Chapter 1: What is the main topic discussed in this episode?
Welcome back to the Rundown, interview edition. Today, I'm talking to Will Marshall, the co-founder and CEO of Planet Labs. Planet Labs is one of the most talked about space companies right now, and for good reason. They operate over 200 satellites that have photographed the entire earth every single day. And the stock has gone from about $2.50 to over $30 in the past year.
So in today's conversation with Will, we get into how he started the company by strapping iPhones to satellites, how the business actually works, who their customers are, the NVIDIA and Google partnerships, and why he thinks that AI will be a huge boost for the business. This was a really fun conversation. I think you guys are going to really enjoy it. So let's get into it.
Will Marshall, welcome to The Rundown. Happy to be here. I'm super excited about today's conversation, Will. I want to start with a bit of your background. I was reading about your background, and I got to say, your resume absolutely stacked. I want to paint the audience a picture here. You got a PhD from Oxford in physics. Then you were a scientist at NASA for a number of years.
And then in 2010, you and a couple of colleagues decided to strap iPhones to a satellite. And then you founded a company called Space Labs. So Planet Labs. Sorry, not Space Labs, Planet Labs. What a mess up. This is why it's not live. You founded a company called Planet Labs. So I'm just kind of curious, what made you think strapping smartphones to a satellite would work?
And what was the inspiration behind starting the company?
Well, firstly, I mean, most people's background of successful startups, they dropped out. So obviously I failed on that front. But we left NASA with this idea that we could really innovate and make satellite costs way cheaper and bring a totally new kind of data set that's possible by putting up far more satellites, doing Earth imaging, scanning the whole Earth every day.
And the reason for that wasn't just frivolous and it's cool tech, and it is cool tech, and I can talk about that, but it's that this data could help the earth economy, could help insurance, finance, agriculture, and governments with floods and fires and defense and intelligence applications. And we saw that more rapid information about the Earth could help us make smarter decisions.
It could be like the Bloomberg terminal with data feeds, but for Earth imaging, powering businesses all around the world, countries all around the world with smarter data to make smarter decisions.
So you have over 200 satellites in orbit now. You're taking pictures every day like you mentioned. But haven't there been government satellites that have been, I don't know, taking pictures for decades? I remember Google Earth when it came out back in 2005. You could see, like, images of the Earth. So, like, what is different about Planet Labs that wasn't done before?
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Chapter 2: What inspired Will Marshall to start Planet Labs?
Maybe Apple TV, because I feel like they have like the best screensavers when it comes to space stuff. I don't know if they're buying your stuff too. Can you walk me through who like the breakdown of your customers? Is it, how much is it government versus how much is it commercial?
MR. Yeah. Well, we split it sort of into three buckets. Defense and intelligence, which is now the biggest area, which is where we're helping countries with security, monitoring for new threats, doing maritime domain awareness, as I said, so tracking ships, making sure they're not doing illegal things.
So, for example, right now we're supporting NATO doing monitoring in the Baltic Sea for hybrid Russian warfare activities. We're helping the U.S. Navy monitor 8 million square kilometers of the South China Sea for illicit activities that the Chinese are doing and other things like that. So that's what we call defense intelligence. Secondly, there's civil government.
So there we work with countries environment agencies doing environmental monitoring, agriculture agencies doing agricultural monitoring, helping agriculture subsidies, disaster response. We work with NASA a lot on science missions, so our data underpins a myriad of systems sciences papers from researchers across the United States.
Another example is we work with Brazil federal police on monitoring deforestation. Another example is we work with the Welsh Government on monitoring agriculture and biodiversity across Wales. We work with German government on environmental disaster response after floods. So that's civil government. And then the final bucket is commercial.
So there, our biggest sets of clients are in agriculture, so we help big ag companies help their farmers improve crop yields by monitoring every farm field around the world every day, we can help them do precision agriculture. So there we work with Bayer, Syngenta. We also work with insurance companies, so doing parametric drought insurance.
So companies like AXA and Swiss Re use our data for automated insurance claims. Rather than having to send out people to check that there's been a disaster or flooding or what have you, they can just look in our image and then do the automatic claim without having to send a person out, and that saves them money. So if you like,
It's a very wide capable tool set that can help a wide variety of applications. And those are the three major markets that we think about.
How does the pricing actually work? I mean, is it like a subscription where someone pays monthly for, I don't know, if you want a certain area, then you pay a certain price. If you want more coverage, you pay a higher price. Is it per image? I'm just kind of curious how that works. MR. You get it.
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Chapter 3: How does Planet Labs differentiate from traditional satellite imaging?
Why would you put a data center in space when you can just put it in space?
We have a lot of land in Texas.
There's a lot of land. Actually, what's surprising, though, is that when you do the math, when you look at all the costs of data centers, and there's a lot, the building, the permits, the land, the water cooling, all the different pieces, of course, the compute infrastructure itself, And then you do the same for space.
There's the launch cost, the satellite cost, the processing cost, all these things. And you put them side by side. We did this as an exercise with Google about eight or nine years ago now. And we assessed that simply when launch costs came down to about $200 to $300 a kilogram, it's simply cheaper to put in space. And let me just give you a little insight that might help you see why.
In many ways, data center growth is a lot of power problem because they can't build enough power to power these data centers. They can't just plug it in. There's just not enough capacity in the grid. So the cheapest by far way of getting power these days is solar. You just put a solar panel on the ground. It costs very little. It's the cheapest.
more than anything else, nuclear, fossil fuels, gas. It's the cheapest. But, of course, on the ground, you don't want an intermittent power, right? You don't want a day-night cycle for your data center. You want it on 24-7. So you would make it the cheap way only. Then you have to add batteries, or you have to have a nuclear power plant, or you have to have gas, all of which is more expensive.
In space, you just don't need that. Your solar panel can be in a, you can put your satellite in a sun-synchronous dawn-dusk orbit where it's in permanent sunlight. That means you get five times more power because of the absorption of the atmosphere that you don't have to deal with and various other things.
You get five times more power per solar panel and you don't have to carry batteries or anything else because it's always in the sun. So here you've solved your power problem really neatly. The only issue is you have to get that sucker into orbit. You don't have to have buildings. You don't have to have cooling. Permits. You don't have to have permits.
There's a lot of other stuff that can just go faster. And in space, there's a lot of space. I mean, you say it's land space. Actually, there's a lot of competition. We're seeing data centers drive agricultural attention, land with cities who don't want these data centers in their backyard, driving up power costs, driving water problems, and they can't get their water supply.
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Chapter 4: Who are the main customers of Planet Labs and how do they use the data?
You know, what I like hearing from you is that, like, a lot of investors are looking for, like, that AI angle for a lot of companies, and sometimes it feels too forced. This seems like a natural just evolution of the company where you can just layer AI on. It doesn't feel like it's forced, and it just is an accelerant to your business, and then you just laid it out. Why?
Absolutely. And, you know, I think the companies are going to split into those that are relevant for AI and those that are not. Sadly, and it's just a fact, I think, the way AI is going. And Planet is hyper relevant for AI. Space in general is. But Planet in particular, I mean, our data set, as I said, isn't just relevant to AI. It is potentially foundational to these next generations of AI that
where companies are all trying to build real-world models. Because, again, the LLMs, your ChatGPT, your Gemini, your anthropic code, only know about human knowledge. They're multimodal, so they understand pictures and text these days, videos and stuff. But they don't really know much about the physical world. Again, they're blind to the physical world.
So if you want a physical world model, obviously, an AI is only as good as the training data. If you want a physical world model, which all of these companies, Dario's talked about that, Demi's talked about that, Sam's talked about that. Everyone's trying to build world models. To have real world models, you need real world data. Stands to reason.
Planet's sitting on arguably, and obviously I'm biased, but arguably the best data set for starting off real world models that exist. 3,000 images for every point on the Earth is a whole land mass. So yes, we're very relevant to AI.
you know, I was just, you know, before I was a podcaster in my previous career, I was a civil engineer. So, you know, I used to have to track the progress on construction sites and things like that. And the way we would do it, we would have people take pictures or have a drone go out once a week.
And I'm just thinking to myself, like, if I had access to data like this, I could just literally look at the pictures in the development every single day, have an eye, just break it down and summarize it for me instead of having to drive an hour to the job site every day, back and forth.
So that's right before. First, it was I could do that job for you. You'd look in. It would detect those things. Now you can just have an agent that goes off and goes, OK, well, I want you to monitor the heck out of these things and let me know. Give me a call if I need to be concerned.
And it will go figure out all of that stuff, build a little dashboard for you that you can check in whenever you want. Where things are going is very exciting. But a lot of stuff may get commoditized with AI. But if you have a foundation data set, data is core. to AI and we're part of that.
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