1
00:00:03,120 --> 00:00:18,480
Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.

2
00:00:20,079 --> 00:00:23,919
Speaker 2: Hello and welcome to another episode of the Odd Lots podcast.

3
00:00:24,000 --> 00:00:26,320
Speaker 3: I'm Joe Wisenthal and I'm Tracy Alloway.

4
00:00:26,560 --> 00:00:29,160
Speaker 2: Oddlots listeners, you are going to be listening to a

5
00:00:29,360 --> 00:00:33,760
Speaker 2: special recording of the podcast, one that we recorded live

6
00:00:33,800 --> 00:00:34,640
Speaker 2: in San Francisco.

7
00:00:35,000 --> 00:00:35,880
Speaker 4: Yep, that's right.

8
00:00:36,000 --> 00:00:38,960
Speaker 3: This was a conversation that we had at the San

9
00:00:39,040 --> 00:00:43,720
Speaker 3: Francisco MoMA on November twentieth. It was an event sponsored

10
00:00:43,760 --> 00:00:47,840
Speaker 3: by Principal Asset Management, and our guest is Ethan Kurzweild,

11
00:00:47,840 --> 00:00:51,040
Speaker 3: the founder and managing partner of Chemistry VC.

12
00:00:51,640 --> 00:00:51,880
Speaker 5: Yep.

13
00:00:51,960 --> 00:00:56,880
Speaker 2: We talked about all things tech software investing, how investing

14
00:00:56,960 --> 00:01:00,560
Speaker 2: today is different than it was, say in twenty fourteen

15
00:01:00,680 --> 00:01:04,000
Speaker 2: when rates are at zero. We obviously talked about AI

16
00:01:04,240 --> 00:01:06,920
Speaker 2: and how that changes the game of software investing.

17
00:01:07,520 --> 00:01:08,160
Speaker 4: Take a listen.

18
00:01:08,400 --> 00:01:12,240
Speaker 2: Thrilled to be here with the perfect guest, Ethan Kurzweil,

19
00:01:12,480 --> 00:01:16,440
Speaker 2: as his new fund, Chemistry It's it basically launched like

20
00:01:16,480 --> 00:01:19,560
Speaker 2: three weeks ago or something like that, and prior to

21
00:01:19,600 --> 00:01:24,119
Speaker 2: that sixteen years at Bessemer. So literally the perfect guest

22
00:01:24,160 --> 00:01:28,520
Speaker 2: to talk about, you know, VC's the landscape.

23
00:01:28,000 --> 00:01:29,680
Speaker 4: Changing over time, or something like that.

24
00:01:29,720 --> 00:01:30,640
Speaker 5: Well, thanks for having me.

25
00:01:30,720 --> 00:01:33,880
Speaker 6: This is actually the first episode of anything we've done

26
00:01:33,920 --> 00:01:37,720
Speaker 6: since we launched Chemistry, So that's amazingly or thrilled.

27
00:01:37,920 --> 00:01:39,800
Speaker 4: There is obviously so much.

28
00:01:40,200 --> 00:01:43,360
Speaker 2: There's so much we could talk about, talk about the

29
00:01:43,400 --> 00:01:46,080
Speaker 2: macro environment, we talk about AI, we could talk about

30
00:01:46,080 --> 00:01:48,280
Speaker 2: the political environment. Maybe we'll touch a little bit on

31
00:01:48,320 --> 00:01:49,840
Speaker 2: all of it. So I'm just gonna ask like a

32
00:01:49,880 --> 00:01:54,560
Speaker 2: really simple question to kick it off, which is in

33
00:01:54,600 --> 00:01:57,600
Speaker 2: the twenty tens, you know, people talked about the Zerp era,

34
00:01:57,680 --> 00:02:01,280
Speaker 2: and some people even look on that quite fundly right

35
00:02:01,320 --> 00:02:04,280
Speaker 2: now with nostalgia, even though at the time zerp was

36
00:02:04,320 --> 00:02:07,400
Speaker 2: sort of seen as like this negative thing didn't seem that.

37
00:02:07,400 --> 00:02:08,320
Speaker 4: Bad out here.

38
00:02:08,400 --> 00:02:13,120
Speaker 2: Though strictly from a macro standpoint, you've been in this game,

39
00:02:13,360 --> 00:02:15,399
Speaker 2: so to speak, for a long time. What's the difference

40
00:02:15,680 --> 00:02:18,560
Speaker 2: right now versus say we were having this conversation in

41
00:02:18,560 --> 00:02:19,200
Speaker 2: twenty fourteen.

42
00:02:19,639 --> 00:02:22,000
Speaker 6: Oh, the good old days of twenty fourteen. I missed

43
00:02:22,040 --> 00:02:22,680
Speaker 6: those days too.

44
00:02:22,680 --> 00:02:23,720
Speaker 5: I wish we could go back.

45
00:02:24,120 --> 00:02:27,679
Speaker 6: So right now, there's lots of things happening in sort

46
00:02:27,720 --> 00:02:30,320
Speaker 6: of the tech landscape broadly as well as like venture,

47
00:02:30,360 --> 00:02:32,720
Speaker 6: and so maybe just taking a few around venture. You

48
00:02:32,800 --> 00:02:35,560
Speaker 6: had this era of explosion of different things, lots of

49
00:02:35,600 --> 00:02:40,000
Speaker 6: different funds and products, money being kind of invested in

50
00:02:40,000 --> 00:02:42,720
Speaker 6: the asset class beyond what it could take, beyond the

51
00:02:42,760 --> 00:02:46,040
Speaker 6: capacity of those companies to absorb the capital and do

52
00:02:46,120 --> 00:02:49,000
Speaker 6: good things with it. I'm an optimist about tech and venture.

53
00:02:49,040 --> 00:02:52,120
Speaker 6: I think more is generally better, but there's a limit

54
00:02:52,160 --> 00:02:54,679
Speaker 6: to that. I think everyone would now agree kind of

55
00:02:54,720 --> 00:02:56,640
Speaker 6: in hindsight, we went a little bit beyond that limit.

56
00:02:57,120 --> 00:03:00,000
Speaker 6: Now we're in this kind of new era where that's happened.

57
00:03:00,160 --> 00:03:03,040
Speaker 6: We're kind of digesting the impact of that, of all

58
00:03:03,080 --> 00:03:05,760
Speaker 6: this capital coming into the space, and you have this

59
00:03:05,840 --> 00:03:07,760
Speaker 6: kind of new technology phenomenon.

60
00:03:07,760 --> 00:03:09,880
Speaker 5: And by the way, it's not really new. It's maybe

61
00:03:09,919 --> 00:03:11,280
Speaker 5: new as it applied to startups.

62
00:03:11,320 --> 00:03:12,440
Speaker 4: You've heard about it for a while.

63
00:03:12,480 --> 00:03:14,760
Speaker 6: I've been hearing about AI for I don't know, a

64
00:03:14,760 --> 00:03:16,320
Speaker 6: few decades like that.

65
00:03:16,480 --> 00:03:17,240
Speaker 4: We'll talk about that.

66
00:03:17,800 --> 00:03:22,280
Speaker 6: We'll get there, but that's now kind of the building

67
00:03:22,360 --> 00:03:25,480
Speaker 6: blocks are now there the technology. Startups without a lot

68
00:03:25,520 --> 00:03:27,520
Speaker 6: of capital can take advantage of it. And so that's

69
00:03:27,560 --> 00:03:30,320
Speaker 6: getting people kind of very very excited again. Even as

70
00:03:30,360 --> 00:03:33,440
Speaker 6: everyone knows it's still this fresh memory in everyone's head

71
00:03:33,639 --> 00:03:36,080
Speaker 6: of how we kind of over capitalized everything, and so

72
00:03:36,120 --> 00:03:38,839
Speaker 6: those two forces are sort of countervailing and it's having

73
00:03:38,880 --> 00:03:41,360
Speaker 6: some interesting impacts that I think we'll probably get into.

74
00:03:41,920 --> 00:03:45,240
Speaker 3: We definitely will the new fund. Why does the world

75
00:03:45,320 --> 00:03:48,560
Speaker 3: need a new venture capital fund? Or if I was

76
00:03:48,600 --> 00:03:50,880
Speaker 3: going to phrase it more diplomatically, like what is it

77
00:03:50,960 --> 00:03:53,240
Speaker 3: that you can do at Chemistry that you couldn't do

78
00:03:53,400 --> 00:03:54,000
Speaker 3: at Best Mark?

79
00:03:54,120 --> 00:03:56,520
Speaker 6: The world does not need a new venture capital fund.

80
00:03:56,600 --> 00:04:00,400
Speaker 6: That's the last thing the world needs launched. Yeah, that

81
00:04:00,520 --> 00:04:03,960
Speaker 6: was the last one before Chemistry. Okay, we were over

82
00:04:04,080 --> 00:04:06,840
Speaker 6: supplied on venture capitals. But the world does need the

83
00:04:06,920 --> 00:04:10,080
Speaker 6: right venture capital fund. And I'll get to why we

84
00:04:10,160 --> 00:04:12,840
Speaker 6: launched Chemistry in a second, But I do think that

85
00:04:13,280 --> 00:04:16,080
Speaker 6: the effect of the capital that came into the asset

86
00:04:16,160 --> 00:04:19,320
Speaker 6: class over the past kind of five to ten years

87
00:04:19,720 --> 00:04:22,320
Speaker 6: has been to create a little bit of a misalignment,

88
00:04:22,320 --> 00:04:25,120
Speaker 6: a misalignment between LPs that's who's invest in venture firms

89
00:04:25,320 --> 00:04:28,560
Speaker 6: and the venture managers, and then a misalignment with founders ultimately,

90
00:04:28,839 --> 00:04:30,919
Speaker 6: and that's what got us the sort of passionate idea

91
00:04:30,960 --> 00:04:31,880
Speaker 6: to bring venture.

92
00:04:31,640 --> 00:04:32,480
Speaker 5: Back to its roots.

93
00:04:32,640 --> 00:04:34,640
Speaker 6: Chemistry is sort of a simple idea. It's a boutique

94
00:04:34,680 --> 00:04:38,360
Speaker 6: venture firm. It's small as designed to scale very slowly,

95
00:04:38,600 --> 00:04:40,640
Speaker 6: so we are not a hyper growth startup, even though

96
00:04:40,640 --> 00:04:42,880
Speaker 6: we try to find those to invest in. We want

97
00:04:42,920 --> 00:04:45,719
Speaker 6: to bring some sort of personal service back to venture capital.

98
00:04:45,880 --> 00:04:48,080
Speaker 6: We don't have big teams of people that we are

99
00:04:48,120 --> 00:04:50,279
Speaker 6: the portfolio services team that works kind of hand on,

100
00:04:50,520 --> 00:04:53,679
Speaker 6: hands on with our startups, and that ethos we felt

101
00:04:53,680 --> 00:04:56,000
Speaker 6: like was missing from a lot of the way that

102
00:04:56,200 --> 00:04:59,440
Speaker 6: kind of as the asset clut's got institutionalized, you lost

103
00:04:59,440 --> 00:05:02,080
Speaker 6: a little bit of the personality and the personal relationships,

104
00:05:02,279 --> 00:05:03,120
Speaker 6: and we felt.

105
00:05:02,839 --> 00:05:04,320
Speaker 5: Like it didn't have to be that way.

106
00:05:04,560 --> 00:05:07,480
Speaker 6: There's nothing bad about the way venture used to be practiced,

107
00:05:07,839 --> 00:05:10,919
Speaker 6: and that it really it just became so missing that

108
00:05:10,960 --> 00:05:12,960
Speaker 6: we felt like, Okay, we'll go do this.

109
00:05:13,440 --> 00:05:15,560
Speaker 2: Let's say I have a lot of money. I'm an

110
00:05:15,640 --> 00:05:18,200
Speaker 2: endowment or whatever, and I'm thinking about allocating money to

111
00:05:18,279 --> 00:05:22,720
Speaker 2: a VC fund or firm. That sounds really nice, personal

112
00:05:22,800 --> 00:05:27,480
Speaker 2: relationships all that, but mostly I just care about getting returns.

113
00:05:27,960 --> 00:05:31,680
Speaker 2: And let's say my assumption is okay, yeah, again, it

114
00:05:31,720 --> 00:05:35,160
Speaker 2: all sounds very nice, but there are advantages to scale.

115
00:05:35,240 --> 00:05:38,400
Speaker 2: There's deal flow that large firms see that you know,

116
00:05:38,520 --> 00:05:41,159
Speaker 2: maybe you know, they're the first call in some round

117
00:05:41,320 --> 00:05:45,680
Speaker 2: or something like that. Why would that be wrong? It's

118
00:05:45,760 --> 00:05:47,960
Speaker 2: not wrong, okay, but it's not the only way to

119
00:05:48,000 --> 00:05:48,760
Speaker 2: practice venture.

120
00:05:48,800 --> 00:05:49,120
Speaker 5: Okay.

121
00:05:49,160 --> 00:05:51,760
Speaker 6: There's definitely advantages to scale, but I think it comes

122
00:05:51,800 --> 00:05:56,279
Speaker 6: at a cost of being able to focus uniquely on

123
00:05:56,640 --> 00:05:59,160
Speaker 6: companies that are at this inflection point moment, this pre

124
00:05:59,279 --> 00:06:01,360
Speaker 6: inflection point where they're about to take off.

125
00:06:01,640 --> 00:06:02,920
Speaker 5: Because when you have a lot.

126
00:06:02,720 --> 00:06:05,960
Speaker 6: Of capital to manage, you're going to make decisions that

127
00:06:06,320 --> 00:06:09,560
Speaker 6: aren't necessarily about how do I find that company that

128
00:06:09,640 --> 00:06:12,719
Speaker 6: needs a three to seven million dollar check at that moment,

129
00:06:13,000 --> 00:06:16,320
Speaker 6: You're thinking about how do I move the move the merchandise,

130
00:06:16,400 --> 00:06:18,520
Speaker 6: move the money that I have in the system. And

131
00:06:18,560 --> 00:06:21,080
Speaker 6: so it may be appropriate to make that investment, but

132
00:06:21,120 --> 00:06:22,719
Speaker 6: it may be appropriate to make a whole host of

133
00:06:22,760 --> 00:06:25,800
Speaker 6: others that are at cross purposes with making finding the

134
00:06:25,839 --> 00:06:29,039
Speaker 6: one defining company of that era. And I think for

135
00:06:29,200 --> 00:06:34,640
Speaker 6: us that have been experienced at working adventure firms and

136
00:06:34,720 --> 00:06:37,440
Speaker 6: identifying the patterns that lead to that, we felt like

137
00:06:37,480 --> 00:06:40,599
Speaker 6: we could pick those out pretty well and that we

138
00:06:40,640 --> 00:06:42,839
Speaker 6: would have a good sense of where to spend our time.

139
00:06:43,160 --> 00:06:46,760
Speaker 6: Without the resources and without the brand magnets of other firms.

140
00:06:46,680 --> 00:06:48,200
Speaker 5: And that it's a small community.

141
00:06:48,200 --> 00:06:50,200
Speaker 6: We could get our brand out there pretty quickly to

142
00:06:50,480 --> 00:06:53,240
Speaker 6: folks that are used to identifying those patterns and referring

143
00:06:53,279 --> 00:06:54,360
Speaker 6: those deals on to us.

144
00:06:54,720 --> 00:06:57,839
Speaker 3: So I think you just finished your first fundraising? Was

145
00:06:57,839 --> 00:06:58,960
Speaker 3: it three hundred and fifty million?

146
00:06:59,000 --> 00:07:00,920
Speaker 5: Three hundred fifty million was the first fund and that's right.

147
00:07:01,000 --> 00:07:05,560
Speaker 3: What was the fundraising experience like now versus say, going

148
00:07:05,560 --> 00:07:07,599
Speaker 3: back to the good old days of twenty fourteen.

149
00:07:08,080 --> 00:07:10,560
Speaker 5: Yeah, oh yeah, good, good, good question.

150
00:07:11,200 --> 00:07:13,400
Speaker 6: So it's different in two respects for us because we

151
00:07:13,400 --> 00:07:16,560
Speaker 6: were a new entity too, and so we had a

152
00:07:16,600 --> 00:07:19,440
Speaker 6: whole bunch of vetting around, Hey, what's our track record

153
00:07:19,440 --> 00:07:21,880
Speaker 6: and experience? The founders want to work with us, because

154
00:07:21,880 --> 00:07:24,160
Speaker 6: this whole premise was on we're going to bring the

155
00:07:24,160 --> 00:07:27,000
Speaker 6: individual personal service back. We need to be able to

156
00:07:27,040 --> 00:07:29,720
Speaker 6: say our personal service is good, like you want to

157
00:07:29,760 --> 00:07:32,520
Speaker 6: work with the chemistry team because we're known for that.

158
00:07:32,800 --> 00:07:34,520
Speaker 5: So that was a lot of vetting around that.

159
00:07:34,920 --> 00:07:37,520
Speaker 6: There were LPs that felt like the Acid class had

160
00:07:37,560 --> 00:07:41,640
Speaker 6: under delivered. Those were generally, you know, came into conversations

161
00:07:41,720 --> 00:07:44,920
Speaker 6: very skeptical and our argument to them and some of

162
00:07:44,920 --> 00:07:46,000
Speaker 6: them invested, some of them.

163
00:07:45,840 --> 00:07:47,600
Speaker 5: Didn't but our argument to them was.

164
00:07:47,840 --> 00:07:51,560
Speaker 6: Look, that's true writ Large, but by having exposure to

165
00:07:51,840 --> 00:07:54,560
Speaker 6: just the earliest stages of the acid class going back

166
00:07:54,640 --> 00:07:57,880
Speaker 6: forty years, that at that phase of the market has

167
00:07:57,920 --> 00:08:01,280
Speaker 6: always performed. If you took a slice of the venture

168
00:08:01,320 --> 00:08:04,920
Speaker 6: market and looked at just early stage investing, just the

169
00:08:04,960 --> 00:08:07,280
Speaker 6: phase of scuity, your typical kind of series A and

170
00:08:07,360 --> 00:08:10,320
Speaker 6: Series B investment, maybe the median fund hasn't performed, but

171
00:08:10,360 --> 00:08:13,760
Speaker 6: there's always been outlier funds throughout that period. If you

172
00:08:13,800 --> 00:08:16,280
Speaker 6: looked at all of the asset class writ Large, including

173
00:08:16,320 --> 00:08:18,840
Speaker 6: all the growth checks, the leader stage investments, the sort

174
00:08:18,840 --> 00:08:21,440
Speaker 6: of pre IPO rounds that came on, that asset class

175
00:08:21,520 --> 00:08:23,680
Speaker 6: is really underperformed over the last five years.

176
00:08:23,840 --> 00:08:26,240
Speaker 5: So we were sort of orienting around, we give you.

177
00:08:26,200 --> 00:08:28,600
Speaker 6: Exposure to just the early stages and we don't want

178
00:08:28,640 --> 00:08:29,400
Speaker 6: to do anything else.

179
00:08:29,640 --> 00:08:32,319
Speaker 5: That's what that's We formed the firm just to do that.

180
00:08:32,920 --> 00:08:35,520
Speaker 2: So there's no guarantees ever in venture, and we know

181
00:08:35,559 --> 00:08:39,640
Speaker 2: there's outliers, but the basic idea here is that venture

182
00:08:39,760 --> 00:08:42,800
Speaker 2: may be cyclical or the maybe structural, but early stage

183
00:08:43,200 --> 00:08:46,600
Speaker 2: is not cyclical in the same way that these potential

184
00:08:46,640 --> 00:08:49,360
Speaker 2: returns have been stable at this level if we.

185
00:08:49,280 --> 00:08:52,119
Speaker 6: Make the right if we make the right number of investments.

186
00:08:52,600 --> 00:08:54,440
Speaker 6: We have to make the right investments, and we have

187
00:08:54,520 --> 00:08:56,880
Speaker 6: to get a few right. Maybe there's a little luck involved,

188
00:08:57,040 --> 00:08:59,120
Speaker 6: but if we do that right, that will outperform.

189
00:08:59,160 --> 00:09:01,160
Speaker 5: We won't water it down with bad investments later.

190
00:09:01,280 --> 00:09:05,199
Speaker 2: Let's talk about how to make good investments then, because that's.

191
00:09:05,080 --> 00:09:06,520
Speaker 4: Really what matters.

192
00:09:06,679 --> 00:09:11,240
Speaker 2: Obviously, the twenty tens, the sort of cheap cloud computing

193
00:09:11,320 --> 00:09:14,720
Speaker 2: and all the SaaS trends that made people of fortune.

194
00:09:14,960 --> 00:09:18,960
Speaker 2: How does evaluating a company today? And this is where,

195
00:09:19,000 --> 00:09:21,360
Speaker 2: like I guess the AI part comes in, whether the

196
00:09:21,360 --> 00:09:24,480
Speaker 2: company is AI specific or in some level is going

197
00:09:24,520 --> 00:09:28,120
Speaker 2: to be plugged into an AI model somewhere. How does

198
00:09:28,160 --> 00:09:31,960
Speaker 2: that make the process of evaluation effiluating a company different?

199
00:09:32,880 --> 00:09:36,960
Speaker 6: It makes it radically different and exactly the same all

200
00:09:37,000 --> 00:09:37,680
Speaker 6: at the same time.

201
00:09:37,760 --> 00:09:40,560
Speaker 5: All right, So what do I mean by that radically different?

202
00:09:40,600 --> 00:09:45,280
Speaker 6: In that the entrepreneur can now promise pretty incredible things.

203
00:09:45,320 --> 00:09:46,960
Speaker 6: You can talk to a system and get it to

204
00:09:47,000 --> 00:09:49,280
Speaker 6: code for you. Is something that three or four years

205
00:09:49,280 --> 00:09:51,440
Speaker 6: ago you would have said, sure, good luck with that. Like,

206
00:09:51,640 --> 00:09:53,520
Speaker 6: you know, you need some you need some engineers on

207
00:09:53,559 --> 00:09:57,360
Speaker 6: your team. You can now make promises like that. But ultimately,

208
00:09:57,679 --> 00:09:59,920
Speaker 6: the way we're evaluating companies is thinking about the end

209
00:10:00,120 --> 00:10:03,480
Speaker 6: market that they serve, the business user or the consumer,

210
00:10:03,679 --> 00:10:05,840
Speaker 6: and how are their lives made better? How is this

211
00:10:05,920 --> 00:10:09,000
Speaker 6: process improved? If you're you know, making a you know,

212
00:10:09,080 --> 00:10:12,400
Speaker 6: consumer video editing app, how is that awesome for consumers

213
00:10:12,440 --> 00:10:17,360
Speaker 6: to use? And so you start with like the question

214
00:10:17,480 --> 00:10:21,120
Speaker 6: of can the technology deliver what the entrepreneur says? That's

215
00:10:21,200 --> 00:10:24,120
Speaker 6: radically different, and then you step back to hey, is

216
00:10:24,120 --> 00:10:26,480
Speaker 6: this a good business opportunity or not? Is this something

217
00:10:26,520 --> 00:10:28,200
Speaker 6: that people will pay a lot of money for that

218
00:10:28,240 --> 00:10:30,760
Speaker 6: will be able to monetize itself in some other way?

219
00:10:31,080 --> 00:10:33,280
Speaker 5: That's very similar to how we've always done the job.

220
00:10:33,840 --> 00:10:36,559
Speaker 3: Is there a difference in the sort of due diligence

221
00:10:36,640 --> 00:10:41,199
Speaker 3: process for AI versus you know, old school SaaS.

222
00:10:41,880 --> 00:10:45,439
Speaker 6: Not terribly Honestly, old school SaaS often had a data

223
00:10:45,480 --> 00:10:48,280
Speaker 6: element to it that's somewhat similar to AI, you know,

224
00:10:48,320 --> 00:10:50,280
Speaker 6: how they harness data in the application.

225
00:10:51,000 --> 00:10:54,840
Speaker 5: What's different now is you can apply a frame of

226
00:10:54,920 --> 00:10:57,880
Speaker 5: reference of what's possible. That's just much broader.

227
00:10:57,640 --> 00:11:01,280
Speaker 6: That's just much more interesting, that's just much more potentially

228
00:11:01,320 --> 00:11:04,319
Speaker 6: transformative to business users or consumers.

229
00:11:04,760 --> 00:11:05,800
Speaker 5: That's a little different.

230
00:11:06,320 --> 00:11:09,920
Speaker 6: You might not be as skeptical about founder's ability to deliver.

231
00:11:10,640 --> 00:11:13,680
Speaker 6: There's this whole democratizing element to AI. Just riff on

232
00:11:13,720 --> 00:11:16,560
Speaker 6: that for a second, in that you maybe don't have

233
00:11:16,640 --> 00:11:20,840
Speaker 6: to have the most ultra specialized skill set of engineer

234
00:11:20,880 --> 00:11:23,640
Speaker 6: to be able to deliver something pretty transformative. And so

235
00:11:24,360 --> 00:11:26,439
Speaker 6: if you back up from that and think about a

236
00:11:26,520 --> 00:11:30,319
Speaker 6: due diligence process, you don't necessarily need to spend as

237
00:11:30,360 --> 00:11:33,959
Speaker 6: much time questioning the entrepreneur's ability to deliver. And there

238
00:11:34,000 --> 00:11:37,040
Speaker 6: is this maxim that like most entrepreneurs will build what

239
00:11:37,080 --> 00:11:39,600
Speaker 6: they want to build, just is that the right thing

240
00:11:39,960 --> 00:11:42,719
Speaker 6: and is the timing right with the aier that's you

241
00:11:43,040 --> 00:11:47,160
Speaker 6: get that on steroids. Most products can be built the

242
00:11:47,200 --> 00:11:49,200
Speaker 6: way the entrepreneur says them. Now will they have the

243
00:11:49,200 --> 00:11:52,360
Speaker 6: impact that the entrepreneur thinks they'll have. That's still a

244
00:11:52,480 --> 00:11:54,720
Speaker 6: question that we have to answer when we make our judgments.

245
00:11:54,920 --> 00:11:58,000
Speaker 3: What's the differentiator in that case if it's not necessarily

246
00:11:58,040 --> 00:12:00,439
Speaker 3: about the skill set of the engineer, or what is

247
00:12:00,480 --> 00:12:03,400
Speaker 3: it that makes you think an AI project is better

248
00:12:03,480 --> 00:12:04,960
Speaker 3: than another AI project?

249
00:12:05,720 --> 00:12:07,800
Speaker 6: Ultimately confact to like, what impact will it have in

250
00:12:07,840 --> 00:12:10,720
Speaker 6: the market? I don't think about AI as a category

251
00:12:10,800 --> 00:12:13,000
Speaker 6: so much. I think about AIS and enabling tech just

252
00:12:13,040 --> 00:12:16,680
Speaker 6: like cloud computing or mobile or mainframes back in the day,

253
00:12:16,960 --> 00:12:20,240
Speaker 6: or you know, data center technology. It's just a way

254
00:12:20,240 --> 00:12:24,960
Speaker 6: of building tech that can potentially allow an entrepreneur more.

255
00:12:24,760 --> 00:12:28,040
Speaker 5: Weapons to be able to deploy. But there's nothing inherently

256
00:12:28,120 --> 00:12:29,439
Speaker 5: like the end user.

257
00:12:29,160 --> 00:12:32,880
Speaker 6: That's using a finance application or that's using a communication app.

258
00:12:33,200 --> 00:12:34,960
Speaker 6: At the end of the day, they're using that app

259
00:12:34,960 --> 00:12:36,240
Speaker 6: because they want to do something with it.

260
00:12:36,280 --> 00:12:37,560
Speaker 5: They want to communicate, they.

261
00:12:37,480 --> 00:12:41,120
Speaker 6: Want to run their expense reconciliation process, they want to

262
00:12:41,120 --> 00:12:44,720
Speaker 6: do X, Y or Z. There's nothing different about AI

263
00:12:44,840 --> 00:12:48,320
Speaker 6: that makes that any bit of a different analysis than

264
00:12:48,320 --> 00:12:50,680
Speaker 6: we had before around what is the impact that that

265
00:12:50,880 --> 00:12:52,440
Speaker 6: particular product is going to have.

266
00:12:52,480 --> 00:13:12,400
Speaker 2: In Today we got in Nvidia earnings, a company that

267
00:13:12,520 --> 00:13:15,840
Speaker 2: people may have heard of, and they were really strong.

268
00:13:15,920 --> 00:13:17,439
Speaker 4: I think this doc slipped a little bit, but in

269
00:13:17,440 --> 00:13:19,079
Speaker 4: an important company.

270
00:13:19,160 --> 00:13:22,280
Speaker 2: Yeah, and Jensen Wong saying, you know, AI is full

271
00:13:22,320 --> 00:13:24,800
Speaker 2: steam ahead and they have all these scarcity when you're

272
00:13:24,840 --> 00:13:28,120
Speaker 2: writing a check to a company today, you know, one

273
00:13:28,160 --> 00:13:30,200
Speaker 2: of the things that characterized the twenty Times was just

274
00:13:30,559 --> 00:13:35,640
Speaker 2: persistently falling cost of computing power. When you're writing a

275
00:13:35,800 --> 00:13:39,320
Speaker 2: check with how much of that today is going to

276
00:13:39,360 --> 00:13:43,319
Speaker 2: pay some sort of in Vidia tacks to have access today?

277
00:13:43,360 --> 00:13:46,480
Speaker 2: And how does that make the sort of capital decisions

278
00:13:46,520 --> 00:13:49,480
Speaker 2: of a company or the types of companies that you're

279
00:13:49,520 --> 00:13:51,520
Speaker 2: invested in going to look different than they were.

280
00:13:51,679 --> 00:13:55,000
Speaker 6: Well, there's this interesting kind of tow counter valing forces

281
00:13:55,040 --> 00:13:58,080
Speaker 6: because the more of your check that goes to attacks

282
00:13:58,120 --> 00:14:00,720
Speaker 6: like an Nvidia attacks or an open ai yea.

283
00:14:00,840 --> 00:14:03,880
Speaker 5: Or whoever built on going some generally for us, it's

284
00:14:03,920 --> 00:14:05,880
Speaker 5: being built on a model that they're not using.

285
00:14:05,880 --> 00:14:09,760
Speaker 6: The sort of baar metal of the GPU that there's

286
00:14:09,800 --> 00:14:12,920
Speaker 6: puts in takes there. But the more you invest in that,

287
00:14:14,080 --> 00:14:17,000
Speaker 6: the more you've got your own technology that's more defensible.

288
00:14:17,320 --> 00:14:19,760
Speaker 6: So a lot of times we're seeing open source tools.

289
00:14:19,760 --> 00:14:21,560
Speaker 6: So a lot of times we're seeing not a lot

290
00:14:21,600 --> 00:14:24,120
Speaker 6: of money go towards that, but they're deploying open source

291
00:14:24,160 --> 00:14:26,680
Speaker 6: tools or they're built on models that are freely available

292
00:14:26,680 --> 00:14:29,400
Speaker 6: to anybody. And so it's a question of can the

293
00:14:29,480 --> 00:14:33,640
Speaker 6: founder or the entrepreneur make a process improvement or productize

294
00:14:34,040 --> 00:14:39,480
Speaker 6: commercially available technology to everyone in a radically different, unique, ten.

295
00:14:39,480 --> 00:14:41,800
Speaker 5: X better way. So much better of a user experience.

296
00:14:42,360 --> 00:14:44,640
Speaker 6: The deep tech founders who are doing what you're saying,

297
00:14:44,680 --> 00:14:47,160
Speaker 6: where a lot of the check goes to building the

298
00:14:47,200 --> 00:14:50,800
Speaker 6: core technology, you have to believe then in the business

299
00:14:50,800 --> 00:14:52,360
Speaker 6: outcome being so great.

300
00:14:52,360 --> 00:14:55,440
Speaker 5: That it's so it's worth it. It's worth this huge

301
00:14:55,560 --> 00:14:55,800
Speaker 5: R and.

302
00:14:55,800 --> 00:14:59,280
Speaker 6: D investment, or this huge investment in training specialized models.

303
00:14:59,360 --> 00:15:01,640
Speaker 2: But just to be clear, you say, okay, the non

304
00:15:01,680 --> 00:15:05,440
Speaker 2: deep tech ones that are build it using some existing models,

305
00:15:06,000 --> 00:15:08,920
Speaker 2: is the amount of money that's going to say an

306
00:15:08,960 --> 00:15:11,680
Speaker 2: open AI or some entity that already built the model,

307
00:15:11,800 --> 00:15:16,760
Speaker 2: is that fundamentally look different than say the expense sheet

308
00:15:16,920 --> 00:15:20,000
Speaker 2: of another software company in twenty fourteen, when they think

309
00:15:20,040 --> 00:15:21,880
Speaker 2: about how much outside tech they're paying for.

310
00:15:22,160 --> 00:15:24,160
Speaker 6: It's not radically different for the ones that are the

311
00:15:24,200 --> 00:15:26,720
Speaker 6: thin layer. Yeah, the thin layer ones are not that

312
00:15:26,880 --> 00:15:29,120
Speaker 6: radically different. I think of it as a as a

313
00:15:29,240 --> 00:15:32,200
Speaker 6: small incremental tax on top of their Amazon Web Services

314
00:15:32,200 --> 00:15:35,359
Speaker 6: built it they could already be paying. And the technology

315
00:15:35,400 --> 00:15:38,000
Speaker 6: is so good, it's so performing, it's so available to

316
00:15:38,040 --> 00:15:40,920
Speaker 6: everyone that most of the companies we look at because

317
00:15:41,280 --> 00:15:44,040
Speaker 6: we believe in the lean startup and most startups that

318
00:15:44,280 --> 00:15:47,000
Speaker 6: can be built on that kind of technology will build

319
00:15:47,000 --> 00:15:48,800
Speaker 6: on that kind of technology.

320
00:15:48,320 --> 00:15:49,960
Speaker 5: It's not a huge tax and costs.

321
00:15:50,120 --> 00:15:51,800
Speaker 6: The huge tax and cost comes when you try to

322
00:15:51,840 --> 00:15:54,400
Speaker 6: sell it and you scale up the go to market operation,

323
00:15:54,520 --> 00:15:55,920
Speaker 6: the sales and marketing, but.

324
00:15:55,960 --> 00:15:56,920
Speaker 5: The tech itself.

325
00:15:57,600 --> 00:16:00,800
Speaker 6: There's only a handful of companies where that's a real

326
00:16:00,880 --> 00:16:02,480
Speaker 6: barrier to entry, and there are some.

327
00:16:03,480 --> 00:16:06,440
Speaker 3: Just on this sort of big versus small point. I

328
00:16:06,480 --> 00:16:09,280
Speaker 3: think one of the weirdest things about the you know,

329
00:16:09,440 --> 00:16:11,600
Speaker 3: the sudden rise of AI over the past couple of

330
00:16:11,680 --> 00:16:15,240
Speaker 3: years has been the fact that Microsoft has been really

331
00:16:15,240 --> 00:16:17,480
Speaker 3: good at it, which I think, you know, three years

332
00:16:17,600 --> 00:16:21,120
Speaker 3: or so, no one would have expected going forward. Do

333
00:16:21,200 --> 00:16:23,560
Speaker 3: you think, like, who's going to be the best at this?

334
00:16:23,800 --> 00:16:25,560
Speaker 3: Is it going to be the incumbents who now have

335
00:16:25,600 --> 00:16:28,320
Speaker 3: a head start, who have the deep pockets, the access

336
00:16:28,360 --> 00:16:30,160
Speaker 3: to data, or is it going to be, you know,

337
00:16:30,200 --> 00:16:33,160
Speaker 3: the leaner startups who are maybe experimenting with new things

338
00:16:33,200 --> 00:16:35,160
Speaker 3: and building on top of existing models.

339
00:16:35,560 --> 00:16:38,040
Speaker 6: Our view would be at the foundational layer, like the

340
00:16:38,080 --> 00:16:40,200
Speaker 6: model layers that a lot of people build on top of,

341
00:16:40,280 --> 00:16:42,200
Speaker 6: or that we as consumers use for sort of our

342
00:16:42,240 --> 00:16:45,960
Speaker 6: basic kind of chatbot style applications, is going to go

343
00:16:46,000 --> 00:16:48,720
Speaker 6: to the big players plus maybe one or two new entrants,

344
00:16:48,840 --> 00:16:51,000
Speaker 6: and that looks like that game is sort of established.

345
00:16:51,040 --> 00:16:52,800
Speaker 6: I mean, I don't know if you count open ai

346
00:16:52,840 --> 00:16:55,560
Speaker 6: as a separate company from Microsoft, but that they're clearly

347
00:16:55,560 --> 00:16:57,600
Speaker 6: around to stay, and maybe there'll be one or two others,

348
00:16:57,920 --> 00:17:01,800
Speaker 6: but that's not, in our view, a humongous startup opportunity

349
00:17:01,840 --> 00:17:04,879
Speaker 6: because there's such amazing capital investments that need to be

350
00:17:04,960 --> 00:17:07,960
Speaker 6: made there. On top of that, la how do we

351
00:17:08,080 --> 00:17:11,879
Speaker 6: take that tech, take those abilities that the amazing researchers

352
00:17:11,880 --> 00:17:15,960
Speaker 6: that open ai, aided by Microsoft and others, have built,

353
00:17:16,040 --> 00:17:18,240
Speaker 6: and make it useful to the end consumer and to

354
00:17:18,320 --> 00:17:20,480
Speaker 6: the end business user. I think that's where we're going

355
00:17:20,520 --> 00:17:22,760
Speaker 6: to see kind of this new era, kind of like

356
00:17:22,800 --> 00:17:24,040
Speaker 6: we saw cloud computing.

357
00:17:23,720 --> 00:17:26,399
Speaker 5: Where there were a few early entrants in our tech.

358
00:17:26,200 --> 00:17:29,720
Speaker 6: And financial applications and things like that, and then this

359
00:17:29,840 --> 00:17:32,960
Speaker 6: explosion of cloud computing applications that disrupted the status quo.

360
00:17:33,440 --> 00:17:36,880
Speaker 2: The platforms that emerged to dumins in twenty ten just

361
00:17:36,880 --> 00:17:41,240
Speaker 2: like exerted to varying degrees, but just tremendous locking for

362
00:17:41,320 --> 00:17:44,639
Speaker 2: their clients and some I'm not talking in the formal

363
00:17:44,720 --> 00:17:48,320
Speaker 2: legal sense, although maybe we'll get to this, but like monopolies,

364
00:17:48,359 --> 00:17:52,800
Speaker 2: but like de facto just like some without truly without competition,

365
00:17:53,280 --> 00:17:56,200
Speaker 2: And there's like this debate about like when it comes

366
00:17:56,240 --> 00:17:59,760
Speaker 2: to these foundational models, there seem to be you know,

367
00:17:59,760 --> 00:18:02,840
Speaker 2: there's there's a lot of entities. There's not thousands, but

368
00:18:02,880 --> 00:18:07,560
Speaker 2: there's quite a few that can make incredibly impressive performance models,

369
00:18:07,600 --> 00:18:10,960
Speaker 2: some open source, some closed source. Do you see any

370
00:18:11,000 --> 00:18:14,000
Speaker 2: of them emerging with the same sort of like true

371
00:18:14,080 --> 00:18:17,520
Speaker 2: lock in kind of dominance, or when you look at

372
00:18:17,560 --> 00:18:21,200
Speaker 2: the companies that you're funding, do they seem like issues like, yeah,

373
00:18:21,240 --> 00:18:24,679
Speaker 2: we could use open eye, but also without too much trouble,

374
00:18:24,960 --> 00:18:27,520
Speaker 2: we could switch to another provider fairly trivially.

375
00:18:28,000 --> 00:18:29,200
Speaker 5: I think it's a really good question.

376
00:18:29,320 --> 00:18:33,040
Speaker 6: I think the lock in is not too dissimilar from

377
00:18:33,040 --> 00:18:36,320
Speaker 6: the cloud era, where you could switch off of one

378
00:18:36,359 --> 00:18:38,600
Speaker 6: cloud computing vendor from one to the other. But there

379
00:18:38,600 --> 00:18:41,000
Speaker 6: weren't that many of them now in this are there

380
00:18:41,080 --> 00:18:43,000
Speaker 6: might be a few more, And I think open source

381
00:18:43,200 --> 00:18:47,359
Speaker 6: there's no open source cloud computing provider. You know, someone's

382
00:18:47,400 --> 00:18:50,520
Speaker 6: got to plug in the hardware, air condition the data center,

383
00:18:50,840 --> 00:18:53,920
Speaker 6: make the networking work with large language models.

384
00:18:53,920 --> 00:18:55,840
Speaker 5: There are open source models that are going to.

385
00:18:55,840 --> 00:18:58,040
Speaker 6: Get to be pretty good, and so I think that's

386
00:18:58,080 --> 00:19:00,520
Speaker 6: another element here that's a little different from the cloud era,

387
00:19:00,560 --> 00:19:03,600
Speaker 6: where probably allows a little more fluidity than even you

388
00:19:03,640 --> 00:19:05,679
Speaker 6: have among clouds. But there's not going to be dozens

389
00:19:05,680 --> 00:19:08,639
Speaker 6: and dozens of models because to be performant, to be

390
00:19:08,840 --> 00:19:12,439
Speaker 6: humanlike be able to provide people with responses that make sense,

391
00:19:12,480 --> 00:19:17,199
Speaker 6: that have emotion, that really fulfill on the promise of

392
00:19:17,240 --> 00:19:20,639
Speaker 6: what AI can do. There's only so much money that

393
00:19:20,680 --> 00:19:22,640
Speaker 6: can there's so much money needed to that that there's

394
00:19:22,680 --> 00:19:24,840
Speaker 6: not that many companies that can capitalize on it.

395
00:19:25,440 --> 00:19:29,280
Speaker 3: Setting aside the big foundational models themselves, what's the coolest

396
00:19:29,400 --> 00:19:32,720
Speaker 3: application of AI that you've seen so far that sort

397
00:19:32,760 --> 00:19:34,040
Speaker 3: of built on the big guys.

398
00:19:34,440 --> 00:19:37,879
Speaker 6: Well, the consumer applications, the companion apps are probably the

399
00:19:38,400 --> 00:19:41,040
Speaker 6: coolest right now. They're not very realistic yet, although they're

400
00:19:41,080 --> 00:19:43,840
Speaker 6: sort of getting getting up there in that, you know,

401
00:19:43,880 --> 00:19:46,760
Speaker 6: they start to emulate real people in.

402
00:19:46,720 --> 00:19:48,520
Speaker 5: The world and can do it.

403
00:19:48,600 --> 00:19:52,240
Speaker 6: Podcasters, I think there should be a Tracy There should

404
00:19:52,240 --> 00:19:54,600
Speaker 6: be a Tracy character app that's out there in the

405
00:19:54,640 --> 00:19:58,520
Speaker 6: public and could in fact with Google's with a Google system,

406
00:19:58,560 --> 00:20:02,280
Speaker 6: you can actually create a whole podcast from a notebook

407
00:20:02,320 --> 00:20:03,240
Speaker 6: that you used.

408
00:20:03,280 --> 00:20:04,240
Speaker 4: Some people have done.

409
00:20:05,960 --> 00:20:08,240
Speaker 5: It's lacking a little color. But this is a little This.

410
00:20:08,280 --> 00:20:11,680
Speaker 2: Is really important because I've listened to some of those

411
00:20:12,000 --> 00:20:15,040
Speaker 2: Google Creator. I mentioned this on another episode. I've listened

412
00:20:15,040 --> 00:20:17,440
Speaker 2: to some of those and they're not as good as

413
00:20:17,480 --> 00:20:20,240
Speaker 2: me and Tracy are, but nothing could.

414
00:20:20,000 --> 00:20:22,440
Speaker 4: Be completely unbiased, but they're not terrible.

415
00:20:22,720 --> 00:20:26,840
Speaker 2: Like it sort of disturbed me because I listened to

416
00:20:26,840 --> 00:20:30,960
Speaker 2: to the AI generated podcast about some document that the

417
00:20:31,000 --> 00:20:33,479
Speaker 2: Department of Energy made and I was like, oh, shoot,

418
00:20:33,480 --> 00:20:37,400
Speaker 2: this isn't that bad. Like, it's not totally boring, It's

419
00:20:37,400 --> 00:20:39,399
Speaker 2: not a terrible way of consuming that content.

420
00:20:39,640 --> 00:20:41,800
Speaker 6: So someone I know, well, how to read an entire

421
00:20:41,840 --> 00:20:45,640
Speaker 6: book and generate a twelve minute podcast on that book.

422
00:20:46,080 --> 00:20:48,240
Speaker 5: And it was not it was. I totally agree it was.

423
00:20:48,720 --> 00:20:50,360
Speaker 4: It was sort of like the.

424
00:20:50,359 --> 00:20:51,600
Speaker 5: Personality was lacking.

425
00:20:51,640 --> 00:20:54,040
Speaker 6: It's right, it was, and they tried to make it

426
00:20:54,080 --> 00:20:55,840
Speaker 6: personable and it just fell flat.

427
00:20:56,160 --> 00:20:59,000
Speaker 5: And I think that's a little bit what's missing today.

428
00:20:59,040 --> 00:21:02,640
Speaker 5: But I will better. Yeah, not as good as you guys.

429
00:21:02,680 --> 00:21:05,800
Speaker 4: Shoot, no odd lack.

430
00:21:05,880 --> 00:21:08,359
Speaker 2: Well, Okay, I'm just gonna ask this question. You know,

431
00:21:08,480 --> 00:21:12,600
Speaker 2: most people in this room have probably been talking a

432
00:21:12,680 --> 00:21:17,359
Speaker 2: lot about AI for two years now. Probably in this

433
00:21:17,440 --> 00:21:19,959
Speaker 2: room it's three years in the rest of the country,

434
00:21:20,000 --> 00:21:23,720
Speaker 2: it's probably about two years. You, as you alluded to,

435
00:21:24,600 --> 00:21:27,719
Speaker 2: have been probably thinking about AI in some respect for

436
00:21:27,800 --> 00:21:29,280
Speaker 2: thirty probably forty years.

437
00:21:29,800 --> 00:21:31,680
Speaker 4: Tell us a little.

438
00:21:31,480 --> 00:21:33,840
Speaker 2: Bit about your background having thought about this for at

439
00:21:33,920 --> 00:21:36,960
Speaker 2: least three or four decades longer than the rest of us.

440
00:21:37,000 --> 00:21:40,400
Speaker 2: And how does that inform when you make predictions now

441
00:21:40,640 --> 00:21:43,360
Speaker 2: when you try to pick winners, How does forty years

442
00:21:43,400 --> 00:21:46,119
Speaker 2: worth of experience inform your choice today?

443
00:21:46,280 --> 00:21:47,440
Speaker 5: All right, so a confession.

444
00:21:47,480 --> 00:21:51,159
Speaker 6: The book that the podcasts that I just mentioned is

445
00:21:51,160 --> 00:21:55,520
Speaker 6: writing about is my dad's book. Okay, my father AI

446
00:21:55,640 --> 00:21:58,600
Speaker 6: Technology Future as has been thinking about AI for about

447
00:21:58,640 --> 00:22:01,959
Speaker 6: sixty five years. What he would say and large language

448
00:22:01,960 --> 00:22:05,400
Speaker 6: models for forty because that's his field. To his pattern recognitions,

449
00:22:05,600 --> 00:22:08,240
Speaker 6: being able to recognize patterns and apply them to language.

450
00:22:08,320 --> 00:22:09,960
Speaker 5: That was one of his first companies was that.

451
00:22:10,680 --> 00:22:11,720
Speaker 4: Is a good job of toing.

452
00:22:12,160 --> 00:22:13,440
Speaker 5: So I was debating with him.

453
00:22:13,560 --> 00:22:15,400
Speaker 6: He thought it was great, and I said, I think

454
00:22:15,400 --> 00:22:17,639
Speaker 6: the podcasters aren't as good as Tracy and Joe. It's

455
00:22:18,280 --> 00:22:20,400
Speaker 6: thank you now your names. That's sort of what I said.

456
00:22:20,400 --> 00:22:22,600
Speaker 6: And that was the debate we had. But they got

457
00:22:22,640 --> 00:22:25,520
Speaker 6: the it got the substance right. But yes to your

458
00:22:25,640 --> 00:22:30,080
Speaker 6: question AI, and it's maybe made me both more excited

459
00:22:30,080 --> 00:22:34,040
Speaker 6: about AI and slightly more cynical about this moment because

460
00:22:34,359 --> 00:22:37,000
Speaker 6: AI has been around for a long time and now

461
00:22:37,000 --> 00:22:40,679
Speaker 6: it's become cycles. Yeah, and we will be in a

462
00:22:40,720 --> 00:22:44,840
Speaker 6: disappointment about what I brings hype cycle in about by

463
00:22:44,840 --> 00:22:48,000
Speaker 6: my calculations, two point six months from now, then we

464
00:22:48,080 --> 00:22:50,919
Speaker 6: will come back out. It's a very precise estimate. And

465
00:22:50,920 --> 00:22:53,520
Speaker 6: then we will come back out from that, and then

466
00:22:53,520 --> 00:22:57,680
Speaker 6: it will start after the four point three Okay, wait, what's.

467
00:22:57,520 --> 00:22:59,280
Speaker 3: The catalyst for disappointment?

468
00:22:59,680 --> 00:23:03,000
Speaker 6: What's that's some of the companies that have been hyped

469
00:23:03,359 --> 00:23:11,040
Speaker 6: to fulfill on this promise of completely human like lifelike understanding, reasoning, abilities,

470
00:23:11,280 --> 00:23:14,600
Speaker 6: and emotion won't quite fulfill on that promise right away,

471
00:23:14,800 --> 00:23:18,720
Speaker 6: and we'll have to wait another four point nine months.

472
00:23:18,400 --> 00:23:20,760
Speaker 5: For that, or longer a couple of years.

473
00:23:20,760 --> 00:23:22,440
Speaker 4: I can't tell how much of a joke anyway.

474
00:23:23,520 --> 00:23:25,840
Speaker 3: You know what else was coming in about two months

475
00:23:26,000 --> 00:23:28,440
Speaker 3: is a new administration.

476
00:23:28,200 --> 00:23:29,600
Speaker 5: And I've heard about that.

477
00:23:29,800 --> 00:23:32,080
Speaker 3: Yeah, it's kind of been in the news. You talk

478
00:23:32,160 --> 00:23:34,480
Speaker 3: to lots of people in the VC space and in

479
00:23:34,760 --> 00:23:38,400
Speaker 3: you know, the founder space. What are people saying about

480
00:23:38,560 --> 00:23:42,320
Speaker 3: the incoming administration, Like what are the hopes, dreams, fears

481
00:23:42,359 --> 00:23:43,840
Speaker 3: that people are talking about.

482
00:23:44,000 --> 00:23:46,199
Speaker 6: You know, there's people that are prominted out there that

483
00:23:46,480 --> 00:23:49,360
Speaker 6: advocated for this or for or against it, that are

484
00:23:50,240 --> 00:23:52,480
Speaker 6: you know, have their passionate points of view about why

485
00:23:52,520 --> 00:23:54,720
Speaker 6: the new administration is good or bad. At the kind

486
00:23:54,720 --> 00:23:58,360
Speaker 6: of surface level day to day this felt like, Okay,

487
00:23:58,720 --> 00:24:00,879
Speaker 6: you know there's a change coming, and there's just not

488
00:24:01,040 --> 00:24:04,879
Speaker 6: a lot of translation between that and the day to

489
00:24:04,960 --> 00:24:07,280
Speaker 6: day of venture capital because you know, technology is this

490
00:24:07,400 --> 00:24:10,920
Speaker 6: force that sort of plows through market cycle, plows through

491
00:24:10,920 --> 00:24:15,080
Speaker 6: market cycles technology administrations, unless you're in a very highly

492
00:24:15,119 --> 00:24:18,680
Speaker 6: regulated industry, you know, crypto for instance, or something like that.

493
00:24:19,119 --> 00:24:21,880
Speaker 6: I feel like in my circles around you know, how

494
00:24:21,920 --> 00:24:24,200
Speaker 6: is AI going to be commercialized for business and consumer?

495
00:24:24,920 --> 00:24:27,399
Speaker 6: It's not an event that people are as focused on

496
00:24:27,520 --> 00:24:30,280
Speaker 6: as perhaps the most prominent personalities of it.

497
00:24:46,560 --> 00:24:49,879
Speaker 2: I'm very curious about like the sort of tech inflicted

498
00:24:49,960 --> 00:24:53,200
Speaker 2: side of this administration, the influence of Elon Musk gd

499
00:24:53,240 --> 00:24:55,920
Speaker 2: Evans having been to VC. But the really low hanging

500
00:24:56,000 --> 00:25:01,679
Speaker 2: fruit policy question is on the merger side, and we

501
00:25:01,720 --> 00:25:04,360
Speaker 2: don't know who's going to run the FDC. We don't

502
00:25:04,359 --> 00:25:08,720
Speaker 2: know who's going to run the DOJ. It certainly seems plausible, however,

503
00:25:09,200 --> 00:25:12,200
Speaker 2: that the new administration will have a much more liberal

504
00:25:12,240 --> 00:25:15,240
Speaker 2: attitude towards letting mergers go through. How much is just

505
00:25:15,280 --> 00:25:17,960
Speaker 2: from a notes and bold steadpoint, when you think about returns,

506
00:25:18,000 --> 00:25:22,520
Speaker 2: when you think about investments, exits of various flavors, there's

507
00:25:22,560 --> 00:25:25,679
Speaker 2: a big sort of ninety degree turn or maybe one

508
00:25:25,800 --> 00:25:29,080
Speaker 2: eighty degree turn on merger policy change your thinking.

509
00:25:30,080 --> 00:25:32,800
Speaker 6: Not a ton, but there's no question at the current administration,

510
00:25:33,560 --> 00:25:36,080
Speaker 6: not just here but in the EU, in the UK

511
00:25:36,400 --> 00:25:38,320
Speaker 6: where I remember any global merger now is to get

512
00:25:38,320 --> 00:25:41,720
Speaker 6: through basically three anti trust bodies has been really a

513
00:25:41,800 --> 00:25:44,840
Speaker 6: lot tougher than any administration we've seen, Democrat or Republican

514
00:25:44,880 --> 00:25:47,120
Speaker 6: in the past. So do you when you're getting when

515
00:25:47,119 --> 00:25:49,600
Speaker 6: you're sitting with an entrepreneur getting excited about some big

516
00:25:49,720 --> 00:25:51,360
Speaker 6: dreams or attech, are you thinking about well, I wonder

517
00:25:51,400 --> 00:25:54,679
Speaker 6: what Lena Kahan's thinking about, you know, the consolidation of

518
00:25:54,680 --> 00:25:56,400
Speaker 6: the market for design tools.

519
00:25:56,760 --> 00:25:59,240
Speaker 5: No, not at all, but is it probably a.

520
00:25:59,200 --> 00:26:02,000
Speaker 6: Good thing for entrepreneurs options to be able to exit

521
00:26:02,040 --> 00:26:04,800
Speaker 6: their business and our business which relies on that.

522
00:26:04,880 --> 00:26:07,800
Speaker 2: Yeah, probably, in the reality of like the prospect of

523
00:26:07,840 --> 00:26:11,840
Speaker 2: an exit into machine design tools didn't like, or the

524
00:26:11,880 --> 00:26:13,800
Speaker 2: prospects of who would be a buyer. Those kind of

525
00:26:13,840 --> 00:26:16,240
Speaker 2: conversations weren't coming up in the early stages of a

526
00:26:16,280 --> 00:26:17,480
Speaker 2: conversation with Ann.

527
00:26:17,400 --> 00:26:19,960
Speaker 6: Not with an early stage found now with in a

528
00:26:20,000 --> 00:26:22,919
Speaker 6: growth context, it's tremendously important because if you don't have

529
00:26:23,000 --> 00:26:25,360
Speaker 6: the option to exit for billions and billions of dollars,

530
00:26:25,680 --> 00:26:28,240
Speaker 6: you can only go public. There's a pretty narrow set

531
00:26:28,280 --> 00:26:29,920
Speaker 6: of criteria you have to meet to be able to

532
00:26:29,920 --> 00:26:33,200
Speaker 6: go public. So then it's a pretty material thing. We're

533
00:26:33,280 --> 00:26:36,719
Speaker 6: at chemistry focused on the earliest stages this technology going

534
00:26:36,720 --> 00:26:39,119
Speaker 6: to get out and have an impact there. You just

535
00:26:39,240 --> 00:26:42,080
Speaker 6: kind of take a flyer. You kind of assume that

536
00:26:42,400 --> 00:26:45,720
Speaker 6: if it does, it'll be valuable in any kind of context,

537
00:26:45,760 --> 00:26:47,520
Speaker 6: whether it's in an M and A one or some

538
00:26:47,560 --> 00:26:48,119
Speaker 6: other exit.

539
00:26:48,760 --> 00:26:52,960
Speaker 3: So another aspect of the incoming administration is they seem

540
00:26:53,040 --> 00:26:56,600
Speaker 3: to be crypto friendly. And I'm going to co opt

541
00:26:56,640 --> 00:27:00,119
Speaker 3: a question that's been submitted by the audience, but what

542
00:27:00,119 --> 00:27:02,600
Speaker 3: do you think about crypto in general as an investment?

543
00:27:02,720 --> 00:27:04,239
Speaker 3: Is it something you're interested in?

544
00:27:04,760 --> 00:27:07,280
Speaker 6: That's one where the administration probably matters a lot. I

545
00:27:07,400 --> 00:27:11,280
Speaker 6: have been pro crypto for certain use cases in the past,

546
00:27:11,680 --> 00:27:15,000
Speaker 6: thinking about what's the infrastructure layer needed to make crypto

547
00:27:15,680 --> 00:27:18,639
Speaker 6: a part of the financial system, And so that's the security,

548
00:27:18,680 --> 00:27:21,520
Speaker 6: the protocols, the permissioning, the privacy, all that kind of

549
00:27:21,520 --> 00:27:24,320
Speaker 6: stuff I think is really necessary because crypto is still

550
00:27:24,840 --> 00:27:27,080
Speaker 6: such a wild west to where you have to be

551
00:27:27,119 --> 00:27:29,280
Speaker 6: pretty deep in it to benefit from it. And so

552
00:27:29,320 --> 00:27:31,639
Speaker 6: I still think there's that like bridge technology to make

553
00:27:31,640 --> 00:27:34,080
Speaker 6: it useful for kind of everyone in their everyday lives,

554
00:27:34,080 --> 00:27:36,359
Speaker 6: Like you know, the coinbase is kind of wallet type

555
00:27:36,400 --> 00:27:41,040
Speaker 6: software for everything else. And it's probably true to the

556
00:27:41,080 --> 00:27:42,919
Speaker 6: extent you can kind of read the tea leaves on

557
00:27:42,920 --> 00:27:44,840
Speaker 6: these things that the current administration is a lot more

558
00:27:44,880 --> 00:27:48,080
Speaker 6: friendly there at least that's messaging how will that manifest

559
00:27:48,160 --> 00:27:51,239
Speaker 6: itself and policy no idea, but right now a lot

560
00:27:51,280 --> 00:27:52,960
Speaker 6: of people are scared of the space because there's a

561
00:27:53,040 --> 00:27:54,280
Speaker 6: lot of uncertainty around it.

562
00:27:55,160 --> 00:27:58,159
Speaker 2: Then the other element is just sort of the people

563
00:27:58,359 --> 00:28:03,639
Speaker 2: in the orbit, you know, tech accelerationism, exciting things getting

564
00:28:03,680 --> 00:28:04,320
Speaker 2: to Mars.

565
00:28:04,840 --> 00:28:05,440
Speaker 4: Sindeka said.

566
00:28:05,480 --> 00:28:08,360
Speaker 2: The Mars question specifically, a lot of it seems very

567
00:28:08,440 --> 00:28:09,960
Speaker 2: vibes based, and I don't know.

568
00:28:09,880 --> 00:28:14,960
Speaker 4: What policy levers. Any of that means in your view, are.

569
00:28:14,840 --> 00:28:19,040
Speaker 2: There other policy levers that could be pulled that would

570
00:28:19,080 --> 00:28:24,000
Speaker 2: be good for the American tech infrastructure or sorry not

571
00:28:24,240 --> 00:28:24,880
Speaker 2: the industry.

572
00:28:26,000 --> 00:28:26,880
Speaker 5: Probably yes.

573
00:28:27,200 --> 00:28:29,760
Speaker 6: It's really hard to start a company and have it

574
00:28:29,800 --> 00:28:32,840
Speaker 6: be successful and have it There's so many things stacked

575
00:28:32,840 --> 00:28:33,359
Speaker 6: against you.

576
00:28:34,000 --> 00:28:35,320
Speaker 5: So what are the things you.

577
00:28:35,240 --> 00:28:39,200
Speaker 6: Can do to remove all the unknown obstacles that might

578
00:28:39,240 --> 00:28:41,920
Speaker 6: come up beyond the really hard ones of like will

579
00:28:41,960 --> 00:28:44,280
Speaker 6: you deliver the product on time? Well, the product, can

580
00:28:44,280 --> 00:28:46,920
Speaker 6: you deliver it for a reasonable cost, and we'll have

581
00:28:46,960 --> 00:28:50,320
Speaker 6: an impact on the market. The regulation that kind of

582
00:28:50,640 --> 00:28:53,160
Speaker 6: is another curveball that you might have to answer to.

583
00:28:53,760 --> 00:28:57,000
Speaker 6: That's an impediment, that's a blockage that serves at the

584
00:28:57,040 --> 00:29:00,520
Speaker 6: cost of innovation for sure. And so I think what

585
00:29:00,800 --> 00:29:03,680
Speaker 6: probably has an impact, just maybe using crypto as an example,

586
00:29:04,120 --> 00:29:08,160
Speaker 6: is clear regulation and lack of uncertainty, where you have

587
00:29:08,200 --> 00:29:10,440
Speaker 6: a sense of what are the rules going to be,

588
00:29:11,040 --> 00:29:13,880
Speaker 6: Not that you know, we'll apply these arcane tests and

589
00:29:13,920 --> 00:29:15,800
Speaker 6: we don't know exactly how a court will interpret it,

590
00:29:15,840 --> 00:29:19,040
Speaker 6: but like, exactly, do these six steps and you'll be fine.

591
00:29:19,400 --> 00:29:22,560
Speaker 6: That's taking out any uncertainty that's beyond the sort of

592
00:29:22,640 --> 00:29:25,160
Speaker 6: normal startup risks is a good thing for innovation.

593
00:29:25,800 --> 00:29:28,760
Speaker 3: Another question from the audience. I mentioned that obviously we've

594
00:29:28,760 --> 00:29:31,160
Speaker 3: been talking about AI a lot, and you talked about

595
00:29:31,200 --> 00:29:35,520
Speaker 3: the disappointment and redemption cycle. Are there any other nascent

596
00:29:35,920 --> 00:29:39,600
Speaker 3: tech areas or growth areas that you are excited about

597
00:29:40,600 --> 00:29:41,880
Speaker 3: beyond AI. Yeah.

598
00:29:42,040 --> 00:29:44,200
Speaker 4: Oh, I think that's hard one.

599
00:29:44,280 --> 00:29:47,160
Speaker 5: Beyond the AI rubicon. Let me think about that.

600
00:29:47,520 --> 00:29:51,080
Speaker 6: I mean, I think the democratization of tech broadly, this

601
00:29:51,160 --> 00:29:54,520
Speaker 6: is aided by a but not principally, the fact that

602
00:29:54,840 --> 00:29:58,080
Speaker 6: a normal business user or even a consumer can now

603
00:29:58,880 --> 00:30:02,160
Speaker 6: create an intelligence system, doesn't have to be a coder

604
00:30:02,200 --> 00:30:04,880
Speaker 6: necessarily to be able to write a complex kind of

605
00:30:04,920 --> 00:30:07,400
Speaker 6: logic flow and be able to build a build an

606
00:30:07,400 --> 00:30:11,200
Speaker 6: application or a messaging tool or something for a business context.

607
00:30:11,360 --> 00:30:13,280
Speaker 6: I think that's pretty powerful. I mean, I've always been

608
00:30:13,280 --> 00:30:15,959
Speaker 6: interested in the democratization of tech. Like even cloud computing

609
00:30:16,000 --> 00:30:18,520
Speaker 6: had a democratizing impact because you could give lots of

610
00:30:18,560 --> 00:30:21,440
Speaker 6: people logins to a system and let them have impact,

611
00:30:21,560 --> 00:30:22,680
Speaker 6: even if they weren't technical.

612
00:30:22,760 --> 00:30:25,280
Speaker 5: You could let people kind of edit.

613
00:30:25,160 --> 00:30:28,040
Speaker 6: The flow on a website, personalize a page, be able

614
00:30:28,080 --> 00:30:30,600
Speaker 6: to engage with their customers directly on the website without

615
00:30:30,600 --> 00:30:32,760
Speaker 6: having to code anything. And so I think there's this

616
00:30:32,840 --> 00:30:36,960
Speaker 6: democrat to that tizing aspect of the Internet. Of cloud

617
00:30:36,960 --> 00:30:39,479
Speaker 6: computing ais a part of this that's going to give

618
00:30:39,560 --> 00:30:42,920
Speaker 6: more people the ability to be more creative, and that's

619
00:30:42,960 --> 00:30:44,720
Speaker 6: going to have a kind of second order impact on

620
00:30:44,960 --> 00:30:46,280
Speaker 6: just the kinds of things we're going to be able

621
00:30:46,280 --> 00:30:48,520
Speaker 6: to do, even for like little niche audiences.

622
00:30:48,040 --> 00:30:53,680
Speaker 2: For do you see yourself writing checks to companies that

623
00:30:53,760 --> 00:30:59,520
Speaker 2: are making apps for virtual reality goggles for virtuality what goggles?

624
00:31:01,000 --> 00:31:02,440
Speaker 5: Possibly is that I've.

625
00:31:02,280 --> 00:31:06,320
Speaker 6: Written one before, but virtuality the goggles like a goggles?

626
00:31:06,520 --> 00:31:08,320
Speaker 4: Yeah, Like is that exciting to you?

627
00:31:08,400 --> 00:31:12,960
Speaker 6: Yeah, virtuality gaming could be a thing, virtuality, messaging, communication,

628
00:31:13,440 --> 00:31:16,720
Speaker 6: working in virtuality. Before I was a venture capolist, I

629
00:31:16,720 --> 00:31:18,680
Speaker 6: worked at a company on Lindenlab, which is coming behind SETH.

630
00:31:18,920 --> 00:31:22,560
Speaker 6: Of course, you remember, it's not a core theme for

631
00:31:22,640 --> 00:31:25,800
Speaker 6: us at Chemistry, so odds are we won't but I'm

632
00:31:25,840 --> 00:31:26,400
Speaker 6: open to it.

633
00:31:27,160 --> 00:31:29,960
Speaker 3: Sorry, I just I had a flashback to the time

634
00:31:30,000 --> 00:31:32,840
Speaker 3: when you thought electric scooters were the future of training.

635
00:31:32,880 --> 00:31:36,960
Speaker 4: They are. They're so great. I've said I love the

636
00:31:37,000 --> 00:31:37,840
Speaker 4: electric scooters.

637
00:31:37,920 --> 00:31:40,440
Speaker 5: Yeah, it's a part of the future.

638
00:31:40,640 --> 00:31:42,320
Speaker 4: So I'm just gonna this will be now.

639
00:31:43,000 --> 00:31:45,400
Speaker 2: I forget when was that that we came out here

640
00:31:45,440 --> 00:31:47,360
Speaker 2: and I was like, oh my god, Lime scooters are

641
00:31:47,360 --> 00:31:50,200
Speaker 2: going to change the world. But for me, like I've

642
00:31:50,240 --> 00:31:53,040
Speaker 2: taken it's wimo this time, and like I'm just so

643
00:31:53,160 --> 00:31:56,120
Speaker 2: completely wamo pilled. I'm just gonna, I'm just it's just

644
00:31:56,120 --> 00:31:58,000
Speaker 2: so amazing. I don't never ever, I never want to

645
00:31:58,120 --> 00:31:58,800
Speaker 2: an Uber again.

646
00:31:58,920 --> 00:32:01,560
Speaker 6: The wow of the Way experience is greater than the

647
00:32:01,560 --> 00:32:03,360
Speaker 6: wow of the Lime scooter experience.

648
00:32:03,440 --> 00:32:04,040
Speaker 4: Yeah, it is.

649
00:32:04,240 --> 00:32:05,720
Speaker 5: There's a lot less risk of death.

650
00:32:05,760 --> 00:32:07,560
Speaker 6: Well, I mean maybe if you think the way I'm

651
00:32:07,680 --> 00:32:08,880
Speaker 6: might crash, but they don't know.

652
00:32:09,000 --> 00:32:11,760
Speaker 2: It feels so safe and I felt so comfortable. And

653
00:32:11,840 --> 00:32:14,800
Speaker 2: actually then I took an Uber today and it felt

654
00:32:15,040 --> 00:32:17,280
Speaker 2: worse and like it was it was.

655
00:32:17,400 --> 00:32:18,680
Speaker 4: It was a worse experience.

656
00:32:18,800 --> 00:32:20,160
Speaker 2: And now when I go back to New York and

657
00:32:20,160 --> 00:32:21,760
Speaker 2: take an Uber, it's like going back to the land

658
00:32:21,760 --> 00:32:22,280
Speaker 2: of flip phone.

659
00:32:22,280 --> 00:32:23,600
Speaker 5: You're going to have to move out to the less

660
00:32:23,640 --> 00:32:24,400
Speaker 5: cust Yeah.

661
00:32:24,960 --> 00:32:26,120
Speaker 6: Yeah, okay.

662
00:32:26,240 --> 00:32:28,600
Speaker 3: So when it comes to AI, one of the one

663
00:32:28,640 --> 00:32:30,400
Speaker 3: of the debates that's been going on is like, well,

664
00:32:30,440 --> 00:32:33,240
Speaker 3: do you invest in the actual AI companies or maybe

665
00:32:33,280 --> 00:32:36,200
Speaker 3: you invest in sort of picks and shovels and data

666
00:32:36,240 --> 00:32:39,320
Speaker 3: centers and things like that. Is that like on your

667
00:32:39,440 --> 00:32:41,720
Speaker 3: radar at all, or do you this is a question

668
00:32:41,760 --> 00:32:43,840
Speaker 3: from the audience, at a minimum, do you look at,

669
00:32:43,840 --> 00:32:47,400
Speaker 3: for instance, investing in new technology that could help AI

670
00:32:47,760 --> 00:32:49,960
Speaker 3: manage energy usage or something like that.

671
00:32:51,000 --> 00:32:52,640
Speaker 6: Yeah, I think there's a lot of like second order

672
00:32:52,640 --> 00:32:56,160
Speaker 6: of fects of AI that we could make investments to

673
00:32:56,200 --> 00:32:59,160
Speaker 6: make better energy usage being one of those, or helping

674
00:32:59,680 --> 00:33:02,920
Speaker 6: create primitives to allow developers easier access to some of

675
00:33:02,960 --> 00:33:06,320
Speaker 6: the more advanced functionalities of AI. There's a whole side

676
00:33:06,360 --> 00:33:10,360
Speaker 6: theme that's maybe orthogonal to your question around the provident,

677
00:33:10,480 --> 00:33:13,479
Speaker 6: like how you don't really know what data has been

678
00:33:13,520 --> 00:33:17,360
Speaker 6: inputed into an AI system is relevant to your answer.

679
00:33:17,480 --> 00:33:19,680
Speaker 6: So it could have stolen some content, or it could

680
00:33:19,680 --> 00:33:22,680
Speaker 6: have like who knows what trained it to provide you

681
00:33:22,760 --> 00:33:25,640
Speaker 6: with that particular thing that it said. And so there's

682
00:33:25,680 --> 00:33:27,280
Speaker 6: a whole side theme of like, okay, how do you

683
00:33:27,320 --> 00:33:29,520
Speaker 6: make that okay for the for the people that made

684
00:33:29,560 --> 00:33:32,680
Speaker 6: the actual IP that that AI.

685
00:33:32,600 --> 00:33:35,240
Speaker 5: Was trained on. So that's another kind of side theme of.

686
00:33:35,240 --> 00:33:36,400
Speaker 4: AI that is so interesting.

687
00:33:36,440 --> 00:33:37,560
Speaker 3: So managing the IP.

688
00:33:37,920 --> 00:33:40,760
Speaker 6: Managing the IP, the rights of that the privacy that

689
00:33:41,240 --> 00:33:44,959
Speaker 6: you know, you might for a base level want an

690
00:33:44,960 --> 00:33:47,920
Speaker 6: AI trained on you know, a corpus of data that's

691
00:33:47,960 --> 00:33:50,760
Speaker 6: pretty basic, but then you know the tailor swift of data,

692
00:33:51,160 --> 00:33:53,960
Speaker 6: you know, the really advanced IP holders, you want to

693
00:33:53,960 --> 00:33:55,360
Speaker 6: pay more, but then you want to get some of

694
00:33:55,360 --> 00:33:57,360
Speaker 6: the money to the people that created that IP that

695
00:33:57,680 --> 00:34:01,360
Speaker 6: made that I AI even better. Actually that's hard to

696
00:34:01,400 --> 00:34:03,840
Speaker 6: do right now, but maybe not yet solved.

697
00:34:04,040 --> 00:34:05,880
Speaker 2: I want to go back to what you were saying

698
00:34:05,960 --> 00:34:09,600
Speaker 2: about how having thought about in your life AI for

699
00:34:09,640 --> 00:34:12,719
Speaker 2: four decades that in some ways it makes you more

700
00:34:12,760 --> 00:34:16,000
Speaker 2: optimistic because you see like this grand sweep, but also

701
00:34:16,719 --> 00:34:19,520
Speaker 2: at least a temporary sort of cynicism because you know

702
00:34:19,600 --> 00:34:23,520
Speaker 2: that AI winters exist and it's very plausible, and you

703
00:34:23,560 --> 00:34:26,120
Speaker 2: know there's all kinds of stories about you know, running

704
00:34:26,160 --> 00:34:28,719
Speaker 2: up against current limits of scaling and all. Can you

705
00:34:28,760 --> 00:34:31,040
Speaker 2: tell us a story about what was the past AI

706
00:34:31,120 --> 00:34:33,960
Speaker 2: winter that happened? What was something that at some point

707
00:34:34,040 --> 00:34:36,320
Speaker 2: people are like, oh, we got this, this is moving

708
00:34:36,400 --> 00:34:38,239
Speaker 2: and then they ran into a wall. And what's a

709
00:34:38,360 --> 00:34:39,720
Speaker 2: lesson that can be drawn from us?

710
00:34:39,800 --> 00:34:43,240
Speaker 6: Well, speech recognition was probably a wall where people thought

711
00:34:43,719 --> 00:34:45,359
Speaker 6: AI he'd be able to talk to AI.

712
00:34:45,640 --> 00:34:48,240
Speaker 5: Yeah, when what was the un in the nineties.

713
00:34:48,280 --> 00:34:50,000
Speaker 6: That was one of the one of the eras of

714
00:34:50,040 --> 00:34:53,120
Speaker 6: AI my father was involved with. In fact, he named

715
00:34:53,120 --> 00:34:57,240
Speaker 6: one of his companies Kurzweil. AI and AI stand stood

716
00:34:57,239 --> 00:34:59,960
Speaker 6: for applied intelligence, not artificial intelligence, because it was about

717
00:35:00,160 --> 00:35:03,279
Speaker 6: word to say AI and haven't mean artificial intelligence because

718
00:35:03,280 --> 00:35:06,400
Speaker 6: it felt like it was under delivering on an artificial intelligence.

719
00:35:06,440 --> 00:35:09,080
Speaker 6: It was more applied than the AI that we think

720
00:35:09,120 --> 00:35:11,680
Speaker 6: of today. And so that was an era where.

721
00:35:11,880 --> 00:35:14,000
Speaker 2: And then what do you discreete what it's like the

722
00:35:14,040 --> 00:35:16,560
Speaker 2: moment to like, oh, this is not growing or scaling

723
00:35:16,800 --> 00:35:18,880
Speaker 2: or improving the way we People.

724
00:35:18,760 --> 00:35:23,440
Speaker 6: Forget and then something that that counters that a counterfactual

725
00:35:23,520 --> 00:35:26,120
Speaker 6: comes out and then everyone kind of hones in on

726
00:35:26,239 --> 00:35:28,879
Speaker 6: that and that period where people are forgetting about it,

727
00:35:28,920 --> 00:35:31,239
Speaker 6: that's the troph of disillusionment. Where at the beginning of

728
00:35:31,239 --> 00:35:34,239
Speaker 6: that is the troph of disillusionment period where there's a

729
00:35:34,239 --> 00:35:37,799
Speaker 6: lot of prognostication about how this technology didn't deliver. Then

730
00:35:37,840 --> 00:35:40,480
Speaker 6: people forget something does deliver, and then move on to

731
00:35:40,520 --> 00:35:43,640
Speaker 6: a new cycle and the expectations get high again that

732
00:35:43,840 --> 00:35:46,040
Speaker 6: probably can't be met all right.

733
00:35:46,080 --> 00:35:49,120
Speaker 3: Another question from the audience also sort of a Trump

734
00:35:49,120 --> 00:35:53,200
Speaker 3: related question. Poly market and other prediction markets? Do you

735
00:35:53,200 --> 00:35:56,640
Speaker 3: think those are like in for well, where are they going?

736
00:35:57,920 --> 00:35:59,600
Speaker 5: That's a really good question. I don't know.

737
00:35:59,719 --> 00:36:04,520
Speaker 6: I mean I think that probably I believe that system

738
00:36:04,920 --> 00:36:08,160
Speaker 6: had the best way of taking stock of kind of

739
00:36:08,360 --> 00:36:11,319
Speaker 6: all the known universe of information that we're out there

740
00:36:11,320 --> 00:36:13,640
Speaker 6: and distilling it down into a Okay, what does it

741
00:36:13,680 --> 00:36:15,560
Speaker 6: mean for a particular event like the election?

742
00:36:16,040 --> 00:36:18,600
Speaker 5: So that's kind of cool. Now is it legal? I

743
00:36:18,640 --> 00:36:18,960
Speaker 5: don't know.

744
00:36:19,040 --> 00:36:21,680
Speaker 6: It sounds like maybe not because somebody's going to jail,

745
00:36:21,800 --> 00:36:26,680
Speaker 6: but somebody might face criminal prosecution.

746
00:36:27,040 --> 00:36:29,439
Speaker 2: It easy for an American to use it, which does

747
00:36:29,480 --> 00:36:31,800
Speaker 2: not seem like it's supposed to write.

748
00:36:31,840 --> 00:36:33,799
Speaker 6: You know, people having real money on the line does

749
00:36:33,840 --> 00:36:37,040
Speaker 6: create a more purest system that it's sort of hard

750
00:36:37,040 --> 00:36:40,880
Speaker 6: to replicate with any other any other approach, similar to

751
00:36:40,880 --> 00:36:43,080
Speaker 6: how the stock market sort of works and in theory

752
00:36:43,160 --> 00:36:46,360
Speaker 6: gives you kind of the right price of every particular

753
00:36:46,400 --> 00:36:49,319
Speaker 6: asset that's listed. So I think for prediction markets, that's

754
00:36:49,640 --> 00:36:51,759
Speaker 6: that incentive is hard to replicate it any other way.

755
00:36:52,360 --> 00:36:55,200
Speaker 6: Should there be prediction systems? And there's a policy question

756
00:36:55,239 --> 00:36:58,120
Speaker 6: that I don't know, Speaking.

757
00:36:57,719 --> 00:37:01,359
Speaker 2: Of prediction markets and sort of a broader philosophical question.

758
00:37:01,480 --> 00:37:03,480
Speaker 2: I was wondering, like, we live in an era of

759
00:37:04,080 --> 00:37:07,920
Speaker 2: people betting on everything. In one of the one aspect

760
00:37:08,040 --> 00:37:10,920
Speaker 2: I think of people sort of betting and speculating on

761
00:37:11,000 --> 00:37:13,640
Speaker 2: all kinds of things is that it seems to me

762
00:37:14,600 --> 00:37:18,880
Speaker 2: that the sort of VC mindset of you want to

763
00:37:18,960 --> 00:37:21,520
Speaker 2: just have a couple of gigantic winners and get that

764
00:37:21,640 --> 00:37:25,680
Speaker 2: big score is spread to the non VC world right

765
00:37:25,719 --> 00:37:29,400
Speaker 2: and people really look for those right tail opportunities, both

766
00:37:29,440 --> 00:37:34,200
Speaker 2: in investing in stocks their careers. There seems to be

767
00:37:34,280 --> 00:37:37,759
Speaker 2: a sort like is do you perceive that the sort

768
00:37:37,760 --> 00:37:41,040
Speaker 2: of VC world view has seeped out of the VC

769
00:37:41,480 --> 00:37:44,040
Speaker 2: realm and sort of I don't want to say infected

770
00:37:44,080 --> 00:37:46,960
Speaker 2: because that's like a bad word, but as.

771
00:37:46,800 --> 00:37:50,319
Speaker 4: A transfer, are we all VC? That's exactly.

772
00:37:51,520 --> 00:37:54,040
Speaker 6: One hundred percent what you're pointing out is true, And

773
00:37:54,760 --> 00:37:57,120
Speaker 6: it'd be interesting to do root cause analysis, like are

774
00:37:57,120 --> 00:37:58,160
Speaker 6: we to blame for that?

775
00:37:58,400 --> 00:37:58,720
Speaker 4: Yeah?

776
00:37:58,800 --> 00:38:00,520
Speaker 6: But first of all, it's a bad thing or not,

777
00:38:01,960 --> 00:38:05,000
Speaker 6: it's certainly happening, and I think the cause of it,

778
00:38:05,080 --> 00:38:09,320
Speaker 6: I would say, is well, there's probably a number of

779
00:38:09,400 --> 00:38:12,560
Speaker 6: kind of psychological causes, but there's been this democratization of

780
00:38:12,600 --> 00:38:15,040
Speaker 6: access to private assets that's happened over the last ten

781
00:38:15,080 --> 00:38:15,440
Speaker 6: years too.

782
00:38:15,760 --> 00:38:16,840
Speaker 5: We haven't talked about.

783
00:38:16,640 --> 00:38:19,319
Speaker 6: Either where rather than trading, you know, used to be

784
00:38:19,560 --> 00:38:21,800
Speaker 6: you traded stocks. Then it was sort of a you

785
00:38:21,800 --> 00:38:27,040
Speaker 6: could trade IPOs, and now there's some access for mainStreet

786
00:38:27,120 --> 00:38:31,640
Speaker 6: consumer to private company assets as well, more typically venture vetted,

787
00:38:32,239 --> 00:38:36,000
Speaker 6: and there's investor protection laws, but not it's the move

788
00:38:36,040 --> 00:38:38,920
Speaker 6: has been towards more and more and more democratization, and

789
00:38:39,000 --> 00:38:40,879
Speaker 6: so the VC way of thinking is just seeping into

790
00:38:40,960 --> 00:38:41,560
Speaker 6: more things.

791
00:38:42,160 --> 00:38:45,240
Speaker 5: Is that good? Is that bad? There's probably pros and cons.

792
00:38:45,960 --> 00:38:48,640
Speaker 3: A lot of people here probably want to know how

793
00:38:48,680 --> 00:38:52,080
Speaker 3: to spot winners in the market, but part of this

794
00:38:52,160 --> 00:38:54,880
Speaker 3: is about avoiding losers as well. What's your best tip

795
00:38:55,000 --> 00:38:59,600
Speaker 3: for spotting, I guess or finding identifying froth in tech?

796
00:38:59,760 --> 00:39:02,359
Speaker 6: Well, I'm the wrong person to ask, because we back

797
00:39:02,480 --> 00:39:04,840
Speaker 6: as a good even as a good venture capitals, we

798
00:39:04,920 --> 00:39:08,240
Speaker 6: back so many losers, like it's just an occupational hazard

799
00:39:08,280 --> 00:39:08,799
Speaker 6: of the job.

800
00:39:09,239 --> 00:39:11,959
Speaker 5: Now you asked about froth though, and.

801
00:39:11,920 --> 00:39:15,120
Speaker 6: So that's the sort of like perception disconnect of like

802
00:39:15,239 --> 00:39:18,799
Speaker 6: what what's the reality of a particular tech And I

803
00:39:18,800 --> 00:39:19,719
Speaker 6: think you have to go.

804
00:39:19,760 --> 00:39:20,480
Speaker 5: To the source.

805
00:39:20,520 --> 00:39:22,800
Speaker 6: You have to see like, what what impact is this having?

806
00:39:22,920 --> 00:39:25,440
Speaker 6: Not are what are other people saying about it? What

807
00:39:25,520 --> 00:39:28,719
Speaker 6: are these kind of second order effects? What is does

808
00:39:28,760 --> 00:39:31,680
Speaker 6: the entrepreneur, you know, look the part in some way

809
00:39:31,880 --> 00:39:34,359
Speaker 6: or are they kind of playing a role that makes

810
00:39:34,400 --> 00:39:37,319
Speaker 6: their impact seem like what's the impact of the technology

811
00:39:37,640 --> 00:39:40,560
Speaker 6: And kind of like have blinders on for the noise

812
00:39:40,640 --> 00:39:43,600
Speaker 6: that's out there in the ecosystem, because that's another impact

813
00:39:43,680 --> 00:39:46,560
Speaker 6: of everyone. You know, more and more VC like thinking

814
00:39:46,719 --> 00:39:50,520
Speaker 6: is there's more and more hype around really exciting tech.

815
00:39:50,640 --> 00:39:52,720
Speaker 5: Some of it's real, some of it's froth.

816
00:39:53,320 --> 00:39:57,200
Speaker 2: We talked about this earlier, the fact that there are

817
00:39:57,239 --> 00:40:02,239
Speaker 2: not terrible podcasts that are produced by AI and it

818
00:40:02,280 --> 00:40:05,759
Speaker 2: does cause me as a professional podcaster, like, yeah, it

819
00:40:05,880 --> 00:40:09,160
Speaker 2: causes me anxiety. But like this is the other sort

820
00:40:09,200 --> 00:40:13,880
Speaker 2: of big question, the sort of future of labor question

821
00:40:14,719 --> 00:40:17,960
Speaker 2: in a world where AI gets better and better, and

822
00:40:18,040 --> 00:40:20,960
Speaker 2: like what are we as humans good at? I mean,

823
00:40:21,000 --> 00:40:24,240
Speaker 2: I'll start with that, what are we And I'm talking

824
00:40:24,320 --> 00:40:26,399
Speaker 2: now just like in the next year or five years,

825
00:40:26,440 --> 00:40:29,560
Speaker 2: but like in twenty years or fifty years. And I

826
00:40:29,600 --> 00:40:32,400
Speaker 2: know your dad made predictions that were fifty and sixty

827
00:40:32,480 --> 00:40:35,640
Speaker 2: years out, so you probably think I wouldn't. I imagine

828
00:40:35,680 --> 00:40:38,080
Speaker 2: you also have in your mind predictions that are fifty

829
00:40:38,120 --> 00:40:40,120
Speaker 2: and sixty years out. And so when you think about,

830
00:40:40,160 --> 00:40:43,880
Speaker 2: like what are humans good at? What are we going

831
00:40:43,920 --> 00:40:45,799
Speaker 2: to be good at? People say the same thing might

832
00:40:45,880 --> 00:40:47,480
Speaker 2: be seen by the way it happened for many years.

833
00:40:47,560 --> 00:40:49,520
Speaker 6: Yea, like assist you'll be able to put this right

834
00:40:49,880 --> 00:40:51,080
Speaker 6: data into a system.

835
00:40:50,760 --> 00:40:52,640
Speaker 5: And it'll be better and many And.

836
00:40:52,680 --> 00:40:55,000
Speaker 4: You can't really do it for stocks yet, right.

837
00:40:55,120 --> 00:40:57,480
Speaker 6: I probably at some level there will be systems that

838
00:40:57,560 --> 00:41:00,000
Speaker 6: aid people. But I still think what tends to happen.

839
00:41:00,280 --> 00:41:03,279
Speaker 6: I'm not the fifties, sixty year out your anchor. I'm

840
00:41:03,320 --> 00:41:06,040
Speaker 6: the sort of five year out, okay kind of thing,

841
00:41:06,080 --> 00:41:07,920
Speaker 6: but let's go with that time horizon.

842
00:41:07,960 --> 00:41:09,320
Speaker 4: Yeah, it's we.

843
00:41:09,280 --> 00:41:11,320
Speaker 6: Tend to as humans kind of move up the stack,

844
00:41:11,360 --> 00:41:12,960
Speaker 6: we still have to do the creative work. We still

845
00:41:13,000 --> 00:41:15,160
Speaker 6: have to guide the AI systems. We still have to

846
00:41:15,200 --> 00:41:16,920
Speaker 6: sort of harness the tech that's coming out of them.

847
00:41:17,000 --> 00:41:18,959
Speaker 2: We can'tigure out how to apply it, so we can't

848
00:41:19,000 --> 00:41:21,560
Speaker 2: just plug in more energy into our systems, and like,

849
00:41:21,600 --> 00:41:23,080
Speaker 2: are we just going to like fall further?

850
00:41:23,239 --> 00:41:25,040
Speaker 4: Like sorry, I think we'll still.

851
00:41:24,880 --> 00:41:25,920
Speaker 5: Stay on top of the systems.

852
00:41:25,920 --> 00:41:27,520
Speaker 6: We're going to tell the systems what to do, Like

853
00:41:27,560 --> 00:41:30,560
Speaker 6: what challenges do we want them to solve, what's the

854
00:41:30,600 --> 00:41:33,320
Speaker 6: problem space that we want them interested in? What success

855
00:41:33,400 --> 00:41:35,919
Speaker 6: look like for these systems? I said, there's like there's

856
00:41:35,960 --> 00:41:38,879
Speaker 6: real work that as we move higher and higher up

857
00:41:39,160 --> 00:41:40,960
Speaker 6: and we do less of the grant work. That's the

858
00:41:41,680 --> 00:41:44,440
Speaker 6: sort of pattern as it exists, has it been existing today.

859
00:41:44,640 --> 00:41:47,920
Speaker 3: So we've been focused on software for obvious reasons. But

860
00:41:48,080 --> 00:41:50,919
Speaker 3: when do we get the good robots that can do

861
00:41:51,440 --> 00:41:54,040
Speaker 3: the terrible jobs? Like I don't like the future where

862
00:41:54,560 --> 00:41:58,239
Speaker 3: AI can do a podcast and write songs and poetry

863
00:41:58,320 --> 00:42:00,600
Speaker 3: and all the fun stuff, but I still have to

864
00:42:00,680 --> 00:42:02,520
Speaker 3: vacuum and fold my launch flow.

865
00:42:02,560 --> 00:42:06,680
Speaker 2: There are robot vacuums, yeah are, but they're doing they can't.

866
00:42:06,480 --> 00:42:08,239
Speaker 4: Fold the laundry there, Yeah, talk about that.

867
00:42:08,520 --> 00:42:09,319
Speaker 5: Yeah, I don't know.

868
00:42:09,440 --> 00:42:11,920
Speaker 6: I mean there I have seen some systems now that

869
00:42:12,239 --> 00:42:14,279
Speaker 6: have sort of like some robots and have like kind

870
00:42:14,280 --> 00:42:17,600
Speaker 6: of human like characteristics and walk upstairs and carry things

871
00:42:17,640 --> 00:42:20,560
Speaker 6: and you know, pick things in a warehouse. And so

872
00:42:21,480 --> 00:42:24,279
Speaker 6: because it's hardware, there's less of an exponential to that.

873
00:42:24,360 --> 00:42:27,120
Speaker 6: So it's more of blocking and tackling around the sensors

874
00:42:27,160 --> 00:42:30,239
Speaker 6: and what's the cost of the particular parts that go

875
00:42:30,320 --> 00:42:34,239
Speaker 6: into that. But cars now drive themselves, so that's a

876
00:42:34,280 --> 00:42:37,280
Speaker 6: big step change what we used to have drive themselves

877
00:42:37,280 --> 00:42:41,480
Speaker 6: in like difficult environments and rain, and so it's coming

878
00:42:41,719 --> 00:42:44,279
Speaker 6: now how soon and which industries isn't going to hit?

879
00:42:45,200 --> 00:42:46,120
Speaker 5: That's a hard one.

880
00:42:46,800 --> 00:42:49,520
Speaker 2: The first question I asked you is like, how is

881
00:42:49,600 --> 00:42:53,960
Speaker 2: a venture different today in twenty twenty four verse twenty fourteen,

882
00:42:54,400 --> 00:42:56,480
Speaker 2: And you gave a good answer to the question I asked,

883
00:42:56,600 --> 00:42:58,680
Speaker 2: But actually I meant to just ask you about what

884
00:42:58,760 --> 00:43:02,800
Speaker 2: the impact of five percent interest rates work specifically versus

885
00:43:02,880 --> 00:43:05,400
Speaker 2: zero And then I asked a very vague question, but

886
00:43:05,480 --> 00:43:09,680
Speaker 2: I am curious about the sort of the strictly macro elements.

887
00:43:10,200 --> 00:43:13,239
Speaker 2: We're also in a weird moment because rates have gone up,

888
00:43:13,600 --> 00:43:16,279
Speaker 2: but the Nasdaq is at all time highs, and it

889
00:43:16,440 --> 00:43:20,719
Speaker 2: seems intuitive that private company valuations have some tether to

890
00:43:20,760 --> 00:43:24,719
Speaker 2: public company valuations, either by DENTTI being acquired or by IPOs.

891
00:43:25,160 --> 00:43:27,719
Speaker 2: Is there a difference though, just from the sort of

892
00:43:28,680 --> 00:43:33,360
Speaker 2: macro environment rates side that affects your thinking today versus

893
00:43:33,600 --> 00:43:35,359
Speaker 2: zero percent rates in twenty four Well.

894
00:43:35,320 --> 00:43:36,760
Speaker 5: In theory it should be a lot harder.

895
00:43:36,880 --> 00:43:39,319
Speaker 6: Yeah, because there's so many more internatives for where to

896
00:43:39,320 --> 00:43:41,080
Speaker 6: put your capital these days.

897
00:43:41,040 --> 00:43:42,080
Speaker 5: That are appealing.

898
00:43:42,160 --> 00:43:46,279
Speaker 6: There's macro big tech that's taking advantage of a lot

899
00:43:46,320 --> 00:43:48,640
Speaker 6: of the trends that we're talking about, not just startups.

900
00:43:49,040 --> 00:43:51,840
Speaker 5: And there's you know, risk free treasury bonds.

901
00:43:51,920 --> 00:43:56,759
Speaker 6: Yeah, now countervail that with all this excitement around AI

902
00:43:56,840 --> 00:43:59,120
Speaker 6: and you kind of have the current moment where it

903
00:43:59,200 --> 00:44:01,400
Speaker 6: should just feel like a sort of post two thousand

904
00:44:01,400 --> 00:44:01,920
Speaker 6: era bubble.

905
00:44:01,960 --> 00:44:02,880
Speaker 5: That's what it should feel like.

906
00:44:02,880 --> 00:44:05,719
Speaker 6: If you also feel like financial flows, that's where we

907
00:44:05,719 --> 00:44:09,040
Speaker 6: should be and we're not because there's just excitement about

908
00:44:09,080 --> 00:44:11,280
Speaker 6: what technology can do in the cycles are getting faster

909
00:44:11,360 --> 00:44:13,480
Speaker 6: and faster. It's like people don't feel like it's ten

910
00:44:13,560 --> 00:44:15,960
Speaker 6: years away now, it's it's like almost here.

911
00:44:17,040 --> 00:44:19,880
Speaker 2: Ethan Cursewil, thank you so much. That was fantastic and

912
00:44:20,000 --> 00:44:21,800
Speaker 2: I really appreciate you doing.

913
00:44:21,600 --> 00:44:23,680
Speaker 5: This live odd lots of those Thanks for having me.

914
00:44:23,719 --> 00:44:24,239
Speaker 5: This is fun.

915
00:44:36,680 --> 00:44:40,080
Speaker 2: And that was our conversation with Ethan Curseweil, founder and

916
00:44:40,120 --> 00:44:42,160
Speaker 2: managing partner of Chemistry VC.

917
00:44:42,400 --> 00:44:45,239
Speaker 3: And a big thank you to everyone who came to

918
00:44:45,400 --> 00:44:49,000
Speaker 3: this live recording. It was a very rainy evening in

919
00:44:49,040 --> 00:44:52,360
Speaker 3: San Francisco, so appreciate so many people coming out, and

920
00:44:52,400 --> 00:44:55,200
Speaker 3: a big thank you as well to our sponsor, Principal

921
00:44:55,200 --> 00:44:59,000
Speaker 3: Asset Management for making this possible. Joe, should I leave

922
00:44:59,000 --> 00:44:59,279
Speaker 3: it there?

923
00:44:59,360 --> 00:45:00,160
Speaker 4: Let's leave it there?

924
00:45:00,200 --> 00:45:03,600
Speaker 3: All right? This has been another episode of the Authoughts podcast.

925
00:45:03,680 --> 00:45:06,840
Speaker 3: I'm Tracy Alloway. You can follow me at Tracy Alloway.

926
00:45:06,600 --> 00:45:09,560
Speaker 2: And I'm Jill Wisenthal. You can follow me at the Stalwart.

927
00:45:10,000 --> 00:45:13,600
Speaker 2: Follow our guest Ethan Kurzweil at Ethan Kurz. Follow our

928
00:45:13,640 --> 00:45:17,200
Speaker 2: producers Carmen Rodriguez at Carmen Erman dash Ol Bennett at

929
00:45:17,239 --> 00:45:20,279
Speaker 2: Dashbot and kill Brooks at Kilbrooks. Thank you to our

930
00:45:20,320 --> 00:45:23,800
Speaker 2: producer Moses Ondem. For more Oddlogs content, go to Bloomberg

931
00:45:23,840 --> 00:45:26,279
Speaker 2: dot com slash odd Lots, where you have transcripts, a

932
00:45:26,320 --> 00:45:29,240
Speaker 2: blog and a daily newsletter and you can chat about

933
00:45:29,280 --> 00:45:31,720
Speaker 2: all of these topics twenty four to seven with fellow

934
00:45:31,719 --> 00:45:35,600
Speaker 2: listeners in our discord discord dot gg slash.

935
00:45:35,239 --> 00:45:37,759
Speaker 3: Od lots And if you enjoy a thoughts, if you

936
00:45:37,920 --> 00:45:40,440
Speaker 3: like it when we do these live recordings, then please

937
00:45:40,520 --> 00:45:43,759
Speaker 3: leave us a positive review on your favorite podcast platform,

938
00:45:44,200 --> 00:45:47,640
Speaker 3: and remember if you are a Bloomberg subscriber. In addition

939
00:45:47,760 --> 00:45:51,800
Speaker 3: to getting the first heads up about these types of events.

940
00:45:51,920 --> 00:45:54,680
Speaker 3: You can also listen to all of the Authoughts episodes

941
00:45:54,760 --> 00:45:57,720
Speaker 3: absolutely ad free. All you need to do is connect

942
00:45:57,760 --> 00:46:01,160
Speaker 3: your Bloomberg account with Apple Podcasts. In order to do that,

943
00:46:01,480 --> 00:46:04,520
Speaker 3: just find the Bloomberg channel on Apple Podcasts and follow

944
00:46:04,560 --> 00:46:06,760
Speaker 3: the instructions there. Thanks for listening.