46: Space Robots Are Helping Hedge Funds Invest

for you. I'm actually really excited about this. It's Matt Levine. He is, of course, the Bloomberg View columnist and quite possibly one of the funniest, most original financial writers out there. So thanks so much Matt for joining us today. Thanks for all right, Matt, Let's start this off with a thought exercise. UM, let's say you're an investor and you're interested in investing in the shares of a company. Let's

say it's something like pet Smart. How do you actually go about figuring out how well pet Smart as a business is doing well? I probably go there with my dog and see it all. Right, that sounds like a very systematic approach. Now, I probably read the financial statements right, look at the ten K you would look at their publicly available earning statements. You would try to figure out what's going on in terms of their revenue trends, things

like that. If you were really fancy about it, you might go there with your dog and try to see how many customers are visiting. But that's not really going to help you if you only go to one pet Smart, Right, So what if you could do something different? What if you could actually see how many people were visiting a pet Smart or pet smarts across the country. What if you could see big data, as people like to put it, and figure out how well the pet smart business was

actually doing. It? Sounds like it would be pretty helpful, all right. So on today's episode, we are going to talk to a company that is helping investors do just that, and one of the ways they're doing it is by using satellite data. So having satellite imagery that looks at things like factory activity, things like how many cars are parked out in parking lots behind pet smarts and walmarts and other retailers around the world to try to gauge

how those businesses are actually doing. And I know you've written a lot about data on Wall Street how people use it. So I think you might be into this topic. That sounds great. Alright, So without further ado, let's bring in our guest for today. It is James Crawford. He is the founder and CEO of Orbital Insight. He's also a former NASA scientist, so Matt, you can ask him many robotics questions you might be into as well. All right, James, thank you so much for joining us today. Hey tre Z,

very happy to be here. Maybe just to begin, you could walk us through what your company actually does, the kind of technology you employ, and how people like potential

pet smart investors might find it useful. Sure, over the last few years, there's been a tremendous growth in the number of satellites over our heads, and it's interesting that the enabler for that has been a lot of the same technologies that bring you cell phones, So the miniaturization of electronics, rapid reductions in the cost of launch, and so just a lot more images are being taken now.

Now taking the image that was only the first start, only the start, because once you take the image, it just sits in on a disk somewhere until somebody actually looks at it and we're getting to the point where there's way more images and there are people that want

to stare at them. So, to continue your pet smart analogy, if if you, if somebody were to deliver to you a million pictures of pet smart stores, you'd be a long time looking through, flipping through all of them, trying to decide how many cars were in each one of them or what was going on in each picture. So what we really do is we complete that supply chain, if you will. We take the images from all the different satellite companies, We run them through artificial intelligence software

to count cars. We can count trucks, we can count train cars, we can count ships, we can look at agricultural fields see what the productivity is likely to be. And then we aggregate up, add up all those numbers and deliver an analysis of you know, how different retailers are doing, what the corn yield in the US is

likely to be, or other interesting economic questions. And who are your clients at the moment for this kind of data, and what data sets are most popular because you mentioned a whole bunch of different types of things just then,

like agriculture, retail, manufacturing, economy, mix, what's most useful. So we've been going through we're a startup or b around startups, so we've been going through a prioritization exercise with our prospects of our customers and and and the first thing we built was the one you alluded to at the beginning, which is the retail car counting. So we're now covering a hundred US retailers, providing daily updates on the number

of cars we're seeing in their parking lots. Now, it's important to say we don't necessarily see every store of every retailer every day. In fact, we see a small fraction of each retailer every day. So you have to look at a moving average over time to get some statistically significant picture of what's going on the different retailers. But but we we're pull in a lot of images

of retailers. We've also been working on oil because there's there's so much debate right now about the price of oil, and there's so much volatility in the price of oil, and trying to understand just a basic simple question how much oil is sitting in all the storage tanks, so all the oil that's been pumped out of the ground, but not y ever find and that number goes up when too much oil is being pumped and there's not enough demand. That obviously goes down if there's a lot

of demand. And so that's that's a really important, perhaps the single most important determinant of the direction of the price of oil. And it's not well known. It's pretty well known for the US, but when you look across the world, it's not well known. So the other major product that we're that we're shipping now is is tracking the oil inventory, the crude oil inventory, and then a lot of the other things I mentioned are all things that we're working on is earlier phase products that will

that will have available you in future quarters. So where do where do ideas come from for things to kind of look for account Is that stuff that that you and your team comes up with, or is that client feedback. It's it's really a combination of both. Our Our favorite thing to do and and we've done a fair amount of this recently, is to get in a room with some creative portfolio managers from large financial managers from either hedge funds or mutual funds or other folks um in

different places, some from wall streets. I'm from London, some from Hong Kong and just brainstorm with them. You know, it's like we asked them, what are your data gaps? What is it that you'd like to know about the world that you don't know, what would help you make better trading decisions? And then we we run that up against what is feasible in terms of what satellites can see and what they can't see, um and we come up with We've come up with a very long list

of great ideas. This is the challenge of being a startup as we have way more ideas for this than we have, you know, resources to actually build, but we're knocking them down pretty fast at this point. And uh and working down the list of the of the really top priority things we think we can measure so retail like store car counting, it feels like a thing that

has that people have done for a long time. You know, hedge bunds will send an analyst to their local mall and obviously you can do it on a on a very different scale. Is there is there something that couldn't have been done at all before that has made possible by satellite technology. That's like, that's that's just a totally

new kind of data. It's a good question. In the case of retail, I think I think scale is tremendously important when we do correlations with say SEC reported revenue UM, there's a there's a tremendous increase in our ability to

predict that with scale. And if you have just a few observations, if you just go down to look at your local store, I can tell you you're not getting a very good prediction because because a lot of these chains have, you know, thousands of stores in them, and so UM just working at scale and the satellites that have launched over the last few years, plus the artificial intelligence to be able to count literally millions of parking lots is really critical. It qualitatively changes the value of

the signal. The oil signal is one that would be very hard to do without satellites because these oil tanks are located in every country in the world. So they're in Singapore, they're in Hong Kong, they are all over China, they're all over Nigeria, South America, Venezuela, the US, Europe, and you're not going to be able to see them just driving past. It's it's when you can look from above and you can actually see the shadows on the top of oil tank that you can actually get a

sense of what's in them. People have in the past flown helicopters over the major oilfields in the US to measure crude oil inventory. But you're not going to fly helicopters over all the oil fields in China, or even all the oil fields in Europe and Africa and South America. So I think that might be an example of something that's just incredibly hard to do if you don't have the satellite coverage. You mentioned that when potential clients come to you with ideas for data sets, that feasibility is

one of the things that you consider. Are there any other considerations that you have to take into account, like privacy or trade secrecy, Like if a hedge one comes to you with with an idea saying, for instance, like they want to figure out the future price of hog futures, so they want to look in the backyards of every American family and figure out whether or not they're buying barbecue sets or something like that. Would you do it? That's interesting question. I don't think you could see that.

So the so the legal limit for images in the US is thirty centimeter pixels. That's about the size at the top of your laptop. So you can tell cars. You can see you know how many cars are parked in people's driveways. You can see how many cars are parked at Walmart. You can't really tell whether it's a Ford Fiesta or or a Mazda in three or something. Um, you can roughly tell a car from a truck. I don't think you can tell whether somebody's got a barbecue set.

Generally speaking, we work at such a such a broad level of aggregation, so we look at We might look at, you know, how many questions similar to what you're asking. You might look at how many solar panels have been installed in all the roofs and all of Colorado, and how fast has that grown over the last five years. UM that that tends to be the level of aggregation that we work. The imagery, UM doesn't. It's it's hard to get enough imagery to work at a very low

level of granularity. And you have what you said, some sometimes privacy concerns, although our our resolution is so poor, I don't think that's a major issue. So usually the limitation is do we have enough imagery? And is the imagery of sufficiently high resolution to see whatever it is that we want to count, and then we count it as I say, very coarse levels of aggregation for investors.

But even if you're aggregating the data, if it's for something like say manufacturing activity in China, where the government publishes official statistics, it publishes p M, I is that a lot of people mistrust. I mean, I'm sure China doesn't necessarily want a bunch of satellites pointed at it saying actually, it looks like activity from your manufacturing sector is slowing a lot more than official figures suggest. Does

that ever come up as an issue? No, not really, because the control of the satellites rests in the country that launched the satellites. So we are mostly using salaries and satellites from all over the world, but the majority of them are flown out of either the U S. Canada, or Europe, and then a few other countries as well. But there's no because when you put a satellite and lower thor of it, it necessarily passes over every square foot of the Earth about every two weeks, that in

each individual satellite. So the government of China can't control this. The only restriction the US government imposes generally is if there's areas where US troops are in active combat, satellites that are run by US companies are not allowed to distribute the imagery of those regions. But that's incredibly small percentage of the world. UM, So generally speaking, now this is not a problem. It's it's a matter of providing visibility for everybody. And we don't single out any particular

country in a particular industry. Um. You know, we're trying to understand, you know, very broad trends and and provide everybody a better insight into what's going on in the world. Is your product sort of reports and insight and analysis, or are you, like in some cases like feeding raw data to algorithmic trading firms, Like like, are people coming to you for kind of like rass signals or for

for the higher level insight. It's it's actually so both, um, And it tends to be as your question implies, it tends to be more the quantitative firms that want the raw data, and um, a lot of the more fundamental firms there are a little bit more interested in in aggregator results charts and graphs that actually give them some higher level insights into what the data is saying, how much do you actually charge for this data? Yeah, unfortunately

we don't. We don't give that out publicly, and it and it and it varies a lot by by them, in the case of retailers, for instance, by how many retailers um the individual customer wants to track. Okay, we are going to take a short break for a message from our sponsor. We'll be back in one second. But knowledge to work and grow your business with c i T from transportation to healthcare to manufacturing. C i T offers commercial lending, leasing, and treasury management services for small

and middle market businesses. Learn more at c i T dot com put knowledge to Work. Okay, we're back with James Crawford. He is the founder and CEO of Orbital Insight, and we are talking satellite data and analysis how investors

on Wall Street can actually use it. You know, James, I just asked you about the cost, and this gets to I think one of the issues that people sometimes have with these sorts of businesses, which is that we're ultimately talking about proprietary data that sometimes you have to pay a lot of money for that isn't accessible to mom and pop or your average retail investor, and people sometimes think that that's unfair. How do you respond to those?

So I guess I would say that having spent a lot of time with with the financial investors, hedge funds as well as the mutual fund guys and in the general financial community, that that the number of different data sources these guys are working from in modern investing is really pretty oppressive UM in terms of what they get from social media, UM, what they get from folks like or Square, from the credit card companies from US and UM.

I think it's I think it's difficult overall for individuals to compete with that on a on a retail name by retail name basis. UM. I think that, and I think that's probably unfortunately or fortunately, depend on how you look at it, going to be more and more true going forward that individuals are going to be primarily either in broad index funds or in funds that are managed by people that actually do aggregate up enough investment capital

that they can pull in. There's pretty rich collection of data because the folks we work with, it's not like they use you know, um SEC reports plus over inside data, they'll be pulling in literally dozens of different data sources to create mosaics of information to to inform their thinking about these investments. Is your client based like does it

skew to sort of quantity? Sophisticated hedge funds are like the big mutual fund complex is also using your data along with other things actually actually both UM, We've we've got a really nice mix of of of mutual funds as well as quantit edge funds and fundamentals. We actually have by account, we have more customers on the fundamental side, but we we get more revenue from from each of the quant funds just because they typically if they buy

the data, will buy every single name. James, can you give us an example of when your technology or analysis UM really surprised you or a client, like, where it found something really counterintuitive or something that you weren't expecting. UM. I'll give you one one recent example where it was it was really something. It was a little bit of a surprise. I was surprised at how clear it was.

So we had we had heard some people say adotally that they felt like the Internet online shopping was affecting the lower grade malls more than the grade A malls. The great A malls are typically the ones that will have, you know, we're shopping is an experience where they'll have roller coasters in the mall, and them all will have a hundred stores, and and they'll have Santa there at Christmas and so on, and whereas the bad C D

class malls are more like strip malls. So we we had heard this speculation, but only as a speculation that that there the Internet was really hurting the lower grade malls were not really affecting the larger malls. And when we actually aggregated the data together for the last five years for US malls, it just immediately jumped out that that the overall carcounts year on year kept going up for the for the top rated malls, and and we're

gradually sinking for the C and D class malls. And it's like, wow, I guess, I guess those guys there when they were when they were thinking about that, they knew what they're they knew what they were thinking about. Can you this is sort of a dumb question, but can you can you tell me a little more about the process of like going from here's an idea that we want to know how to count to having the

sort of software to account it. So, you know, you talk a lot about cars at mall parking lots, which is sort of a you know, colored rectangle on a big black rectangle. Uh. Whenever I read articles about this kind of thing, there are always these beautiful geometric pictures and then the caption says these are you know, terrorist fields in China, And I sometimes wonder, how do you know? And like, is there sort of like is this a sort of primarily like the software can kind of figure

out what stuff is? Or is this human analysts looking at pictures and trying to teach the software how to match patterns? Or is it sometimes human analysts getting on a plane and saying, you know, what is this thing that we're looking at and trying to go look at it in person? Yeah? I actually I think that's a great question. So, UM, we tend to start Let's say, let's say somebody comes in and they want to track, um, the development of wind farms in China, just as a

random example. So um, we we would start just by pulling up the images, um, and let's see what they look like from space? What do these wind farms look like from space? Is it is it clear when a human looks at them, um, where the wind farms are. UM. If it's not, then yeah, we may have to get a wind form expert on the phone and have them look at the image and explain to us what these

things look like from above. So we get into the point where where we have a few humans who are able to actually find the object we want to count. Then we build what we call the labeled training set, where we have the humans actually go in and click the mouse on this thing that we want to find,

whether it's trucks or windmills or solar panels or whatever. UM. We build a landing, a labeled training set, often consisting of hundreds or if we if we're doing this as a really we want to get really high accuracy thousands of images and UM. From that, the machine vision algorithms can take over and they can learn, um, inductively from those examples what this object looks like, and then we can count, you know, a billion of the thing, which

is what we did with cars. We trained it on a few thousand and images and then it counts that we've now counted three point seven billion cars. The process that takes some work. Yeah, what's the accuracy rate on cars, like how many like shopping car return areas or or you know, painted squares get counted as cars. Do you have any idea We've actually gotten to be quite good at that because that's been something we've been working on now for a couple of years. So we're about we're

about accurate on cars. So if you train up the analysts and managed to pinpoint things as accurately as possible, there are still limitations to the value of that data. Right, So you can see how many cars are parked at shopping walls or at pet smarts or wherever, but you can't actually tell how much people are spending once they're inside. That's right. There's always what we call con founders, things that things that confound you when you try to do

the analysis. So right, so, so not knowing exactly how much people spend, not knowing whether they actually spend anything at all are are major confounders. UM. In the case of retail, another confounder is if you have a multilayer parking garage at some malls, for instance, you can only see the top level UM, and if you have somebody going into a mall, you don't necessarily know which store

they're going to. UM that they may park by the Macy's, but not actually shopping the Macy's, just walk straight through it and shop at some other store. That's what my dad does. Yeah, there's a there's a there's a variety of of con founders, and that's why you don't get You know, if if our data shows UM an increase in in car accounts, it doesn't necessarily always mean there's

an increase in sales. That it simply gives you. Basically, it loads the dice in your favor if you're if you're an investor, what do you think the future of this business is like in five or ten years? Is it going to be an absolutely massive industry or does the fact that most of this is proprietary data is sometimes expensive, does that limit its ability to scale up? No, I don't think so. I think there's a I think

I think it's it's mendous opportunity. And the main reason we think that is that the availability of imagery is only going up, So within one to two years we expect to have daily imagery of the Earth at maybe three to five meter pixels, so pretty course, but still

the whole Earth every day. Five years out the kind of time frame you're talking about five to seven years out, we expect to have you know, reasonably good resolution, you know, a meter or less per pixel of the whole world every day, and then we can track I'm not only retail traffic. We can track mining. We can track manufacturing. We can track the car manufacturers. We can track ports and see port port work stoppages. We can see approximate

input imports and exports are in different countries. We can basically track the physical aspects of the economy at that at that level, and that becomes valuable um not only for investors, but also for governments, for non governmental organizations, on for other Fortune five companies that are trying to plan their supply chain and understand what's going on in the economy that surrounds them. We've also been working a

lot recently with non governmental organizations. We've been working with the World Bank on poverty mapping so that we can help them understand um where poverty is and where it isn't and how it's changing. Because a lot of these places they can only do surveys poverty surveys once a decade, and obviously our world is changing faster than that, so we actually think over time this becomes a foundation of the economic analysis, of understanding the physical aspects of the

world to track economies for all kinds of purposes. And I do think that over time, the individual what we call signals, the individual signals like just car accounts are just truck counts, do get do get cheaper? And then um, what people are actually buying is and actually using is an aggregation of all these different things that we're able to count going forward in that future. Are you sort of still the like analysis layer or at some point

our hedge funds saying I want the data. I want to just you know, millions of images, and I want to make my own proprietary signalism, my own proprietary analysis of it. I don't think they would. I don't think there would be the mileage for for them to take the images. I think they some of the quant funds are already looking at a pretty granular level at the counts. So so some of the quant funds will actually take you know, address date count and that's essentially the data

that we give them and they work from there. So I could see that going forward, But the analysis is is something that we use the same analysis routines for the government customers, investors, insurance companies, energy companies, you know, all of our all of our customers, we use the same image analysis and a lot of the same data analysis routines. So I think there's a lot of economies of scale for us in that. But at the same time, you write, some of these quant funds, especially get pretty

granular in the data they take from us. Okay, I think that's a good place to leave it. James Crawford of Orbital Insight, thank you so much for joining us. Absolutely, thanks for all your great questions. All Right, Matt, that was your first episode of Odd Lots. How did you find it? I thought that was fun. I've always been interested in these in these sort of imaging and and proprietary data companies for some of the reasons you alluded to.

You know, I write a lot about insider trading, and one thing I always wonder about is why people get so upset about the lack of a level playing field between retail investors and professional investors. And as James said, you know, there are so many sources of data that professional investors can rely on, you know, flying helicopters over oil fields, flying satellites over oil fields. It seems silly

to worry about any any one source of data. Yeah, there was one thing he said that kind of depressed me, where it was basically like retail hell investors don't have any hope of competing with the big guys in terms of information flow, and so everyone, you know, mom and pop are just going to increasingly herd into passive investing in index funds. Like that kind of worries me a little. Oh not me. I think that's I think that's been

true forever. I think if it's your job to invest, and you have the tools to invest, you're going to be better at it than someone who's doing it as a as a hobby or is something they just you know, do in their spare time. I don't think that you have to herd the passive funds, though, I mean you can hurt you know, if you're a mom and pop investor, you can invest in the mutual funds that it sounds like use his data, you know, alongside the hedge funds.

So you know, the point is not passive versus active, or or retail versus professional. Really, it's that you know there is a professional management layer that that decides where to invest money, and as a retail investor, you can have access to it. I guess where I get uncomfortable is the differentiation or lack of differentiation between access to

information and intelligent analysis. Like one guy can get ahead just because he has better access to information flow, whereas another guy falls behind because even though he's super super smart, he just doesn't have that data that I think that's

what depresses me about the whole thing. I hear you, but there's a lot of data in the world, right, I mean, there's more being generated all the time, and it seems it seems a little hopeless to say we're gonna we're gonna give everyone all the same data at the same time. Because you know, I understand what you're saying. But what would you do with all this satellite data? You know, would you analyze it? If you're a retail

investor and you have to work a day job. Yeah, alongside my day job at Bloomberg, I would be looking at satellite images for hours and hours and hours on end. That sounds that sounds totally like something I would do. All right, Matt, thank you so much for joining us today. That was really good fun. Thankfully, we'll have you on again some point that all right, I'm Tracy Alloway. You can find me on Twitter at Tracy Alloway. And I'm Matt Levine of Bloomberg View and you can find me

on Twitter at Matt underscore Levine. Thanks for joining us, everyone, take care, Put knowledge to work and grow your business with c i T. From transportation to healthcare to manufacturing. C i T offers commercial lending, leasing, and treasury management services for small and middle market businesses. Learn more at c i T dot com. Put Knowledge to Work