1
00:00:00,160 --> 00:00:02,320
Speaker 1: But knowledge to work and grow your business with c

2
00:00:02,520 --> 00:00:06,680
Speaker 1: I T. From transportation to healthcare to manufacturing, c I

3
00:00:06,760 --> 00:00:10,520
Speaker 1: T opers commercial lending, leasing, and treasury management services for

4
00:00:10,600 --> 00:00:13,480
Speaker 1: small and middle market businesses. Learn more at c I

5
00:00:13,560 --> 00:00:25,160
Speaker 1: T dot com put Knowledge to Work. Hello and welcome

6
00:00:25,200 --> 00:00:29,080
Speaker 1: to another edition of the Odd Thoughts Podcast. I'm Tracy Alloway,

7
00:00:29,160 --> 00:00:33,440
Speaker 1: executive editor of Bloomberg Markets. My co host Joe Wisenthal

8
00:00:33,720 --> 00:00:37,040
Speaker 1: is away, and so I have a replacement co host

9
00:00:37,120 --> 00:00:40,800
Speaker 1: for you. I'm actually really excited about this. It's Matt Levine.

10
00:00:40,880 --> 00:00:43,920
Speaker 1: He is, of course, the Bloomberg View columnist and quite

11
00:00:43,960 --> 00:00:48,960
Speaker 1: possibly one of the funniest, most original financial writers out there.

12
00:00:49,040 --> 00:00:51,640
Speaker 1: So thanks so much Matt for joining us today. Thanks

13
00:00:51,680 --> 00:00:56,880
Speaker 1: for all right, Matt, Let's start this off with a

14
00:00:57,080 --> 00:01:01,920
Speaker 1: thought exercise. UM, let's say you're an investor and you're

15
00:01:02,000 --> 00:01:05,920
Speaker 1: interested in investing in the shares of a company. Let's

16
00:01:05,959 --> 00:01:09,560
Speaker 1: say it's something like pet Smart. How do you actually

17
00:01:09,600 --> 00:01:13,440
Speaker 1: go about figuring out how well pet Smart as a

18
00:01:13,480 --> 00:01:16,880
Speaker 1: business is doing well? I probably go there with my

19
00:01:17,000 --> 00:01:21,560
Speaker 1: dog and see it all. Right, that sounds like a

20
00:01:21,640 --> 00:01:25,319
Speaker 1: very systematic approach. Now, I probably read the financial statements right,

21
00:01:26,400 --> 00:01:29,119
Speaker 1: look at the ten K you would look at their

22
00:01:29,160 --> 00:01:33,160
Speaker 1: publicly available earning statements. You would try to figure out

23
00:01:33,200 --> 00:01:37,040
Speaker 1: what's going on in terms of their revenue trends, things

24
00:01:37,120 --> 00:01:39,039
Speaker 1: like that. If you were really fancy about it, you

25
00:01:39,120 --> 00:01:41,840
Speaker 1: might go there with your dog and try to see

26
00:01:41,959 --> 00:01:44,720
Speaker 1: how many customers are visiting. But that's not really going

27
00:01:44,760 --> 00:01:48,240
Speaker 1: to help you if you only go to one pet Smart, Right,

28
00:01:48,640 --> 00:01:51,520
Speaker 1: So what if you could do something different? What if

29
00:01:51,520 --> 00:01:54,480
Speaker 1: you could actually see how many people were visiting a

30
00:01:54,520 --> 00:01:58,520
Speaker 1: pet Smart or pet smarts across the country. What if

31
00:01:58,520 --> 00:02:01,240
Speaker 1: you could see big data, as people like to put it,

32
00:02:01,520 --> 00:02:03,840
Speaker 1: and figure out how well the pet smart business was

33
00:02:03,880 --> 00:02:06,400
Speaker 1: actually doing. It? Sounds like it would be pretty helpful,

34
00:02:07,160 --> 00:02:10,280
Speaker 1: all right. So on today's episode, we are going to

35
00:02:10,320 --> 00:02:13,320
Speaker 1: talk to a company that is helping investors do just that,

36
00:02:13,720 --> 00:02:15,320
Speaker 1: and one of the ways they're doing it is by

37
00:02:15,480 --> 00:02:19,359
Speaker 1: using satellite data. So having satellite imagery that looks at

38
00:02:19,440 --> 00:02:23,720
Speaker 1: things like factory activity, things like how many cars are

39
00:02:23,800 --> 00:02:27,440
Speaker 1: parked out in parking lots behind pet smarts and walmarts

40
00:02:27,440 --> 00:02:30,200
Speaker 1: and other retailers around the world to try to gauge

41
00:02:30,240 --> 00:02:33,560
Speaker 1: how those businesses are actually doing. And I know you've

42
00:02:33,560 --> 00:02:36,160
Speaker 1: written a lot about data on Wall Street how people

43
00:02:36,280 --> 00:02:38,640
Speaker 1: use it. So I think you might be into this topic.

44
00:02:39,080 --> 00:02:42,800
Speaker 1: That sounds great. Alright, So without further ado, let's bring

45
00:02:42,800 --> 00:02:45,880
Speaker 1: in our guest for today. It is James Crawford. He

46
00:02:46,080 --> 00:02:49,880
Speaker 1: is the founder and CEO of Orbital Insight. He's also

47
00:02:50,000 --> 00:02:53,720
Speaker 1: a former NASA scientist, so Matt, you can ask him

48
00:02:53,760 --> 00:03:11,160
Speaker 1: many robotics questions you might be into as well. All right, James,

49
00:03:11,240 --> 00:03:13,760
Speaker 1: thank you so much for joining us today. Hey tre Z,

50
00:03:14,040 --> 00:03:17,040
Speaker 1: very happy to be here. Maybe just to begin, you

51
00:03:17,040 --> 00:03:20,000
Speaker 1: could walk us through what your company actually does, the

52
00:03:20,080 --> 00:03:24,480
Speaker 1: kind of technology you employ, and how people like potential

53
00:03:24,520 --> 00:03:28,720
Speaker 1: pet smart investors might find it useful. Sure, over the

54
00:03:28,800 --> 00:03:31,240
Speaker 1: last few years, there's been a tremendous growth in the

55
00:03:31,280 --> 00:03:34,679
Speaker 1: number of satellites over our heads, and it's interesting that

56
00:03:35,040 --> 00:03:37,000
Speaker 1: the enabler for that has been a lot of the

57
00:03:37,040 --> 00:03:40,120
Speaker 1: same technologies that bring you cell phones, So the miniaturization

58
00:03:40,200 --> 00:03:44,280
Speaker 1: of electronics, rapid reductions in the cost of launch, and

59
00:03:44,400 --> 00:03:47,480
Speaker 1: so just a lot more images are being taken now.

60
00:03:47,840 --> 00:03:49,880
Speaker 1: Now taking the image that was only the first start,

61
00:03:50,000 --> 00:03:52,000
Speaker 1: only the start, because once you take the image, it

62
00:03:52,040 --> 00:03:55,040
Speaker 1: just sits in on a disk somewhere until somebody actually

63
00:03:55,080 --> 00:03:57,160
Speaker 1: looks at it and we're getting to the point where

64
00:03:57,160 --> 00:03:59,120
Speaker 1: there's way more images and there are people that want

65
00:03:59,120 --> 00:04:01,680
Speaker 1: to stare at them. So, to continue your pet smart analogy,

66
00:04:01,720 --> 00:04:03,840
Speaker 1: if if you, if somebody were to deliver to you

67
00:04:04,720 --> 00:04:07,600
Speaker 1: a million pictures of pet smart stores, you'd be a

68
00:04:07,600 --> 00:04:10,080
Speaker 1: long time looking through, flipping through all of them, trying

69
00:04:10,120 --> 00:04:11,760
Speaker 1: to decide how many cars were in each one of

70
00:04:11,760 --> 00:04:13,920
Speaker 1: them or what was going on in each picture. So

71
00:04:13,960 --> 00:04:16,960
Speaker 1: what we really do is we complete that supply chain,

72
00:04:17,000 --> 00:04:18,719
Speaker 1: if you will. We take the images from all the

73
00:04:18,720 --> 00:04:23,680
Speaker 1: different satellite companies, We run them through artificial intelligence software

74
00:04:23,760 --> 00:04:26,119
Speaker 1: to count cars. We can count trucks, we can count

75
00:04:26,120 --> 00:04:28,600
Speaker 1: train cars, we can count ships, we can look at

76
00:04:28,640 --> 00:04:32,080
Speaker 1: agricultural fields see what the productivity is likely to be.

77
00:04:32,560 --> 00:04:36,359
Speaker 1: And then we aggregate up, add up all those numbers

78
00:04:36,360 --> 00:04:40,000
Speaker 1: and deliver an analysis of you know, how different retailers

79
00:04:40,040 --> 00:04:42,279
Speaker 1: are doing, what the corn yield in the US is

80
00:04:42,320 --> 00:04:46,440
Speaker 1: likely to be, or other interesting economic questions. And who

81
00:04:46,520 --> 00:04:48,880
Speaker 1: are your clients at the moment for this kind of data,

82
00:04:48,920 --> 00:04:52,400
Speaker 1: and what data sets are most popular because you mentioned

83
00:04:52,600 --> 00:04:54,760
Speaker 1: a whole bunch of different types of things just then,

84
00:04:54,839 --> 00:05:02,600
Speaker 1: like agriculture, retail, manufacturing, economy, mix, what's most useful. So

85
00:05:02,680 --> 00:05:06,400
Speaker 1: we've been going through we're a startup or b around startups,

86
00:05:06,440 --> 00:05:10,040
Speaker 1: so we've been going through a prioritization exercise with our

87
00:05:10,080 --> 00:05:12,600
Speaker 1: prospects of our customers and and and the first thing

88
00:05:12,640 --> 00:05:15,120
Speaker 1: we built was the one you alluded to at the beginning,

89
00:05:15,160 --> 00:05:17,400
Speaker 1: which is the retail car counting. So we're now covering

90
00:05:17,440 --> 00:05:21,039
Speaker 1: a hundred US retailers, providing daily updates on the number

91
00:05:21,080 --> 00:05:23,400
Speaker 1: of cars we're seeing in their parking lots. Now, it's

92
00:05:23,400 --> 00:05:26,240
Speaker 1: important to say we don't necessarily see every store of

93
00:05:26,240 --> 00:05:28,360
Speaker 1: every retailer every day. In fact, we see a small

94
00:05:28,360 --> 00:05:31,039
Speaker 1: fraction of each retailer every day. So you have to

95
00:05:31,120 --> 00:05:33,360
Speaker 1: look at a moving average over time to get some

96
00:05:33,640 --> 00:05:38,120
Speaker 1: statistically significant picture of what's going on the different retailers.

97
00:05:38,160 --> 00:05:40,279
Speaker 1: But but we we're pull in a lot of images

98
00:05:40,360 --> 00:05:44,520
Speaker 1: of retailers. We've also been working on oil because there's

99
00:05:44,560 --> 00:05:47,080
Speaker 1: there's so much debate right now about the price of oil,

100
00:05:47,120 --> 00:05:49,279
Speaker 1: and there's so much volatility in the price of oil,

101
00:05:49,920 --> 00:05:53,120
Speaker 1: and trying to understand just a basic simple question how

102
00:05:53,200 --> 00:05:55,560
Speaker 1: much oil is sitting in all the storage tanks, so

103
00:05:55,680 --> 00:05:57,320
Speaker 1: all the oil that's been pumped out of the ground,

104
00:05:57,320 --> 00:05:59,800
Speaker 1: but not y ever find and that number goes up

105
00:06:00,600 --> 00:06:02,760
Speaker 1: when too much oil is being pumped and there's not

106
00:06:02,839 --> 00:06:04,719
Speaker 1: enough demand. That obviously goes down if there's a lot

107
00:06:04,760 --> 00:06:07,360
Speaker 1: of demand. And so that's that's a really important, perhaps

108
00:06:07,360 --> 00:06:09,440
Speaker 1: the single most important determinant of the direction of the

109
00:06:09,440 --> 00:06:12,040
Speaker 1: price of oil. And it's not well known. It's pretty

110
00:06:12,080 --> 00:06:14,440
Speaker 1: well known for the US, but when you look across

111
00:06:14,480 --> 00:06:18,000
Speaker 1: the world, it's not well known. So the other major

112
00:06:18,040 --> 00:06:21,240
Speaker 1: product that we're that we're shipping now is is tracking

113
00:06:21,279 --> 00:06:24,080
Speaker 1: the oil inventory, the crude oil inventory, and then a

114
00:06:24,080 --> 00:06:25,720
Speaker 1: lot of the other things I mentioned are all things

115
00:06:25,760 --> 00:06:28,479
Speaker 1: that we're working on is earlier phase products that will

116
00:06:28,600 --> 00:06:32,479
Speaker 1: that will have available you in future quarters. So where

117
00:06:32,520 --> 00:06:34,960
Speaker 1: do where do ideas come from for things to kind

118
00:06:34,960 --> 00:06:37,800
Speaker 1: of look for account Is that stuff that that you

119
00:06:37,920 --> 00:06:41,320
Speaker 1: and your team comes up with, or is that client feedback.

120
00:06:42,040 --> 00:06:44,400
Speaker 1: It's it's really a combination of both. Our Our favorite

121
00:06:44,400 --> 00:06:46,520
Speaker 1: thing to do and and we've done a fair amount

122
00:06:46,520 --> 00:06:48,520
Speaker 1: of this recently, is to get in a room with

123
00:06:48,640 --> 00:06:53,520
Speaker 1: some creative portfolio managers from large financial managers from either

124
00:06:53,560 --> 00:06:56,680
Speaker 1: hedge funds or mutual funds or other folks um in

125
00:06:56,680 --> 00:06:58,800
Speaker 1: different places, some from wall streets. I'm from London, some

126
00:06:58,880 --> 00:07:01,359
Speaker 1: from Hong Kong and just brainstorm with them. You know,

127
00:07:01,440 --> 00:07:03,119
Speaker 1: it's like we asked them, what are your data gaps?

128
00:07:03,160 --> 00:07:04,760
Speaker 1: What is it that you'd like to know about the

129
00:07:04,800 --> 00:07:07,200
Speaker 1: world that you don't know, what would help you make

130
00:07:07,279 --> 00:07:10,520
Speaker 1: better trading decisions? And then we we run that up

131
00:07:10,560 --> 00:07:13,760
Speaker 1: against what is feasible in terms of what satellites can

132
00:07:13,800 --> 00:07:17,480
Speaker 1: see and what they can't see, um and we come

133
00:07:17,520 --> 00:07:19,280
Speaker 1: up with We've come up with a very long list

134
00:07:19,320 --> 00:07:21,800
Speaker 1: of great ideas. This is the challenge of being a

135
00:07:21,840 --> 00:07:23,920
Speaker 1: startup as we have way more ideas for this than

136
00:07:23,960 --> 00:07:27,800
Speaker 1: we have, you know, resources to actually build, but we're

137
00:07:27,920 --> 00:07:30,360
Speaker 1: knocking them down pretty fast at this point. And uh

138
00:07:30,440 --> 00:07:32,720
Speaker 1: and working down the list of the of the really

139
00:07:32,720 --> 00:07:36,320
Speaker 1: top priority things we think we can measure so retail

140
00:07:36,520 --> 00:07:38,720
Speaker 1: like store car counting, it feels like a thing that

141
00:07:38,760 --> 00:07:40,840
Speaker 1: has that people have done for a long time. You know,

142
00:07:41,120 --> 00:07:43,520
Speaker 1: hedge bunds will send an analyst to their local mall

143
00:07:43,880 --> 00:07:45,760
Speaker 1: and obviously you can do it on a on a

144
00:07:45,880 --> 00:07:50,240
Speaker 1: very different scale. Is there is there something that couldn't

145
00:07:50,240 --> 00:07:52,440
Speaker 1: have been done at all before that has made possible

146
00:07:52,440 --> 00:07:55,760
Speaker 1: by satellite technology. That's like, that's that's just a totally

147
00:07:55,760 --> 00:07:58,280
Speaker 1: new kind of data. It's a good question. In the

148
00:07:58,320 --> 00:08:01,360
Speaker 1: case of retail, I think I think scale is tremendously

149
00:08:01,400 --> 00:08:07,200
Speaker 1: important when we do correlations with say SEC reported revenue UM,

150
00:08:07,240 --> 00:08:09,880
Speaker 1: there's a there's a tremendous increase in our ability to

151
00:08:09,920 --> 00:08:12,320
Speaker 1: predict that with scale. And if you have just a

152
00:08:12,360 --> 00:08:14,280
Speaker 1: few observations, if you just go down to look at

153
00:08:14,320 --> 00:08:16,800
Speaker 1: your local store, I can tell you you're not getting

154
00:08:16,800 --> 00:08:18,920
Speaker 1: a very good prediction because because a lot of these

155
00:08:19,000 --> 00:08:21,920
Speaker 1: chains have, you know, thousands of stores in them, and

156
00:08:22,000 --> 00:08:25,000
Speaker 1: so UM just working at scale and the satellites that

157
00:08:25,040 --> 00:08:27,680
Speaker 1: have launched over the last few years, plus the artificial

158
00:08:27,720 --> 00:08:30,360
Speaker 1: intelligence to be able to count literally millions of parking

159
00:08:30,400 --> 00:08:34,360
Speaker 1: lots is really critical. It qualitatively changes the value of

160
00:08:34,360 --> 00:08:37,920
Speaker 1: the signal. The oil signal is one that would be

161
00:08:38,040 --> 00:08:41,520
Speaker 1: very hard to do without satellites because these oil tanks

162
00:08:41,520 --> 00:08:44,280
Speaker 1: are located in every country in the world. So they're

163
00:08:44,280 --> 00:08:46,760
Speaker 1: in Singapore, they're in Hong Kong, they are all over China,

164
00:08:46,800 --> 00:08:51,640
Speaker 1: they're all over Nigeria, South America, Venezuela, the US, Europe,

165
00:08:51,920 --> 00:08:55,079
Speaker 1: and you're not going to be able to see them

166
00:08:55,120 --> 00:08:57,719
Speaker 1: just driving past. It's it's when you can look from

167
00:08:57,760 --> 00:08:59,640
Speaker 1: above and you can actually see the shadows on the

168
00:08:59,679 --> 00:09:01,480
Speaker 1: top of oil tank that you can actually get a

169
00:09:01,480 --> 00:09:04,240
Speaker 1: sense of what's in them. People have in the past

170
00:09:04,280 --> 00:09:07,600
Speaker 1: flown helicopters over the major oilfields in the US to

171
00:09:07,720 --> 00:09:10,480
Speaker 1: measure crude oil inventory. But you're not going to fly

172
00:09:10,600 --> 00:09:13,160
Speaker 1: helicopters over all the oil fields in China, or even

173
00:09:13,200 --> 00:09:16,160
Speaker 1: all the oil fields in Europe and Africa and South America.

174
00:09:16,679 --> 00:09:18,600
Speaker 1: So I think that might be an example of something

175
00:09:18,640 --> 00:09:20,880
Speaker 1: that's just incredibly hard to do if you don't have

176
00:09:20,920 --> 00:09:25,360
Speaker 1: the satellite coverage. You mentioned that when potential clients come

177
00:09:25,400 --> 00:09:29,280
Speaker 1: to you with ideas for data sets, that feasibility is

178
00:09:29,440 --> 00:09:32,120
Speaker 1: one of the things that you consider. Are there any

179
00:09:32,120 --> 00:09:34,839
Speaker 1: other considerations that you have to take into account, like

180
00:09:35,200 --> 00:09:40,079
Speaker 1: privacy or trade secrecy, Like if a hedge one comes

181
00:09:40,120 --> 00:09:43,360
Speaker 1: to you with with an idea saying, for instance, like

182
00:09:43,679 --> 00:09:46,160
Speaker 1: they want to figure out the future price of hog futures,

183
00:09:46,480 --> 00:09:48,760
Speaker 1: so they want to look in the backyards of every

184
00:09:48,800 --> 00:09:51,680
Speaker 1: American family and figure out whether or not they're buying

185
00:09:51,720 --> 00:09:55,000
Speaker 1: barbecue sets or something like that. Would you do it?

186
00:09:55,600 --> 00:09:57,800
Speaker 1: That's interesting question. I don't think you could see that.

187
00:09:57,920 --> 00:10:00,520
Speaker 1: So the so the legal limit for images in the

188
00:10:00,600 --> 00:10:04,679
Speaker 1: US is thirty centimeter pixels. That's about the size at

189
00:10:04,679 --> 00:10:08,720
Speaker 1: the top of your laptop. So you can tell cars.

190
00:10:09,160 --> 00:10:10,960
Speaker 1: You can see you know how many cars are parked

191
00:10:10,960 --> 00:10:12,840
Speaker 1: in people's driveways. You can see how many cars are

192
00:10:12,840 --> 00:10:16,200
Speaker 1: parked at Walmart. You can't really tell whether it's a

193
00:10:16,280 --> 00:10:20,319
Speaker 1: Ford Fiesta or or a Mazda in three or something. Um,

194
00:10:20,640 --> 00:10:23,760
Speaker 1: you can roughly tell a car from a truck. I

195
00:10:23,800 --> 00:10:26,120
Speaker 1: don't think you can tell whether somebody's got a barbecue set.

196
00:10:26,760 --> 00:10:30,160
Speaker 1: Generally speaking, we work at such a such a broad

197
00:10:30,280 --> 00:10:33,520
Speaker 1: level of aggregation, so we look at We might look at,

198
00:10:33,600 --> 00:10:36,280
Speaker 1: you know, how many questions similar to what you're asking.

199
00:10:36,320 --> 00:10:38,200
Speaker 1: You might look at how many solar panels have been

200
00:10:38,240 --> 00:10:41,600
Speaker 1: installed in all the roofs and all of Colorado, and

201
00:10:41,679 --> 00:10:43,920
Speaker 1: how fast has that grown over the last five years.

202
00:10:44,480 --> 00:10:47,840
Speaker 1: UM that that tends to be the level of aggregation

203
00:10:47,920 --> 00:10:51,520
Speaker 1: that we work. The imagery, UM doesn't. It's it's hard

204
00:10:51,559 --> 00:10:53,559
Speaker 1: to get enough imagery to work at a very low

205
00:10:53,640 --> 00:10:55,839
Speaker 1: level of granularity. And you have what you said, some

206
00:10:55,840 --> 00:10:58,960
Speaker 1: sometimes privacy concerns, although our our resolution is so poor,

207
00:10:59,000 --> 00:11:01,160
Speaker 1: I don't think that's a major issue. So usually the

208
00:11:01,240 --> 00:11:03,840
Speaker 1: limitation is do we have enough imagery? And is the

209
00:11:03,840 --> 00:11:06,840
Speaker 1: imagery of sufficiently high resolution to see whatever it is

210
00:11:06,880 --> 00:11:08,960
Speaker 1: that we want to count, and then we count it

211
00:11:09,280 --> 00:11:12,240
Speaker 1: as I say, very coarse levels of aggregation for investors.

212
00:11:12,840 --> 00:11:16,160
Speaker 1: But even if you're aggregating the data, if it's for

213
00:11:16,240 --> 00:11:21,480
Speaker 1: something like say manufacturing activity in China, where the government

214
00:11:21,640 --> 00:11:25,200
Speaker 1: publishes official statistics, it publishes p M, I is that

215
00:11:25,280 --> 00:11:28,920
Speaker 1: a lot of people mistrust. I mean, I'm sure China

216
00:11:29,000 --> 00:11:32,280
Speaker 1: doesn't necessarily want a bunch of satellites pointed at it

217
00:11:32,400 --> 00:11:36,280
Speaker 1: saying actually, it looks like activity from your manufacturing sector

218
00:11:36,360 --> 00:11:40,840
Speaker 1: is slowing a lot more than official figures suggest. Does

219
00:11:40,880 --> 00:11:44,160
Speaker 1: that ever come up as an issue? No, not really,

220
00:11:44,200 --> 00:11:48,200
Speaker 1: because the control of the satellites rests in the country

221
00:11:48,240 --> 00:11:52,240
Speaker 1: that launched the satellites. So we are mostly using salaries

222
00:11:52,280 --> 00:11:54,160
Speaker 1: and satellites from all over the world, but the majority

223
00:11:54,160 --> 00:11:56,240
Speaker 1: of them are flown out of either the U S. Canada,

224
00:11:56,320 --> 00:11:59,440
Speaker 1: or Europe, and then a few other countries as well.

225
00:11:59,480 --> 00:12:02,679
Speaker 1: But there's no because when you put a satellite and

226
00:12:02,760 --> 00:12:05,760
Speaker 1: lower thor of it, it necessarily passes over every square

227
00:12:05,760 --> 00:12:08,640
Speaker 1: foot of the Earth about every two weeks, that in

228
00:12:09,000 --> 00:12:12,599
Speaker 1: each individual satellite. So the government of China can't control this.

229
00:12:13,160 --> 00:12:17,559
Speaker 1: The only restriction the US government imposes generally is if

230
00:12:17,559 --> 00:12:21,199
Speaker 1: there's areas where US troops are in active combat, satellites

231
00:12:21,240 --> 00:12:23,240
Speaker 1: that are run by US companies are not allowed to

232
00:12:23,280 --> 00:12:27,040
Speaker 1: distribute the imagery of those regions. But that's incredibly small

233
00:12:27,120 --> 00:12:30,160
Speaker 1: percentage of the world. UM, So generally speaking, now this

234
00:12:30,280 --> 00:12:32,440
Speaker 1: is not a problem. It's it's a matter of providing

235
00:12:32,480 --> 00:12:35,600
Speaker 1: visibility for everybody. And we don't single out any particular

236
00:12:35,640 --> 00:12:38,400
Speaker 1: country in a particular industry. Um. You know, we're trying

237
00:12:38,440 --> 00:12:41,480
Speaker 1: to understand, you know, very broad trends and and provide

238
00:12:41,559 --> 00:12:44,360
Speaker 1: everybody a better insight into what's going on in the world.

239
00:12:44,960 --> 00:12:50,000
Speaker 1: Is your product sort of reports and insight and analysis,

240
00:12:50,520 --> 00:12:53,440
Speaker 1: or are you, like in some cases like feeding raw

241
00:12:53,600 --> 00:12:56,480
Speaker 1: data to algorithmic trading firms, Like like, are people coming

242
00:12:56,520 --> 00:12:59,480
Speaker 1: to you for kind of like rass signals or for

243
00:12:59,480 --> 00:13:04,160
Speaker 1: for the higher level insight. It's it's actually so both, um,

244
00:13:04,200 --> 00:13:06,240
Speaker 1: And it tends to be as your question implies, it

245
00:13:06,240 --> 00:13:09,000
Speaker 1: tends to be more the quantitative firms that want the

246
00:13:09,080 --> 00:13:11,880
Speaker 1: raw data, and um, a lot of the more fundamental

247
00:13:11,920 --> 00:13:15,120
Speaker 1: firms there are a little bit more interested in in

248
00:13:15,920 --> 00:13:19,240
Speaker 1: aggregator results charts and graphs that actually give them some

249
00:13:19,559 --> 00:13:22,959
Speaker 1: higher level insights into what the data is saying, how

250
00:13:23,040 --> 00:13:26,559
Speaker 1: much do you actually charge for this data? Yeah, unfortunately

251
00:13:26,559 --> 00:13:28,320
Speaker 1: we don't. We don't give that out publicly, and it

252
00:13:28,400 --> 00:13:31,080
Speaker 1: and it and it varies a lot by by them,

253
00:13:31,160 --> 00:13:34,360
Speaker 1: in the case of retailers, for instance, by how many

254
00:13:34,400 --> 00:13:38,600
Speaker 1: retailers um the individual customer wants to track. Okay, we

255
00:13:38,640 --> 00:13:41,640
Speaker 1: are going to take a short break for a message

256
00:13:41,679 --> 00:13:44,640
Speaker 1: from our sponsor. We'll be back in one second. But

257
00:13:44,800 --> 00:13:47,040
Speaker 1: knowledge to work and grow your business with c i

258
00:13:47,120 --> 00:13:51,240
Speaker 1: T from transportation to healthcare to manufacturing. C i T

259
00:13:51,400 --> 00:13:55,400
Speaker 1: offers commercial lending, leasing, and treasury management services for small

260
00:13:55,480 --> 00:13:58,040
Speaker 1: and middle market businesses. Learn more at c i T

261
00:13:58,240 --> 00:14:03,319
Speaker 1: dot com put knowledge to Work. Okay, we're back with

262
00:14:03,400 --> 00:14:06,720
Speaker 1: James Crawford. He is the founder and CEO of Orbital Insight,

263
00:14:06,800 --> 00:14:12,280
Speaker 1: and we are talking satellite data and analysis how investors

264
00:14:12,320 --> 00:14:16,440
Speaker 1: on Wall Street can actually use it. You know, James,

265
00:14:16,520 --> 00:14:19,080
Speaker 1: I just asked you about the cost, and this gets

266
00:14:19,120 --> 00:14:23,240
Speaker 1: to I think one of the issues that people sometimes

267
00:14:23,280 --> 00:14:26,640
Speaker 1: have with these sorts of businesses, which is that we're

268
00:14:26,720 --> 00:14:30,440
Speaker 1: ultimately talking about proprietary data that sometimes you have to

269
00:14:30,440 --> 00:14:33,680
Speaker 1: pay a lot of money for that isn't accessible to

270
00:14:34,120 --> 00:14:38,600
Speaker 1: mom and pop or your average retail investor, and people

271
00:14:38,920 --> 00:14:42,640
Speaker 1: sometimes think that that's unfair. How do you respond to those?

272
00:14:43,400 --> 00:14:46,400
Speaker 1: So I guess I would say that having spent a

273
00:14:46,400 --> 00:14:50,720
Speaker 1: lot of time with with the financial investors, hedge funds

274
00:14:50,720 --> 00:14:52,640
Speaker 1: as well as the mutual fund guys and in the

275
00:14:52,720 --> 00:14:56,240
Speaker 1: general financial community, that that the number of different data

276
00:14:56,280 --> 00:14:59,080
Speaker 1: sources these guys are working from in modern investing is

277
00:14:59,360 --> 00:15:02,320
Speaker 1: really pretty oppressive UM in terms of what they get

278
00:15:02,440 --> 00:15:06,280
Speaker 1: from social media, UM, what they get from folks like

279
00:15:06,560 --> 00:15:10,520
Speaker 1: or Square, from the credit card companies from US and UM.

280
00:15:10,560 --> 00:15:14,720
Speaker 1: I think it's I think it's difficult overall for individuals

281
00:15:14,800 --> 00:15:18,840
Speaker 1: to compete with that on a on a retail name

282
00:15:18,880 --> 00:15:22,240
Speaker 1: by retail name basis. UM. I think that, and I

283
00:15:22,240 --> 00:15:24,800
Speaker 1: think that's probably unfortunately or fortunately, depend on how you

284
00:15:24,840 --> 00:15:26,240
Speaker 1: look at it, going to be more and more true

285
00:15:26,240 --> 00:15:29,680
Speaker 1: going forward that individuals are going to be primarily either

286
00:15:29,880 --> 00:15:34,520
Speaker 1: in broad index funds or in funds that are managed

287
00:15:34,520 --> 00:15:38,080
Speaker 1: by people that actually do aggregate up enough investment capital

288
00:15:38,120 --> 00:15:40,720
Speaker 1: that they can pull in. There's pretty rich collection of

289
00:15:40,800 --> 00:15:43,160
Speaker 1: data because the folks we work with, it's not like

290
00:15:43,240 --> 00:15:47,720
Speaker 1: they use you know, um SEC reports plus over inside data,

291
00:15:48,040 --> 00:15:51,360
Speaker 1: they'll be pulling in literally dozens of different data sources

292
00:15:51,400 --> 00:15:55,400
Speaker 1: to create mosaics of information to to inform their thinking

293
00:15:55,400 --> 00:15:58,480
Speaker 1: about these investments. Is your client based like does it

294
00:15:58,560 --> 00:16:03,160
Speaker 1: skew to sort of quantity? Sophisticated hedge funds are like

295
00:16:03,240 --> 00:16:06,320
Speaker 1: the big mutual fund complex is also using your data

296
00:16:06,320 --> 00:16:10,480
Speaker 1: along with other things actually actually both UM, We've we've

297
00:16:10,520 --> 00:16:13,360
Speaker 1: got a really nice mix of of of mutual funds

298
00:16:13,440 --> 00:16:17,120
Speaker 1: as well as quantit edge funds and fundamentals. We actually

299
00:16:17,120 --> 00:16:20,440
Speaker 1: have by account, we have more customers on the fundamental side,

300
00:16:20,480 --> 00:16:23,960
Speaker 1: but we we get more revenue from from each of

301
00:16:24,040 --> 00:16:27,000
Speaker 1: the quant funds just because they typically if they buy

302
00:16:27,000 --> 00:16:30,320
Speaker 1: the data, will buy every single name. James, can you

303
00:16:30,320 --> 00:16:34,640
Speaker 1: give us an example of when your technology or analysis

304
00:16:34,840 --> 00:16:37,880
Speaker 1: UM really surprised you or a client, like, where it

305
00:16:37,960 --> 00:16:44,040
Speaker 1: found something really counterintuitive or something that you weren't expecting. UM.

306
00:16:44,080 --> 00:16:46,840
Speaker 1: I'll give you one one recent example where it was

307
00:16:46,880 --> 00:16:48,760
Speaker 1: it was really something. It was a little bit of

308
00:16:48,800 --> 00:16:51,240
Speaker 1: a surprise. I was surprised at how clear it was.

309
00:16:51,280 --> 00:16:54,880
Speaker 1: So we had we had heard some people say adotally

310
00:16:54,960 --> 00:16:58,960
Speaker 1: that they felt like the Internet online shopping was affecting

311
00:16:59,400 --> 00:17:03,280
Speaker 1: the lower grade malls more than the grade A malls.

312
00:17:03,320 --> 00:17:05,560
Speaker 1: The great A malls are typically the ones that will have,

313
00:17:06,240 --> 00:17:08,600
Speaker 1: you know, we're shopping is an experience where they'll have

314
00:17:08,720 --> 00:17:10,760
Speaker 1: roller coasters in the mall, and them all will have

315
00:17:10,760 --> 00:17:13,439
Speaker 1: a hundred stores, and and they'll have Santa there at

316
00:17:13,480 --> 00:17:16,440
Speaker 1: Christmas and so on, and whereas the bad C D

317
00:17:16,600 --> 00:17:18,879
Speaker 1: class malls are more like strip malls. So we we

318
00:17:18,920 --> 00:17:22,560
Speaker 1: had heard this speculation, but only as a speculation that

319
00:17:22,560 --> 00:17:25,440
Speaker 1: that there the Internet was really hurting the lower grade

320
00:17:25,440 --> 00:17:28,280
Speaker 1: malls were not really affecting the larger malls. And when

321
00:17:28,280 --> 00:17:31,320
Speaker 1: we actually aggregated the data together for the last five

322
00:17:31,400 --> 00:17:35,159
Speaker 1: years for US malls, it just immediately jumped out that

323
00:17:35,160 --> 00:17:39,440
Speaker 1: that the overall carcounts year on year kept going up

324
00:17:39,720 --> 00:17:42,280
Speaker 1: for the for the top rated malls, and and we're

325
00:17:42,320 --> 00:17:45,239
Speaker 1: gradually sinking for the C and D class malls. And

326
00:17:45,280 --> 00:17:47,520
Speaker 1: it's like, wow, I guess, I guess those guys there

327
00:17:47,520 --> 00:17:49,000
Speaker 1: when they were when they were thinking about that, they

328
00:17:49,040 --> 00:17:50,680
Speaker 1: knew what they're they knew what they were thinking about.

329
00:17:51,200 --> 00:17:53,080
Speaker 1: Can you this is sort of a dumb question, but

330
00:17:53,119 --> 00:17:54,600
Speaker 1: can you can you tell me a little more about

331
00:17:54,640 --> 00:17:57,359
Speaker 1: the process of like going from here's an idea that

332
00:17:57,400 --> 00:18:00,440
Speaker 1: we want to know how to count to having the

333
00:18:00,520 --> 00:18:02,919
Speaker 1: sort of software to account it. So, you know, you

334
00:18:02,960 --> 00:18:05,800
Speaker 1: talk a lot about cars at mall parking lots, which

335
00:18:05,840 --> 00:18:08,600
Speaker 1: is sort of a you know, colored rectangle on a

336
00:18:08,600 --> 00:18:12,280
Speaker 1: big black rectangle. Uh. Whenever I read articles about this

337
00:18:12,359 --> 00:18:15,200
Speaker 1: kind of thing, there are always these beautiful geometric pictures

338
00:18:15,440 --> 00:18:18,919
Speaker 1: and then the caption says these are you know, terrorist

339
00:18:19,000 --> 00:18:22,639
Speaker 1: fields in China, And I sometimes wonder, how do you know?

340
00:18:23,200 --> 00:18:25,639
Speaker 1: And like, is there sort of like is this a

341
00:18:25,760 --> 00:18:28,560
Speaker 1: sort of primarily like the software can kind of figure

342
00:18:28,560 --> 00:18:31,320
Speaker 1: out what stuff is? Or is this human analysts looking

343
00:18:31,359 --> 00:18:33,280
Speaker 1: at pictures and trying to teach the software how to

344
00:18:33,320 --> 00:18:36,800
Speaker 1: match patterns? Or is it sometimes human analysts getting on

345
00:18:36,840 --> 00:18:38,680
Speaker 1: a plane and saying, you know, what is this thing

346
00:18:38,680 --> 00:18:40,280
Speaker 1: that we're looking at and trying to go look at

347
00:18:40,320 --> 00:18:42,760
Speaker 1: it in person? Yeah? I actually I think that's a

348
00:18:42,760 --> 00:18:46,760
Speaker 1: great question. So, UM, we tend to start Let's say,

349
00:18:46,840 --> 00:18:49,560
Speaker 1: let's say somebody comes in and they want to track, um,

350
00:18:50,000 --> 00:18:52,639
Speaker 1: the development of wind farms in China, just as a

351
00:18:52,720 --> 00:18:56,760
Speaker 1: random example. So um, we we would start just by

352
00:18:56,800 --> 00:18:59,639
Speaker 1: pulling up the images, um, and let's see what they

353
00:18:59,680 --> 00:19:01,560
Speaker 1: look like from space? What do these wind farms look

354
00:19:01,600 --> 00:19:03,440
Speaker 1: like from space? Is it is it clear when a

355
00:19:03,520 --> 00:19:06,720
Speaker 1: human looks at them, um, where the wind farms are. UM.

356
00:19:07,080 --> 00:19:09,399
Speaker 1: If it's not, then yeah, we may have to get

357
00:19:09,440 --> 00:19:11,800
Speaker 1: a wind form expert on the phone and have them

358
00:19:11,800 --> 00:19:14,160
Speaker 1: look at the image and explain to us what these

359
00:19:14,160 --> 00:19:16,760
Speaker 1: things look like from above. So we get into the

360
00:19:16,800 --> 00:19:19,359
Speaker 1: point where where we have a few humans who are

361
00:19:19,359 --> 00:19:21,760
Speaker 1: able to actually find the object we want to count.

362
00:19:22,119 --> 00:19:24,399
Speaker 1: Then we build what we call the labeled training set,

363
00:19:24,440 --> 00:19:27,359
Speaker 1: where we have the humans actually go in and click

364
00:19:27,440 --> 00:19:29,840
Speaker 1: the mouse on this thing that we want to find,

365
00:19:29,840 --> 00:19:33,960
Speaker 1: whether it's trucks or windmills or solar panels or whatever. UM.

366
00:19:34,000 --> 00:19:37,639
Speaker 1: We build a landing, a labeled training set, often consisting

367
00:19:37,680 --> 00:19:40,439
Speaker 1: of hundreds or if we if we're doing this as

368
00:19:40,480 --> 00:19:43,120
Speaker 1: a really we want to get really high accuracy thousands

369
00:19:43,119 --> 00:19:48,119
Speaker 1: of images and UM. From that, the machine vision algorithms

370
00:19:48,240 --> 00:19:52,440
Speaker 1: can take over and they can learn, um, inductively from

371
00:19:52,480 --> 00:19:55,280
Speaker 1: those examples what this object looks like, and then we

372
00:19:55,320 --> 00:19:57,760
Speaker 1: can count, you know, a billion of the thing, which

373
00:19:57,800 --> 00:19:59,439
Speaker 1: is what we did with cars. We trained it on

374
00:19:59,480 --> 00:20:01,440
Speaker 1: a few thousand and images and then it counts that

375
00:20:01,560 --> 00:20:04,920
Speaker 1: we've now counted three point seven billion cars. The process

376
00:20:04,920 --> 00:20:08,280
Speaker 1: that takes some work. Yeah, what's the accuracy rate on cars,

377
00:20:08,320 --> 00:20:12,359
Speaker 1: like how many like shopping car return areas or or

378
00:20:12,600 --> 00:20:15,040
Speaker 1: you know, painted squares get counted as cars. Do you

379
00:20:15,119 --> 00:20:17,040
Speaker 1: have any idea We've actually gotten to be quite good

380
00:20:17,040 --> 00:20:18,800
Speaker 1: at that because that's been something we've been working on

381
00:20:18,840 --> 00:20:20,400
Speaker 1: now for a couple of years. So we're about we're

382
00:20:20,400 --> 00:20:25,840
Speaker 1: about accurate on cars. So if you train up the

383
00:20:25,880 --> 00:20:30,120
Speaker 1: analysts and managed to pinpoint things as accurately as possible,

384
00:20:30,160 --> 00:20:34,520
Speaker 1: there are still limitations to the value of that data. Right,

385
00:20:34,560 --> 00:20:37,000
Speaker 1: So you can see how many cars are parked at

386
00:20:37,040 --> 00:20:40,440
Speaker 1: shopping walls or at pet smarts or wherever, but you

387
00:20:40,640 --> 00:20:44,680
Speaker 1: can't actually tell how much people are spending once they're inside.

388
00:20:45,240 --> 00:20:49,200
Speaker 1: That's right. There's always what we call con founders, things

389
00:20:49,200 --> 00:20:50,879
Speaker 1: that things that confound you when you try to do

390
00:20:50,920 --> 00:20:53,760
Speaker 1: the analysis. So right, so, so not knowing exactly how

391
00:20:53,800 --> 00:20:56,879
Speaker 1: much people spend, not knowing whether they actually spend anything

392
00:20:56,920 --> 00:21:00,640
Speaker 1: at all are are major confounders. UM. In the case

393
00:21:00,680 --> 00:21:04,600
Speaker 1: of retail, another confounder is if you have a multilayer

394
00:21:04,680 --> 00:21:07,760
Speaker 1: parking garage at some malls, for instance, you can only

395
00:21:07,800 --> 00:21:11,159
Speaker 1: see the top level UM, and if you have somebody

396
00:21:11,160 --> 00:21:14,040
Speaker 1: going into a mall, you don't necessarily know which store

397
00:21:14,040 --> 00:21:16,480
Speaker 1: they're going to. UM that they may park by the Macy's,

398
00:21:16,520 --> 00:21:18,760
Speaker 1: but not actually shopping the Macy's, just walk straight through

399
00:21:18,760 --> 00:21:20,560
Speaker 1: it and shop at some other store. That's what my

400
00:21:20,680 --> 00:21:24,120
Speaker 1: dad does. Yeah, there's a there's a there's a variety

401
00:21:24,200 --> 00:21:28,080
Speaker 1: of of con founders, and that's why you don't get

402
00:21:28,160 --> 00:21:31,760
Speaker 1: You know, if if our data shows UM an increase

403
00:21:31,800 --> 00:21:34,800
Speaker 1: in in car accounts, it doesn't necessarily always mean there's

404
00:21:34,800 --> 00:21:37,960
Speaker 1: an increase in sales. That it simply gives you. Basically,

405
00:21:38,000 --> 00:21:40,000
Speaker 1: it loads the dice in your favor if you're if

406
00:21:40,000 --> 00:21:42,639
Speaker 1: you're an investor, what do you think the future of

407
00:21:42,640 --> 00:21:45,480
Speaker 1: this business is like in five or ten years? Is

408
00:21:45,480 --> 00:21:49,080
Speaker 1: it going to be an absolutely massive industry or does

409
00:21:49,440 --> 00:21:52,359
Speaker 1: the fact that most of this is proprietary data is

410
00:21:52,400 --> 00:21:56,679
Speaker 1: sometimes expensive, does that limit its ability to scale up? No,

411
00:21:56,840 --> 00:21:58,800
Speaker 1: I don't think so. I think there's a I think

412
00:21:58,840 --> 00:22:01,720
Speaker 1: I think it's it's mendous opportunity. And the main reason

413
00:22:01,800 --> 00:22:04,000
Speaker 1: we think that is that the availability of imagery is

414
00:22:04,040 --> 00:22:08,199
Speaker 1: only going up, So within one to two years we

415
00:22:08,240 --> 00:22:11,000
Speaker 1: expect to have daily imagery of the Earth at maybe

416
00:22:11,160 --> 00:22:13,879
Speaker 1: three to five meter pixels, so pretty course, but still

417
00:22:13,920 --> 00:22:16,680
Speaker 1: the whole Earth every day. Five years out the kind

418
00:22:16,720 --> 00:22:18,720
Speaker 1: of time frame you're talking about five to seven years out,

419
00:22:18,720 --> 00:22:22,760
Speaker 1: we expect to have you know, reasonably good resolution, you know,

420
00:22:22,800 --> 00:22:25,120
Speaker 1: a meter or less per pixel of the whole world

421
00:22:25,200 --> 00:22:28,040
Speaker 1: every day, and then we can track I'm not only

422
00:22:28,080 --> 00:22:31,120
Speaker 1: retail traffic. We can track mining. We can track manufacturing.

423
00:22:31,200 --> 00:22:34,160
Speaker 1: We can track the car manufacturers. We can track ports

424
00:22:34,600 --> 00:22:39,000
Speaker 1: and see port port work stoppages. We can see approximate

425
00:22:39,000 --> 00:22:41,960
Speaker 1: input imports and exports are in different countries. We can

426
00:22:42,000 --> 00:22:45,600
Speaker 1: basically track the physical aspects of the economy at that

427
00:22:45,720 --> 00:22:50,480
Speaker 1: at that level, and that becomes valuable um not only

428
00:22:50,600 --> 00:22:54,600
Speaker 1: for investors, but also for governments, for non governmental organizations,

429
00:22:55,240 --> 00:22:58,080
Speaker 1: on for other Fortune five companies that are trying to

430
00:22:58,119 --> 00:23:00,480
Speaker 1: plan their supply chain and understand what's going on in

431
00:23:00,480 --> 00:23:03,439
Speaker 1: the economy that surrounds them. We've also been working a

432
00:23:03,480 --> 00:23:06,600
Speaker 1: lot recently with non governmental organizations. We've been working with

433
00:23:06,640 --> 00:23:09,440
Speaker 1: the World Bank on poverty mapping so that we can

434
00:23:09,480 --> 00:23:13,040
Speaker 1: help them understand um where poverty is and where it

435
00:23:13,080 --> 00:23:15,080
Speaker 1: isn't and how it's changing. Because a lot of these

436
00:23:15,080 --> 00:23:18,920
Speaker 1: places they can only do surveys poverty surveys once a decade,

437
00:23:18,960 --> 00:23:22,080
Speaker 1: and obviously our world is changing faster than that, so

438
00:23:22,480 --> 00:23:27,199
Speaker 1: we actually think over time this becomes a foundation of

439
00:23:27,240 --> 00:23:30,480
Speaker 1: the economic analysis, of understanding the physical aspects of the

440
00:23:30,520 --> 00:23:34,399
Speaker 1: world to track economies for all kinds of purposes. And

441
00:23:34,440 --> 00:23:37,520
Speaker 1: I do think that over time, the individual what we

442
00:23:37,560 --> 00:23:40,440
Speaker 1: call signals, the individual signals like just car accounts are

443
00:23:40,480 --> 00:23:44,600
Speaker 1: just truck counts, do get do get cheaper? And then um,

444
00:23:44,640 --> 00:23:47,639
Speaker 1: what people are actually buying is and actually using is

445
00:23:47,720 --> 00:23:50,600
Speaker 1: an aggregation of all these different things that we're able

446
00:23:50,600 --> 00:23:53,520
Speaker 1: to count going forward in that future. Are you sort

447
00:23:53,560 --> 00:23:56,560
Speaker 1: of still the like analysis layer or at some point

448
00:23:57,000 --> 00:23:59,880
Speaker 1: our hedge funds saying I want the data. I want

449
00:23:59,880 --> 00:24:02,600
Speaker 1: to just you know, millions of images, and I want

450
00:24:02,680 --> 00:24:06,440
Speaker 1: to make my own proprietary signalism, my own proprietary analysis

451
00:24:06,480 --> 00:24:08,879
Speaker 1: of it. I don't think they would. I don't think

452
00:24:08,880 --> 00:24:11,000
Speaker 1: there would be the mileage for for them to take

453
00:24:11,040 --> 00:24:14,160
Speaker 1: the images. I think they some of the quant funds

454
00:24:14,160 --> 00:24:17,560
Speaker 1: are already looking at a pretty granular level at the counts.

455
00:24:17,600 --> 00:24:20,160
Speaker 1: So so some of the quant funds will actually take

456
00:24:20,640 --> 00:24:25,800
Speaker 1: you know, address date count and that's essentially the data

457
00:24:25,840 --> 00:24:27,840
Speaker 1: that we give them and they work from there. So

458
00:24:27,920 --> 00:24:30,760
Speaker 1: I could see that going forward, But the analysis is

459
00:24:30,760 --> 00:24:33,800
Speaker 1: is something that we use the same analysis routines for

460
00:24:34,119 --> 00:24:38,000
Speaker 1: the government customers, investors, insurance companies, energy companies, you know,

461
00:24:38,359 --> 00:24:40,280
Speaker 1: all of our all of our customers, we use the

462
00:24:40,359 --> 00:24:42,400
Speaker 1: same image analysis and a lot of the same data

463
00:24:42,400 --> 00:24:44,760
Speaker 1: analysis routines. So I think there's a lot of economies

464
00:24:44,760 --> 00:24:47,640
Speaker 1: of scale for us in that. But at the same time,

465
00:24:47,680 --> 00:24:50,440
Speaker 1: you write, some of these quant funds, especially get pretty

466
00:24:50,440 --> 00:24:53,199
Speaker 1: granular in the data they take from us. Okay, I

467
00:24:53,200 --> 00:24:55,760
Speaker 1: think that's a good place to leave it. James Crawford

468
00:24:55,840 --> 00:24:59,160
Speaker 1: of Orbital Insight, thank you so much for joining us. Absolutely,

469
00:24:59,200 --> 00:25:14,560
Speaker 1: thanks for all your great questions. All Right, Matt, that

470
00:25:14,640 --> 00:25:18,879
Speaker 1: was your first episode of Odd Lots. How did you

471
00:25:18,920 --> 00:25:22,280
Speaker 1: find it? I thought that was fun. I've always been

472
00:25:22,320 --> 00:25:25,840
Speaker 1: interested in these in these sort of imaging and and

473
00:25:26,080 --> 00:25:29,399
Speaker 1: proprietary data companies for some of the reasons you alluded to.

474
00:25:29,520 --> 00:25:31,919
Speaker 1: You know, I write a lot about insider trading, and

475
00:25:31,960 --> 00:25:35,200
Speaker 1: one thing I always wonder about is why people get

476
00:25:35,240 --> 00:25:38,200
Speaker 1: so upset about the lack of a level playing field

477
00:25:38,200 --> 00:25:42,119
Speaker 1: between retail investors and professional investors. And as James said,

478
00:25:42,200 --> 00:25:44,760
Speaker 1: you know, there are so many sources of data that

479
00:25:44,800 --> 00:25:48,240
Speaker 1: professional investors can rely on, you know, flying helicopters over

480
00:25:48,280 --> 00:25:51,760
Speaker 1: oil fields, flying satellites over oil fields. It seems silly

481
00:25:51,800 --> 00:25:55,280
Speaker 1: to worry about any any one source of data. Yeah,

482
00:25:55,320 --> 00:25:57,919
Speaker 1: there was one thing he said that kind of depressed me,

483
00:25:58,040 --> 00:26:01,080
Speaker 1: where it was basically like retail hell investors don't have

484
00:26:01,119 --> 00:26:04,920
Speaker 1: any hope of competing with the big guys in terms

485
00:26:04,920 --> 00:26:08,200
Speaker 1: of information flow, and so everyone, you know, mom and

486
00:26:08,240 --> 00:26:11,240
Speaker 1: pop are just going to increasingly herd into passive investing

487
00:26:11,280 --> 00:26:14,800
Speaker 1: in index funds. Like that kind of worries me a little.

488
00:26:15,119 --> 00:26:17,199
Speaker 1: Oh not me. I think that's I think that's been

489
00:26:17,240 --> 00:26:19,720
Speaker 1: true forever. I think if it's your job to invest,

490
00:26:20,240 --> 00:26:22,240
Speaker 1: and you have the tools to invest, you're going to

491
00:26:22,320 --> 00:26:24,240
Speaker 1: be better at it than someone who's doing it as

492
00:26:24,280 --> 00:26:26,639
Speaker 1: a as a hobby or is something they just you know,

493
00:26:26,720 --> 00:26:28,639
Speaker 1: do in their spare time. I don't think that you

494
00:26:28,680 --> 00:26:31,240
Speaker 1: have to herd the passive funds, though, I mean you

495
00:26:31,280 --> 00:26:34,440
Speaker 1: can hurt you know, if you're a mom and pop investor,

496
00:26:34,520 --> 00:26:36,399
Speaker 1: you can invest in the mutual funds that it sounds

497
00:26:36,440 --> 00:26:39,760
Speaker 1: like use his data, you know, alongside the hedge funds.

498
00:26:40,040 --> 00:26:43,840
Speaker 1: So you know, the point is not passive versus active,

499
00:26:43,960 --> 00:26:47,359
Speaker 1: or or retail versus professional. Really, it's that you know

500
00:26:47,480 --> 00:26:51,640
Speaker 1: there is a professional management layer that that decides where

501
00:26:51,680 --> 00:26:54,479
Speaker 1: to invest money, and as a retail investor, you can

502
00:26:54,520 --> 00:26:57,480
Speaker 1: have access to it. I guess where I get uncomfortable

503
00:26:57,680 --> 00:27:03,040
Speaker 1: is the differentiation or lack of differentiation between access to

504
00:27:03,119 --> 00:27:07,840
Speaker 1: information and intelligent analysis. Like one guy can get ahead

505
00:27:07,920 --> 00:27:12,240
Speaker 1: just because he has better access to information flow, whereas

506
00:27:12,240 --> 00:27:15,760
Speaker 1: another guy falls behind because even though he's super super smart,

507
00:27:16,160 --> 00:27:19,480
Speaker 1: he just doesn't have that data that I think that's

508
00:27:19,520 --> 00:27:23,199
Speaker 1: what depresses me about the whole thing. I hear you,

509
00:27:23,320 --> 00:27:25,520
Speaker 1: but there's a lot of data in the world, right,

510
00:27:25,520 --> 00:27:28,800
Speaker 1: I mean, there's more being generated all the time, and

511
00:27:29,200 --> 00:27:32,080
Speaker 1: it seems it seems a little hopeless to say we're

512
00:27:32,080 --> 00:27:34,119
Speaker 1: gonna we're gonna give everyone all the same data at

513
00:27:34,160 --> 00:27:37,560
Speaker 1: the same time. Because you know, I understand what you're saying.

514
00:27:37,600 --> 00:27:39,600
Speaker 1: But what would you do with all this satellite data?

515
00:27:39,640 --> 00:27:41,879
Speaker 1: You know, would you analyze it? If you're a retail

516
00:27:41,880 --> 00:27:44,119
Speaker 1: investor and you have to work a day job. Yeah,

517
00:27:44,320 --> 00:27:47,200
Speaker 1: alongside my day job at Bloomberg, I would be looking

518
00:27:47,200 --> 00:27:49,840
Speaker 1: at satellite images for hours and hours and hours on end.

519
00:27:49,960 --> 00:27:52,600
Speaker 1: That sounds that sounds totally like something I would do.

520
00:27:53,440 --> 00:27:56,560
Speaker 1: All right, Matt, thank you so much for joining us today.

521
00:27:56,600 --> 00:27:59,600
Speaker 1: That was really good fun. Thankfully, we'll have you on

522
00:27:59,640 --> 00:28:03,080
Speaker 1: again some point that all right, I'm Tracy Alloway. You

523
00:28:03,080 --> 00:28:06,360
Speaker 1: can find me on Twitter at Tracy Alloway. And I'm

524
00:28:06,400 --> 00:28:08,480
Speaker 1: Matt Levine of Bloomberg View and you can find me

525
00:28:08,520 --> 00:28:12,840
Speaker 1: on Twitter at Matt underscore Levine. Thanks for joining us, everyone,

526
00:28:13,040 --> 00:28:21,080
Speaker 1: take care, Put knowledge to work and grow your business

527
00:28:21,119 --> 00:28:25,199
Speaker 1: with c i T. From transportation to healthcare to manufacturing.

528
00:28:25,400 --> 00:28:28,800
Speaker 1: C i T offers commercial lending, leasing, and treasury management

529
00:28:28,840 --> 00:28:32,200
Speaker 1: services for small and middle market businesses. Learn more at

530
00:28:32,200 --> 00:28:34,680
Speaker 1: c i T dot com. Put Knowledge to Work