Speaker 1: But knowledge to work and grow your business with c
Speaker 1: I T. From transportation to healthcare to manufacturing, c I
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Speaker 1: T dot com put Knowledge to Work. Hello and welcome
Speaker 1: to another edition of the Odd Thoughts Podcast. I'm Tracy Alloway,
Speaker 1: executive editor of Bloomberg Markets. My co host Joe Wisenthal
Speaker 1: is away, and so I have a replacement co host
Speaker 1: for you. I'm actually really excited about this. It's Matt Levine.
Speaker 1: He is, of course, the Bloomberg View columnist and quite
Speaker 1: possibly one of the funniest, most original financial writers out there.
Speaker 1: So thanks so much Matt for joining us today. Thanks
Speaker 1: for all right, Matt, Let's start this off with a
Speaker 1: thought exercise. UM, let's say you're an investor and you're
Speaker 1: interested in investing in the shares of a company. Let's
Speaker 1: say it's something like pet Smart. How do you actually
Speaker 1: go about figuring out how well pet Smart as a
Speaker 1: business is doing well? I probably go there with my
Speaker 1: dog and see it all. Right, that sounds like a
Speaker 1: very systematic approach. Now, I probably read the financial statements right,
Speaker 1: look at the ten K you would look at their
Speaker 1: publicly available earning statements. You would try to figure out
Speaker 1: what's going on in terms of their revenue trends, things
Speaker 1: like that. If you were really fancy about it, you
Speaker 1: might go there with your dog and try to see
Speaker 1: how many customers are visiting. But that's not really going
Speaker 1: to help you if you only go to one pet Smart, Right,
Speaker 1: So what if you could do something different? What if
Speaker 1: you could actually see how many people were visiting a
Speaker 1: pet Smart or pet smarts across the country. What if
Speaker 1: you could see big data, as people like to put it,
Speaker 1: and figure out how well the pet smart business was
Speaker 1: actually doing. It? Sounds like it would be pretty helpful,
Speaker 1: all right. So on today's episode, we are going to
Speaker 1: talk to a company that is helping investors do just that,
Speaker 1: and one of the ways they're doing it is by
Speaker 1: using satellite data. So having satellite imagery that looks at
Speaker 1: things like factory activity, things like how many cars are
Speaker 1: parked out in parking lots behind pet smarts and walmarts
Speaker 1: and other retailers around the world to try to gauge
Speaker 1: how those businesses are actually doing. And I know you've
Speaker 1: written a lot about data on Wall Street how people
Speaker 1: use it. So I think you might be into this topic.
Speaker 1: That sounds great. Alright, So without further ado, let's bring
Speaker 1: in our guest for today. It is James Crawford. He
Speaker 1: is the founder and CEO of Orbital Insight. He's also
Speaker 1: a former NASA scientist, so Matt, you can ask him
Speaker 1: many robotics questions you might be into as well. All right, James,
Speaker 1: thank you so much for joining us today. Hey tre Z,
Speaker 1: very happy to be here. Maybe just to begin, you
Speaker 1: could walk us through what your company actually does, the
Speaker 1: kind of technology you employ, and how people like potential
Speaker 1: pet smart investors might find it useful. Sure, over the
Speaker 1: last few years, there's been a tremendous growth in the
Speaker 1: number of satellites over our heads, and it's interesting that
Speaker 1: the enabler for that has been a lot of the
Speaker 1: same technologies that bring you cell phones, So the miniaturization
Speaker 1: of electronics, rapid reductions in the cost of launch, and
Speaker 1: so just a lot more images are being taken now.
Speaker 1: Now taking the image that was only the first start,
Speaker 1: only the start, because once you take the image, it
Speaker 1: just sits in on a disk somewhere until somebody actually
Speaker 1: looks at it and we're getting to the point where
Speaker 1: there's way more images and there are people that want
Speaker 1: to stare at them. So, to continue your pet smart analogy,
Speaker 1: if if you, if somebody were to deliver to you
Speaker 1: a million pictures of pet smart stores, you'd be a
Speaker 1: long time looking through, flipping through all of them, trying
Speaker 1: to decide how many cars were in each one of
Speaker 1: them or what was going on in each picture. So
Speaker 1: what we really do is we complete that supply chain,
Speaker 1: if you will. We take the images from all the
Speaker 1: different satellite companies, We run them through artificial intelligence software
Speaker 1: to count cars. We can count trucks, we can count
Speaker 1: train cars, we can count ships, we can look at
Speaker 1: agricultural fields see what the productivity is likely to be.
Speaker 1: And then we aggregate up, add up all those numbers
Speaker 1: and deliver an analysis of you know, how different retailers
Speaker 1: are doing, what the corn yield in the US is
Speaker 1: likely to be, or other interesting economic questions. And who
Speaker 1: are your clients at the moment for this kind of data,
Speaker 1: and what data sets are most popular because you mentioned
Speaker 1: a whole bunch of different types of things just then,
Speaker 1: like agriculture, retail, manufacturing, economy, mix, what's most useful. So
Speaker 1: we've been going through we're a startup or b around startups,
Speaker 1: so we've been going through a prioritization exercise with our
Speaker 1: prospects of our customers and and and the first thing
Speaker 1: we built was the one you alluded to at the beginning,
Speaker 1: which is the retail car counting. So we're now covering
Speaker 1: a hundred US retailers, providing daily updates on the number
Speaker 1: of cars we're seeing in their parking lots. Now, it's
Speaker 1: important to say we don't necessarily see every store of
Speaker 1: every retailer every day. In fact, we see a small
Speaker 1: fraction of each retailer every day. So you have to
Speaker 1: look at a moving average over time to get some
Speaker 1: statistically significant picture of what's going on the different retailers.
Speaker 1: But but we we're pull in a lot of images
Speaker 1: of retailers. We've also been working on oil because there's
Speaker 1: there's so much debate right now about the price of oil,
Speaker 1: and there's so much volatility in the price of oil,
Speaker 1: and trying to understand just a basic simple question how
Speaker 1: much oil is sitting in all the storage tanks, so
Speaker 1: all the oil that's been pumped out of the ground,
Speaker 1: but not y ever find and that number goes up
Speaker 1: when too much oil is being pumped and there's not
Speaker 1: enough demand. That obviously goes down if there's a lot
Speaker 1: of demand. And so that's that's a really important, perhaps
Speaker 1: the single most important determinant of the direction of the
Speaker 1: price of oil. And it's not well known. It's pretty
Speaker 1: well known for the US, but when you look across
Speaker 1: the world, it's not well known. So the other major
Speaker 1: product that we're that we're shipping now is is tracking
Speaker 1: the oil inventory, the crude oil inventory, and then a
Speaker 1: lot of the other things I mentioned are all things
Speaker 1: that we're working on is earlier phase products that will
Speaker 1: that will have available you in future quarters. So where
Speaker 1: do where do ideas come from for things to kind
Speaker 1: of look for account Is that stuff that that you
Speaker 1: and your team comes up with, or is that client feedback.
Speaker 1: It's it's really a combination of both. Our Our favorite
Speaker 1: thing to do and and we've done a fair amount
Speaker 1: of this recently, is to get in a room with
Speaker 1: some creative portfolio managers from large financial managers from either
Speaker 1: hedge funds or mutual funds or other folks um in
Speaker 1: different places, some from wall streets. I'm from London, some
Speaker 1: from Hong Kong and just brainstorm with them. You know,
Speaker 1: it's like we asked them, what are your data gaps?
Speaker 1: What is it that you'd like to know about the
Speaker 1: world that you don't know, what would help you make
Speaker 1: better trading decisions? And then we we run that up
Speaker 1: against what is feasible in terms of what satellites can
Speaker 1: see and what they can't see, um and we come
Speaker 1: up with We've come up with a very long list
Speaker 1: of great ideas. This is the challenge of being a
Speaker 1: startup as we have way more ideas for this than
Speaker 1: we have, you know, resources to actually build, but we're
Speaker 1: knocking them down pretty fast at this point. And uh
Speaker 1: and working down the list of the of the really
Speaker 1: top priority things we think we can measure so retail
Speaker 1: like store car counting, it feels like a thing that
Speaker 1: has that people have done for a long time. You know,
Speaker 1: hedge bunds will send an analyst to their local mall
Speaker 1: and obviously you can do it on a on a
Speaker 1: very different scale. Is there is there something that couldn't
Speaker 1: have been done at all before that has made possible
Speaker 1: by satellite technology. That's like, that's that's just a totally
Speaker 1: new kind of data. It's a good question. In the
Speaker 1: case of retail, I think I think scale is tremendously
Speaker 1: important when we do correlations with say SEC reported revenue UM,
Speaker 1: there's a there's a tremendous increase in our ability to
Speaker 1: predict that with scale. And if you have just a
Speaker 1: few observations, if you just go down to look at
Speaker 1: your local store, I can tell you you're not getting
Speaker 1: a very good prediction because because a lot of these
Speaker 1: chains have, you know, thousands of stores in them, and
Speaker 1: so UM just working at scale and the satellites that
Speaker 1: have launched over the last few years, plus the artificial
Speaker 1: intelligence to be able to count literally millions of parking
Speaker 1: lots is really critical. It qualitatively changes the value of
Speaker 1: the signal. The oil signal is one that would be
Speaker 1: very hard to do without satellites because these oil tanks
Speaker 1: are located in every country in the world. So they're
Speaker 1: in Singapore, they're in Hong Kong, they are all over China,
Speaker 1: they're all over Nigeria, South America, Venezuela, the US, Europe,
Speaker 1: and you're not going to be able to see them
Speaker 1: just driving past. It's it's when you can look from
Speaker 1: above and you can actually see the shadows on the
Speaker 1: top of oil tank that you can actually get a
Speaker 1: sense of what's in them. People have in the past
Speaker 1: flown helicopters over the major oilfields in the US to
Speaker 1: measure crude oil inventory. But you're not going to fly
Speaker 1: helicopters over all the oil fields in China, or even
Speaker 1: all the oil fields in Europe and Africa and South America.
Speaker 1: So I think that might be an example of something
Speaker 1: that's just incredibly hard to do if you don't have
Speaker 1: the satellite coverage. You mentioned that when potential clients come
Speaker 1: to you with ideas for data sets, that feasibility is
Speaker 1: one of the things that you consider. Are there any
Speaker 1: other considerations that you have to take into account, like
Speaker 1: privacy or trade secrecy, Like if a hedge one comes
Speaker 1: to you with with an idea saying, for instance, like
Speaker 1: they want to figure out the future price of hog futures,
Speaker 1: so they want to look in the backyards of every
Speaker 1: American family and figure out whether or not they're buying
Speaker 1: barbecue sets or something like that. Would you do it?
Speaker 1: That's interesting question. I don't think you could see that.
Speaker 1: So the so the legal limit for images in the
Speaker 1: US is thirty centimeter pixels. That's about the size at
Speaker 1: the top of your laptop. So you can tell cars.
Speaker 1: You can see you know how many cars are parked
Speaker 1: in people's driveways. You can see how many cars are
Speaker 1: parked at Walmart. You can't really tell whether it's a
Speaker 1: Ford Fiesta or or a Mazda in three or something. Um,
Speaker 1: you can roughly tell a car from a truck. I
Speaker 1: don't think you can tell whether somebody's got a barbecue set.
Speaker 1: Generally speaking, we work at such a such a broad
Speaker 1: level of aggregation, so we look at We might look at,
Speaker 1: you know, how many questions similar to what you're asking.
Speaker 1: You might look at how many solar panels have been
Speaker 1: installed in all the roofs and all of Colorado, and
Speaker 1: how fast has that grown over the last five years.
Speaker 1: UM that that tends to be the level of aggregation
Speaker 1: that we work. The imagery, UM doesn't. It's it's hard
Speaker 1: to get enough imagery to work at a very low
Speaker 1: level of granularity. And you have what you said, some
Speaker 1: sometimes privacy concerns, although our our resolution is so poor,
Speaker 1: I don't think that's a major issue. So usually the
Speaker 1: limitation is do we have enough imagery? And is the
Speaker 1: imagery of sufficiently high resolution to see whatever it is
Speaker 1: that we want to count, and then we count it
Speaker 1: as I say, very coarse levels of aggregation for investors.
Speaker 1: But even if you're aggregating the data, if it's for
Speaker 1: something like say manufacturing activity in China, where the government
Speaker 1: publishes official statistics, it publishes p M, I is that
Speaker 1: a lot of people mistrust. I mean, I'm sure China
Speaker 1: doesn't necessarily want a bunch of satellites pointed at it
Speaker 1: saying actually, it looks like activity from your manufacturing sector
Speaker 1: is slowing a lot more than official figures suggest. Does
Speaker 1: that ever come up as an issue? No, not really,
Speaker 1: because the control of the satellites rests in the country
Speaker 1: that launched the satellites. So we are mostly using salaries
Speaker 1: and satellites from all over the world, but the majority
Speaker 1: of them are flown out of either the U S. Canada,
Speaker 1: or Europe, and then a few other countries as well.
Speaker 1: But there's no because when you put a satellite and
Speaker 1: lower thor of it, it necessarily passes over every square
Speaker 1: foot of the Earth about every two weeks, that in
Speaker 1: each individual satellite. So the government of China can't control this.
Speaker 1: The only restriction the US government imposes generally is if
Speaker 1: there's areas where US troops are in active combat, satellites
Speaker 1: that are run by US companies are not allowed to
Speaker 1: distribute the imagery of those regions. But that's incredibly small
Speaker 1: percentage of the world. UM, So generally speaking, now this
Speaker 1: is not a problem. It's it's a matter of providing
Speaker 1: visibility for everybody. And we don't single out any particular
Speaker 1: country in a particular industry. Um. You know, we're trying
Speaker 1: to understand, you know, very broad trends and and provide
Speaker 1: everybody a better insight into what's going on in the world.
Speaker 1: Is your product sort of reports and insight and analysis,
Speaker 1: or are you, like in some cases like feeding raw
Speaker 1: data to algorithmic trading firms, Like like, are people coming
Speaker 1: to you for kind of like rass signals or for
Speaker 1: for the higher level insight. It's it's actually so both, um,
Speaker 1: And it tends to be as your question implies, it
Speaker 1: tends to be more the quantitative firms that want the
Speaker 1: raw data, and um, a lot of the more fundamental
Speaker 1: firms there are a little bit more interested in in
Speaker 1: aggregator results charts and graphs that actually give them some
Speaker 1: higher level insights into what the data is saying, how
Speaker 1: much do you actually charge for this data? Yeah, unfortunately
Speaker 1: we don't. We don't give that out publicly, and it
Speaker 1: and it and it varies a lot by by them,
Speaker 1: in the case of retailers, for instance, by how many
Speaker 1: retailers um the individual customer wants to track. Okay, we
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Speaker 1: dot com put knowledge to Work. Okay, we're back with
Speaker 1: James Crawford. He is the founder and CEO of Orbital Insight,
Speaker 1: and we are talking satellite data and analysis how investors
Speaker 1: on Wall Street can actually use it. You know, James,
Speaker 1: I just asked you about the cost, and this gets
Speaker 1: to I think one of the issues that people sometimes
Speaker 1: have with these sorts of businesses, which is that we're
Speaker 1: ultimately talking about proprietary data that sometimes you have to
Speaker 1: pay a lot of money for that isn't accessible to
Speaker 1: mom and pop or your average retail investor, and people
Speaker 1: sometimes think that that's unfair. How do you respond to those?
Speaker 1: So I guess I would say that having spent a
Speaker 1: lot of time with with the financial investors, hedge funds
Speaker 1: as well as the mutual fund guys and in the
Speaker 1: general financial community, that that the number of different data
Speaker 1: sources these guys are working from in modern investing is
Speaker 1: really pretty oppressive UM in terms of what they get
Speaker 1: from social media, UM, what they get from folks like
Speaker 1: or Square, from the credit card companies from US and UM.
Speaker 1: I think it's I think it's difficult overall for individuals
Speaker 1: to compete with that on a on a retail name
Speaker 1: by retail name basis. UM. I think that, and I
Speaker 1: think that's probably unfortunately or fortunately, depend on how you
Speaker 1: look at it, going to be more and more true
Speaker 1: going forward that individuals are going to be primarily either
Speaker 1: in broad index funds or in funds that are managed
Speaker 1: by people that actually do aggregate up enough investment capital
Speaker 1: that they can pull in. There's pretty rich collection of
Speaker 1: data because the folks we work with, it's not like
Speaker 1: they use you know, um SEC reports plus over inside data,
Speaker 1: they'll be pulling in literally dozens of different data sources
Speaker 1: to create mosaics of information to to inform their thinking
Speaker 1: about these investments. Is your client based like does it
Speaker 1: skew to sort of quantity? Sophisticated hedge funds are like
Speaker 1: the big mutual fund complex is also using your data
Speaker 1: along with other things actually actually both UM, We've we've
Speaker 1: got a really nice mix of of of mutual funds
Speaker 1: as well as quantit edge funds and fundamentals. We actually
Speaker 1: have by account, we have more customers on the fundamental side,
Speaker 1: but we we get more revenue from from each of
Speaker 1: the quant funds just because they typically if they buy
Speaker 1: the data, will buy every single name. James, can you
Speaker 1: give us an example of when your technology or analysis
Speaker 1: UM really surprised you or a client, like, where it
Speaker 1: found something really counterintuitive or something that you weren't expecting. UM.
Speaker 1: I'll give you one one recent example where it was
Speaker 1: it was really something. It was a little bit of
Speaker 1: a surprise. I was surprised at how clear it was.
Speaker 1: So we had we had heard some people say adotally
Speaker 1: that they felt like the Internet online shopping was affecting
Speaker 1: the lower grade malls more than the grade A malls.
Speaker 1: The great A malls are typically the ones that will have,
Speaker 1: you know, we're shopping is an experience where they'll have
Speaker 1: roller coasters in the mall, and them all will have
Speaker 1: a hundred stores, and and they'll have Santa there at
Speaker 1: Christmas and so on, and whereas the bad C D
Speaker 1: class malls are more like strip malls. So we we
Speaker 1: had heard this speculation, but only as a speculation that
Speaker 1: that there the Internet was really hurting the lower grade
Speaker 1: malls were not really affecting the larger malls. And when
Speaker 1: we actually aggregated the data together for the last five
Speaker 1: years for US malls, it just immediately jumped out that
Speaker 1: that the overall carcounts year on year kept going up
Speaker 1: for the for the top rated malls, and and we're
Speaker 1: gradually sinking for the C and D class malls. And
Speaker 1: it's like, wow, I guess, I guess those guys there
Speaker 1: when they were when they were thinking about that, they
Speaker 1: knew what they're they knew what they were thinking about.
Speaker 1: Can you this is sort of a dumb question, but
Speaker 1: can you can you tell me a little more about
Speaker 1: the process of like going from here's an idea that
Speaker 1: we want to know how to count to having the
Speaker 1: sort of software to account it. So, you know, you
Speaker 1: talk a lot about cars at mall parking lots, which
Speaker 1: is sort of a you know, colored rectangle on a
Speaker 1: big black rectangle. Uh. Whenever I read articles about this
Speaker 1: kind of thing, there are always these beautiful geometric pictures
Speaker 1: and then the caption says these are you know, terrorist
Speaker 1: fields in China, And I sometimes wonder, how do you know?
Speaker 1: And like, is there sort of like is this a
Speaker 1: sort of primarily like the software can kind of figure
Speaker 1: out what stuff is? Or is this human analysts looking
Speaker 1: at pictures and trying to teach the software how to
Speaker 1: match patterns? Or is it sometimes human analysts getting on
Speaker 1: a plane and saying, you know, what is this thing
Speaker 1: that we're looking at and trying to go look at
Speaker 1: it in person? Yeah? I actually I think that's a
Speaker 1: great question. So, UM, we tend to start Let's say,
Speaker 1: let's say somebody comes in and they want to track, um,
Speaker 1: the development of wind farms in China, just as a
Speaker 1: random example. So um, we we would start just by
Speaker 1: pulling up the images, um, and let's see what they
Speaker 1: look like from space? What do these wind farms look
Speaker 1: like from space? Is it is it clear when a
Speaker 1: human looks at them, um, where the wind farms are. UM.
Speaker 1: If it's not, then yeah, we may have to get
Speaker 1: a wind form expert on the phone and have them
Speaker 1: look at the image and explain to us what these
Speaker 1: things look like from above. So we get into the
Speaker 1: point where where we have a few humans who are
Speaker 1: able to actually find the object we want to count.
Speaker 1: Then we build what we call the labeled training set,
Speaker 1: where we have the humans actually go in and click
Speaker 1: the mouse on this thing that we want to find,
Speaker 1: whether it's trucks or windmills or solar panels or whatever. UM.
Speaker 1: We build a landing, a labeled training set, often consisting
Speaker 1: of hundreds or if we if we're doing this as
Speaker 1: a really we want to get really high accuracy thousands
Speaker 1: of images and UM. From that, the machine vision algorithms
Speaker 1: can take over and they can learn, um, inductively from
Speaker 1: those examples what this object looks like, and then we
Speaker 1: can count, you know, a billion of the thing, which
Speaker 1: is what we did with cars. We trained it on
Speaker 1: a few thousand and images and then it counts that
Speaker 1: we've now counted three point seven billion cars. The process
Speaker 1: that takes some work. Yeah, what's the accuracy rate on cars,
Speaker 1: like how many like shopping car return areas or or
Speaker 1: you know, painted squares get counted as cars. Do you
Speaker 1: have any idea We've actually gotten to be quite good
Speaker 1: at that because that's been something we've been working on
Speaker 1: now for a couple of years. So we're about we're
Speaker 1: about accurate on cars. So if you train up the
Speaker 1: analysts and managed to pinpoint things as accurately as possible,
Speaker 1: there are still limitations to the value of that data. Right,
Speaker 1: So you can see how many cars are parked at
Speaker 1: shopping walls or at pet smarts or wherever, but you
Speaker 1: can't actually tell how much people are spending once they're inside.
Speaker 1: That's right. There's always what we call con founders, things
Speaker 1: that things that confound you when you try to do
Speaker 1: the analysis. So right, so, so not knowing exactly how
Speaker 1: much people spend, not knowing whether they actually spend anything
Speaker 1: at all are are major confounders. UM. In the case
Speaker 1: of retail, another confounder is if you have a multilayer
Speaker 1: parking garage at some malls, for instance, you can only
Speaker 1: see the top level UM, and if you have somebody
Speaker 1: going into a mall, you don't necessarily know which store
Speaker 1: they're going to. UM that they may park by the Macy's,
Speaker 1: but not actually shopping the Macy's, just walk straight through
Speaker 1: it and shop at some other store. That's what my
Speaker 1: dad does. Yeah, there's a there's a there's a variety
Speaker 1: of of con founders, and that's why you don't get
Speaker 1: You know, if if our data shows UM an increase
Speaker 1: in in car accounts, it doesn't necessarily always mean there's
Speaker 1: an increase in sales. That it simply gives you. Basically,
Speaker 1: it loads the dice in your favor if you're if
Speaker 1: you're an investor, what do you think the future of
Speaker 1: this business is like in five or ten years? Is
Speaker 1: it going to be an absolutely massive industry or does
Speaker 1: the fact that most of this is proprietary data is
Speaker 1: sometimes expensive, does that limit its ability to scale up? No,
Speaker 1: I don't think so. I think there's a I think
Speaker 1: I think it's it's mendous opportunity. And the main reason
Speaker 1: we think that is that the availability of imagery is
Speaker 1: only going up, So within one to two years we
Speaker 1: expect to have daily imagery of the Earth at maybe
Speaker 1: three to five meter pixels, so pretty course, but still
Speaker 1: the whole Earth every day. Five years out the kind
Speaker 1: of time frame you're talking about five to seven years out,
Speaker 1: we expect to have you know, reasonably good resolution, you know,
Speaker 1: a meter or less per pixel of the whole world
Speaker 1: every day, and then we can track I'm not only
Speaker 1: retail traffic. We can track mining. We can track manufacturing.
Speaker 1: We can track the car manufacturers. We can track ports
Speaker 1: and see port port work stoppages. We can see approximate
Speaker 1: input imports and exports are in different countries. We can
Speaker 1: basically track the physical aspects of the economy at that
Speaker 1: at that level, and that becomes valuable um not only
Speaker 1: for investors, but also for governments, for non governmental organizations,
Speaker 1: on for other Fortune five companies that are trying to
Speaker 1: plan their supply chain and understand what's going on in
Speaker 1: the economy that surrounds them. We've also been working a
Speaker 1: lot recently with non governmental organizations. We've been working with
Speaker 1: the World Bank on poverty mapping so that we can
Speaker 1: help them understand um where poverty is and where it
Speaker 1: isn't and how it's changing. Because a lot of these
Speaker 1: places they can only do surveys poverty surveys once a decade,
Speaker 1: and obviously our world is changing faster than that, so
Speaker 1: we actually think over time this becomes a foundation of
Speaker 1: the economic analysis, of understanding the physical aspects of the
Speaker 1: world to track economies for all kinds of purposes. And
Speaker 1: I do think that over time, the individual what we
Speaker 1: call signals, the individual signals like just car accounts are
Speaker 1: just truck counts, do get do get cheaper? And then um,
Speaker 1: what people are actually buying is and actually using is
Speaker 1: an aggregation of all these different things that we're able
Speaker 1: to count going forward in that future. Are you sort
Speaker 1: of still the like analysis layer or at some point
Speaker 1: our hedge funds saying I want the data. I want
Speaker 1: to just you know, millions of images, and I want
Speaker 1: to make my own proprietary signalism, my own proprietary analysis
Speaker 1: of it. I don't think they would. I don't think
Speaker 1: there would be the mileage for for them to take
Speaker 1: the images. I think they some of the quant funds
Speaker 1: are already looking at a pretty granular level at the counts.
Speaker 1: So so some of the quant funds will actually take
Speaker 1: you know, address date count and that's essentially the data
Speaker 1: that we give them and they work from there. So
Speaker 1: I could see that going forward, But the analysis is
Speaker 1: is something that we use the same analysis routines for
Speaker 1: the government customers, investors, insurance companies, energy companies, you know,
Speaker 1: all of our all of our customers, we use the
Speaker 1: same image analysis and a lot of the same data
Speaker 1: analysis routines. So I think there's a lot of economies
Speaker 1: of scale for us in that. But at the same time,
Speaker 1: you write, some of these quant funds, especially get pretty
Speaker 1: granular in the data they take from us. Okay, I
Speaker 1: think that's a good place to leave it. James Crawford
Speaker 1: of Orbital Insight, thank you so much for joining us. Absolutely,
Speaker 1: thanks for all your great questions. All Right, Matt, that
Speaker 1: was your first episode of Odd Lots. How did you
Speaker 1: find it? I thought that was fun. I've always been
Speaker 1: interested in these in these sort of imaging and and
Speaker 1: proprietary data companies for some of the reasons you alluded to.
Speaker 1: You know, I write a lot about insider trading, and
Speaker 1: one thing I always wonder about is why people get
Speaker 1: so upset about the lack of a level playing field
Speaker 1: between retail investors and professional investors. And as James said,
Speaker 1: you know, there are so many sources of data that
Speaker 1: professional investors can rely on, you know, flying helicopters over
Speaker 1: oil fields, flying satellites over oil fields. It seems silly
Speaker 1: to worry about any any one source of data. Yeah,
Speaker 1: there was one thing he said that kind of depressed me,
Speaker 1: where it was basically like retail hell investors don't have
Speaker 1: any hope of competing with the big guys in terms
Speaker 1: of information flow, and so everyone, you know, mom and
Speaker 1: pop are just going to increasingly herd into passive investing
Speaker 1: in index funds. Like that kind of worries me a little.
Speaker 1: Oh not me. I think that's I think that's been
Speaker 1: true forever. I think if it's your job to invest,
Speaker 1: and you have the tools to invest, you're going to
Speaker 1: be better at it than someone who's doing it as
Speaker 1: a as a hobby or is something they just you know,
Speaker 1: do in their spare time. I don't think that you
Speaker 1: have to herd the passive funds, though, I mean you
Speaker 1: can hurt you know, if you're a mom and pop investor,
Speaker 1: you can invest in the mutual funds that it sounds
Speaker 1: like use his data, you know, alongside the hedge funds.
Speaker 1: So you know, the point is not passive versus active,
Speaker 1: or or retail versus professional. Really, it's that you know
Speaker 1: there is a professional management layer that that decides where
Speaker 1: to invest money, and as a retail investor, you can
Speaker 1: have access to it. I guess where I get uncomfortable
Speaker 1: is the differentiation or lack of differentiation between access to
Speaker 1: information and intelligent analysis. Like one guy can get ahead
Speaker 1: just because he has better access to information flow, whereas
Speaker 1: another guy falls behind because even though he's super super smart,
Speaker 1: he just doesn't have that data that I think that's
Speaker 1: what depresses me about the whole thing. I hear you,
Speaker 1: but there's a lot of data in the world, right,
Speaker 1: I mean, there's more being generated all the time, and
Speaker 1: it seems it seems a little hopeless to say we're
Speaker 1: gonna we're gonna give everyone all the same data at
Speaker 1: the same time. Because you know, I understand what you're saying.
Speaker 1: But what would you do with all this satellite data?
Speaker 1: You know, would you analyze it? If you're a retail
Speaker 1: investor and you have to work a day job. Yeah,
Speaker 1: alongside my day job at Bloomberg, I would be looking
Speaker 1: at satellite images for hours and hours and hours on end.
Speaker 1: That sounds that sounds totally like something I would do.
Speaker 1: All right, Matt, thank you so much for joining us today.
Speaker 1: That was really good fun. Thankfully, we'll have you on
Speaker 1: again some point that all right, I'm Tracy Alloway. You
Speaker 1: can find me on Twitter at Tracy Alloway. And I'm
Speaker 1: Matt Levine of Bloomberg View and you can find me
Speaker 1: on Twitter at Matt underscore Levine. Thanks for joining us, everyone,
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