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Speaker 1: Pushkin. I'm Jacob Goldstein and this is What's Your Problem

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Speaker 1: show about people using technology to solve problems that matter.

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Speaker 1: My guest today is Johnny Dyer. He's the co founder

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Speaker 1: and CEO of a company called Muon Space. Newon Space

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Speaker 1: was founded in twenty twenty one, and the company designs, builds,

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Speaker 1: and operates satellites. Their first big project is a constellation

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Speaker 1: of satellites called fire Sat. And Johnny's problem is this,

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Speaker 1: how do you build satellites that can capture data from

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Speaker 1: space to help manage wildfires around the world in near

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Speaker 1: real time. In our conversation, Johnny and I also talked

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Speaker 1: about his company's work with the US Department of Defense,

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Speaker 1: and more generally, the global geopolitics of satellites, which basically

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Speaker 1: means the US versus China in space. To start, we

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Speaker 1: talked about a satellite company called Skybox. Johnny joined that

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Speaker 1: company just after it was founded in two thousand and nine.

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Speaker 1: Up to around that time, satellites had mostly been giant, expensive,

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Speaker 1: bespoke things, but this was a moment when a few

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Speaker 1: people were starting to think that satellites could get much

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Speaker 1: smaller and much cheaper. Johnny told me that the idea

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Speaker 1: was partly inspired by the emergence of the iPhone.

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Speaker 2: You shrink cameras down enough, you shrink compute down enough,

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Speaker 2: all of a sudden you have this thing that is

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Speaker 2: possible in a much smaller package than was ever possible before.

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Speaker 2: And so there was a strong analogy. We thought with

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Speaker 2: satellites that if you can use these commercial technologies, you

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Speaker 2: can do a lot of the things that these big

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Speaker 2: billion dollar systems are doing in a much smaller package.

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Speaker 2: If you can do it in a smaller package, everything

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Speaker 2: gets faster, cheaper, because most of the challenge of getting

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Speaker 2: stuff in space is launching, and that on a per

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Speaker 2: kilogram basis, like it just costs per kilogram to get

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Speaker 2: stuff in space. If you make it smaller, everything gets better.

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Speaker 2: And so we, you know, we did this and we

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Speaker 2: were very successful at it. We ended up building twenty

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Speaker 2: one satellites that were you know, took data of the

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Speaker 2: same quality that literally, you know, one hundred million dollar

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Speaker 2: systems before us were collecting. We got bought by Google

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Speaker 2: and in the process we learned a lot about what

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Speaker 2: it takes to do this, what works, what doesn't work.

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Speaker 2: But also, you know, there there was sort of a

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Speaker 2: lot of kind of frustrations we had about the fact

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Speaker 2: that we solved this problem, this technical problem, and figured

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Speaker 2: out how to deploy these things in space, but we

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Speaker 2: really were only applying it to one small use case,

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Speaker 2: which was this high resolution visible imagery. Think about what

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Speaker 2: you see in Google Maps. So that's what we were doing.

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Speaker 2: We're taking like submeters roughly one meter resolution visible images

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Speaker 2: like you see Google Maps.

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Speaker 1: So when I look on Google Maps and I see

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Speaker 1: the trampoline in my backyard, which I do, Uh, is

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Speaker 1: that you taking that picture?

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Speaker 3: So probably the trampoline in your backyard.

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Speaker 2: Probably not that was probably taken it from an aircraft

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Speaker 2: because that's probably more like ten centimeter imagery.

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Speaker 3: But if you go to.

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Speaker 2: A big trampoline, yeah, I mean you would see it

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Speaker 2: in our imagery.

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Speaker 3: You would definitely see a trampoline.

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Speaker 2: If you go a little bit outside a major urban

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Speaker 2: area where it's too expensive to go collect aircraft imagery,

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Speaker 2: all of that imagery and Google Maps would be the

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Speaker 2: type of stuff we were collecting. So you can go

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Speaker 2: search around Google Maps and you can see a bunch

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Speaker 2: of that stuff. And so you know, we got we

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Speaker 2: went through that journey. Eventually it ended, but stuck in

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Speaker 2: the back of my head was we've shown, you know,

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Speaker 2: we've shown a different paradigm for doing this, and there's

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Speaker 2: so many possible applications of that new paradigm.

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Speaker 1: So so kind of the basic narrative of how you

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Speaker 1: get to muon just to collapse it is Skybox gets

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Speaker 1: acquired by Google, and then at some point after that

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Speaker 1: you get a call from somebody who wants to build

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Speaker 1: satellites to track methane emissions, this project called Methane and Set,

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Speaker 1: which seems like it is sort of what puts you

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Speaker 1: on the path to starting mewon. The company're running, Now,

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Speaker 1: how did that happen? What happened there?

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Speaker 2: I got a call from some people I knew from

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Speaker 2: Skybox at the Environmental Defense Fund saying we think there's

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Speaker 2: an opportunity, you know, approaching the problem the way that

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Speaker 2: companies like Skybox did, to go build a system that

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Speaker 2: can map methane globally in the world. And this is

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Speaker 2: you know, a huge first order a greenhouse driver. It's

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Speaker 2: in the near term, it's the most important greenhouse gas,

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Speaker 2: even more important than CO two. And we don't know,

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Speaker 2: we don't there's no good source of data for actually

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Speaker 2: how much is getting omitted by all these different sources

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Speaker 2: where not quantitatively at least, And so, you know, that

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Speaker 2: was a really exciting prospect for me to be part

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Speaker 2: of that because it seems like a really important problem

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Speaker 2: that needed to be solved. It was also really interesting

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Speaker 2: to me that here we are in this world where

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Speaker 2: there's a global nonprofitia the Environmental Defense Fund, so philanthropically

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Speaker 2: funded nonprofit that is basically believes they can go out

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Speaker 2: and build old what you know, traditionally would have been

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Speaker 2: only the realm of a government, like a major government

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Speaker 2: project like a NASA mission or an e submission, And

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Speaker 2: so that just seemed like an extremely compelling prospect, like,

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Speaker 2: if you can do that kind of thing and apply

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Speaker 2: it in all these different ways, it's going to blow

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Speaker 2: the door open on the types of things we imagined

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Speaker 2: doing from space.

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Speaker 3: And so for all those reasons.

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Speaker 2: I was really excited to you know, be part of

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Speaker 2: Methane SAD and join the advisory board for it. And

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Speaker 2: we went through this long process of basically, for lack

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Speaker 2: of a better term, trying to recreate some of the

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Speaker 2: things about what we did at Skybox, but with this

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Speaker 2: totally different outcome in mind. And it was really hard.

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Speaker 2: And one of the primary reasons it was really hard

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Speaker 2: was that at Skybox everything was within our control and

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Speaker 2: we built everything ourselves. We literally, you know, we were

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Speaker 2: literally building the circuit boards that went in the satellites.

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Speaker 2: We wrote all the software, we were building the cameras

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Speaker 2: that went into the telescope.

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Speaker 1: You know.

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Speaker 2: Environmental Defense Fund does not have a deep aerospace engineering

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Speaker 2: function that can go build all this stuff. And so

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Speaker 2: they essentially went out to the market and they said, Okay, well,

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Speaker 2: companies like Skybox and Planet exist. There must be now

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Speaker 2: a rich ecosystem of suppliers in the market that we

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Speaker 2: can go buy what we need to go do this mission.

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Speaker 2: The long and the short of it is that is

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Speaker 2: not true. And so what they what we kind of

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Speaker 2: ran up against was the state of the overall ecosystem

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Speaker 2: was still very much like that old mainframe in space,

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Speaker 2: you know, billion dollar ecosystem or these little tiny cottage

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Speaker 2: industry startups that we're trying to build some small piece

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Speaker 2: of a component or a little bit of software or something.

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Speaker 2: And the thing about space is really the hardest part

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Speaker 2: in many ways is integrating all these things together and

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Speaker 2: making them work as a system that can ultimately deliver

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Speaker 2: you know, an answer or a solution to a problem.

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Speaker 2: And so you know, if you fast forward and we

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Speaker 2: spent like five years helping edf kind get up the

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Speaker 2: learning curve doing that, and it was I would say, ultimately,

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Speaker 2: you know, successful in the sense that a satellite got built,

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Speaker 2: it got launched, it collected data. It was way slower,

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Speaker 2: it was way more expensive, it was way harder than

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Speaker 2: everybody thought, and it really kind of oriented me towards

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Speaker 2: there's a huge opportunity here. Like you know, we learned

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Speaker 2: how to solve a lot of these problems at Skybox.

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Speaker 2: There's clearly a need and so we have this opportunity

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Speaker 2: to frankly flood the sky with you know, entirely new

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Speaker 2: types of sensors collecting data that could be incredibly impactful

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Speaker 2: on how we manage the planet. But the supply chain,

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Speaker 2: the ecosystem to go execute that efficiently. You know, when

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Speaker 2: we started Mwon, I felt had not mature, it had

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Speaker 2: did not exist at the state that it needed to

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Speaker 2: to realize that. And so that was a lot of

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Speaker 2: the kind of origin thought process that led us to

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Speaker 2: start the company, is we can go build an entity

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Speaker 2: that is really able to deliver on these really hard,

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Speaker 2: difficult type of missions, but in the way that we

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Speaker 2: did it at Skybox. So we're ten times faster and

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Speaker 2: ten times cheaper. And if we do that, it's going

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Speaker 2: to blow the door open on the types of applications

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Speaker 2: people are doing in space.

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Speaker 3: And that's what we're ultimately trying to do.

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Speaker 1: And so you create me on in twenty twenty one,

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Speaker 1: is it right? And the first project that I'm aware

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Speaker 1: of that you were working on is fire SAT. Was

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Speaker 1: that sort of part of the creation of the company.

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Speaker 2: Well, it predated the creation of the company a little

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Speaker 2: bit in the sense that what it came out of

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Speaker 2: was there's a group of people I worked with at

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Speaker 2: Google when I was at Google and research that we're

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Speaker 2: looking at how to is kind of AI applications, so

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Speaker 2: looking at how they could take a lot of the

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Speaker 2: AI work going on and applied to very particular impact

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Speaker 2: applications like climate, like wildfire. One of the biggest problems

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Speaker 2: with wildfire is there's these data gaps. It's very hard

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Speaker 2: to understand just how little they know when they're making

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Speaker 2: decisions during a wildfire, like where the fire is, how

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Speaker 2: fast it's moving, how hot, it's burning, what's in danger.

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Speaker 2: Usually it is largely from reports coming in from boots

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Speaker 2: on the ground.

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Speaker 3: They have firefires on the line.

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Speaker 2: Did the end of the day come in and say,

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Speaker 2: this is what we saw, this is roughly where the

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Speaker 2: fire line was, and people are hand drawing it on maps.

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Speaker 3: That's literally often like it's.

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Speaker 1: Nineteen fifty or at nineteen hundred.

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Speaker 3: Nineteen hundred.

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Speaker 2: It really is not that different than I think the

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Speaker 2: way wildfires were fought in nineteen hundred. And you know,

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Speaker 2: if you think about most elements of society, most elements

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Speaker 2: of our lives, they've progressed dramatically past that point. You know,

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Speaker 2: we could pull up a map on our phone that

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Speaker 2: has incredible near real time date on traffic.

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Speaker 1: I mean, let me ask us a dumb question about that.

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Speaker 1: They fly planes over wildfires. Once these images of that,

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Speaker 1: can the planes not give them pretty good data about

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Speaker 1: what's happening? At least once they know it's there.

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Speaker 3: They can.

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Speaker 2: At times, the planes are very limited, so they they're

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Speaker 2: off not position in a place where they can get

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Speaker 2: them to an active fire fast enough to get the

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Speaker 2: data they need. So there's all those challenges, and they

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Speaker 2: often can't fly due to the conditions too, so if

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Speaker 2: it's too smoky, they can't fly. If there's really high winds,

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Speaker 2: which was obviously the case in the LA fires last year,

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Speaker 2: so they weren't able to fly the aircraft over those

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Speaker 2: fires because the winds.

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Speaker 3: Were too high.

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Speaker 2: So they can provide very good situational awareness data, but

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Speaker 2: they are very limited in a lot of ways, both

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Speaker 2: from a cost per perspective, from sort of a breadth

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Speaker 2: and geographic passy perspective, and then just from an operational perspective.

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Speaker 2: The thing that satellites give you that is unique as

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Speaker 2: this kind of persistent overhead view.

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Speaker 3: Right A satellite's giving you this.

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Speaker 2: You know, constant kind of persistent overhead map like view

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Speaker 2: of where the fire boundary is, how hot it is,

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Speaker 2: how fast it's moving, and that's just, you know, really

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Speaker 2: transformational for detecting the fires early. You don't have to say, hey,

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Speaker 2: I'm going to go fly a plane and look for

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Speaker 2: a fire. The satellite's going to cover the space and

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Speaker 2: detect a fire if it starts. And that's one of

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Speaker 2: the goals of firesat is to build to attack to

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Speaker 2: fire within about twenty minutes of its starting, and then

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Speaker 2: once it's burning we have this you know, ongoing time

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Speaker 2: series of understanding of how it's moving, which is going

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Speaker 2: to be absolutely transformational in terms of how they think

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Speaker 2: about positioning resources, how to manage risk. The other thing,

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Speaker 2: the other part of this that I think is important,

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Speaker 2: is that the goal is not to eliminate fire like

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Speaker 2: fire is really important. In fact, we don't have enough

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Speaker 2: fire in many in California, we don't have enough fire.

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Speaker 2: Historically there was a lot more natural wildfire, and we've

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Speaker 2: kind of eliminated that.

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Speaker 1: Just to be clear about that, like the ideas, California,

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Speaker 1: like much of the world, all of the American West,

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Speaker 1: evolved to burn, to happen right, fires periodically, and we

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Speaker 1: had this century essentially of not letting anything burn that's right,

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Speaker 1: and you get too much fuel, too much underbrush, and

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Speaker 1: then when there is a fire, it's too hot and

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Speaker 1: it burns too big in a way that it would

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Speaker 1: not have if we had let fires burn in the

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Speaker 1: first place.

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Speaker 3: Perfect, man, you've clearly studied this topic.

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Speaker 1: Yeah, it's really interesting, right, it's not entirely intuitive, but

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Speaker 1: not into about it and it's like, oh interesting, Yeah.

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Speaker 1: I spend a lot of time in the Sierras where

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Speaker 1: It's like you see like Manzanita are like pumping out

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Speaker 1: oil because they want there to be a fire.

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Speaker 2: Yeah, and Manzanita is like a blow torch when it

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Speaker 2: starts on fire. So yeah, so yeah, I mean that's

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Speaker 2: a huge problem. And I think the point is that

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Speaker 2: it's not simple enough to say when a fire starts,

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Speaker 2: put it out. There has to be a decision made

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Speaker 2: about there's you know, the way that this has best

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Speaker 2: been described to me. We work with an advisor named

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Speaker 2: Kate Dargan. He used to be the fire chief of California,

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Speaker 2: and she talks about good fire and bad fire. Right

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Speaker 2: when you get to what you were describing, where you

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Speaker 2: have these over fuel loated forests that burst into flame

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Speaker 2: and are burning basically so hot that they're killing everything

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Speaker 2: in a very unhealthy way.

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Speaker 3: That's bad fire, and like you want it.

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Speaker 2: You don't want that to burn, But we need a

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Speaker 2: lot more low intensity fire through the forest to keep

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Speaker 2: the forest healthy. And so right now, there's basically no

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Speaker 2: way for the fire agencies to distinguish that for the

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Speaker 2: most part. I mean, they're typically because of the risks involved,

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Speaker 2: they're just going to try and put the fires out

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Speaker 2: because there's so much at risk.

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Speaker 1: Well, like politically, if you think about it, politically, you're

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Speaker 1: never going to get in trouble for putting the fires, right,

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Speaker 1: But if you're like, well, you know we made the call,

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Speaker 1: and you know our model said it should burn, like,

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Speaker 1: that's just not going to play if anything bad happens.

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Speaker 3: That's right exactly. Yeah.

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Speaker 2: Kate's big thing is if we can measure the intensity

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Speaker 2: on a near real time basis, we can make way

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Speaker 2: better decisions about how we deploy resources to combat the

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Speaker 2: bad fire while allowing the good fire to burn. And

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Speaker 2: that's that's a very heavily kind of risk based decision

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Speaker 2: process that needs to be much better informed by data.

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Speaker 2: And I think that's in many ways we love fire

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Speaker 2: Set because I think it is such a tangible example

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Speaker 2: of the power of these space based data collection systems

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Speaker 2: to not only tell us something about the world or

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Speaker 2: you know, oh that's interesting, but actually drive decision making

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Speaker 2: on the ground. We now have actionable data that can

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Speaker 2: be used to say we're going to position firefighters here

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Speaker 2: instead of there, and that's not only going to protect this,

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Speaker 2: you know, this town, but it's also going to allow

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Speaker 2: this other part of the forest to burn.

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Speaker 3: So there's less risk next year that it's going to burn.

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Speaker 1: Right. So you say we now have actionable data, but like,

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Speaker 1: we don't literally have it now, right, because the project

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Speaker 1: is still ongoing. And so just to be clear, the

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Speaker 1: goal of the project is to have So it's an

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Speaker 1: infrared image, right, which makes sense of every piece of

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Speaker 1: the earth out of resolution. You said about the size

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Speaker 1: of a swimming pool, right, which is a nice image.

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Speaker 2: Yeah, the actual resolution is a lot bigger than that.

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Speaker 2: It's about fifty meters, so think about half of a

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Speaker 2: football field. But we can see we can detect a

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Speaker 2: fire within that fifty meters down to about the size

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Speaker 2: of a swimming pool. So if there was a swimming

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Speaker 2: pool size fire on half a football field, we would

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Speaker 2: detect that fire.

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Speaker 1: Okay, and to do that of the whole earth every.

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Speaker 3: Twent the minutes, that's right.

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Speaker 1: It sounds like a lot.

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Speaker 3: It is a lot of year.

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Speaker 1: It is a lot, right. And so so you start

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Speaker 1: this company five years ago around the time this project

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Speaker 1: is kind of coming into view, right, and you the

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Speaker 1: company and the project sort of get going together.

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Speaker 3: Yes, that's right, yep.

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Speaker 1: And so I mean you talked about how you had

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Speaker 1: solved a lot of these things at Skybox. But plainly

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Speaker 1: there's a lot to figure out here. So when you

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Speaker 1: launch the company, what do you have to figure out

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Speaker 1: to do this?

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Speaker 2: Yeah, so there's a couple of things. I think one

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Speaker 2: thing is be able to build a small and low

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Speaker 2: cost infrared camera, which is a critical enabler for this

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Speaker 2: to work. And that's something that we bid off very

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Speaker 2: early in the process, very much using again the same

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Speaker 2: kind of approach that we use at Skybox.

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Speaker 3: At Skybox, we were.

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Speaker 2: Much we were focused on visible imagery, which is a

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Speaker 2: different thing that's very much a solved problem now for space.

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Speaker 2: But if you ask anybody when we started the company,

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Speaker 2: what does it take to build like a very high

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Speaker 2: sense it'sivity infrared imager from space, they'd probably tell you

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Speaker 2: it's like a ten to thirty million dollar problem per unit.

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Speaker 1: Wait, you mean it's going to cost you ten million

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Speaker 1: dollars for each camera?

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Speaker 2: Yeah, I mean if you went out to Lockheed or

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Speaker 2: Northrope or whoever builds these things, because you know commercially

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Speaker 2: they're not built.

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Speaker 3: Yeah, that's what they would have told you. That's what

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Speaker 3: you know.

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Speaker 2: In many ways, like the government is paying today for

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Speaker 2: that type of payload.

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Speaker 3: And we couldn't do.

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Speaker 2: That because, you know, ultimately, to get to that twenty minutes,

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Speaker 2: which everybody agrees was a critical goal, you need like

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Speaker 2: fifty satellites and so if you're if just the camera

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Speaker 2: on each satellite costs twenty million dollars, it's pretty hard

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Speaker 2: to imagine that making sense.

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Speaker 3: But we needed like a ten x.

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Speaker 2: I mean, we needed to get this thing down to

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Speaker 2: like a million dollars or you know, no more than

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Speaker 2: two million dollars. And so again we had to go

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Speaker 2: to the out and say, like, what are technologies that

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Speaker 2: have been developed for other industries that we can apply

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Speaker 2: to this problem that haven't been used in space before,

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Speaker 2: because typically there's a lot of industries that have spent

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Speaker 2: billions of dollars on technology and getting the cost down

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Speaker 2: and the performance up. And so what we found was

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Speaker 2: there was there had been work on It was largely

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Speaker 2: for tactical military applications, so not things that go in space,

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Speaker 2: but things like the night vision that a soldier wears

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Speaker 2: in the field, where they're building hundreds of thousands of

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Speaker 2: these things at fairly low cost, and the underlying camera

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Speaker 2: technology is applicable to it's what we needed for this

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Speaker 2: it's a very different way to do it. Again, it

363
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Speaker 2: was another case of like we literally had people saying

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Speaker 2: this will never work, you can't do it that way.

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Speaker 1: While as you say that, well, I mean when people

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Speaker 1: say that too.

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Speaker 2: I love it when people tell me something's not possible,

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Speaker 2: because that's just like that's a great challenge, like Okay,

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Speaker 2: I'm gonna prove you wrong. So yeah, So that was

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Speaker 2: a big challenge that we had to solve and we did.

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Speaker 2: I think another challenge that we really had to solve,

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Speaker 2: which is related to fire set, but it's also related

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Speaker 2: to the bigger picture for the company is you know,

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Speaker 2: these things produce enormous amounts of data, and they often

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Speaker 2: are constrained in various ways in terms of the ability

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Speaker 2: to get that data back to Earth and then into

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Speaker 2: the hands of people that need it, and certainly on

378
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Speaker 2: reasonable time scales, so like typical satellite systems maybe only

379
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Speaker 2: might take an hour and a half or two hours

380
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Speaker 2: from the time a picture is taken to when it's

381
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Speaker 2: actually usable in somebody's hand.

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Speaker 3: And again for the fire.

383
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Speaker 2: Problem, obviously that's not going to work if we're going

384
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Speaker 2: to put all these satellites up to fly over every

385
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Speaker 2: fifteen minutes, Like, waiting an hour and a half for

386
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Speaker 2: somebody to get that data is not helpful. So we

387
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Speaker 2: had to put a lot of time and effort into

388
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Speaker 2: thinking about how you can build extremely high bandwidth, extremely

389
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Speaker 2: low latency pipelines basically to get data from satellites and

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Speaker 2: out to users. And the reason I say it's more

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Speaker 2: than just fire sat is because I do think that's

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Speaker 2: something that is an unlock for a whole bunch of

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Speaker 2: other things too.

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Speaker 1: So how'd you figure that one out?

395
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Speaker 2: So there's kind of two different pieces of it. There's

396
00:19:01,356 --> 00:19:04,596
Speaker 2: kind of a phase one and a phase two. The

397
00:19:04,636 --> 00:19:09,476
Speaker 2: phase one is clever, you know, basically clever archets picture

398
00:19:09,596 --> 00:19:11,916
Speaker 2: of where we put antennas around the world, and how

399
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Speaker 2: we design our radios to be able to continuously downlink

400
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Speaker 2: while the cameras are collecting, which most satellites don't do.

401
00:19:18,116 --> 00:19:21,316
Speaker 2: Most satellites collect some data, store it, down link it.

402
00:19:22,556 --> 00:19:25,276
Speaker 2: Our system is designed to be able to continuously do both,

403
00:19:25,436 --> 00:19:28,956
Speaker 2: and to do it to antennas that are basically placed

404
00:19:28,956 --> 00:19:31,796
Speaker 2: in geographies where wildfire really matters, so it's a near

405
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Speaker 2: real time connection all the time.

406
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Speaker 3: That's kind of phase one.

407
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Speaker 2: So the other thing kind of the phase two of

408
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Speaker 2: this that we're now bringing on board is we have

409
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Speaker 2: this partnership with SpaceX where we'll basically be connecting these

410
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Speaker 2: satellites into the Starlink network in space.

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Speaker 1: Is that the laser part? Are you not saying lasers?

412
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Speaker 3: It is lasers and space. Lasers and space. Yeah, well,

413
00:19:50,356 --> 00:19:51,156
Speaker 3: lasers in space.

414
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Speaker 2: So now rather than having to wait to come over

415
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Speaker 2: a ground station or try and place one in exactly

416
00:19:57,716 --> 00:20:00,516
Speaker 2: the right spot to keep that latency down as we're

417
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Speaker 2: collecting data and space, we will have essentially what's like

418
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Speaker 2: an always on broadband connection to the Internet and we

419
00:20:05,516 --> 00:20:09,356
Speaker 2: can just continuously stream that data back for users. That's

420
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Speaker 2: completely transformative, right in terms of how you think about some.

421
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Speaker 3: Of these problems.

422
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Speaker 1: So where is fire SAT now?

423
00:20:16,156 --> 00:20:19,076
Speaker 2: So we launched what we called the protoflight satellite, so

424
00:20:19,076 --> 00:20:22,236
Speaker 2: a first instance last year and it's up collecting data.

425
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Speaker 2: We're launching the first three of what we call the

426
00:20:25,436 --> 00:20:31,596
Speaker 2: operational constellation in June and then basically we have a

427
00:20:31,676 --> 00:20:34,116
Speaker 2: you know, a roadmap of launches that gets us out

428
00:20:34,196 --> 00:20:37,716
Speaker 2: to the first kind of big like milestone we're shooting

429
00:20:37,716 --> 00:20:41,276
Speaker 2: for is hourly So having a picture every hour of

430
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Speaker 2: the entire planet, which we should get to in sort

431
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Speaker 2: of late twenty seven early twenty twenty eight, and there

432
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Speaker 2: will ultimately be about twenty in that hour hourly constellation.

433
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Speaker 2: And then after that the goal is to expand that

434
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Speaker 2: ultimately to fifty two.

435
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Speaker 3: That'll get us to fifteen minutes.

436
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Speaker 1: Why might it not work?

437
00:21:00,676 --> 00:21:05,916
Speaker 2: Yeah, So one of our biggest worries and concerns, because

438
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Speaker 2: there's a lot of history of this is up to

439
00:21:09,356 --> 00:21:11,236
Speaker 2: by the operational community.

440
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Speaker 1: And I mean if there's no customers essentially.

441
00:21:14,916 --> 00:21:17,356
Speaker 2: Yeah, our users, or if it's difficult for them to

442
00:21:17,556 --> 00:21:20,436
Speaker 2: incorporate into their operational flows, and.

443
00:21:20,396 --> 00:21:23,516
Speaker 1: So somehow it's not actually helpful in fighting fires or

444
00:21:23,596 --> 00:21:24,836
Speaker 1: understanding fires.

445
00:21:24,636 --> 00:21:27,556
Speaker 2: Right, And I think we're very confident that the data

446
00:21:27,596 --> 00:21:30,996
Speaker 2: itself is helpful, but it's only helpful, you know, if

447
00:21:31,036 --> 00:21:33,196
Speaker 2: you can get you know the term the water to

448
00:21:33,236 --> 00:21:35,276
Speaker 2: the end of the road, right, So it's not helpful

449
00:21:35,836 --> 00:21:38,676
Speaker 2: if all this data ends in some cloud bucket somewhere

450
00:21:38,716 --> 00:21:40,956
Speaker 2: that nobody can get to. It's only helpful if it

451
00:21:40,996 --> 00:21:45,276
Speaker 2: actually ends up out in these operational command centers, in

452
00:21:45,356 --> 00:21:48,076
Speaker 2: the tools that the fire operators are using on a

453
00:21:48,116 --> 00:21:51,556
Speaker 2: daily basis. And so that's actually not a trivial problem

454
00:21:51,636 --> 00:21:56,716
Speaker 2: because it's a huge patchwork of different systems and tools

455
00:21:57,276 --> 00:22:00,276
Speaker 2: even in the US, like there is no unified system.

456
00:22:01,116 --> 00:22:06,036
Speaker 2: So like our priority earliest integration partner is CalFire For

457
00:22:06,036 --> 00:22:09,076
Speaker 2: that reason, CalFire is the most sophisticated fire agency.

458
00:22:09,156 --> 00:22:10,716
Speaker 3: They have the best tools to do this.

459
00:22:11,356 --> 00:22:13,516
Speaker 2: But you know, ultimately we want to do a lot

460
00:22:13,556 --> 00:22:16,596
Speaker 2: more than that, Like, yeah, this is not just for California,

461
00:22:16,636 --> 00:22:18,956
Speaker 2: it's not just for the US, it's for the world.

462
00:22:19,316 --> 00:22:21,796
Speaker 2: And so that long tail of how do you actually

463
00:22:21,836 --> 00:22:23,236
Speaker 2: get the water to the end of the row with

464
00:22:23,276 --> 00:22:25,196
Speaker 2: the users that need it, I think is actually the

465
00:22:25,516 --> 00:22:27,636
Speaker 2: heart and not the hardest problem, but it's one of

466
00:22:27,676 --> 00:22:30,516
Speaker 2: the harder problems, and I think it's the biggest risk to.

467
00:22:30,636 --> 00:22:31,956
Speaker 3: Ultimately having impact.

468
00:22:32,116 --> 00:22:34,596
Speaker 2: Like, you know, if we get all these satellites in

469
00:22:34,596 --> 00:22:36,476
Speaker 2: space and they're wonderful and they're producing data, but we

470
00:22:36,556 --> 00:22:38,956
Speaker 2: don't solve that problem, it's all for nothing.

471
00:22:39,436 --> 00:22:40,836
Speaker 1: Yeah, that could totally happen.

472
00:22:42,276 --> 00:22:44,556
Speaker 2: Well, we're going to try and make it not not

473
00:22:44,676 --> 00:22:45,116
Speaker 2: a question.

474
00:22:45,236 --> 00:22:47,516
Speaker 1: Yeah, not technically a question to say that could totally happen.

475
00:22:50,996 --> 00:22:59,996
Speaker 1: We'll be back in just a minute. Hey, it's Jacob,

476
00:23:00,076 --> 00:23:01,836
Speaker 1: and I want to tell you that I am hosting

477
00:23:01,876 --> 00:23:05,676
Speaker 1: a new show called Business History. It's about the incredible

478
00:23:05,676 --> 00:23:10,156
Speaker 1: innovations and massive failures and unbelievable characters in the history

479
00:23:10,156 --> 00:23:13,516
Speaker 1: of business, and I hope I think the show provides

480
00:23:13,556 --> 00:23:16,956
Speaker 1: insights about how business works today. At the end of

481
00:23:16,956 --> 00:23:19,436
Speaker 1: today's episode of What's Your Problem, We're going to play

482
00:23:19,436 --> 00:23:22,636
Speaker 1: you a clip from Business History. It's the story behind

483
00:23:22,716 --> 00:23:26,156
Speaker 1: the video game company Atari, and they're surprising early hire

484
00:23:26,316 --> 00:23:29,396
Speaker 1: of a young hippie named Steve Jobs. The show's called

485
00:23:29,436 --> 00:23:31,956
Speaker 1: Business History. You can listen to it wherever you're listening

486
00:23:32,036 --> 00:23:34,396
Speaker 1: right now, and we'll play that clip at the end

487
00:23:34,396 --> 00:23:40,756
Speaker 1: of today's episode. Tell me about your work with the

488
00:23:40,796 --> 00:23:41,716
Speaker 1: Department of Defense.

489
00:23:42,956 --> 00:23:48,076
Speaker 3: Yeah, So there's a couple of worlds out there right now.

490
00:23:48,116 --> 00:23:50,836
Speaker 2: There's the commercial world, and I would put fire sat

491
00:23:50,876 --> 00:23:51,236
Speaker 2: in that.

492
00:23:51,156 --> 00:23:53,356
Speaker 3: World, and then there's sort of the national security world,

493
00:23:53,516 --> 00:23:56,476
Speaker 3: and they're related in a lot of ways.

494
00:23:57,156 --> 00:23:59,916
Speaker 2: It's kind of hard to separate them completely, but there

495
00:23:59,996 --> 00:24:05,116
Speaker 2: is right now a I would say existential need, especially

496
00:24:05,116 --> 00:24:09,716
Speaker 2: in the US, for new technology and space. And the

497
00:24:09,716 --> 00:24:12,316
Speaker 2: way I would think about this is that we've spent

498
00:24:12,556 --> 00:24:18,476
Speaker 2: sixty or seventy years building very expensive, high performance, exquisite

499
00:24:18,476 --> 00:24:23,036
Speaker 2: systems for space. On the national security side, most people

500
00:24:23,036 --> 00:24:28,916
Speaker 2: don't even realize exist, and they're absolutely absolutely critical to

501
00:24:29,076 --> 00:24:31,636
Speaker 2: our country's ability to operate in the world, like in

502
00:24:31,636 --> 00:24:34,316
Speaker 2: a way that you know, we're very very sensitive too.

503
00:24:34,556 --> 00:24:40,156
Speaker 2: So historically we have dominated space as a country that's

504
00:24:40,196 --> 00:24:42,716
Speaker 2: not really been contested, and so this has been both

505
00:24:42,716 --> 00:24:46,996
Speaker 2: a superpower but also sort of a capability. It's kind

506
00:24:46,996 --> 00:24:49,516
Speaker 2: of taken for granted. The armed services don't even think

507
00:24:49,516 --> 00:24:51,916
Speaker 2: about the idea that some of these things, these tools

508
00:24:51,916 --> 00:24:54,036
Speaker 2: that they have that they rely upon, would not be

509
00:24:55,396 --> 00:24:56,996
Speaker 2: available or useful.

510
00:24:56,956 --> 00:24:59,836
Speaker 1: Like that some enemy might shoot them down exactly what

511
00:24:59,876 --> 00:25:03,236
Speaker 1: you're alluding to, Yeah, or jam them or jam them, right,

512
00:25:03,276 --> 00:25:05,116
Speaker 1: I guess jam them is actually what happened.

513
00:25:05,236 --> 00:25:07,276
Speaker 2: Well, I mean the Chinese shot a satellite down a

514
00:25:07,276 --> 00:25:09,636
Speaker 2: couple of years ago, right to demonstrate they can. So

515
00:25:09,716 --> 00:25:13,356
Speaker 2: like there's a lot of concern about that. So right

516
00:25:13,356 --> 00:25:16,836
Speaker 2: now we're in the midst of this period where those

517
00:25:16,836 --> 00:25:19,796
Speaker 2: old systems have were designed in an era where there

518
00:25:19,796 --> 00:25:24,316
Speaker 2: weren't threats. There's now threats. There's also a lot of

519
00:25:25,156 --> 00:25:27,716
Speaker 2: like China has become much more capable in space and

520
00:25:27,756 --> 00:25:30,596
Speaker 2: it's now more like a peer to us. Versus like

521
00:25:30,956 --> 00:25:31,876
Speaker 2: a strong second.

522
00:25:32,636 --> 00:25:33,396
Speaker 3: And so there's this.

523
00:25:33,436 --> 00:25:39,636
Speaker 2: Huge push to basically modernize and refresh all of the

524
00:25:40,436 --> 00:25:42,636
Speaker 2: kind of space infrastructure that the US uses on the

525
00:25:42,716 --> 00:25:46,876
Speaker 2: national security side, but very much leveraging the commercial approach

526
00:25:47,036 --> 00:25:49,316
Speaker 2: that we've been talking about that I think we kind

527
00:25:49,316 --> 00:25:52,116
Speaker 2: of pioneered at Skybox a decade ago, and at the

528
00:25:52,156 --> 00:25:54,436
Speaker 2: time it was perceived as too risky and too early,

529
00:25:54,996 --> 00:25:57,156
Speaker 2: but there's now a recognition that that is the future,

530
00:25:57,236 --> 00:25:59,596
Speaker 2: this is how these systems need to be built, and

531
00:25:59,676 --> 00:26:04,356
Speaker 2: that there is very quickly becoming this vibrant commercial industry

532
00:26:04,476 --> 00:26:06,836
Speaker 2: that can can build a lot of these systems.

533
00:26:06,876 --> 00:26:09,956
Speaker 1: Analogous to what SpaceX did for rocket rockets used to

534
00:26:10,036 --> 00:26:13,836
Speaker 1: be whatever, three two whatever. It was one military contractor

535
00:26:14,316 --> 00:26:15,956
Speaker 1: and then a private company game. It was like, look,

536
00:26:15,996 --> 00:26:18,836
Speaker 1: we can do this cheaper, right, That's happening for satellites

537
00:26:18,876 --> 00:26:20,716
Speaker 1: now for the government, for the military.

538
00:26:20,796 --> 00:26:23,916
Speaker 2: Yeah, And very similarly, SpaceX, you know, did it in

539
00:26:23,956 --> 00:26:27,516
Speaker 2: a way that was commercially oriented. They're launching commercial communication

540
00:26:27,596 --> 00:26:32,556
Speaker 2: satellites but also supporting national security missions. So I think

541
00:26:32,596 --> 00:26:38,236
Speaker 2: that's space inherently is very difficult to separate from that perspective.

542
00:26:38,716 --> 00:26:43,156
Speaker 1: Separate private from the military essentially, Yeah, but just because

543
00:26:43,156 --> 00:26:44,156
Speaker 1: that's where the money is.

544
00:26:45,236 --> 00:26:48,196
Speaker 2: That's where the money is. But also because anything you

545
00:26:48,236 --> 00:26:52,196
Speaker 2: put in space inherently has the ability to do both, right,

546
00:26:52,316 --> 00:26:54,516
Speaker 2: I mean in the limit, if you really want to

547
00:26:54,516 --> 00:26:57,716
Speaker 2: think about the limit. And it's interesting just to make

548
00:26:57,756 --> 00:27:00,796
Speaker 2: this make this point pretty clear, China does not really

549
00:27:00,836 --> 00:27:05,316
Speaker 2: distinguish between commercial satellites and military satellites. We historically have

550
00:27:05,436 --> 00:27:08,436
Speaker 2: distinguished that. But one of the big fears more and

551
00:27:08,476 --> 00:27:12,076
Speaker 2: more is that that you know, our government should be

552
00:27:12,076 --> 00:27:15,956
Speaker 2: depending on commercial satellites. Do those then become targets in

553
00:27:15,996 --> 00:27:16,556
Speaker 2: a conflict?

554
00:27:16,676 --> 00:27:16,836
Speaker 4: Right?

555
00:27:16,916 --> 00:27:19,956
Speaker 3: And so there's it's a little another analogy.

556
00:27:19,996 --> 00:27:22,156
Speaker 1: I think that's a good thinking of the Lusitania.

557
00:27:22,756 --> 00:27:25,316
Speaker 2: I I was gonna say naval like kind of pre

558
00:27:25,436 --> 00:27:29,996
Speaker 2: World War two naval conflict, right, birch and ship can

559
00:27:30,036 --> 00:27:31,556
Speaker 2: be used for goods, but it can also be used

560
00:27:31,556 --> 00:27:34,036
Speaker 2: as smuggle weapons and and you know, in a lot

561
00:27:34,076 --> 00:27:37,036
Speaker 2: of ways, our navy after the Second World War sort

562
00:27:37,036 --> 00:27:41,956
Speaker 2: of police the world's oceans to kind of guarantee a

563
00:27:41,996 --> 00:27:45,236
Speaker 2: lot of the kind of separation between commercial and military

564
00:27:45,276 --> 00:27:49,516
Speaker 2: affairs in the ocean. There's no equivalents of that in space.

565
00:27:49,596 --> 00:27:52,436
Speaker 2: And so that's I think that's a big concern people

566
00:27:52,476 --> 00:27:55,036
Speaker 2: have right now, is that even if you want to

567
00:27:55,116 --> 00:27:57,716
Speaker 2: say this is a commercial system, this is a military system,

568
00:27:58,076 --> 00:28:00,156
Speaker 2: it's very hard to draw that line in a way

569
00:28:00,196 --> 00:28:02,876
Speaker 2: that China will respect it or some other adversary will

570
00:28:02,916 --> 00:28:06,116
Speaker 2: respect it. And so all to be said, the technologies

571
00:28:06,116 --> 00:28:10,276
Speaker 2: are very similar. The technology are the same, right you know,

572
00:28:10,276 --> 00:28:13,276
Speaker 2: if you put something in space, it's it's very hard

573
00:28:13,316 --> 00:28:16,036
Speaker 2: to tell what it's for for somebody else looking on

574
00:28:16,116 --> 00:28:17,156
Speaker 2: like it's very easy for.

575
00:28:17,876 --> 00:28:21,556
Speaker 1: It's just a satellite. It's satellite country taking a picture.

576
00:28:21,356 --> 00:28:24,436
Speaker 2: Scout radios and sensors on it. Are those radios and

577
00:28:24,476 --> 00:28:27,156
Speaker 2: sensors communicating to commercial stuff? Are they community in the military?

578
00:28:27,316 --> 00:28:30,956
Speaker 2: Are they taking pictures of you know, people's trampolines in

579
00:28:30,996 --> 00:28:33,556
Speaker 2: their backyard. Are they taking pictures of military sites? It's

580
00:28:33,636 --> 00:28:35,276
Speaker 2: very hard to separate. And I think we're seeing a

581
00:28:35,276 --> 00:28:38,676
Speaker 2: big convergence right now in and a recognized convergence of

582
00:28:38,716 --> 00:28:40,876
Speaker 2: those those spheres. And so there's you know, there's both

583
00:28:40,956 --> 00:28:43,476
Speaker 2: huge opportunities and risks with that. You know, the opportunity

584
00:28:43,476 --> 00:28:46,316
Speaker 2: for us is that the things we're building are applicable

585
00:28:46,316 --> 00:28:49,076
Speaker 2: to both There's a huge need on the national security side.

586
00:28:49,116 --> 00:28:53,036
Speaker 2: And to give a very specific example of this, you know,

587
00:28:53,076 --> 00:28:55,716
Speaker 2: we want a contract with the Space Force last year

588
00:28:56,476 --> 00:28:58,716
Speaker 2: to basically take what we developed for fire SAT, this

589
00:28:58,796 --> 00:29:02,316
Speaker 2: infrared camera, this low cost infrared camera, and adapt to

590
00:29:03,396 --> 00:29:06,436
Speaker 2: a mission set that they've had for forty years that

591
00:29:06,556 --> 00:29:08,996
Speaker 2: has been very challenging for them to upgrade, which is

592
00:29:09,916 --> 00:29:12,996
Speaker 2: what they call They call it theater imagery and cloud characterization,

593
00:29:13,076 --> 00:29:15,756
Speaker 2: but it's basically weather forecasting. So the DoD has to

594
00:29:15,796 --> 00:29:18,116
Speaker 2: forecast weather. If they want to fly planes, they need

595
00:29:18,156 --> 00:29:19,396
Speaker 2: to know what the weather is, so they have their

596
00:29:19,436 --> 00:29:23,316
Speaker 2: own weather forecasting capability, and they have satellites to feed that.

597
00:29:23,636 --> 00:29:27,596
Speaker 2: And historically these have been these billion dollar, twenty year

598
00:29:27,836 --> 00:29:32,316
Speaker 2: bespoke things. And what we've been able to identify as

599
00:29:32,316 --> 00:29:34,716
Speaker 2: the same technology we've built for fire Sat is very

600
00:29:34,716 --> 00:29:38,116
Speaker 2: applicable to deploying satellites to solve that mission for them.

601
00:29:38,196 --> 00:29:40,996
Speaker 2: So I think that's a really good example of kind

602
00:29:41,036 --> 00:29:44,356
Speaker 2: of this crossover and how and how inherently dual use

603
00:29:44,356 --> 00:29:45,076
Speaker 2: a lot of these things.

604
00:29:45,116 --> 00:29:48,956
Speaker 1: Are you were saying there's opportunities and risk. That's the opportunity.

605
00:29:48,996 --> 00:29:50,276
Speaker 1: What's the risk for you?

606
00:29:50,836 --> 00:29:52,836
Speaker 2: I think there's like a perceptive risk, and I think

607
00:29:52,876 --> 00:29:57,876
Speaker 2: there's a physical risk. The perceptive risk is. You know,

608
00:29:58,156 --> 00:30:02,636
Speaker 2: we started the company very much to do positive in

609
00:30:02,676 --> 00:30:05,596
Speaker 2: the world, and I firmly believe that's what we're doing.

610
00:30:05,676 --> 00:30:07,876
Speaker 2: I think Fire SAD is a canonical example of how

611
00:30:07,876 --> 00:30:10,476
Speaker 2: we're trying to do this. I believe there's a lot

612
00:30:10,476 --> 00:30:12,676
Speaker 2: of things in national security sphere that we can do

613
00:30:12,716 --> 00:30:15,796
Speaker 2: that also have that characteristic that are positive things for

614
00:30:15,796 --> 00:30:16,636
Speaker 2: the world.

615
00:30:17,156 --> 00:30:18,476
Speaker 3: Not everybody agrees with that.

616
00:30:19,156 --> 00:30:22,596
Speaker 1: Yeah, well, and I mean, you know, somewhat famously, at

617
00:30:22,636 --> 00:30:26,396
Speaker 1: other big tech companies, right, people who worked at the

618
00:30:26,396 --> 00:30:29,996
Speaker 1: companies didn't like it when Google and Microsoft started doing

619
00:30:29,996 --> 00:30:31,116
Speaker 1: work for the Department of Defense.

620
00:30:31,236 --> 00:30:31,436
Speaker 5: Right.

621
00:30:31,516 --> 00:30:33,676
Speaker 2: I was at Google when the Project Maven thing happened,

622
00:30:33,676 --> 00:30:36,556
Speaker 2: like when there was the big backlash against their work

623
00:30:36,676 --> 00:30:39,796
Speaker 2: on that. So, like, have you encountered any of that

624
00:30:39,876 --> 00:30:42,796
Speaker 2: at MWON. Yeah, No, We've definitely had people with concerns.

625
00:30:43,076 --> 00:30:44,876
Speaker 2: I mean, I think that what we've tried to do

626
00:30:44,956 --> 00:30:47,676
Speaker 2: is just be very open about why we're doing it

627
00:30:47,716 --> 00:30:50,116
Speaker 2: and why we see it as a positive addition to

628
00:30:50,196 --> 00:30:52,716
Speaker 2: our mission set. And for the most part, that's been

629
00:30:52,796 --> 00:30:56,436
Speaker 2: very successful. I mean, people typically are engineers, are very

630
00:30:56,516 --> 00:31:01,876
Speaker 2: rational people usually, and so I think if we can transparently.

631
00:31:01,276 --> 00:31:05,596
Speaker 1: Present rational I mean you rational people can disagree about

632
00:31:05,636 --> 00:31:08,316
Speaker 1: the most prevailance of the American Department of Defense.

633
00:31:08,316 --> 00:31:11,316
Speaker 2: Oh, you're right, And so I mean again, like my

634
00:31:11,436 --> 00:31:13,956
Speaker 2: perspective on this is like I never want to jam

635
00:31:13,956 --> 00:31:16,396
Speaker 2: this down anybody's throat, Like I think people need to

636
00:31:16,596 --> 00:31:18,916
Speaker 2: make their own decisions and be very comfortable with what

637
00:31:18,916 --> 00:31:20,876
Speaker 2: we're doing, and we're just trying to be as transparent

638
00:31:20,916 --> 00:31:21,756
Speaker 2: about that as we can.

639
00:31:22,676 --> 00:31:25,516
Speaker 1: Let's talk now about the broader state of the industry,

640
00:31:25,556 --> 00:31:27,796
Speaker 1: of the space industry, such as it is. I mean,

641
00:31:27,836 --> 00:31:30,116
Speaker 1: it's particularly interesting given that you have seen now this

642
00:31:30,276 --> 00:31:33,156
Speaker 1: rather long arc, right when there has been this big

643
00:31:33,196 --> 00:31:38,156
Speaker 1: transformation in the industry over what fifteen years or twenty years? Yeah,

644
00:31:38,196 --> 00:31:45,076
Speaker 1: twenty years. So you know, how has it changed? That's

645
00:31:45,076 --> 00:31:47,676
Speaker 1: the big dumb question. Like, yeah, I mean there's the

646
00:31:47,716 --> 00:31:49,596
Speaker 1: obvious stuff that you've done, but like, what can you

647
00:31:49,636 --> 00:31:50,876
Speaker 1: do now that you couldn't have done?

648
00:31:50,876 --> 00:31:51,036
Speaker 4: Then?

649
00:31:51,116 --> 00:31:53,076
Speaker 1: What's you know, what's interesting, what's surprising.

650
00:31:53,476 --> 00:31:58,436
Speaker 2: So I'll give some anecdotes. So I interned at SpaceX

651
00:31:58,476 --> 00:32:00,796
Speaker 2: in like two thousand and four. I was there's like

652
00:32:00,876 --> 00:32:03,996
Speaker 2: forty people there, and you know, my first job was

653
00:32:04,796 --> 00:32:07,276
Speaker 2: helping build the first Merlin, which is like the first

654
00:32:07,276 --> 00:32:10,236
Speaker 2: stage engine for Falcon one, and testing it. And you know,

655
00:32:11,476 --> 00:32:14,756
Speaker 2: this was kind of when I was there, there was

656
00:32:14,796 --> 00:32:18,276
Speaker 2: this excitement about how space This was very early, right,

657
00:32:18,276 --> 00:32:20,556
Speaker 2: there's forty people, and there was this belief that they

658
00:32:20,596 --> 00:32:25,836
Speaker 2: were going to totally transform the industry that early. You know,

659
00:32:25,956 --> 00:32:27,876
Speaker 2: I learned very quickly that we were a long way

660
00:32:28,116 --> 00:32:30,276
Speaker 2: away from that. The first time we put this engine together,

661
00:32:30,436 --> 00:32:33,556
Speaker 2: probably tore itself apart, burned the test stand down, and

662
00:32:33,636 --> 00:32:35,356
Speaker 2: you know, I spent the rest of the summer like

663
00:32:35,476 --> 00:32:37,596
Speaker 2: rewiring this test dan in the summer heat in the

664
00:32:37,636 --> 00:32:40,436
Speaker 2: Texas eat and it took him another like five years

665
00:32:40,436 --> 00:32:43,516
Speaker 2: before they even got a first rocket into space after that,

666
00:32:45,196 --> 00:32:48,596
Speaker 2: And if I fast forward from there and then think

667
00:32:48,636 --> 00:32:51,276
Speaker 2: about starting Skybox in two thousand and nine, which is

668
00:32:51,356 --> 00:32:54,516
Speaker 2: roughly at the same time, again we had this perspective

669
00:32:54,516 --> 00:32:56,836
Speaker 2: that it's like, okay, well it took a while, but

670
00:32:56,916 --> 00:33:00,076
Speaker 2: SpaceX is solving this problem. They solve this launch problem

671
00:33:00,116 --> 00:33:02,116
Speaker 2: where access to space is going to be easier. They

672
00:33:02,116 --> 00:33:04,436
Speaker 2: now have Falcon one. We actually were going to buy

673
00:33:04,516 --> 00:33:07,676
Speaker 2: two Falcon ones. It's Skybox. Early on and right at

674
00:33:07,716 --> 00:33:11,356
Speaker 2: the last minute, Elon said, there's not a Falcon one

675
00:33:11,476 --> 00:33:12,436
Speaker 2: is not business competitive.

676
00:33:12,436 --> 00:33:13,476
Speaker 3: We're gonna build a bigger rocket.

677
00:33:13,476 --> 00:33:15,356
Speaker 2: We're gonna build Falcon nine, and all the contracts that

678
00:33:15,356 --> 00:33:17,076
Speaker 2: we're gonna do for Falcon one are now Nolan bought

679
00:33:17,156 --> 00:33:20,796
Speaker 2: Void and so this was existential for us at Skybox.

680
00:33:20,836 --> 00:33:22,156
Speaker 3: We ended up having.

681
00:33:21,916 --> 00:33:25,636
Speaker 2: To go to Russia, of all places, and buy these

682
00:33:25,676 --> 00:33:29,316
Speaker 2: converted Cold War era ICBMs to launch our satellites with.

683
00:33:29,316 --> 00:33:32,076
Speaker 2: That's we were literally launching our satellites out of a

684
00:33:32,076 --> 00:33:34,436
Speaker 2: missile base in southern Russia off ICBMs.

685
00:33:35,076 --> 00:33:36,276
Speaker 3: And so again like.

686
00:33:36,436 --> 00:33:38,876
Speaker 2: We were like, okay, well, this problem we thought was solved,

687
00:33:38,876 --> 00:33:40,596
Speaker 2: it was going to blow the door open, is still

688
00:33:40,636 --> 00:33:45,676
Speaker 2: not solved. It is now solved fundamentally. And I mean

689
00:33:45,836 --> 00:33:48,156
Speaker 2: there's all kinds of stats that show this from the

690
00:33:48,196 --> 00:33:51,036
Speaker 2: cost of launch that has come down, and launch launch

691
00:33:51,116 --> 00:33:55,556
Speaker 2: has been the big choke point for most of the

692
00:33:55,596 --> 00:33:57,916
Speaker 2: history of space. So that's a huge unlocked.

693
00:33:57,476 --> 00:33:59,916
Speaker 1: Hard to get there space hard to get there now,

694
00:33:59,956 --> 00:34:01,316
Speaker 1: not as hard, not as hard.

695
00:34:01,556 --> 00:34:05,876
Speaker 2: I don't think you can underestimate the impact of solving

696
00:34:05,876 --> 00:34:09,196
Speaker 2: the launch problem. I'll call it in terms of how

697
00:34:09,276 --> 00:34:11,516
Speaker 2: the world opens up, in terms of what you can.

698
00:34:11,436 --> 00:34:11,996
Speaker 3: Do with space.

699
00:34:12,996 --> 00:34:15,116
Speaker 2: The other thing I would say is I think we're

700
00:34:17,436 --> 00:34:20,076
Speaker 2: even when we were doing Skybox and thinking very differently

701
00:34:20,316 --> 00:34:23,116
Speaker 2: about how to build satellites, we were still building in

702
00:34:23,196 --> 00:34:25,836
Speaker 2: a silo. We were building this thing that the analogy

703
00:34:25,836 --> 00:34:28,596
Speaker 2: I like to use most of stuff in space is

704
00:34:28,676 --> 00:34:31,756
Speaker 2: kind of like species on the Galapagos Island. They're like

705
00:34:32,436 --> 00:34:35,516
Speaker 2: these pieces of technology that have been like separated from

706
00:34:35,556 --> 00:34:37,796
Speaker 2: the mainland for so long. They've kind of evolved in

707
00:34:37,836 --> 00:34:39,116
Speaker 2: these like perverse ways.

708
00:34:39,676 --> 00:34:41,916
Speaker 1: And if you actually like this's whatever, like the motors

709
00:34:41,956 --> 00:34:44,356
Speaker 1: and the cameras and whatever, they're like, oh, I understand

710
00:34:44,356 --> 00:34:46,156
Speaker 1: how this used to be like a motor or camera

711
00:34:46,156 --> 00:34:48,756
Speaker 1: on Earth, But now it's totally different, totally.

712
00:34:48,396 --> 00:34:50,236
Speaker 2: Like literally to the point where if you looked at

713
00:34:50,276 --> 00:34:53,276
Speaker 2: like the computer on even a satellite that got launched

714
00:34:53,316 --> 00:34:56,156
Speaker 2: ten years ago, it would look like something that TI

715
00:34:56,236 --> 00:34:58,596
Speaker 2: put in a calculator in the nineteen seventies, right, I mean,

716
00:34:58,596 --> 00:35:01,756
Speaker 2: it's crazy. So there's this kind of like frozen in

717
00:35:01,796 --> 00:35:04,156
Speaker 2: time nature to a lot of the technology in space,

718
00:35:05,036 --> 00:35:06,756
Speaker 2: and because of the launch, but also because of these

719
00:35:06,756 --> 00:35:09,596
Speaker 2: things that we've been talking about, there's a big turnover

720
00:35:09,676 --> 00:35:12,036
Speaker 2: where now the technology in space is looking a lot

721
00:35:12,076 --> 00:35:14,316
Speaker 2: more like the stuff that goes into data center, where

722
00:35:14,356 --> 00:35:14,796
Speaker 2: that goes.

723
00:35:14,636 --> 00:35:15,516
Speaker 3: In your smartphone.

724
00:35:16,676 --> 00:35:20,756
Speaker 2: And because of that, there's also a lot more interconnectedness

725
00:35:20,756 --> 00:35:24,076
Speaker 2: between those technologies and the technologies on Earth. And I

726
00:35:24,076 --> 00:35:25,916
Speaker 2: think we're just in the early stages of this and

727
00:35:25,956 --> 00:35:28,196
Speaker 2: it's going to cause it's already causing, but it's going

728
00:35:28,276 --> 00:35:31,316
Speaker 2: to cause this huge convergence in what we think about

729
00:35:31,356 --> 00:35:34,276
Speaker 2: as space technology and every other bit of technology in

730
00:35:34,316 --> 00:35:36,996
Speaker 2: our life, to the point where we're going to realize that.

731
00:35:37,876 --> 00:35:40,796
Speaker 2: You know, right now, I do I use space technology

732
00:35:40,796 --> 00:35:43,836
Speaker 2: on here GPS, right, satellites are in space and when

733
00:35:43,836 --> 00:35:45,556
Speaker 2: I look at my map, it's telling me where I

734
00:35:45,596 --> 00:35:48,036
Speaker 2: am based on satellites and space. But nobody thinks about

735
00:35:48,036 --> 00:35:50,476
Speaker 2: those satellites, you know, when they look at their map

736
00:35:50,516 --> 00:35:53,956
Speaker 2: and ask where they're going. That's going to be true

737
00:35:53,996 --> 00:35:56,956
Speaker 2: of like a whole bunch of different things, Okay, what

738
00:35:57,156 --> 00:35:59,916
Speaker 2: things and when one of the most exciting things for me,

739
00:36:00,116 --> 00:36:02,156
Speaker 2: there's been a lot of discussion about like orbital data

740
00:36:02,156 --> 00:36:04,716
Speaker 2: centers right people are talking about orbital data centers right now.

741
00:36:04,836 --> 00:36:06,916
Speaker 1: Yeah, And just to be clear, like an actual like

742
00:36:07,036 --> 00:36:09,436
Speaker 1: AI data center that is some giant.

743
00:36:09,236 --> 00:36:13,316
Speaker 2: Gigawatts of solar power and space. Yeah yeah, yeah, and

744
00:36:13,396 --> 00:36:14,436
Speaker 2: it sounds crazy.

745
00:36:14,676 --> 00:36:15,676
Speaker 3: I think it's going to happen.

746
00:36:16,516 --> 00:36:18,436
Speaker 1: Okay, when and why?

747
00:36:19,556 --> 00:36:21,916
Speaker 3: So the why there's there's a bunch of reasons right

748
00:36:21,956 --> 00:36:25,116
Speaker 3: now to build a data center takes between five and

749
00:36:25,196 --> 00:36:30,396
Speaker 3: seven years okay, between permitting, construction, power, interconnect, all of

750
00:36:30,436 --> 00:36:31,076
Speaker 3: these other things.

751
00:36:31,116 --> 00:36:34,876
Speaker 1: I love that it's permitting, permitting, permit will drive data

752
00:36:34,876 --> 00:36:35,556
Speaker 1: centers to space.

753
00:36:36,636 --> 00:36:38,596
Speaker 2: It seems crazy, but I mean it's true, right and

754
00:36:38,636 --> 00:36:40,716
Speaker 2: so like again, and I'm not saying this is easy,

755
00:36:40,796 --> 00:36:43,316
Speaker 2: but if you could get a factory set up and

756
00:36:43,436 --> 00:36:46,756
Speaker 2: rockets launching frequently enough that you can go deploy that

757
00:36:46,756 --> 00:36:49,036
Speaker 2: one hundred megawatts of data center to space in two

758
00:36:49,116 --> 00:36:52,316
Speaker 2: years instead of five years, there's a huge time value

759
00:36:52,396 --> 00:36:53,316
Speaker 2: money component to that.

760
00:36:53,316 --> 00:36:56,476
Speaker 1: That's it's so wild. What give me the over under?

761
00:36:56,476 --> 00:36:57,636
Speaker 1: What year is that going to happen?

762
00:36:57,836 --> 00:36:58,676
Speaker 3: Twenty thirty five?

763
00:36:59,556 --> 00:37:00,876
Speaker 1: That soon? That soon?

764
00:37:01,196 --> 00:37:03,316
Speaker 3: I think that scale could happen that soon and.

765
00:37:03,396 --> 00:37:05,996
Speaker 1: Is part of what has to happen there, Like it

766
00:37:06,116 --> 00:37:08,916
Speaker 1: keeps getting cheaper to go to space absolute kilogram.

767
00:37:09,276 --> 00:37:09,516
Speaker 3: Yeah.

768
00:37:09,556 --> 00:37:11,996
Speaker 2: So so like we've done a bit of work on

769
00:37:12,036 --> 00:37:13,236
Speaker 2: this with some hyperscalers.

770
00:37:13,316 --> 00:37:16,676
Speaker 1: So they've come to you and asked hyperscalts is whatever.

771
00:37:16,756 --> 00:37:20,596
Speaker 1: This is four giant tech companies. They're all actually calling

772
00:37:20,636 --> 00:37:22,116
Speaker 1: you and saying, yeah, I mean it take to put

773
00:37:22,156 --> 00:37:23,116
Speaker 1: a data center in space.

774
00:37:23,196 --> 00:37:24,596
Speaker 3: Google has announced it publicly.

775
00:37:25,556 --> 00:37:30,596
Speaker 2: Okay, so you know the cost per kilogram to put

776
00:37:30,636 --> 00:37:33,276
Speaker 2: something in space is the key metric. And I think

777
00:37:33,756 --> 00:37:36,596
Speaker 2: for economic parody, if you think about, like how what

778
00:37:36,636 --> 00:37:40,196
Speaker 2: it would take to get to a similar cost per

779
00:37:40,316 --> 00:37:42,836
Speaker 2: unit capability of compute and a data center on Earth.

780
00:37:42,916 --> 00:37:46,156
Speaker 2: Right now, we're still off by about a factor of six.

781
00:37:46,356 --> 00:37:49,196
Speaker 2: So like it's six, but it's not one hundred, it's

782
00:37:49,756 --> 00:37:51,756
Speaker 2: one hundred. It's not even an order of magnetus.

783
00:37:51,836 --> 00:37:53,156
Speaker 3: It's not even an order of magnitudes.

784
00:37:53,236 --> 00:37:54,156
Speaker 5: And so is the.

785
00:37:54,036 --> 00:37:58,436
Speaker 1: Reason it's cheaper once you're up there? What the energy permitting?

786
00:37:58,476 --> 00:38:02,196
Speaker 1: Like why is it? Why is it only six? So

787
00:38:02,236 --> 00:38:03,036
Speaker 1: it only effect us.

788
00:38:03,156 --> 00:38:05,636
Speaker 2: So here's here's a really important factor. If you put

789
00:38:05,716 --> 00:38:09,036
Speaker 2: a satellite into space in the right orbit, you can

790
00:38:09,356 --> 00:38:11,316
Speaker 2: have solar rays that are in this sign one hundred

791
00:38:11,316 --> 00:38:14,476
Speaker 2: percent of the time. So it's baseload power. Now it's

792
00:38:14,516 --> 00:38:17,796
Speaker 2: solar is no baseload power. I mean, solar is extremely

793
00:38:17,876 --> 00:38:20,236
Speaker 2: cheap right now, but you can't use just solar to

794
00:38:20,236 --> 00:38:23,116
Speaker 2: power data center because it's not consistent, so you end

795
00:38:23,196 --> 00:38:26,676
Speaker 2: up needing massive some other form of massive energy storage,

796
00:38:26,876 --> 00:38:27,156
Speaker 2: and it.

797
00:38:27,076 --> 00:38:30,756
Speaker 3: Has become very expensive. So I think that's a big deal.

798
00:38:30,916 --> 00:38:33,396
Speaker 2: I think that's one of the reasons in my opinion,

799
00:38:33,396 --> 00:38:36,476
Speaker 2: that it at six x you know, it's cost competitive

800
00:38:36,476 --> 00:38:39,436
Speaker 2: because it's when you actually factor in all those other

801
00:38:39,516 --> 00:38:41,996
Speaker 2: costs to terrestrial data centers for things like power, it's

802
00:38:42,036 --> 00:38:46,676
Speaker 2: actually higher than you would think. Okay, So again, the

803
00:38:46,716 --> 00:38:48,676
Speaker 2: other thing I just think is an important point is

804
00:38:50,316 --> 00:38:52,116
Speaker 2: and I think this is being missed a little bit, like,

805
00:38:52,476 --> 00:38:56,396
Speaker 2: we don't go from here to gigawat data centers in space. Right,

806
00:38:56,436 --> 00:38:59,196
Speaker 2: there's this huge spectrum between where we are now and that,

807
00:38:59,836 --> 00:39:02,076
Speaker 2: and I have no doubt we're going to be going

808
00:39:02,116 --> 00:39:04,676
Speaker 2: way down that spectrum over the next ten years. Right,

809
00:39:04,756 --> 00:39:07,476
Speaker 2: so where it actually lands, Are we actually going to

810
00:39:07,556 --> 00:39:09,076
Speaker 2: have gigawat data centers in space?

811
00:39:09,276 --> 00:39:09,356
Speaker 4: Like?

812
00:39:09,796 --> 00:39:13,516
Speaker 3: Who knows? Right, who knows if the models will continue scaling.

813
00:39:13,236 --> 00:39:14,796
Speaker 1: The scaling laws will hold.

814
00:39:14,956 --> 00:39:17,236
Speaker 3: Yeah, there's a stuff. What's the midpoint?

815
00:39:17,276 --> 00:39:20,756
Speaker 1: What's the like easier to imagine launches don't have to

816
00:39:20,756 --> 00:39:24,196
Speaker 1: get that much cheaper. We're almost there now, but still

817
00:39:24,236 --> 00:39:25,356
Speaker 1: cool thing yeah.

818
00:39:25,396 --> 00:39:27,436
Speaker 2: I mean I think it's that there are these kind

819
00:39:27,516 --> 00:39:30,396
Speaker 2: of multi tiered layers of what we would think about

820
00:39:30,436 --> 00:39:33,476
Speaker 2: as like cloud infrastructure in space, right, so very high

821
00:39:33,516 --> 00:39:35,756
Speaker 2: speed network. So now you have this high speed network

822
00:39:35,796 --> 00:39:37,836
Speaker 2: that everything is connected to all the time in space,

823
00:39:37,876 --> 00:39:38,956
Speaker 2: which is not the case.

824
00:39:38,796 --> 00:39:42,236
Speaker 1: Now what we thought of a data center ten years ago,

825
00:39:42,516 --> 00:39:44,436
Speaker 1: like where they're like whatever.

826
00:39:45,836 --> 00:39:47,996
Speaker 2: You know what you're getting Netflix served from? And I

827
00:39:47,996 --> 00:39:50,476
Speaker 2: mean Starlink's already doing this a little bit. Starlink cash's

828
00:39:50,516 --> 00:39:53,996
Speaker 2: Netflix on the satellites, right. And what will happen is

829
00:39:54,036 --> 00:39:57,196
Speaker 2: as that builds out, there will be reasons to move

830
00:39:57,316 --> 00:40:00,436
Speaker 2: certain things that are in the cloud on data centers

831
00:40:00,436 --> 00:40:02,516
Speaker 2: and Earth now up to space to make the latency

832
00:40:02,556 --> 00:40:05,676
Speaker 2: lower or the processing more efficient or whatever. But we

833
00:40:05,676 --> 00:40:08,196
Speaker 2: won't even think about it. That'll just be in the background.

834
00:40:08,356 --> 00:40:10,316
Speaker 2: We'll use the thing and nobody will know whether it's

835
00:40:10,356 --> 00:40:12,036
Speaker 2: happening in space or happening in the ground.

836
00:40:15,276 --> 00:40:17,436
Speaker 1: We'll be back in a minute with the lightning round.

837
00:40:30,756 --> 00:40:34,756
Speaker 1: Let's finish with the lightning round. What's the secret to

838
00:40:34,796 --> 00:40:37,236
Speaker 1: having one of the best breaking balls in the Stanford bullpen?

839
00:40:39,876 --> 00:40:44,756
Speaker 3: Oh man, it's top spin.

840
00:40:46,436 --> 00:40:48,476
Speaker 1: What's your favorite satellite that you didn't build.

841
00:40:51,156 --> 00:40:52,356
Speaker 3: The Corona satellites.

842
00:40:53,196 --> 00:40:54,636
Speaker 1: Tell me about the Corona satellites.

843
00:40:55,076 --> 00:40:59,036
Speaker 2: So the Corona satellites were the first imaging satellites we

844
00:40:59,076 --> 00:41:02,916
Speaker 2: built in the early sixties. And they were film based systems,

845
00:41:02,956 --> 00:41:05,996
Speaker 2: so they had film in them that ran through them

846
00:41:05,996 --> 00:41:08,716
Speaker 2: that you know, this incredible film technology that ran through

847
00:41:08,716 --> 00:41:10,996
Speaker 2: them that you know, one hundreds defeat per second to

848
00:41:11,116 --> 00:41:13,836
Speaker 2: keep up with the speed that they were flying. And

849
00:41:13,836 --> 00:41:17,036
Speaker 2: that would get loaded into these canisters and dropped off

850
00:41:17,076 --> 00:41:20,596
Speaker 2: in reentry vehicles parachute down to Earth and then we

851
00:41:20,636 --> 00:41:22,396
Speaker 2: would go pick them up by scooping them out of

852
00:41:22,436 --> 00:41:23,756
Speaker 2: the air behind a plane.

853
00:41:23,956 --> 00:41:25,756
Speaker 3: To go develop the film and distribute it.

854
00:41:26,076 --> 00:41:28,996
Speaker 1: Why was I just reading about that? I was reading

855
00:41:28,996 --> 00:41:31,876
Speaker 1: about the special planes, right, they made special planes to

856
00:41:31,956 --> 00:41:33,476
Speaker 1: catch the canisters as they're.

857
00:41:33,316 --> 00:41:34,996
Speaker 3: Coming down, these like tohoks.

858
00:41:35,196 --> 00:41:38,596
Speaker 1: It's so wild and like physical, right. You're a mechanical engineer, right,

859
00:41:38,636 --> 00:41:41,716
Speaker 1: that's like very yeah, so mechanical, yeah, yeah.

860
00:41:41,516 --> 00:41:43,196
Speaker 2: I mean, and you look inside these things and there's

861
00:41:43,236 --> 00:41:45,316
Speaker 2: just like all these rollers and motors and stuff to

862
00:41:45,396 --> 00:41:47,596
Speaker 2: like move the film around and develop it and you know,

863
00:41:47,636 --> 00:41:48,116
Speaker 2: put in the.

864
00:41:48,076 --> 00:41:49,756
Speaker 3: Canister and drop the canister off.

865
00:41:50,276 --> 00:41:54,236
Speaker 1: So analog, right, it's just all physical stuff. What's something

866
00:41:54,276 --> 00:41:57,396
Speaker 1: you learned working on autonomous vehicles that lift that's been

867
00:41:57,476 --> 00:41:59,076
Speaker 1: useful to you at new.

868
00:42:00,316 --> 00:42:01,996
Speaker 3: Yeah, I mean I actually thought about this a lot.

869
00:42:03,036 --> 00:42:08,276
Speaker 2: The way that robotics and av used data is totally

870
00:42:09,476 --> 00:42:15,076
Speaker 2: revolutionary from somebody that's come from aerospace because of some

871
00:42:15,116 --> 00:42:17,556
Speaker 2: of the things we talked about about how primitive a

872
00:42:17,596 --> 00:42:22,596
Speaker 2: lot of historically, like compute on aerospace systems is, you

873
00:42:22,756 --> 00:42:26,316
Speaker 2: end up having to be very conservative about data and

874
00:42:26,356 --> 00:42:29,476
Speaker 2: processing and all of these things. And avs are one

875
00:42:29,556 --> 00:42:32,636
Speaker 2: hundred percent the opposite. They're just these like data vacuums

876
00:42:32,716 --> 00:42:34,676
Speaker 2: and so, but what it does is it means that

877
00:42:35,556 --> 00:42:38,516
Speaker 2: you think about these problems totally differently, like solving problems

878
00:42:38,516 --> 00:42:39,236
Speaker 2: totally differently.

879
00:42:39,516 --> 00:42:41,316
Speaker 1: So what does it mean to you in your work?

880
00:42:41,516 --> 00:42:42,276
Speaker 1: How do you use that?

881
00:42:42,836 --> 00:42:45,356
Speaker 2: I mean by architecting our systems to look more like

882
00:42:45,396 --> 00:42:47,276
Speaker 2: avs than satellites.

883
00:42:48,276 --> 00:42:50,236
Speaker 1: Just to be able to handle more data.

884
00:42:50,356 --> 00:42:53,156
Speaker 2: Yeah, I mean we can use the same technology. So

885
00:42:53,196 --> 00:42:56,076
Speaker 2: we are putting massive amounts of compute and networking and

886
00:42:56,356 --> 00:42:59,676
Speaker 2: you know, bandwidth on our satellites, and so we can

887
00:42:59,916 --> 00:43:00,236
Speaker 2: do that.

888
00:43:02,436 --> 00:43:05,756
Speaker 1: Will evidence of extraterrestrial life be discovered in your lifetime?

889
00:43:06,396 --> 00:43:06,596
Speaker 3: Yes?

890
00:43:08,116 --> 00:43:11,836
Speaker 1: Okay, Uh, there is there anything you know that I

891
00:43:11,876 --> 00:43:12,876
Speaker 1: don't know that makes you.

892
00:43:12,916 --> 00:43:15,516
Speaker 3: Say that, No, I just think that.

893
00:43:16,796 --> 00:43:19,676
Speaker 2: I think I think we'll discover very simple extraterrestrial life.

894
00:43:19,676 --> 00:43:23,316
Speaker 2: I think there's life arose so early in the history

895
00:43:23,396 --> 00:43:27,596
Speaker 2: of the planet, simple life that it just seems like

896
00:43:27,676 --> 00:43:30,876
Speaker 2: it's very likely to have risen in other places or

897
00:43:30,956 --> 00:43:32,796
Speaker 2: have been transplanted here from other places.

898
00:43:34,116 --> 00:43:36,276
Speaker 3: I think complex life is a lot harder.

899
00:43:36,316 --> 00:43:36,396
Speaker 5: Like.

900
00:43:36,476 --> 00:43:39,156
Speaker 3: I do not think we will discover complex life in

901
00:43:39,196 --> 00:43:39,676
Speaker 3: my life.

902
00:43:39,836 --> 00:43:43,516
Speaker 1: We'll find like bacteria on a moon of Jupiter or something.

903
00:43:43,876 --> 00:43:46,636
Speaker 3: Yeah, I think like single cell procaryot like things.

904
00:43:46,956 --> 00:43:52,196
Speaker 1: Yeah. Will you go to space before you die?

905
00:43:55,716 --> 00:43:57,996
Speaker 3: I would love to? I think probably not.

906
00:43:59,156 --> 00:43:59,476
Speaker 1: Why not?

907
00:44:02,916 --> 00:44:05,916
Speaker 2: I'm not convinced there's great reason to send humans to

908
00:44:05,956 --> 00:44:08,316
Speaker 2: space A lot I think. I think there's a lot

909
00:44:08,356 --> 00:44:10,876
Speaker 2: of things we can do without sending humans to space,

910
00:44:11,236 --> 00:44:11,516
Speaker 2: all right.

911
00:44:11,556 --> 00:44:12,996
Speaker 3: I think where we do, it'll be limited.

912
00:44:14,836 --> 00:44:15,756
Speaker 1: Why would you love to?

913
00:44:16,996 --> 00:44:19,236
Speaker 2: I'd love to see Earth from space? I think the

914
00:44:19,396 --> 00:44:22,876
Speaker 2: overview effects you I've heard that astronauts talk about the

915
00:44:22,916 --> 00:44:27,236
Speaker 2: total transformation and perspective that comes from seeing the Earth

916
00:44:27,236 --> 00:44:27,836
Speaker 2: from space.

917
00:44:28,796 --> 00:44:30,036
Speaker 3: So I'd love to experience that.

918
00:44:36,876 --> 00:44:40,556
Speaker 1: Johnny Dyer is the co founder and CEO of muon Space.

919
00:44:41,236 --> 00:44:44,316
Speaker 1: Today's show was produced by Gabriel Hunter Chang. It was

920
00:44:44,356 --> 00:44:48,596
Speaker 1: engineered by Sarah Bruguire and edited by Lydia Jane Kott.

921
00:44:48,796 --> 00:44:51,796
Speaker 1: I'm Jacob Goldstein. You can find me on x at

922
00:44:51,916 --> 00:44:54,476
Speaker 1: Jacob Goldstein. You can find me on LinkedIn. You can

923
00:44:54,516 --> 00:44:58,716
Speaker 1: also email us at problem at pushkin dot FM. We'll

924
00:44:58,716 --> 00:45:01,556
Speaker 1: be back next week with another episode of the show.

925
00:45:10,556 --> 00:45:13,716
Speaker 1: I'm Jacob Goldstein and right now we're going to play

926
00:45:13,756 --> 00:45:16,356
Speaker 1: you a clip of a new show that I co host.

927
00:45:16,716 --> 00:45:19,996
Speaker 1: The show's called Business History. My co host is Robert Smith,

928
00:45:20,196 --> 00:45:22,796
Speaker 1: and this clip is from an episode we did about

929
00:45:22,836 --> 00:45:27,556
Speaker 1: how Nolan Bush, now a stoner turned entrepreneur, created the

930
00:45:27,636 --> 00:45:32,756
Speaker 1: video game company Atari and hired a young, inexperienced Steve Jobs.

931
00:45:33,156 --> 00:45:35,236
Speaker 1: I really hope you like the clip, and if you

932
00:45:35,236 --> 00:45:38,356
Speaker 1: want to hear more, you can find Business History wherever

933
00:45:38,396 --> 00:45:42,636
Speaker 1: you're listening to this show. Right now, this like nineteen

934
00:45:42,716 --> 00:45:45,316
Speaker 1: year old hippie kid walks in and he says he

935
00:45:45,396 --> 00:45:48,436
Speaker 1: won't leave until they give him a job, and the

936
00:45:48,436 --> 00:45:52,076
Speaker 1: receptionist calls the head engineer and she goes, yeah, we

937
00:45:52,116 --> 00:45:54,116
Speaker 1: got a hippiekid in the lobby says, he won't leave

938
00:45:54,156 --> 00:45:56,836
Speaker 1: until we hire him. Should we call the cops or

939
00:45:56,916 --> 00:45:59,476
Speaker 1: let him in? And the engineer says.

940
00:45:59,276 --> 00:46:00,076
Speaker 4: Bring him on in.

941
00:46:00,316 --> 00:46:03,596
Speaker 5: It's nineteen seventies, a Silicon valley, and this guy wanders

942
00:46:03,636 --> 00:46:06,716
Speaker 5: in and he is whenever you tell a story like this,

943
00:46:06,756 --> 00:46:07,276
Speaker 5: you know who it is.

944
00:46:07,316 --> 00:46:07,996
Speaker 4: It's Steve Jobs.

945
00:46:08,036 --> 00:46:13,636
Speaker 1: It's Steve Job fun. It's so delightful and perfectly. Steve

946
00:46:13,716 --> 00:46:16,916
Speaker 1: Jobs is very good at his job, yes, and very

947
00:46:16,956 --> 00:46:21,636
Speaker 1: unpleasant to wrqu with. On surprising, he tells Bushnell that

948
00:46:21,716 --> 00:46:24,636
Speaker 1: like everybody's soldering wrong, right, they're actually putting together the hardware,

949
00:46:24,676 --> 00:46:25,436
Speaker 1: soldering the hardware.

950
00:46:25,516 --> 00:46:27,476
Speaker 4: He's probably right, and bush Nell's like, yeah, he was right.

951
00:46:27,836 --> 00:46:30,596
Speaker 1: He keeps calling his manager a dumb shit. Probably was

952
00:46:31,036 --> 00:46:35,636
Speaker 1: not kind, but not necessary. Bushnell winds up putting Jobs

953
00:46:35,676 --> 00:46:38,996
Speaker 1: on the night shift, partly so he won't bother so

954
00:46:39,036 --> 00:46:42,316
Speaker 1: many people, and partly because he knew that Jobs like

955
00:46:42,356 --> 00:46:45,436
Speaker 1: to hang out at night with his buddy Steve Wozniak,

956
00:46:45,796 --> 00:46:47,756
Speaker 1: who was a great engineer, would be great to have

957
00:46:48,196 --> 00:46:51,636
Speaker 1: hanging around atari And in fact, Jobs and Wozniak helped

958
00:46:51,636 --> 00:46:55,436
Speaker 1: to make Breakout great a target. Remember Breakout, of.

959
00:46:55,356 --> 00:46:57,356
Speaker 4: Course you're trying to knock down the bricks in a wall.

960
00:46:57,636 --> 00:46:59,436
Speaker 1: Still games like that, when you got a little paddle

961
00:46:59,436 --> 00:47:00,196
Speaker 1: at the bottom.

962
00:46:59,916 --> 00:47:01,436
Speaker 4: End, there's a moment when it goes through the wall

963
00:47:01,476 --> 00:47:02,836
Speaker 4: and then goes so.

964
00:47:04,436 --> 00:47:07,676
Speaker 1: Good. So Jobs worked at Atari for a little while

965
00:47:07,756 --> 00:47:09,876
Speaker 1: and then decided he wanted to go out to India

966
00:47:09,916 --> 00:47:14,236
Speaker 1: to find his guru. Perfect asked Atari to pay for

967
00:47:14,276 --> 00:47:17,596
Speaker 1: the trip. Nobody ever said he lacked moxie and wound

968
00:47:17,716 --> 00:47:20,436
Speaker 1: up making a deal with Atari where they pay him

969
00:47:20,476 --> 00:47:22,956
Speaker 1: to go part of the way there. They had exported

970
00:47:22,956 --> 00:47:24,396
Speaker 1: some games to Germany and there was some kind of

971
00:47:24,436 --> 00:47:26,116
Speaker 1: problem with the games in Germany, and they're like, we'll

972
00:47:26,156 --> 00:47:28,796
Speaker 1: send you to Germany to fix the games and then

973
00:47:28,836 --> 00:47:30,356
Speaker 1: you can get the rest of the way to India.

974
00:47:30,916 --> 00:47:33,716
Speaker 1: And you know, the Germans said Jobs was terrible to

975
00:47:33,756 --> 00:47:36,876
Speaker 1: work with, but he fixed the games. I was thinking

976
00:47:36,876 --> 00:47:39,356
Speaker 1: about like the link between Atarian Jobs and what did

977
00:47:39,356 --> 00:47:41,596
Speaker 1: he learn there, and it felt like maybe a little overdetermined,

978
00:47:41,636 --> 00:47:44,796
Speaker 1: but I do think, you know, clearly he had this

979
00:47:44,956 --> 00:47:49,356
Speaker 1: profound sense of aesthetics and of delight, right, like think

980
00:47:49,396 --> 00:47:52,116
Speaker 1: of the Macintosh, right, this breakthrough Apple machine in the eighties.

981
00:47:52,236 --> 00:47:55,076
Speaker 1: It was round and instead of squares, it was rounded,

982
00:47:55,116 --> 00:47:57,436
Speaker 1: and it cost them more money, but it was beautiful

983
00:47:57,436 --> 00:47:59,756
Speaker 1: and it was fun and you were engaged with it

984
00:47:59,956 --> 00:48:00,516
Speaker 1: like a game.

985
00:48:01,116 --> 00:48:03,196
Speaker 5: It almost looked a little bit like an arcade game

986
00:48:03,276 --> 00:48:05,876
Speaker 5: with the curves. Yeah, but more than that, I think

987
00:48:05,916 --> 00:48:08,356
Speaker 5: that especially at Silicon Valley at the time when they

988
00:48:08,356 --> 00:48:10,956
Speaker 5: were dealing in action Silicon. Right, if you're making chips

989
00:48:10,956 --> 00:48:14,156
Speaker 5: for somebody, you're thinking about the future and the computers.

990
00:48:14,356 --> 00:48:16,796
Speaker 5: You're not thinking about the psychology of the customer, because

991
00:48:16,836 --> 00:48:20,356
Speaker 5: the customer was another electronics company, right, And so Atari

992
00:48:20,476 --> 00:48:22,596
Speaker 5: and the strength of it was really the first time

993
00:48:22,636 --> 00:48:26,236
Speaker 5: where they're just like, how will a regular human being

994
00:48:26,316 --> 00:48:29,516
Speaker 5: who has no training whatsoever interact with technology?

995
00:48:29,676 --> 00:48:30,236
Speaker 3: Yeah?

996
00:48:30,316 --> 00:48:36,196
Speaker 1: So Atari now mid seventies, they're selling all the machines

997
00:48:36,236 --> 00:48:39,356
Speaker 1: they can make. They need more space, and so they

998
00:48:39,396 --> 00:48:42,596
Speaker 1: rent an abandoned roller rink and they turn it into

999
00:48:42,596 --> 00:48:44,876
Speaker 1: this office slash video game factory.

1000
00:48:45,356 --> 00:48:47,636
Speaker 4: I've been ten years older, I would have loved to

1001
00:48:47,636 --> 00:48:49,596
Speaker 4: have worked in a roller rink video game factory.

1002
00:48:49,676 --> 00:48:52,836
Speaker 1: Yes, everybody smoked weed. Yeah, there was a hot tub.

1003
00:48:54,476 --> 00:48:56,636
Speaker 1: There was a pool party where everybody ended up naked

1004
00:48:56,636 --> 00:49:00,556
Speaker 1: in the pool at Bushnell himself looked back on it

1005
00:49:00,636 --> 00:49:03,876
Speaker 1: later and said if that isn't a horror show for

1006
00:49:03,916 --> 00:49:06,796
Speaker 1: any HR person today, I don't know what is