, far

929
00:59:00,520 --> 00:59:04,400
Speaker 1: less other people knowing it. Right, We were really bound

930
00:59:04,440 --> 00:59:08,480
Speaker 1: by our geography and by our networks, and those networks

931
00:59:08,520 --> 00:59:11,760
Speaker 1: were pretty small. Like who'd you grow up with, who'd

932
00:59:11,760 --> 00:59:14,360
Speaker 1: you go to school with, who'd you mary? Where are

933
00:59:14,360 --> 00:59:16,560
Speaker 1: your kids going to school? What church do you go to?

934
00:59:16,680 --> 00:59:19,360
Speaker 1: That kind of stuff pretty random. You're just random where

935
00:59:19,400 --> 00:59:21,480
Speaker 1: you were born. I was just dumb. Luck was kind

936
00:59:21,480 --> 00:59:24,640
Speaker 1: of dumb. Luck. You could move, of course, but changing

937
00:59:24,720 --> 00:59:27,200
Speaker 1: your digital zip code is a hell of a lot

938
00:59:27,240 --> 00:59:31,320
Speaker 1: easier than changing your physical zip code. But more importantly,

939
00:59:31,600 --> 00:59:36,080
Speaker 1: we now are interconnected. I can find somebody who's a

940
00:59:36,200 --> 00:59:39,040
Speaker 1: genius who happens to live in Bangladesh. I would have

941
00:59:39,200 --> 00:59:42,880
Speaker 1: never under the old system ever known about that person.

942
00:59:43,760 --> 00:59:46,880
Speaker 1: Now I have the ability to know about that person

943
00:59:47,280 --> 00:59:51,240
Speaker 1: and find and fund them. The whole idea behind the

944
00:59:51,320 --> 00:59:54,760
Speaker 1: fellowships was we wanted to come up with something that

945
00:59:54,960 --> 01:00:00,640
Speaker 1: highlighted the fact that there are tons millions of brilliant

946
01:00:00,800 --> 01:00:05,320
Speaker 1: people who in the past just didn't have the right connections,

947
01:00:05,680 --> 01:00:09,400
Speaker 1: didn't have the right credentials, you'd name it, to get

948
01:00:09,440 --> 01:00:12,640
Speaker 1: into a place where they could get funding, they could

949
01:00:12,640 --> 01:00:16,120
Speaker 1: make their idea come to life. And so the idea

950
01:00:16,160 --> 01:00:19,320
Speaker 1: is quite simple. We're gonna find and fund them and

951
01:00:19,640 --> 01:00:23,760
Speaker 1: see what comes from that. I think that it allows

952
01:00:23,920 --> 01:00:26,880
Speaker 1: for so many things, Like it allows we have a

953
01:00:26,920 --> 01:00:29,520
Speaker 1: guy who got one of our grants, which is the

954
01:00:29,600 --> 01:00:32,880
Speaker 1: smaller amount. It's ten thousand. The fellowships are one hundred

955
01:00:32,880 --> 01:00:35,320
Speaker 1: thousand over a year, no strings.

956
01:00:35,080 --> 01:00:37,400
Speaker 2: No strings attached. He has a check for one hundred k.

957
01:00:38,080 --> 01:00:41,040
Speaker 2: Go do something interesting. We don't care what it is exactly.

958
01:00:41,760 --> 01:00:45,240
Speaker 1: And we wanted to do no strings because like we

959
01:00:45,320 --> 01:00:48,440
Speaker 1: don't want Gotcha's we don't want. But you've got to

960
01:00:48,480 --> 01:00:51,360
Speaker 1: do You've got to give us right of first refusal.

961
01:00:52,080 --> 01:00:54,000
Speaker 1: The way I look at it is if if we

962
01:00:54,120 --> 01:00:57,360
Speaker 1: got somebody so wrong that they're going to take one

963
01:00:57,440 --> 01:01:01,920
Speaker 1: hundred thousand fellowship from us, develop something really cool, decide

964
01:01:01,960 --> 01:01:04,080
Speaker 1: to start a company around it, and then take it

965
01:01:04,080 --> 01:01:06,880
Speaker 1: to a different person for funding, well, we made the

966
01:01:06,920 --> 01:01:11,120
Speaker 1: mistake right, because generally speaking, what we're finding is they

967
01:01:11,240 --> 01:01:15,080
Speaker 1: love being part of the community. Because I'm also a

968
01:01:15,320 --> 01:01:20,160
Speaker 1: huge believer in cognitive diversity, right, there's a great quote

969
01:01:20,160 --> 01:01:23,360
Speaker 1: that is like, no matter how smart somebody is, no

970
01:01:23,440 --> 01:01:29,160
Speaker 1: matter how insightful, no matter how brilliant, you can't ask

971
01:01:29,360 --> 01:01:31,959
Speaker 1: them to make a list of things that would never

972
01:01:32,120 --> 01:01:35,800
Speaker 1: occur to them. And so essentially what happens when you

973
01:01:35,880 --> 01:01:39,480
Speaker 1: get all of these really bright people in our fellowship

974
01:01:39,520 --> 01:01:44,280
Speaker 1: and grant community communicating with each other. Wow, the ideas

975
01:01:44,320 --> 01:01:48,560
Speaker 1: that come out of those cross pollenization of ideas are

976
01:01:48,600 --> 01:01:49,760
Speaker 1: really extraordinary.

977
01:01:50,080 --> 01:01:53,520
Speaker 2: But this sounds like this is really an incubator of sorts.

978
01:01:53,960 --> 01:01:57,640
Speaker 1: It can be, but it needn't be. Here's a great example.

979
01:01:58,320 --> 01:02:00,480
Speaker 1: One of the guys that we gave a great aunt too,

980
01:02:00,680 --> 01:02:05,920
Speaker 1: his name's just that's his stage name, was an accountant

981
01:02:06,040 --> 01:02:09,880
Speaker 1: in India who decided he really had music in him

982
01:02:10,200 --> 01:02:14,320
Speaker 1: and he really wanted to do a musical video using

983
01:02:14,440 --> 01:02:19,760
Speaker 1: traditional Indian songs and singing in Hindi and other Indian dialects.

984
01:02:20,400 --> 01:02:25,600
Speaker 1: He went super viral, tens of millions of downloads of

985
01:02:25,640 --> 01:02:29,040
Speaker 1: his song. He's being put on all of their Good

986
01:02:29,160 --> 01:02:32,760
Speaker 1: Morning India. You know, we have Good Morning America being

987
01:02:32,800 --> 01:02:37,360
Speaker 1: written about in all of their newspapers. And essentially that

988
01:02:37,520 --> 01:02:40,640
Speaker 1: was because we thought, Wow, this guy's got talent. Let's

989
01:02:40,640 --> 01:02:44,479
Speaker 1: see what happens. We're not incubating him for anything. Right,

990
01:02:44,560 --> 01:02:46,680
Speaker 1: if he goes off and signs a deal with a

991
01:02:46,800 --> 01:02:50,880
Speaker 1: music company, we don't do music, so God bless.

992
01:02:50,760 --> 01:02:53,920
Speaker 2: This sounds a little bit like the MacArthur Genius Awards,

993
01:02:54,280 --> 01:02:57,280
Speaker 2: where here's a chunk of money, go be a genius.

994
01:02:57,680 --> 01:03:02,960
Speaker 1: There's just so much potential around the world, Barry, that

995
01:03:03,400 --> 01:03:08,680
Speaker 1: I feel compelled to amplify. Everybody loves to bag on

996
01:03:09,040 --> 01:03:14,040
Speaker 1: the generation before or after them, Right, Listen, the kids today,

997
01:03:14,280 --> 01:03:18,520
Speaker 1: young people today are digital natives. They know how to

998
01:03:18,680 --> 01:03:22,120
Speaker 1: use these tools in ways that we boomers probably are

999
01:03:22,200 --> 01:03:25,840
Speaker 1: never going to get to. And I say, let's empower them.

1000
01:03:26,360 --> 01:03:32,120
Speaker 1: Let's demonstrate to the world that this makes real practical sense. Right,

1001
01:03:32,240 --> 01:03:38,040
Speaker 1: Now let's take somebody else who is turning his grant

1002
01:03:38,120 --> 01:03:43,160
Speaker 1: into a company. It's a guy in Africa who faced

1003
01:03:43,240 --> 01:03:46,880
Speaker 1: a problem I knew nothing about, which was the cost

1004
01:03:46,960 --> 01:03:51,880
Speaker 1: of sanitary napkins for women who are menstruating is out

1005
01:03:51,880 --> 01:03:55,360
Speaker 1: of reach. They are all imported from the West and

1006
01:03:55,600 --> 01:03:58,520
Speaker 1: they can't buy them because they don't have enough money. Well,

1007
01:03:58,560 --> 01:04:02,360
Speaker 1: he came up with an idea where his mostly female

1008
01:04:02,400 --> 01:04:08,400
Speaker 1: staff and researchers use banana leaves and other biodegradable products

1009
01:04:08,720 --> 01:04:12,320
Speaker 1: that they can make on the ground in Africa sell

1010
01:04:12,400 --> 01:04:17,200
Speaker 1: for a fraction of the cost that the important ones

1011
01:04:17,320 --> 01:04:21,440
Speaker 1: work just as well. Now I believe he is turning

1012
01:04:21,480 --> 01:04:25,120
Speaker 1: that into an enterprise. He's founding a company. Will take

1013
01:04:25,120 --> 01:04:28,200
Speaker 1: a look at investing in it, because of course he's

1014
01:04:28,280 --> 01:04:31,880
Speaker 1: asked us to. It can be on the business side,

1015
01:04:31,880 --> 01:04:35,440
Speaker 1: definitely an incubator, but on the social side, on the

1016
01:04:35,520 --> 01:04:38,240
Speaker 1: music side, on the art side. So for example, this year,

1017
01:04:38,800 --> 01:04:42,120
Speaker 1: I really want to have a fine artist get one

1018
01:04:42,160 --> 01:04:46,160
Speaker 1: of these grants, because again I want really people to

1019
01:04:46,200 --> 01:04:50,120
Speaker 1: be able to see there is so much talent in

1020
01:04:50,160 --> 01:04:52,880
Speaker 1: the world, and I always try to look for things

1021
01:04:52,920 --> 01:04:58,240
Speaker 1: to root for as opposed to against. They're so easy

1022
01:04:58,480 --> 01:05:01,120
Speaker 1: to root against something thing, right, you don't have to

1023
01:05:01,160 --> 01:05:05,440
Speaker 1: be terribly bright to say that sucks. That sucks. Here's why.

1024
01:05:06,360 --> 01:05:09,320
Speaker 1: How about doing things the other way around? How about

1025
01:05:09,360 --> 01:05:13,400
Speaker 1: finding things you can root for? And then the results

1026
01:05:13,440 --> 01:05:16,240
Speaker 1: have been kind of like the coolest things we've ever seen,

1027
01:05:16,360 --> 01:05:19,840
Speaker 1: like the guy going viral in India, like we have.

1028
01:05:20,200 --> 01:05:24,120
Speaker 1: We funded a guy trying to advance open source quantum computing.

1029
01:05:24,520 --> 01:05:28,720
Speaker 1: He now is a big deal in quantum computing. It's

1030
01:05:28,760 --> 01:05:30,800
Speaker 1: a great thing to do in general.

1031
01:05:30,960 --> 01:05:34,160
Speaker 2: Tell us about some of the first few you tried.

1032
01:05:34,520 --> 01:05:37,320
Speaker 2: Who were the people that were the first couple of

1033
01:05:37,400 --> 01:05:38,240
Speaker 2: recipients of.

1034
01:05:38,520 --> 01:05:42,680
Speaker 1: The guy I just mentioned with the quantum computing. He

1035
01:05:42,720 --> 01:05:44,760
Speaker 1: had me at hello because I love that stuff.

1036
01:05:44,840 --> 01:05:48,920
Speaker 2: What about people who are looking at markets in the economy,

1037
01:05:48,960 --> 01:05:51,440
Speaker 2: I know that that's a pieve of yours. Oh.

1038
01:05:51,520 --> 01:05:56,280
Speaker 1: Absolutely, the thing there is we wanted it to be

1039
01:05:56,400 --> 01:06:02,200
Speaker 1: significantly different than our traditional quant One of the reasons

1040
01:06:02,280 --> 01:06:05,640
Speaker 1: I became so interested in machine learning and AI was

1041
01:06:05,760 --> 01:06:09,200
Speaker 1: I viewed that as the next frontier for quant The

1042
01:06:09,200 --> 01:06:13,240
Speaker 1: dirty little secret of week quants is if you really

1043
01:06:13,280 --> 01:06:17,440
Speaker 1: press us and ask us to really explain your model

1044
01:06:17,480 --> 01:06:20,160
Speaker 1: like you would to a five year old we're using

1045
01:06:20,280 --> 01:06:24,680
Speaker 1: pretty much the same stuff, right, So what we wanted

1046
01:06:24,720 --> 01:06:28,440
Speaker 1: to do there was push the needle as far as

1047
01:06:28,440 --> 01:06:32,160
Speaker 1: we possibly could. But then one of the first people

1048
01:06:32,280 --> 01:06:35,440
Speaker 1: to get one of the fellowships was a married couple,

1049
01:06:35,680 --> 01:06:39,200
Speaker 1: Matt and Martha Sharp, and what they wanted to do

1050
01:06:39,360 --> 01:06:44,480
Speaker 1: was make a documentary about non traditional schools for their kids.

1051
01:06:44,520 --> 01:06:47,479
Speaker 1: They have a bunch of young kids below the age

1052
01:06:47,480 --> 01:06:52,200
Speaker 1: of seven, and they put out a great documentary about

1053
01:06:52,520 --> 01:06:56,520
Speaker 1: a particular school, which was really novel. And so we

1054
01:06:56,640 --> 01:06:59,680
Speaker 1: really are all over the map in the type of

1055
01:06:59,760 --> 01:07:04,440
Speaker 1: per groups that were willing to consider. Yet another was

1056
01:07:04,720 --> 01:07:09,480
Speaker 1: a refugee in Ireland who found that she couldn't figure

1057
01:07:09,520 --> 01:07:13,560
Speaker 1: out a way in her native language to work her

1058
01:07:13,640 --> 01:07:18,480
Speaker 1: way through the halls of the bureaucracy, to figure out

1059
01:07:18,520 --> 01:07:20,200
Speaker 1: how do I get a place to live? How do

1060
01:07:20,280 --> 01:07:23,280
Speaker 1: I do all of these things? So we funded her

1061
01:07:23,400 --> 01:07:26,280
Speaker 1: to make an app. And then finally another one that

1062
01:07:26,360 --> 01:07:29,919
Speaker 1: I just love is we have a doctor who came

1063
01:07:30,000 --> 01:07:32,480
Speaker 1: to us and said what he wanted to do was

1064
01:07:32,640 --> 01:07:36,000
Speaker 1: make an app for an iPhone or an Android where

1065
01:07:36,040 --> 01:07:40,200
Speaker 1: you could completely non invasively, I could point the phone

1066
01:07:40,240 --> 01:07:44,040
Speaker 1: at you, get your vitals on the phone, just by

1067
01:07:44,320 --> 01:07:48,200
Speaker 1: the camera on the phone. Really yeah, And what was

1068
01:07:48,240 --> 01:07:52,080
Speaker 1: cool for us was we really pushed him. We're like, why, why,

1069
01:07:52,200 --> 01:07:56,600
Speaker 1: why why? And finally at the end of our interview

1070
01:07:56,640 --> 01:08:00,120
Speaker 1: with him, he was near tears and he went the

1071
01:08:00,200 --> 01:08:03,080
Speaker 1: real reason for this is my dad died of a

1072
01:08:03,120 --> 01:08:06,400
Speaker 1: stroke and I was in medical school and I didn't

1073
01:08:06,440 --> 01:08:10,160
Speaker 1: save him. I didn't even know that he had a problem.

1074
01:08:10,640 --> 01:08:14,160
Speaker 1: And so this is why I'm so passionate about this.

1075
01:08:14,640 --> 01:08:18,519
Speaker 1: To get a life saving thing in the hands of

1076
01:08:18,800 --> 01:08:22,960
Speaker 1: an on something that we all carry with us, these smartphones,

1077
01:08:23,320 --> 01:08:26,360
Speaker 1: is what motivated him. And on top of that, looks

1078
01:08:26,400 --> 01:08:28,440
Speaker 1: like it could also be a great business.

1079
01:08:28,720 --> 01:08:33,120
Speaker 2: Well that's really interesting. Let's stay with AI and talk

1080
01:08:33,400 --> 01:08:38,320
Speaker 2: about medicine in particular. I'm fascinated by the concept of

1081
01:08:38,680 --> 01:08:45,480
Speaker 2: AI running through billions or even trillions of molecular combinations

1082
01:08:45,960 --> 01:08:50,599
Speaker 2: to identify promising drugs, some of which are already out there,

1083
01:08:50,640 --> 01:08:53,599
Speaker 2: some of which haven't been created. But it really gives

1084
01:08:53,720 --> 01:08:57,960
Speaker 2: us the ability to take millennial worth of experimentation and

1085
01:08:58,080 --> 01:09:00,400
Speaker 2: do it in a really very short period of time.

1086
01:09:00,600 --> 01:09:05,360
Speaker 1: It's a world changer. The ability to, as you mentioned,

1087
01:09:05,520 --> 01:09:09,280
Speaker 1: take different molecules where there isn't a drug addressing a

1088
01:09:09,320 --> 01:09:14,200
Speaker 1: certain problem and or taking existing research from drugs and

1089
01:09:14,479 --> 01:09:19,680
Speaker 1: repurposing it. AI can go into all of those spaces

1090
01:09:19,720 --> 01:09:23,800
Speaker 1: that we humans simply can't do and find the connections

1091
01:09:23,840 --> 01:09:26,960
Speaker 1: on an existing drug. You know what, this drug was

1092
01:09:27,000 --> 01:09:32,000
Speaker 1: originally done for malaria, Well it doesn't work for malaria,

1093
01:09:32,200 --> 01:09:35,799
Speaker 1: but it works really well for this disease over here,

1094
01:09:36,200 --> 01:09:40,640
Speaker 1: and then new drugs that the discovery is going to

1095
01:09:40,680 --> 01:09:43,160
Speaker 1: be amazing. And you've got to remember a lot of

1096
01:09:43,200 --> 01:09:45,800
Speaker 1: this stuff can be done what they call in silico.

1097
01:09:46,280 --> 01:09:50,080
Speaker 1: You don't have to test it on humans or animals.

1098
01:09:50,360 --> 01:09:54,200
Speaker 1: You can test it on the clone of we humans

1099
01:09:54,600 --> 01:09:59,400
Speaker 1: that you set up in the computer. And so these

1100
01:09:59,439 --> 01:10:02,360
Speaker 1: types of thing things like I honestly don't think it's

1101
01:10:02,400 --> 01:10:07,320
Speaker 1: an overstatement to say, like this, this AI and it's

1102
01:10:07,640 --> 01:10:11,720
Speaker 1: many use cases belong up there with the wheel and

1103
01:10:11,880 --> 01:10:16,839
Speaker 1: fire and the printing press, because it is a multi

1104
01:10:17,360 --> 01:10:22,120
Speaker 1: use technology that's going to affect everything from drug discovery

1105
01:10:22,640 --> 01:10:27,759
Speaker 1: to financial analysis. What about we had trained an AI

1106
01:10:28,560 --> 01:10:32,439
Speaker 1: to generate nothing but null sets? Right, Like, if you're

1107
01:10:32,439 --> 01:10:36,240
Speaker 1: a medical researcher and you're trying to get funding, what

1108
01:10:36,240 --> 01:10:39,120
Speaker 1: do you want to do? You want to prove something new? Right,

1109
01:10:39,200 --> 01:10:41,040
Speaker 1: you don't. You're not going to get funded to prove

1110
01:10:41,479 --> 01:10:44,559
Speaker 1: you know that aspirin works, but you want to find

1111
01:10:44,600 --> 01:10:47,160
Speaker 1: something new and you also want it to be a

1112
01:10:47,280 --> 01:10:53,640
Speaker 1: positive finding. So what happens is the incentives preclude a

1113
01:10:53,640 --> 01:10:57,679
Speaker 1: lot of brilliant scientists from looking for things that don't work.

1114
01:10:58,080 --> 01:11:02,080
Speaker 1: And yet, like the dog that didn't bark in Sherlock Holmes,

1115
01:11:02,560 --> 01:11:09,400
Speaker 1: there's a lot of really cool information, useful information via negativia.

1116
01:11:09,960 --> 01:11:12,240
Speaker 1: And so one of the things that we want to

1117
01:11:12,280 --> 01:11:17,840
Speaker 1: do is just have a large language model, churnout hypothesis

1118
01:11:17,880 --> 01:11:21,679
Speaker 1: after hypothesis that is going to generate a null set,

1119
01:11:21,920 --> 01:11:24,920
Speaker 1: publish them to a database that all scientists can have

1120
01:11:24,960 --> 01:11:28,960
Speaker 1: access to. Because there's a wealth of information in the

1121
01:11:29,040 --> 01:11:31,800
Speaker 1: stuff that doesn't work. Here are things you don't want

1122
01:11:31,800 --> 01:11:34,960
Speaker 1: to waste your time exactly. Let's talk a bit about

1123
01:11:35,120 --> 01:11:38,479
Speaker 1: stability AI. You're on the board of directors, you're the

1124
01:11:38,520 --> 01:11:42,400
Speaker 1: executive chair, and you started back in September twenty twenty two.

1125
01:11:42,479 --> 01:11:45,599
Speaker 1: Pretty good timing. Tell us a little bit about what

1126
01:11:45,640 --> 01:11:49,240
Speaker 1: stability AI does and how does this relate to the

1127
01:11:49,280 --> 01:11:55,360
Speaker 1: rest of O'Shaughnessy ventures. So stability AI builds foundational open

1128
01:11:55,439 --> 01:12:00,799
Speaker 1: source models. I had a very pointed point of view

1129
01:12:01,479 --> 01:12:05,920
Speaker 1: that with a technology this powerful, I did not want

1130
01:12:06,000 --> 01:12:11,439
Speaker 1: it controlled by a panopticon controlled by a few, and

1131
01:12:11,760 --> 01:12:15,200
Speaker 1: I saw that with that kind of power could come

1132
01:12:15,320 --> 01:12:22,519
Speaker 1: some pretty negative externalities. And so Stability AI was the

1133
01:12:22,840 --> 01:12:26,360
Speaker 1: one that really caught my eye because they really were

1134
01:12:26,360 --> 01:12:29,120
Speaker 1: the ones who shot the gun. Back in the summer

1135
01:12:29,240 --> 01:12:35,000
Speaker 1: of August of twenty two, they released a stable diffusion

1136
01:12:35,040 --> 01:12:39,320
Speaker 1: model which generates images, right, but they did something that

1137
01:12:39,360 --> 01:12:42,400
Speaker 1: no one had done before. They released that model with

1138
01:12:42,560 --> 01:12:46,639
Speaker 1: all of its weights. Now, not to get too geeky here,

1139
01:12:47,120 --> 01:12:50,240
Speaker 1: but the only way people can build on that type

1140
01:12:50,240 --> 01:12:53,360
Speaker 1: of model is to know what the weights are. And

1141
01:12:53,479 --> 01:12:57,839
Speaker 1: so what they did was show it all. They released

1142
01:12:57,920 --> 01:13:02,560
Speaker 1: the whole thing, full open source, driven source, fully transparent,

1143
01:13:02,920 --> 01:13:09,599
Speaker 1: and bury the Cambrian like explosion of creativity that happened

1144
01:13:09,720 --> 01:13:16,360
Speaker 1: almost immediately really proved to me. Yeah, back to cognitive diversity, right,

1145
01:13:16,760 --> 01:13:20,320
Speaker 1: when you allow all of these clever people the ability

1146
01:13:20,479 --> 01:13:23,599
Speaker 1: to play with it, to tinker with it, you get

1147
01:13:23,880 --> 01:13:27,519
Speaker 1: a much better model. For example, that's why Linux runs

1148
01:13:27,600 --> 01:13:31,960
Speaker 1: the web. Linux is open source, right, and it does

1149
01:13:32,040 --> 01:13:35,560
Speaker 1: so because a bunch of different people work on different problems.

1150
01:13:36,080 --> 01:13:39,360
Speaker 1: And so my point of view was I'm all for

1151
01:13:39,439 --> 01:13:42,200
Speaker 1: the open I use open AI. I use all of

1152
01:13:42,240 --> 01:13:44,320
Speaker 1: the commercial Uh.

1153
01:13:44,360 --> 01:13:48,519
Speaker 2: What are some of the commercial apps? So perplexity, I

1154
01:13:48,560 --> 01:13:49,320
Speaker 2: love perplexity.

1155
01:13:49,320 --> 01:13:54,679
Speaker 1: It's on my phone's open AI. I'm looking at Claude,

1156
01:13:54,760 --> 01:13:56,760
Speaker 1: the new Claude that you know.

1157
01:13:56,960 --> 01:14:01,120
Speaker 2: Perplexity can be driven by either Claude or there's like

1158
01:14:01,240 --> 01:14:02,280
Speaker 2: four different engines.

1159
01:14:02,320 --> 01:14:04,760
Speaker 1: That which really interesting. One of the things I love

1160
01:14:04,800 --> 01:14:05,960
Speaker 1: about perplexity.

1161
01:14:06,160 --> 01:14:09,439
Speaker 2: It's just so great and it's cheap and it's so useful.

1162
01:14:09,640 --> 01:14:14,920
Speaker 2: Exactly every interview I do, I don't start with perplexity,

1163
01:14:15,080 --> 01:14:18,080
Speaker 2: I finished with perplexity and what did I miss?

1164
01:14:18,200 --> 01:14:19,040
Speaker 1: What did I get wrong?

1165
01:14:19,360 --> 01:14:21,760
Speaker 2: Although you still have to be careful because every now

1166
01:14:21,800 --> 01:14:25,599
Speaker 2: and then, like O'Shaughnessy is not the rarest of names,

1167
01:14:26,200 --> 01:14:30,720
Speaker 2: you know. I had Bill Dudley, former New York Fed chair,

1168
01:14:31,240 --> 01:14:34,240
Speaker 2: and I learned that he was a running back in

1169
01:14:34,320 --> 01:14:37,439
Speaker 2: the NFL in the forties, which is kind of interesting

1170
01:14:37,479 --> 01:14:41,080
Speaker 2: because he wasn't born till the fifties. But every now

1171
01:14:41,160 --> 01:14:44,800
Speaker 2: and then something will pop up that is a little off.

1172
01:14:45,000 --> 01:14:48,000
Speaker 2: I love the phrase hallucination for that. What else do

1173
01:14:48,040 --> 01:14:50,280
Speaker 2: you use besides perplexity and chatching.

1174
01:14:50,000 --> 01:14:57,000
Speaker 1: The stability AIS for his models. Are they available? Are

1175
01:14:57,080 --> 01:14:59,559
Speaker 1: they accessible to the lay person? Like, that's the beauty

1176
01:14:59,600 --> 01:15:04,439
Speaker 1: of they are, but through different APIs. We really wanted

1177
01:15:04,479 --> 01:15:09,040
Speaker 1: to focus on being the builder, right, so we did

1178
01:15:09,080 --> 01:15:12,120
Speaker 1: not want to try to compete in the direct to

1179
01:15:12,160 --> 01:15:17,800
Speaker 1: consumer space. And so what we're focusing on is multimodals,

1180
01:15:17,960 --> 01:15:24,880
Speaker 1: including generative models, including specific models for medical research, obviously,

1181
01:15:25,439 --> 01:15:30,760
Speaker 1: generative art models, movie models, et cetera. The thing I

1182
01:15:30,880 --> 01:15:34,000
Speaker 1: wanted to mention when you were talking about perplexity in

1183
01:15:34,040 --> 01:15:39,360
Speaker 1: it coming up with I also passionately believe that the

1184
01:15:39,560 --> 01:15:42,639
Speaker 1: models that are going to win, or not the models,

1185
01:15:42,760 --> 01:15:48,240
Speaker 1: the approach that's going to win is human plus machine,

1186
01:15:48,800 --> 01:15:52,519
Speaker 1: so called Senator model. I think that you're going to see,

1187
01:15:52,600 --> 01:15:55,559
Speaker 1: you know, we're going to see a deluge of AI

1188
01:15:56,000 --> 01:16:00,479
Speaker 1: only generated stuff, content, movies, et cetera. And to be honest,

1189
01:16:00,720 --> 01:16:05,120
Speaker 1: most of it's gonna suck. Right. The magic comes when

1190
01:16:05,240 --> 01:16:08,759
Speaker 1: you add a human in the loop. The magic comes

1191
01:16:09,280 --> 01:16:13,200
Speaker 1: by being able to partner with that and co create

1192
01:16:13,479 --> 01:16:17,200
Speaker 1: and sometimes iterate on your own stuff. Like you said,

1193
01:16:17,760 --> 01:16:21,559
Speaker 1: the ideas that you can generate through putting your own

1194
01:16:21,680 --> 01:16:26,320
Speaker 1: stuff into the various models is really cool. We invest

1195
01:16:26,520 --> 01:16:31,040
Speaker 1: in a startup called wand and what they do is

1196
01:16:31,120 --> 01:16:34,400
Speaker 1: it's for graphic artists and it's an AI, but it

1197
01:16:34,439 --> 01:16:38,320
Speaker 1: has an actual tool, thus the name wand And. What

1198
01:16:38,360 --> 01:16:41,080
Speaker 1: the artist is able to do is feed their own

1199
01:16:41,280 --> 01:16:45,439
Speaker 1: work into the model and then ask it hey, spin

1200
01:16:45,520 --> 01:16:48,479
Speaker 1: out variations on it, and then the artists will look

1201
01:16:48,479 --> 01:16:50,559
Speaker 1: at it and say, wow, I never thought about it

1202
01:16:50,600 --> 01:16:53,200
Speaker 1: that way. That's really cool. And then he or she

1203
01:16:53,680 --> 01:16:56,680
Speaker 1: will iterate, iterate, send it back and this is an

1204
01:16:56,840 --> 01:17:01,479
Speaker 1: iterative process. But what's really cool is they end up

1205
01:17:01,520 --> 01:17:04,559
Speaker 1: in places. We had one artists say to me, I

1206
01:17:04,640 --> 01:17:08,360
Speaker 1: would never have thought to do it this way, but

1207
01:17:08,400 --> 01:17:12,240
Speaker 1: I absolutely love it. It's his work. He's iterating on his

1208
01:17:12,320 --> 01:17:16,680
Speaker 1: own work, but he's using a tool, the WAND, that

1209
01:17:16,840 --> 01:17:21,719
Speaker 1: makes it infinitely easier for him to get these great ideas. Huh.

1210
01:17:21,840 --> 01:17:26,759
Speaker 2: Really interesting. Last question before we jump to our favorites,

1211
01:17:26,800 --> 01:17:29,720
Speaker 2: we ask all our guests, which is I want to

1212
01:17:29,760 --> 01:17:34,880
Speaker 2: bring this back to stocks. I know thanks to perplexity

1213
01:17:34,920 --> 01:17:38,040
Speaker 2: as an example, but there are lots of other tools.

1214
01:17:38,479 --> 01:17:41,920
Speaker 2: I find myself going to Google a whole lot less

1215
01:17:41,960 --> 01:17:46,040
Speaker 2: than I used to, and in fact, the Google search

1216
01:17:46,120 --> 01:17:51,559
Speaker 2: results like, suddenly you realize these are crude. They're much

1217
01:17:51,640 --> 01:17:55,559
Speaker 2: less useful than they used to be. They're festooned with

1218
01:17:55,600 --> 01:17:59,880
Speaker 2: a lot of advertising and a lot of Google in

1219
01:18:00,000 --> 01:18:03,160
Speaker 2: internal products dominate that first page.

1220
01:18:04,800 --> 01:18:07,439
Speaker 1: What else is AI? What other companies?

1221
01:18:07,439 --> 01:18:12,480
Speaker 2: What other sectors might AI affect, either positively or negatively?

1222
01:18:12,880 --> 01:18:15,880
Speaker 1: Well, honestly, how much time do you have it? I

1223
01:18:15,920 --> 01:18:20,280
Speaker 1: think that AI is going to transform virtually every industry.

1224
01:18:20,840 --> 01:18:24,280
Speaker 1: And one of the things that people they get afraid

1225
01:18:24,280 --> 01:18:27,799
Speaker 1: when they hear that, and my view is quite different.

1226
01:18:28,000 --> 01:18:31,640
Speaker 1: It's going to transform for a lot of industries, the

1227
01:18:31,760 --> 01:18:36,360
Speaker 1: pure drudge work, the pure copy and paste stuff. What

1228
01:18:36,479 --> 01:18:39,120
Speaker 1: do you want? Do you like copying and pasting? I

1229
01:18:39,160 --> 01:18:42,720
Speaker 1: hate it? And so it also is going to be

1230
01:18:42,840 --> 01:18:46,280
Speaker 1: able to create jobs that we can't even conceive of

1231
01:18:46,680 --> 01:18:49,960
Speaker 1: right now. Right like two years ago, would you have

1232
01:18:50,080 --> 01:18:54,559
Speaker 1: known what a prompt engineer was? No, I certainly wouldn't have, right,

1233
01:18:54,760 --> 01:18:57,439
Speaker 1: and yet there's a lot of people doing really well

1234
01:18:58,000 --> 01:19:01,920
Speaker 1: pursuing that is a career. And so I think that

1235
01:19:02,320 --> 01:19:08,240
Speaker 1: entertainment is going to be materially affected media, materially affected search,

1236
01:19:08,880 --> 01:19:11,800
Speaker 1: as you well point out, like you can do a

1237
01:19:11,880 --> 01:19:15,799
Speaker 1: customized search just for Barry and it, you know, depending

1238
01:19:15,840 --> 01:19:18,240
Speaker 1: on how much information you want to give that AI

1239
01:19:18,360 --> 01:19:20,880
Speaker 1: about yourself. You're going to be at a place where

1240
01:19:20,880 --> 01:19:22,960
Speaker 1: you're going to be able to say, hey, what was

1241
01:19:22,960 --> 01:19:25,400
Speaker 1: that place that I had lunch with Jim last time?

1242
01:19:25,400 --> 01:19:27,479
Speaker 1: We both really really liked it. I'd like to go

1243
01:19:27,560 --> 01:19:29,960
Speaker 1: there again and guess what, it's going to give you

1244
01:19:30,000 --> 01:19:34,599
Speaker 1: the name and address of that restaurant. Because it has

1245
01:19:34,680 --> 01:19:38,160
Speaker 1: access to your calendar, It has access to all of

1246
01:19:38,160 --> 01:19:39,160
Speaker 1: that type of stuff.

1247
01:19:39,240 --> 01:19:43,800
Speaker 2: It feels like I'll never forget. I tweeted out this

1248
01:19:43,920 --> 01:19:48,720
Speaker 2: really interesting Roman Pizza place, and Roman Pizza is a

1249
01:19:48,840 --> 01:19:52,680
Speaker 2: different type of and I just you know, I used

1250
01:19:52,720 --> 01:19:55,960
Speaker 2: Sarah to speak into the iPhone. Hey we had a fend.

1251
01:19:56,040 --> 01:19:59,760
Speaker 2: This is really different than your usual pizza. And some

1252
01:20:00,120 --> 01:20:05,280
Speaker 2: how it showed up on Twitter as woman Pizza and like, wait,

1253
01:20:05,400 --> 01:20:09,400
Speaker 2: I'm standing right in front of the place. Any correlation

1254
01:20:09,600 --> 01:20:13,519
Speaker 2: between my geotag and business I'm in front of it

1255
01:20:13,680 --> 01:20:17,360
Speaker 2: just felt like technology should have figured that out. Yeah,

1256
01:20:17,400 --> 01:20:22,480
Speaker 2: what you're saying is that sort of access to your contacts,

1257
01:20:22,600 --> 01:20:26,400
Speaker 2: access to your where you are, access to your calendar,

1258
01:20:27,000 --> 01:20:30,360
Speaker 2: once there's an intelligent agent running all of that. A

1259
01:20:30,400 --> 01:20:33,880
Speaker 2: lot of these sort of silly why can't Siri talk

1260
01:20:33,960 --> 01:20:37,800
Speaker 2: with this person, Why can't Alexa? It just seems like

1261
01:20:38,680 --> 01:20:42,679
Speaker 2: the pre AI era was filled with a lot.

1262
01:20:42,520 --> 01:20:46,960
Speaker 1: Of pretty dombai. It's starting to get smarter. Yeah, And

1263
01:20:47,400 --> 01:20:50,240
Speaker 1: that's the thing going back to your Right Brothers example.

1264
01:20:50,760 --> 01:20:54,920
Speaker 1: You know when the Right Brothers did that very brief flight,

1265
01:20:55,000 --> 01:20:57,880
Speaker 1: it was only a matter of eight yeah, I think

1266
01:20:57,880 --> 01:21:00,320
Speaker 1: it was twelve seconds, and I think they went like

1267
01:21:00,320 --> 01:21:04,040
Speaker 1: one hundred and odd feet. Like you could see why

1268
01:21:04,320 --> 01:21:07,200
Speaker 1: a lot of people would going, Eh, they didn't accomplish much,

1269
01:21:07,760 --> 01:21:10,880
Speaker 1: but I like the person who was watching and said,

1270
01:21:11,040 --> 01:21:16,479
Speaker 1: this changes everything, And so that's kind of how I

1271
01:21:16,560 --> 01:21:20,040
Speaker 1: see AI. Of course, we're in the early innings of this,

1272
01:21:20,120 --> 01:21:22,679
Speaker 1: and of course it's going to this is the worst

1273
01:21:22,760 --> 01:21:26,280
Speaker 1: you're ever going to see it. It's going to improve, improve, improve.

1274
01:21:26,640 --> 01:21:29,120
Speaker 1: But the other thing I want to really underline here

1275
01:21:29,680 --> 01:21:32,639
Speaker 1: is it's the quality of the data that you train

1276
01:21:32,720 --> 01:21:36,880
Speaker 1: your AI on that determines its value to you. And

1277
01:21:37,200 --> 01:21:39,640
Speaker 1: one of the big reasons I'm a huge believer in

1278
01:21:39,760 --> 01:21:43,960
Speaker 1: private AIS is that you will feel if you know

1279
01:21:44,040 --> 01:21:46,679
Speaker 1: that no one else can have access to that right,

1280
01:21:47,080 --> 01:21:49,880
Speaker 1: you're going to give it a lot more access to

1281
01:21:50,040 --> 01:21:54,479
Speaker 1: things than you might otherwise. That's happening right now. Wow.

1282
01:21:54,680 --> 01:21:57,920
Speaker 1: And so one of the things, you know, a lot

1283
01:21:57,960 --> 01:22:01,080
Speaker 1: of people see this as, you know, like the great

1284
01:22:01,240 --> 01:22:04,599
Speaker 1: model that will figure everything out. I don't see it

1285
01:22:04,640 --> 01:22:07,240
Speaker 1: that way at all. I see it as a lot

1286
01:22:07,280 --> 01:22:13,960
Speaker 1: of smaller but incredibly useful AI agents doing specific things

1287
01:22:14,000 --> 01:22:17,960
Speaker 1: for each of us. Again, Canvas fits in beautifully. Here.

1288
01:22:18,040 --> 01:22:22,800
Speaker 1: We are now in an era of mass customization. We

1289
01:22:22,840 --> 01:22:25,599
Speaker 1: are in an era where it's going to be able

1290
01:22:25,720 --> 01:22:29,560
Speaker 1: to design it just for you and your likes and dislikes.

1291
01:22:30,280 --> 01:22:33,519
Speaker 1: That's really profound when you think about.

1292
01:22:33,320 --> 01:22:36,920
Speaker 2: It, really fascinating. So let's jump to our speed round,

1293
01:22:37,080 --> 01:22:40,599
Speaker 2: our favorite questions. We ask all of our guests, starting

1294
01:22:40,720 --> 01:22:43,519
Speaker 2: with what has been keeping you entertained these days? What

1295
01:22:43,560 --> 01:22:45,559
Speaker 2: are you either watching or listening to?

1296
01:22:46,240 --> 01:22:51,479
Speaker 1: So we rewatched True Detective, my wife and I I

1297
01:22:51,520 --> 01:22:56,240
Speaker 1: would highly recommend rewatching the first season of that. It

1298
01:22:56,320 --> 01:23:00,439
Speaker 1: was brilliant. It led us into a reward of the

1299
01:23:00,600 --> 01:23:04,920
Speaker 1: entire series. And now we're on number three, the second one.

1300
01:23:05,200 --> 01:23:07,559
Speaker 1: Here's one of the funny things, like in memory, I

1301
01:23:07,640 --> 01:23:09,639
Speaker 1: kind of my wife and I were both kind of like, yeah,

1302
01:23:09,680 --> 01:23:13,360
Speaker 1: that second one wasn't very good. It was good, and

1303
01:23:13,840 --> 01:23:19,599
Speaker 1: so we're doing that Masters of the air that's on. Yeah, great,

1304
01:23:19,880 --> 01:23:23,879
Speaker 1: really loving that. I loved Band of Brothers, so we're

1305
01:23:23,920 --> 01:23:28,839
Speaker 1: both really really liking that. And then we are also

1306
01:23:29,400 --> 01:23:34,560
Speaker 1: watching a series, or I guess I should say rewatching

1307
01:23:34,880 --> 01:23:39,519
Speaker 1: a series which kind of kicked off the idea of

1308
01:23:39,840 --> 01:23:41,840
Speaker 1: the Golden Age of television. It was one of the

1309
01:23:41,880 --> 01:23:44,679
Speaker 1: earlier ones. I'm not the Sopranos, but The Wire.

1310
01:23:45,000 --> 01:23:49,840
Speaker 2: Now, I recall The Wire being very brutal and difficult.

1311
01:23:49,439 --> 01:23:53,720
Speaker 1: It watch it is, But what's so cool if you

1312
01:23:53,840 --> 01:23:58,160
Speaker 1: choose to watch it again, you see that the reason

1313
01:23:58,240 --> 01:24:01,599
Speaker 1: it kicked off that kind of tea was because it

1314
01:24:01,720 --> 01:24:05,679
Speaker 1: was brutally honest about things. It wasn't trying to lie

1315
01:24:05,720 --> 01:24:11,200
Speaker 1: to you about anything, and the characters are incredibly complex,

1316
01:24:11,280 --> 01:24:17,479
Speaker 1: even though even the evil guys are incredibly complex, And

1317
01:24:17,600 --> 01:24:21,720
Speaker 1: so watching it now from the vantage point of like

1318
01:24:22,439 --> 01:24:27,160
Speaker 1: twenty years or more, it's really amazing.

1319
01:24:27,080 --> 01:24:31,000
Speaker 2: Really interesting. Tell us about your mentors who helped to

1320
01:24:31,160 --> 01:24:32,200
Speaker 2: shape your career.

1321
01:24:32,640 --> 01:24:37,760
Speaker 1: Primarily, I would list my grandfather. I was lucky enough,

1322
01:24:37,920 --> 01:24:42,599
Speaker 1: he was very successful in the oil industry, and I

1323
01:24:42,640 --> 01:24:45,719
Speaker 1: am the youngest of the third generation at least the males.

1324
01:24:46,160 --> 01:24:49,439
Speaker 1: I have one younger female cousin and she's just a

1325
01:24:49,439 --> 01:24:52,639
Speaker 1: few months behind me. But I lived in the same

1326
01:24:52,720 --> 01:24:56,640
Speaker 1: town my grandfather did, and after my grandmother died, he

1327
01:24:56,680 --> 01:24:59,080
Speaker 1: would come to our house twice a week for dinner

1328
01:25:00,000 --> 01:25:04,080
Speaker 1: and literally I would literally sit at his knee. And

1329
01:25:04,240 --> 01:25:10,200
Speaker 1: he was a wonderful storyteller. He was a wonderful teacher,

1330
01:25:10,640 --> 01:25:14,640
Speaker 1: and he taught me this idea of premeditating that I

1331
01:25:14,920 --> 01:25:17,880
Speaker 1: have written a lot about and use all the time.

1332
01:25:18,560 --> 01:25:21,960
Speaker 1: Another was a wonderful man, not related to me at all,

1333
01:25:22,040 --> 01:25:25,280
Speaker 1: by the name of Jim Myers. Any entrepreneur, you hit

1334
01:25:25,320 --> 01:25:28,040
Speaker 1: some rough spots, sure, and I had hit a really

1335
01:25:28,120 --> 01:25:32,880
Speaker 1: rough spot and was basically broke and trying to pay

1336
01:25:32,960 --> 01:25:37,720
Speaker 1: for a house because we'd moved to Greenwich and keep

1337
01:25:37,720 --> 01:25:40,960
Speaker 1: my business afloat and all of that, and the banks

1338
01:25:41,000 --> 01:25:44,200
Speaker 1: are like, dude, like, you're an entrepreneur. This is back

1339
01:25:44,240 --> 01:25:46,599
Speaker 1: in the nineties. Yeah, sorry, we're not going to give

1340
01:25:46,640 --> 01:25:50,280
Speaker 1: you a mortgage. He stepped in and he's like, Jim,

1341
01:25:50,600 --> 01:25:55,080
Speaker 1: I believe you're going to be tremendously successful and gave

1342
01:25:55,120 --> 01:25:59,320
Speaker 1: me one on handshake, which I was able to repay rapidly.

1343
01:25:59,479 --> 01:26:03,920
Speaker 1: But more than that, just being a super high quality man,

1344
01:26:04,680 --> 01:26:10,240
Speaker 1: he taught me more about real business than any textbook

1345
01:26:10,880 --> 01:26:14,280
Speaker 1: because I was young, right, and I started with him

1346
01:26:14,760 --> 01:26:19,720
Speaker 1: when I was in my early twenties and just an

1347
01:26:19,760 --> 01:26:24,040
Speaker 1: amazing man. And then finally the other mentors that I

1348
01:26:24,040 --> 01:26:27,559
Speaker 1: would say are like the greatest minds of history. I

1349
01:26:27,600 --> 01:26:32,200
Speaker 1: love to read. I particularly like to read biographies about

1350
01:26:32,280 --> 01:26:35,720
Speaker 1: people I admire. And you know what, Barry life was

1351
01:26:35,760 --> 01:26:39,000
Speaker 1: not easy. We remember them now, right, like, oh, they

1352
01:26:39,000 --> 01:26:42,800
Speaker 1: were this huge success. When you read their biographies, you

1353
01:26:42,880 --> 01:26:46,439
Speaker 1: see they went through a lot of muck to get

1354
01:26:46,439 --> 01:26:49,559
Speaker 1: where they got and so kind of universal lessons.

1355
01:26:49,880 --> 01:26:53,320
Speaker 2: So perfect segue. Let's talk about some of your favorite

1356
01:26:53,320 --> 01:26:55,280
Speaker 2: books and what are you reading right now?

1357
01:26:56,240 --> 01:27:00,759
Speaker 1: So right now I am reading about four different books

1358
01:27:01,880 --> 01:27:03,520
Speaker 1: and I which.

1359
01:27:03,240 --> 01:27:06,599
Speaker 2: By the way, is an occupational hazard for folks like us,

1360
01:27:06,800 --> 01:27:09,920
Speaker 2: because there's always a book I'm prepping for a podcast,

1361
01:27:09,960 --> 01:27:12,599
Speaker 2: there's a book I'm reading for work, and then there's

1362
01:27:12,600 --> 01:27:14,600
Speaker 2: a book I'm just like, I'm going to relax and

1363
01:27:14,640 --> 01:27:14,960
Speaker 2: read this.

1364
01:27:15,240 --> 01:27:19,240
Speaker 1: Yeah, so for fun. Right now I'm reading Burned book

1365
01:27:19,360 --> 01:27:24,920
Speaker 1: by Karra What's her last Swisher? Which I find very interesting.

1366
01:27:25,520 --> 01:27:29,680
Speaker 1: She's always fascinating, yeah, kind of an inside look. My

1367
01:27:29,760 --> 01:27:33,120
Speaker 1: only comment there was she might be a little guilty

1368
01:27:33,200 --> 01:27:35,759
Speaker 1: of the things that she accuses the people she doesn't

1369
01:27:35,880 --> 01:27:39,240
Speaker 1: like are. But other than that, it's a fun and

1370
01:27:39,439 --> 01:27:44,520
Speaker 1: kind of a rollicking read. I am reading or rereading

1371
01:27:44,920 --> 01:27:48,440
Speaker 1: several of the books from Will Durant's Story of Civilization,

1372
01:27:49,360 --> 01:27:51,920
Speaker 1: which I read as a kid or a young man,

1373
01:27:52,320 --> 01:27:57,120
Speaker 1: loved and thought, you know what, we moved recently, and

1374
01:27:57,200 --> 01:27:59,040
Speaker 1: so I was going through all my books and I

1375
01:27:59,120 --> 01:28:02,040
Speaker 1: found that I'm like, I should reread some of these

1376
01:28:02,160 --> 01:28:05,000
Speaker 1: just to see if it still stands up. Barry, It's

1377
01:28:05,080 --> 01:28:10,200
Speaker 1: still stuff, really really stands up. And then just finished

1378
01:28:10,479 --> 01:28:16,519
Speaker 1: an additional biography about Teddy Roosevelt Teddy Rex And then

1379
01:28:16,600 --> 01:28:22,240
Speaker 1: finally I'm reading a lot about AI and scientific development.

1380
01:28:22,800 --> 01:28:26,120
Speaker 1: The book I'd recommend there is written by a pair

1381
01:28:26,439 --> 01:28:30,480
Speaker 1: of authors, one an AI expert, the other a great storyteller,

1382
01:28:30,840 --> 01:28:35,840
Speaker 1: and it's called AI twenty forty one, Ten Visions of

1383
01:28:35,880 --> 01:28:38,559
Speaker 1: Our AI Future. Huh, highly recommend.

1384
01:28:38,560 --> 01:28:41,040
Speaker 2: I'm going to check that out. We've been talking about

1385
01:28:41,040 --> 01:28:43,439
Speaker 2: the Ripe Brothers. Did you ever read the David McCullough

1386
01:28:43,439 --> 01:28:44,759
Speaker 2: biography of the Ripe Brothers?

1387
01:28:44,800 --> 01:28:45,160
Speaker 1: I did.

1388
01:28:45,400 --> 01:28:50,000
Speaker 2: Fascinating, right, really really really fascinating. And our final two questions,

1389
01:28:50,400 --> 01:28:52,960
Speaker 2: what sort of advice would you give to a recent

1390
01:28:53,040 --> 01:28:58,520
Speaker 2: college graduate interested in a career in either quantitative analysis,

1391
01:28:59,040 --> 01:29:00,759
Speaker 2: finance as management.

1392
01:29:00,920 --> 01:29:05,800
Speaker 1: What's your advice for them? My advice is to focus

1393
01:29:05,960 --> 01:29:11,520
Speaker 1: on the parts of learning that might not be included

1394
01:29:11,680 --> 01:29:15,599
Speaker 1: in a business or finance degree. My line is that

1395
01:29:15,840 --> 01:29:20,840
Speaker 1: markets change second by second, but human nature barely budgees

1396
01:29:21,120 --> 01:29:26,720
Speaker 1: millennia by millennia. Arbitraging. Human nature is the last sustainable

1397
01:29:26,960 --> 01:29:31,320
Speaker 1: edge in investing. And so if you read about evolutionary

1398
01:29:31,360 --> 01:29:36,840
Speaker 1: psychology and biology, regular psychology and biology and history, what

1399
01:29:36,880 --> 01:29:40,200
Speaker 1: you're going to see is no, history doesn't repeat, but

1400
01:29:40,360 --> 01:29:43,840
Speaker 1: it rhymes, and you can see in you know, all

1401
01:29:43,880 --> 01:29:45,639
Speaker 1: you got to do is go read a book about

1402
01:29:45,680 --> 01:29:48,640
Speaker 1: the south Sea Scandal where Isaac Newton, one of the

1403
01:29:48,680 --> 01:29:52,160
Speaker 1: most brilliant guys of his era, lost a fortune, causing

1404
01:29:52,240 --> 01:29:54,519
Speaker 1: him to lament that he could measure the motion of

1405
01:29:54,560 --> 01:29:58,120
Speaker 1: heavenly bodies but not the madness of men. And guess

1406
01:29:58,160 --> 01:30:01,360
Speaker 1: what we're not changing. So you can read it in

1407
01:30:01,400 --> 01:30:06,719
Speaker 1: a market related way, or just understand human nature better,

1408
01:30:07,360 --> 01:30:10,160
Speaker 1: you're going to be miles ahead of the people who

1409
01:30:10,240 --> 01:30:14,480
Speaker 1: are just studying math or finance or economics.

1410
01:30:15,280 --> 01:30:18,920
Speaker 2: Really interesting, and our final question, what do you know

1411
01:30:18,960 --> 01:30:21,960
Speaker 2: about the world of investing today you wish you knew

1412
01:30:22,200 --> 01:30:25,400
Speaker 2: forty or so years ago when you were first getting started.

1413
01:30:26,080 --> 01:30:28,320
Speaker 1: I think maybe just the advice that I just gave,

1414
01:30:28,680 --> 01:30:31,720
Speaker 1: I wish that I would have known forty years ago

1415
01:30:32,200 --> 01:30:38,160
Speaker 1: that markets are market prices are determined by human beings,

1416
01:30:39,040 --> 01:30:43,280
Speaker 1: and if you are ignorant of all of the ways

1417
01:30:43,320 --> 01:30:47,719
Speaker 1: that we let things affect us, from whether we're hungry

1418
01:30:47,800 --> 01:30:51,600
Speaker 1: or not, or whether we're angry, or whether we're calm,

1419
01:30:51,800 --> 01:30:55,960
Speaker 1: I would have understood that it was not just numbers

1420
01:30:55,960 --> 01:31:01,639
Speaker 1: on a page, that markets are full blooded, almost human

1421
01:31:01,880 --> 01:31:06,599
Speaker 1: like things because they're driven and created by humans. If

1422
01:31:06,760 --> 01:31:11,240
Speaker 1: I could have told Jim of age twenty three that

1423
01:31:11,920 --> 01:31:17,160
Speaker 1: it would have hastened but also improved the pretty circuitous

1424
01:31:17,200 --> 01:31:21,440
Speaker 1: path that I took to becoming a quat really interesting.

1425
01:31:22,160 --> 01:31:24,880
Speaker 2: Thank you, Jim for being so generous with your time.

1426
01:31:25,120 --> 01:31:29,560
Speaker 2: We have been speaking with Jim O'Shaughnessy, founder of OSAM

1427
01:31:29,640 --> 01:31:34,840
Speaker 2: Asset Management and currently CEO and founder of O'Shaughnessy Ventures

1428
01:31:34,880 --> 01:31:39,519
Speaker 2: and host of the Infinite Loops podcast. If you enjoy

1429
01:31:39,600 --> 01:31:41,280
Speaker 2: this conversation, well.

1430
01:31:41,200 --> 01:31:42,840
Speaker 1: Be sure and check out any of.

1431
01:31:42,760 --> 01:31:46,840
Speaker 2: The five hundred previous discussions we've had over the past

1432
01:31:46,880 --> 01:31:47,519
Speaker 2: ten years.

1433
01:31:47,960 --> 01:31:49,519
Speaker 1: You can find those at.

1434
01:31:49,520 --> 01:31:55,080
Speaker 2: iTunes, Spotify, YouTube, wherever you find your favorite podcast. Be

1435
01:31:55,160 --> 01:31:58,360
Speaker 2: sure and sign up for my new podcast At the Money,

1436
01:31:58,680 --> 01:32:01,960
Speaker 2: where we speak with an X and give you information

1437
01:32:02,439 --> 01:32:06,599
Speaker 2: on a topic relative to your money in short eight

1438
01:32:06,640 --> 01:32:10,360
Speaker 2: to twelve minute batches. You can find those in the

1439
01:32:10,360 --> 01:32:14,559
Speaker 2: Masters in Business podcast feed, or wherever you get your

1440
01:32:14,600 --> 01:32:17,920
Speaker 2: favorite podcasts. I would be remiss if I did not

1441
01:32:18,040 --> 01:32:20,760
Speaker 2: thank the Cracked team that helps us put these conversations

1442
01:32:20,800 --> 01:32:25,320
Speaker 2: together each week. My audio engineer is Sebastian Escobar, My

1443
01:32:25,600 --> 01:32:29,080
Speaker 2: producer is Anna Luke. Sean Russo is my head of research.

1444
01:32:29,400 --> 01:32:34,240
Speaker 2: Attika Valbrun is my project manager. Sage Bauman is the

1445
01:32:34,280 --> 01:32:35,320
Speaker 2: head of podcasts.

1446
01:32:36,040 --> 01:32:39,479
Speaker 1: I'm Barry Riddolts. You've been listening to Masters in Business

1447
01:32:40,000 --> 01:32:42,160
Speaker 1: on Bloomberg Radio.