1
00:00:03,840 --> 00:00:07,080
Speaker 1: Kyorda, Welcome to Shared Lunch. I'm Sonya. Today we have

2
00:00:07,120 --> 00:00:10,039
Speaker 1: a special episode we will talk about AI and investing.

3
00:00:10,480 --> 00:00:12,840
Speaker 1: We have a new feature coming to Chairs's called Search

4
00:00:12,920 --> 00:00:15,400
Speaker 1: AI and we'll talk more about it in this episode.

5
00:00:15,600 --> 00:00:18,280
Speaker 1: But before we get started, here's some important information.

6
00:00:19,440 --> 00:00:23,200
Speaker 2: Investing involves risk you might lose the money you start with.

7
00:00:23,640 --> 00:00:27,400
Speaker 2: We recommend talking to a licensed financial advisor. We also

8
00:00:27,440 --> 00:00:32,240
Speaker 2: recommend reading product disclosure documents before deciding to invest. Everything

9
00:00:32,280 --> 00:00:34,879
Speaker 2: you're about to see and here is current at the

10
00:00:34,920 --> 00:00:35,760
Speaker 2: time of recording.

11
00:00:35,880 --> 00:00:36,200
Speaker 3: Today.

12
00:00:36,680 --> 00:00:39,280
Speaker 1: We're joined by Richard Clark, who is one of the

13
00:00:39,280 --> 00:00:42,600
Speaker 1: co founders of chairs EA's, and Luke Petett, who is

14
00:00:42,800 --> 00:00:47,479
Speaker 1: the founder and CEO of Telescope. We are talking about AI,

15
00:00:47,640 --> 00:00:50,720
Speaker 1: which is a hot topic right now and something that's

16
00:00:50,920 --> 00:00:53,559
Speaker 1: really of interest to us as we are launching a

17
00:00:53,600 --> 00:00:57,280
Speaker 1: new tool here at chairs EA's called AI Search, and

18
00:00:57,320 --> 00:00:59,480
Speaker 1: so we want to talk about that, but also just

19
00:00:59,600 --> 00:01:02,080
Speaker 1: get a to to talk about AI with two people

20
00:01:02,120 --> 00:01:06,560
Speaker 1: who are deep in that world. So let's kick off

21
00:01:06,560 --> 00:01:10,240
Speaker 1: the conversation. So first, maybe we'll start with you, Richard.

22
00:01:10,360 --> 00:01:11,840
Speaker 1: Can you tell us a bit about.

23
00:01:11,600 --> 00:01:15,000
Speaker 3: You and how you come to be joining us combo today.

24
00:01:16,319 --> 00:01:19,640
Speaker 4: Yeah, sure. So I'm one of the technical co founders

25
00:01:19,680 --> 00:01:23,400
Speaker 4: of Chaza's and you know, came to Chazas after a

26
00:01:23,720 --> 00:01:26,920
Speaker 4: long time in the tech industry. And I think the

27
00:01:26,959 --> 00:01:30,240
Speaker 4: fascinating thing for me about AI has it has always

28
00:01:30,280 --> 00:01:34,839
Speaker 4: been really interesting, you know, the idea of computers doing everything,

29
00:01:34,959 --> 00:01:38,120
Speaker 4: you know, being just like people in some ways. You know,

30
00:01:38,120 --> 00:01:41,120
Speaker 4: it's science fiction everywhere, and so that was always a

31
00:01:41,200 --> 00:01:44,920
Speaker 4: thing that was fascinating to me. I tried to, you know,

32
00:01:45,160 --> 00:01:48,000
Speaker 4: make things ever since I started programming, you know, make

33
00:01:48,080 --> 00:01:52,160
Speaker 4: little companions or whatever, and they were all very good.

34
00:01:53,720 --> 00:01:56,120
Speaker 4: But you know, in the last few years, we've just

35
00:01:56,320 --> 00:01:59,680
Speaker 4: seen this incredible sea change, and it has been so

36
00:01:59,680 --> 00:02:03,560
Speaker 4: so interesting to see something that's gone from like nothing

37
00:02:03,600 --> 00:02:07,960
Speaker 4: to science fiction to to you know, really right there

38
00:02:08,000 --> 00:02:11,040
Speaker 4: on your computer, able to play with it. And you know,

39
00:02:11,120 --> 00:02:13,320
Speaker 4: my so my recent history with that has just been

40
00:02:13,360 --> 00:02:19,000
Speaker 4: really engaging with these new changes, building tools, chatting with

41
00:02:19,080 --> 00:02:23,639
Speaker 4: people like Louk here, and really trying to understand how

42
00:02:23,680 --> 00:02:27,040
Speaker 4: this is going to impact everything and how we can

43
00:02:27,160 --> 00:02:29,840
Speaker 4: use these things to do really really interesting stuff.

44
00:02:29,919 --> 00:02:33,519
Speaker 5: And how about you, Lot, Yeah, I mean, well, first

45
00:02:33,520 --> 00:02:36,000
Speaker 5: of all, you Sion founder of Telescope.

46
00:02:36,040 --> 00:02:39,000
Speaker 6: So I'm definitely trying to keep up with AI.

47
00:02:39,080 --> 00:02:42,880
Speaker 5: It's it's wild, as Richard pointed out, like it's it's

48
00:02:43,000 --> 00:02:46,079
Speaker 5: just moving so quick. It is even for someone well

49
00:02:46,120 --> 00:02:49,800
Speaker 5: and truly involved in the industry and as an engineer,

50
00:02:50,160 --> 00:02:52,640
Speaker 5: you know, it's sort of I feel like I understand

51
00:02:52,680 --> 00:02:56,880
Speaker 5: the fundamentals, but it is. It is crazy how quick

52
00:02:56,880 --> 00:03:00,200
Speaker 5: things are moving. And it certainly appeals. I mean, the

53
00:03:00,240 --> 00:03:04,080
Speaker 5: idea that you can just have a system make intelligent

54
00:03:04,200 --> 00:03:08,560
Speaker 5: choices with like kind of you know, unusual conditions.

55
00:03:08,320 --> 00:03:09,440
Speaker 6: It's so human.

56
00:03:09,520 --> 00:03:11,720
Speaker 5: It feels so human the way it works, and it

57
00:03:11,800 --> 00:03:16,120
Speaker 5: actually has shifted my thinking around how we see humans.

58
00:03:16,120 --> 00:03:18,360
Speaker 5: You know, I kind of feel like some sort of

59
00:03:18,400 --> 00:03:21,440
Speaker 5: finely tuned model that's had all these experiences through my

60
00:03:21,600 --> 00:03:22,400
Speaker 5: life as well.

61
00:03:22,480 --> 00:03:24,080
Speaker 3: You mentioned, you know, you're from Telescope.

62
00:03:24,280 --> 00:03:26,360
Speaker 1: For anyone who's never heard of telescope, do you want

63
00:03:26,400 --> 00:03:29,480
Speaker 1: to just give us the oneliner of what telescope is.

64
00:03:29,960 --> 00:03:30,160
Speaker 6: Yeah.

65
00:03:30,240 --> 00:03:34,720
Speaker 5: So we're generative aur company based in Australia, a small team,

66
00:03:34,800 --> 00:03:38,720
Speaker 5: but we're growing. We're working with brokers and publishers and

67
00:03:38,760 --> 00:03:43,520
Speaker 5: sort of FinTechs all across the world on solving problems

68
00:03:43,560 --> 00:03:46,680
Speaker 5: to do with capital markets and finance and most of

69
00:03:46,720 --> 00:03:49,560
Speaker 5: those problems we found to do with I guess, the

70
00:03:49,600 --> 00:03:53,480
Speaker 5: overwhelming nature of capital markets, of investing, of the choices

71
00:03:53,520 --> 00:03:58,440
Speaker 5: we have when it comes to investing, and just offering

72
00:03:58,720 --> 00:04:03,000
Speaker 5: co pilots or AI tools to help navigate that journey

73
00:04:03,320 --> 00:04:06,480
Speaker 5: when it comes to making a decision or thinking about

74
00:04:06,880 --> 00:04:09,880
Speaker 5: you know, a single stock or looking looking for something

75
00:04:09,920 --> 00:04:12,880
Speaker 5: maybe that matters to you, and just providing more information

76
00:04:13,000 --> 00:04:13,880
Speaker 5: to those investors.

77
00:04:14,000 --> 00:04:14,360
Speaker 3: Yeah.

78
00:04:14,440 --> 00:04:16,880
Speaker 1: So now if we want to kind of take a

79
00:04:16,920 --> 00:04:20,160
Speaker 1: step back and we're just talking about things that for

80
00:04:20,160 --> 00:04:22,560
Speaker 1: anyone who's not finger on the pulse or they're just

81
00:04:22,640 --> 00:04:25,200
Speaker 1: kind of reading the headlines hearing what's going on, if

82
00:04:25,200 --> 00:04:27,080
Speaker 1: we can help fill in a few of those gaps,

83
00:04:27,160 --> 00:04:29,760
Speaker 1: can you just tell us, like, maybe what's what's been

84
00:04:29,800 --> 00:04:33,880
Speaker 1: happening that's driving this real kick kind of into another.

85
00:04:33,640 --> 00:04:36,919
Speaker 4: Gear from where we've been sitting at Shares's And you know,

86
00:04:37,000 --> 00:04:40,440
Speaker 4: my own personal interest, I think probably three to four

87
00:04:40,520 --> 00:04:45,919
Speaker 4: years ago, largely with a technology called GPT two, which

88
00:04:46,160 --> 00:04:49,240
Speaker 4: was you know, the very you're probably aware of GPT four.

89
00:04:49,279 --> 00:04:51,320
Speaker 4: You might have heard that stuff on the headlines chat

90
00:04:51,400 --> 00:04:53,760
Speaker 4: GPT and stuff like that, but GPT two is an

91
00:04:53,760 --> 00:04:58,000
Speaker 4: earlier version of this and when it turned up. It

92
00:04:58,080 --> 00:05:01,400
Speaker 4: was very interesting. It's it was nothing like what we're

93
00:05:01,440 --> 00:05:04,279
Speaker 4: able to see today, but it had those hints. And

94
00:05:04,360 --> 00:05:08,279
Speaker 4: now with GPD four and some of the more recent

95
00:05:08,680 --> 00:05:12,440
Speaker 4: things both from Google and Anthropic and other companies involved

96
00:05:12,480 --> 00:05:17,919
Speaker 4: in this space, is actually it's doing really interesting stuff constantly,

97
00:05:18,160 --> 00:05:24,360
Speaker 4: consistently bringing new and interesting perspectives or creating new opportunities.

98
00:05:24,880 --> 00:05:27,320
Speaker 4: And I think this is the point where it's like,

99
00:05:28,240 --> 00:05:30,920
Speaker 4: you really have to be paying attention to this thing

100
00:05:32,240 --> 00:05:36,560
Speaker 4: as an organization because the potential is enormous and we

101
00:05:37,040 --> 00:05:39,960
Speaker 4: just don't know what we can and can't do with it.

102
00:05:39,960 --> 00:05:41,840
Speaker 4: We're still very much finding out.

103
00:05:42,000 --> 00:05:44,360
Speaker 6: I'd love to explain how it works as well.

104
00:05:45,080 --> 00:05:47,080
Speaker 3: Oh give there to go, don't hold back.

105
00:05:48,279 --> 00:05:50,320
Speaker 5: I was thinking about this morning, and it's very hard

106
00:05:50,320 --> 00:05:54,599
Speaker 5: to articulate sometimes these complex systems in simple ways. But

107
00:05:55,560 --> 00:05:58,440
Speaker 5: you know this, Just think about a simple sentence, like

108
00:05:58,480 --> 00:06:01,680
Speaker 5: this morning, I grabbed my surfboard and I caught an epic.

109
00:06:02,960 --> 00:06:05,400
Speaker 5: You know, I'm going to say most likely to say wave.

110
00:06:05,480 --> 00:06:08,520
Speaker 5: And we experienced this kind of auto predict on our phones.

111
00:06:08,960 --> 00:06:12,360
Speaker 5: And so if you think about a sentence and a conversation,

112
00:06:13,080 --> 00:06:15,800
Speaker 5: you know it follows a certain number of patterns, and

113
00:06:15,839 --> 00:06:19,400
Speaker 5: there's probability and mathematics involved in what's most likely to

114
00:06:19,440 --> 00:06:23,400
Speaker 5: be that final word, But maybe it was actually I

115
00:06:23,440 --> 00:06:26,440
Speaker 5: caught an epic fish, and so what's the probability of

116
00:06:26,480 --> 00:06:27,120
Speaker 5: me saying that?

117
00:06:27,200 --> 00:06:29,000
Speaker 6: And it's probably very low.

118
00:06:29,640 --> 00:06:31,960
Speaker 5: But then what open AI did in the beginning was

119
00:06:32,000 --> 00:06:33,919
Speaker 5: they actually just went, you know what, let's take auto

120
00:06:33,960 --> 00:06:39,039
Speaker 5: predict and let's put it through a large cluster of

121
00:06:39,040 --> 00:06:42,599
Speaker 5: computing units of GPUs, and let's look at all of

122
00:06:42,640 --> 00:06:46,400
Speaker 5: those relationships on a time series and almost think of

123
00:06:46,400 --> 00:06:49,200
Speaker 5: it like in three D space, so like visualize the

124
00:06:49,800 --> 00:06:52,880
Speaker 5: three D space of language, and so you think about

125
00:06:52,920 --> 00:06:55,559
Speaker 5: these kind of relationships between words, but then you start

126
00:06:55,560 --> 00:06:59,200
Speaker 5: to think of those in a multi dimensional manner, so

127
00:06:59,240 --> 00:07:02,800
Speaker 5: you start to think about relationships between paragraphs and the

128
00:07:02,880 --> 00:07:05,640
Speaker 5: question that's been asked, And that's really all that did,

129
00:07:05,680 --> 00:07:09,239
Speaker 5: and it's kind of fascinating that maybe this is all

130
00:07:09,480 --> 00:07:10,760
Speaker 5: intelligence really is.

131
00:07:11,360 --> 00:07:14,280
Speaker 1: Yeah, I've used it to try and you know, create

132
00:07:14,360 --> 00:07:16,520
Speaker 1: meal plans for my you know, one and a half

133
00:07:16,640 --> 00:07:17,040
Speaker 1: year old.

134
00:07:17,200 --> 00:07:20,240
Speaker 3: You know, when I was like and just like stuff

135
00:07:20,280 --> 00:07:20,840
Speaker 3: that I'm like.

136
00:07:20,840 --> 00:07:24,000
Speaker 1: Please help me, you know, and then when you think of,

137
00:07:24,200 --> 00:07:26,640
Speaker 1: you know, what, what mission we're on here, which is like,

138
00:07:26,880 --> 00:07:30,360
Speaker 1: we're trying to create more empowerment for people when it

139
00:07:30,360 --> 00:07:32,920
Speaker 1: comes to money and some of the things that you

140
00:07:32,920 --> 00:07:36,120
Speaker 1: guys have talked about about what is possible here?

141
00:07:36,160 --> 00:07:37,600
Speaker 3: Were AI or where we're at?

142
00:07:37,640 --> 00:07:40,880
Speaker 1: Where AI like, am really interested to, you know, get

143
00:07:40,880 --> 00:07:43,560
Speaker 1: your thoughts on how do you see this kind of

144
00:07:43,680 --> 00:07:46,560
Speaker 1: playing out for people when it comes to money or investing.

145
00:07:46,680 --> 00:07:48,880
Speaker 5: The irony is if this video ends up on YouTube,

146
00:07:48,880 --> 00:07:51,200
Speaker 5: it's probably going to be part of the future model

147
00:07:51,240 --> 00:07:54,880
Speaker 5: training that's going to help build the model. So maybe

148
00:07:55,520 --> 00:07:58,800
Speaker 5: maybe we have our moment to share our ideas personally,

149
00:07:58,840 --> 00:08:02,520
Speaker 5: I think if you think about the best investors of

150
00:08:02,560 --> 00:08:06,120
Speaker 5: all time, like Munger and Buffet and Howard, you know,

151
00:08:06,520 --> 00:08:10,720
Speaker 5: Howard markson all the long list of value investors, the

152
00:08:10,720 --> 00:08:16,000
Speaker 5: ones that actually study financial markets and history and a

153
00:08:16,080 --> 00:08:18,520
Speaker 5: multifaceted approach to investing.

154
00:08:18,920 --> 00:08:19,680
Speaker 6: They've spent a.

155
00:08:19,760 --> 00:08:23,520
Speaker 5: Good part of like thirty forty in Monger's case, maybe

156
00:08:23,560 --> 00:08:27,960
Speaker 5: seventy years of their life. I think Manger famously read

157
00:08:28,040 --> 00:08:31,160
Speaker 5: for thirty eight hours a week. He would allocate two

158
00:08:31,200 --> 00:08:35,240
Speaker 5: hours a week to meetings. So let's just presume he

159
00:08:36,240 --> 00:08:41,719
Speaker 5: spent seventy years of his investing career reading you know,

160
00:08:41,960 --> 00:08:45,600
Speaker 5: all sorts of literature, and not just annual filings and

161
00:08:46,000 --> 00:08:53,160
Speaker 5: SEC filings, but physics and you know, politics, history. I've

162
00:08:53,160 --> 00:08:56,920
Speaker 5: calculated that it's around about two billion words that Manger

163
00:08:56,960 --> 00:08:59,920
Speaker 5: has possibly consumed two to ten billion words in his

164
00:09:00,080 --> 00:09:03,160
Speaker 5: lifetime jet And then the question is that how much

165
00:09:03,160 --> 00:09:06,319
Speaker 5: does he recall? You know, that's forming his knowledge base,

166
00:09:06,840 --> 00:09:10,760
Speaker 5: but he's trained himself to know on that content. And

167
00:09:10,800 --> 00:09:13,079
Speaker 5: I think so there's no First of all, there's no

168
00:09:13,240 --> 00:09:18,679
Speaker 5: denying that the compound knowledge that AI models are going

169
00:09:18,720 --> 00:09:22,520
Speaker 5: through already, the intelligence that they're building on, is growing

170
00:09:22,640 --> 00:09:27,360
Speaker 5: at a large rate, and it will outperform somebody like

171
00:09:27,480 --> 00:09:31,720
Speaker 5: Manga or Buffett or whoever. And so I think the

172
00:09:31,800 --> 00:09:35,280
Speaker 5: opportunity in capital markets when it comes to an investing

173
00:09:35,559 --> 00:09:39,600
Speaker 5: is really interesting because maybe it levels the playing field.

174
00:09:39,679 --> 00:09:41,680
Speaker 6: If we have access to these models.

175
00:09:41,360 --> 00:09:44,200
Speaker 5: And they become super intelligent, and we just know how

176
00:09:44,200 --> 00:09:47,520
Speaker 5: to use them in the right way, and they're available publicly,

177
00:09:47,600 --> 00:09:50,040
Speaker 5: they're not privately run by Hedge Fund in New York,

178
00:09:51,920 --> 00:09:55,840
Speaker 5: maybe that kind of makes the markets more efficient and

179
00:09:55,920 --> 00:09:59,200
Speaker 5: makes them probably more fair. If a balance sheet comes

180
00:09:59,240 --> 00:10:02,520
Speaker 5: out on the stop market and you have something that

181
00:10:02,559 --> 00:10:06,000
Speaker 5: can read it and understand it because you can't, maybe

182
00:10:06,400 --> 00:10:10,240
Speaker 5: you know what to do, and so yeah, I think

183
00:10:10,240 --> 00:10:14,600
Speaker 5: maybe it creates more opportunity for abundance when it comes

184
00:10:14,600 --> 00:10:16,760
Speaker 5: to investing. That's my view on maybe where it could

185
00:10:16,760 --> 00:10:19,800
Speaker 5: go for ten ten years, and I'm an optimist. I'd

186
00:10:19,880 --> 00:10:23,480
Speaker 5: like to hope that it won't be held by you know,

187
00:10:23,760 --> 00:10:26,079
Speaker 5: concentrated powers and on Wall Street.

188
00:10:27,200 --> 00:10:27,840
Speaker 6: That's my view.

189
00:10:28,200 --> 00:10:30,439
Speaker 4: I would love to read thirty eight hours a week.

190
00:10:31,040 --> 00:10:36,560
Speaker 4: That's I love reading, and that sounds great, but you know, yeah,

191
00:10:36,720 --> 00:10:38,880
Speaker 4: but the other reflection that I have, and I think

192
00:10:38,960 --> 00:10:42,400
Speaker 4: is very key to us here at share These, is

193
00:10:42,440 --> 00:10:48,760
Speaker 4: that it's so much about wealth, is personal to you,

194
00:10:49,040 --> 00:10:54,720
Speaker 4: to your your family, your situation, all of these things.

195
00:10:54,800 --> 00:11:01,480
Speaker 4: And until a technology like AI turns up and is

196
00:11:01,559 --> 00:11:05,040
Speaker 4: able to take all of that context into account as

197
00:11:05,040 --> 00:11:10,120
Speaker 4: it provides you within science and take the information that's

198
00:11:10,160 --> 00:11:13,560
Speaker 4: been given. You know, maybe you're a particle physicist. You

199
00:11:13,600 --> 00:11:16,560
Speaker 4: don't need somebody to give you baby words on the

200
00:11:16,640 --> 00:11:19,320
Speaker 4: nature of particle physics, but you're trying to evaluate a

201
00:11:19,360 --> 00:11:21,760
Speaker 4: company that's working in that field, and you don't understand

202
00:11:21,800 --> 00:11:25,440
Speaker 4: the financial terms. You know, it's something that can understand

203
00:11:25,720 --> 00:11:28,440
Speaker 4: what you know, what you don't. The gaps that you

204
00:11:28,480 --> 00:11:33,160
Speaker 4: have in your understanding and what's relevant for you really

205
00:11:33,240 --> 00:11:37,280
Speaker 4: changes the picture in my view, because people can really

206
00:11:37,360 --> 00:11:42,080
Speaker 4: leverage the opportunities that they have personally and solve the

207
00:11:42,120 --> 00:11:46,520
Speaker 4: problems that they have personally and apply their personal values.

208
00:11:47,120 --> 00:11:49,960
Speaker 4: You know, get these tools. And I think we've talked

209
00:11:49,960 --> 00:11:52,760
Speaker 4: about this several times, the idea that AI at the

210
00:11:52,840 --> 00:11:56,040
Speaker 4: moment anyway, we're kind of anticipating and acting in a

211
00:11:56,080 --> 00:11:59,480
Speaker 4: copilot role. It's sitting there at like a friend, a buddy.

212
00:11:59,480 --> 00:12:02,719
Speaker 4: It's help us understand. It's not making decisions for us,

213
00:12:02,760 --> 00:12:06,400
Speaker 4: it's helping us understand the things that are important to us.

214
00:12:06,800 --> 00:12:10,760
Speaker 4: And when that happens, it just feels like it's really

215
00:12:10,800 --> 00:12:15,040
Speaker 4: really magical and really really useful. You know, Sonya's heard

216
00:12:15,080 --> 00:12:18,520
Speaker 4: me say this many many times. People need to be

217
00:12:18,600 --> 00:12:21,920
Speaker 4: building an intuition about these tools and how they work,

218
00:12:22,320 --> 00:12:26,280
Speaker 4: like you can't use them half as effectively. You know,

219
00:12:27,800 --> 00:12:31,680
Speaker 4: almost all of our listeners who've heard anything about this stuff,

220
00:12:32,880 --> 00:12:35,520
Speaker 4: will I have heard equal amounts of like, wow, this

221
00:12:35,640 --> 00:12:40,440
Speaker 4: is amazing at massive hype and also oh hallucinations, they lie,

222
00:12:40,520 --> 00:12:43,920
Speaker 4: they make stuff up, they do all that, and the

223
00:12:44,640 --> 00:12:47,800
Speaker 4: trekkers you get way better outcomes if you invest the

224
00:12:47,880 --> 00:12:51,400
Speaker 4: time to understand the strengths and weaknesses, you know, spend

225
00:12:51,400 --> 00:12:53,600
Speaker 4: some time playing with them. It doesn't really matter whether

226
00:12:53,600 --> 00:12:57,200
Speaker 4: you're doing serious work stuff or whether you're just toying around.

227
00:12:57,360 --> 00:13:01,680
Speaker 5: And if you tried the audio feature within chat GPT,

228
00:13:01,960 --> 00:13:03,760
Speaker 5: so you just have it on in your car and

229
00:13:03,880 --> 00:13:04,439
Speaker 5: talk to it.

230
00:13:05,280 --> 00:13:07,920
Speaker 6: That's fascinating. I've done it with my kids. So I

231
00:13:07,960 --> 00:13:08,880
Speaker 6: picked them up from school.

232
00:13:08,880 --> 00:13:12,720
Speaker 5: We've got three kids, and just take a moment to

233
00:13:12,760 --> 00:13:16,920
Speaker 5: explain my kids' ages, their names, and their interests. And

234
00:13:16,960 --> 00:13:20,079
Speaker 5: then I ask chat GPT to go around the car

235
00:13:20,559 --> 00:13:23,880
Speaker 5: one by one and ask them an interesting question that

236
00:13:23,880 --> 00:13:26,480
Speaker 5: they have to answer, and it becomes a homework task.

237
00:13:26,960 --> 00:13:29,880
Speaker 5: And I just drive and I let this AI agent

238
00:13:29,960 --> 00:13:32,719
Speaker 5: take over the conversation in the car and they love it.

239
00:13:32,720 --> 00:13:33,560
Speaker 6: It's fascinating.

240
00:13:34,320 --> 00:13:36,559
Speaker 3: I love it. It's like your own little family podcast.

241
00:13:36,960 --> 00:13:37,400
Speaker 6: Yeah.

242
00:13:37,559 --> 00:13:40,439
Speaker 5: Well, remember the feeling when we had an Encyclopedia Britannica

243
00:13:40,640 --> 00:13:44,120
Speaker 5: or those you know, or even a map of the world.

244
00:13:44,600 --> 00:13:46,880
Speaker 5: I mean it's the same experience for them, just at

245
00:13:46,880 --> 00:13:47,959
Speaker 5: a much deeper level.

246
00:13:48,240 --> 00:13:50,480
Speaker 3: Yeah. Great. Like so talking about getting stuck.

247
00:13:50,280 --> 00:13:53,400
Speaker 1: In you know, one thing I think of is like,

248
00:13:53,440 --> 00:13:56,040
Speaker 1: sometimes the problems don't change, it's just the solutions. All

249
00:13:56,040 --> 00:13:58,600
Speaker 1: the ways we might solve those problems become different as

250
00:13:58,800 --> 00:14:02,200
Speaker 1: different technologies and things like that. And you know, one

251
00:14:02,240 --> 00:14:05,840
Speaker 1: thing we are launching soon is and working with Telescope

252
00:14:05,840 --> 00:14:09,480
Speaker 1: to launch that is the AI search. And you know,

253
00:14:09,559 --> 00:14:11,800
Speaker 1: one of the problems that exists and one of the

254
00:14:11,880 --> 00:14:14,280
Speaker 1: you know, if people are investing, one of the top

255
00:14:14,360 --> 00:14:15,719
Speaker 1: questions is well, what should I.

256
00:14:15,679 --> 00:14:17,480
Speaker 3: Invest in or how should I think about this? And

257
00:14:17,480 --> 00:14:18,360
Speaker 3: what do I need to know?

258
00:14:18,520 --> 00:14:21,400
Speaker 1: And want to get more and build their knowledge around

259
00:14:21,440 --> 00:14:24,440
Speaker 1: investing and build their confidence in that space. So we're

260
00:14:24,480 --> 00:14:27,920
Speaker 1: launching this in New Zealand. But maybe Richard, do you

261
00:14:27,920 --> 00:14:30,520
Speaker 1: want to give us a bit of a insight into

262
00:14:30,520 --> 00:14:31,880
Speaker 1: what AI search is?

263
00:14:32,320 --> 00:14:32,520
Speaker 5: Yeah?

264
00:14:32,560 --> 00:14:36,720
Speaker 4: Yeah, yeah, So AI search for us is, you know,

265
00:14:36,760 --> 00:14:40,480
Speaker 4: we call the problem discovery. How do you find the

266
00:14:40,480 --> 00:14:44,560
Speaker 4: thing that you're actually interested in, whether you know it

267
00:14:45,080 --> 00:14:50,600
Speaker 4: you know well or not. And AI search is a

268
00:14:50,600 --> 00:14:53,080
Speaker 4: new take on this. It's the idea that you can

269
00:14:53,200 --> 00:14:56,040
Speaker 4: kind of give us a concept. You can search on

270
00:14:56,080 --> 00:14:59,680
Speaker 4: a concept or an idea what change in the world

271
00:15:00,680 --> 00:15:05,520
Speaker 4: might be true and then how would that impact what

272
00:15:05,560 --> 00:15:08,320
Speaker 4: you might look to invest in or what you might

273
00:15:08,440 --> 00:15:09,720
Speaker 4: look to think about.

274
00:15:09,920 --> 00:15:13,800
Speaker 5: I think the initial problem we were solving at Telescope,

275
00:15:13,800 --> 00:15:18,240
Speaker 5: even prior to working with Chasi's, was that scenario playing

276
00:15:18,480 --> 00:15:21,640
Speaker 5: a situation, a theme, or an event or something that

277
00:15:21,720 --> 00:15:26,600
Speaker 5: was personal that mattered to you, and you know, stepping

278
00:15:26,640 --> 00:15:29,800
Speaker 5: through I guess the butterfly effect if you've heard that

279
00:15:29,880 --> 00:15:32,080
Speaker 5: term before, but the cause and effects, and then the

280
00:15:32,080 --> 00:15:35,479
Speaker 5: cause and effect. So going through this process of conducting

281
00:15:36,200 --> 00:15:39,960
Speaker 5: second third order thinking, which is an interesting topic, but

282
00:15:40,040 --> 00:15:44,840
Speaker 5: actually just relying on the one point seven trillion parameters

283
00:15:44,880 --> 00:15:49,560
Speaker 5: that open ai has in their knowledge base to rationally

284
00:15:49,640 --> 00:15:52,600
Speaker 5: just search for stocks based on that theme, so not

285
00:15:52,680 --> 00:15:56,280
Speaker 5: only unpack the idea that you had, but also just

286
00:15:56,320 --> 00:16:01,640
Speaker 5: to rationally pick the stocks that match the idea. And

287
00:16:01,680 --> 00:16:04,760
Speaker 5: so I think, like then, just what's really interesting about

288
00:16:04,760 --> 00:16:07,160
Speaker 5: it is that it changes the way you then interact

289
00:16:07,280 --> 00:16:09,960
Speaker 5: in the real world because you suddenly realize you have

290
00:16:10,040 --> 00:16:13,720
Speaker 5: this tool available. So, for example, we had a user

291
00:16:13,800 --> 00:16:20,800
Speaker 5: go to take their EV to the mechanic and they said, oh,

292
00:16:20,920 --> 00:16:22,640
Speaker 5: the tires have a bit of wear on them, but

293
00:16:22,680 --> 00:16:27,119
Speaker 5: nothing unusual due to it being an EV and having evware,

294
00:16:27,840 --> 00:16:30,560
Speaker 5: And it's like, what do you mean by evwear? And

295
00:16:31,040 --> 00:16:33,960
Speaker 5: you put that into Chasi's ai search and it actually

296
00:16:34,000 --> 00:16:39,160
Speaker 5: explains like, well, they get twenty to twenty five percent

297
00:16:39,280 --> 00:16:42,120
Speaker 5: more wear on their tires because the car's heavier and

298
00:16:42,160 --> 00:16:44,840
Speaker 5: they have more talqu and so actually, like what's an

299
00:16:44,880 --> 00:16:48,960
Speaker 5: interesting side effect of the electrification of vehicles is that

300
00:16:49,480 --> 00:16:52,720
Speaker 5: now there's the tire companies that are starting to benefit

301
00:16:52,760 --> 00:16:56,000
Speaker 5: in their revenues growing. So that's an interesting idea that

302
00:16:56,040 --> 00:16:59,440
Speaker 5: you probably wouldn't have traditionally been able to just search.

303
00:16:59,520 --> 00:17:02,680
Speaker 5: And so I think going into the Chasias app and

304
00:17:02,760 --> 00:17:06,159
Speaker 5: just throwing in these ideas throughout the experience you have

305
00:17:06,280 --> 00:17:09,199
Speaker 5: in your career or you know, at home, and just

306
00:17:09,240 --> 00:17:12,600
Speaker 5: thinking about the products you use and the services you

307
00:17:12,720 --> 00:17:15,760
Speaker 5: use in a different way and being able to search

308
00:17:15,800 --> 00:17:17,840
Speaker 5: for those like you can you can put in you know,

309
00:17:18,320 --> 00:17:22,439
Speaker 5: maybe just something that an ETF doesn't already cover, Like

310
00:17:22,440 --> 00:17:25,679
Speaker 5: what about just female CEOs like that. Unfortunately it's not

311
00:17:25,720 --> 00:17:28,920
Speaker 5: an ETF, it's not an investment product, but perhaps that's

312
00:17:28,920 --> 00:17:32,840
Speaker 5: just something you want to invest in or search for

313
00:17:33,119 --> 00:17:36,479
Speaker 5: and then choose the single stocks that you actually believe in.

314
00:17:37,320 --> 00:17:40,520
Speaker 1: It's so like the opportunity I think and just helping

315
00:17:40,520 --> 00:17:44,520
Speaker 1: people build context, you know. Unfortunately, there's not a use

316
00:17:44,640 --> 00:17:48,440
Speaker 1: or no answer, you know, to any of the world

317
00:17:48,480 --> 00:17:51,840
Speaker 1: of investing. But I think what is really fascinating as

318
00:17:52,640 --> 00:17:55,240
Speaker 1: the more you can kind of understand the richness around

319
00:17:55,240 --> 00:17:59,800
Speaker 1: your decisions or the assumptions or the context, and so

320
00:18:00,080 --> 00:18:03,359
Speaker 1: can kind of maybe recognize some connections that you hadn't

321
00:18:03,480 --> 00:18:08,159
Speaker 1: more like most immediately made. Being able to explore and

322
00:18:08,400 --> 00:18:10,520
Speaker 1: you know, build that knowledge base or show some of

323
00:18:10,560 --> 00:18:14,800
Speaker 1: that logic or flesh those things out is super exciting

324
00:18:14,840 --> 00:18:17,199
Speaker 1: and I think can't wait for this to launch and

325
00:18:17,240 --> 00:18:21,399
Speaker 1: also to get feedback from our investors or around you know,

326
00:18:21,400 --> 00:18:24,720
Speaker 1: how this is helping them searching and get more interested

327
00:18:25,040 --> 00:18:28,080
Speaker 1: and building a portfolio that's right for them. I think

328
00:18:28,119 --> 00:18:30,480
Speaker 1: we've come to the end of our time now, and

329
00:18:30,520 --> 00:18:32,440
Speaker 1: I think just so grateful to have you guys here

330
00:18:32,480 --> 00:18:35,679
Speaker 1: and sharing your insights and what's been a really fascinating

331
00:18:35,800 --> 00:18:39,960
Speaker 1: conversation and shared some cool tools that people can use

332
00:18:40,040 --> 00:18:43,760
Speaker 1: to get stuck in that. Yeah, just did just want

333
00:18:43,760 --> 00:18:46,840
Speaker 1: to say thanks heats for sharing.

334
00:18:47,520 --> 00:18:47,879
Speaker 5: Thank you