WEBVTT - Building Boundary-Breaking Balloons

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<v Speaker 1>Pushkin. Every day around the world, more than a thousand

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<v Speaker 1>weather balloons are launched into the sky. The balloons float

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<v Speaker 1>high up into the atmosphere, sending back information you know, temperature,

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<v Speaker 1>wind speed, air pressure, et cetera, and then a couple

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<v Speaker 1>hours later, the balloons pop. This is basically the way

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<v Speaker 1>weather balloons have worked for decades, and the information the

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<v Speaker 1>balloons send back is really useful for weather forecasts. But

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<v Speaker 1>the information the balloon send back is also pretty limited

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<v Speaker 1>because the balloons only stay in the sky for a

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<v Speaker 1>couple hours, they don't fly very far. If we could

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<v Speaker 1>figure out how to make balloons stay up for longer,

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<v Speaker 1>they could blow in the wind and travel thousands of miles.

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<v Speaker 1>They could travel across continents and across oceans and send

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<v Speaker 1>us back a lot more data and give us a

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<v Speaker 1>much clearer picture of what weather is coming our way.

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<v Speaker 1>I'm Jacob Goldstein and this is What's Your Problem, the

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<v Speaker 1>show where I talk to people who are trying to

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<v Speaker 1>make technological progress. My guest today is Kai Marshland. He's

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<v Speaker 1>the co founder and chief product officer of Windborne Systems.

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<v Speaker 1>Kai's problem is this, can you build weather balloons that

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<v Speaker 1>stay in the air for weeks or months instead of hours,

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<v Speaker 1>And can you pair the data from those balloons with

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<v Speaker 1>AI to make weather forecasts that are way better than

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<v Speaker 1>anything we have today. Kai got interested in balloons back

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<v Speaker 1>in twenty fifteen. He was a freshman at Stanford and

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<v Speaker 1>he joined the Student Space Initiative, which sounds kind of

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<v Speaker 1>fancy but in fact was basically a bunch of college

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<v Speaker 1>kids trying to build their own weather balloons.

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<v Speaker 2>The first half dozen flights of our balloons were complete

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<v Speaker 2>and total failures.

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<v Speaker 1>When do things start working?

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<v Speaker 2>That would be back in June of twenty sixteen. This

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<v Speaker 2>is the end of my freshman year, and I think

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<v Speaker 2>our longest flight that year had been fourteen minutes, so

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<v Speaker 2>shorter than a normal weather balloon.

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<v Speaker 1>Buy a lot, Buy a lot.

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<v Speaker 2>And also the person who had started this project, who

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<v Speaker 2>then became my co founder, had just graduated and was

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<v Speaker 2>about to leave what we thought never to be seen again.

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<v Speaker 2>And school has ended. We can't even get into the

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<v Speaker 2>building because our key cards have stopped working, so we

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<v Speaker 2>have to tailgate someone in to get our balloon launch

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<v Speaker 2>equipment out. Go out to the middle of nowhere in

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<v Speaker 2>the central Valley of California, and if this launch didn't succeed,

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<v Speaker 2>windborne would not exist.

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<v Speaker 1>Okay, so I can guess what happened next based on

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<v Speaker 1>that fact. But tell me what happened.

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<v Speaker 2>Yeah. So it got to be about four am, and

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<v Speaker 2>we're out in the middle of this park, really cold,

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<v Speaker 2>and the sprinklers turn on. They start slowly swiveling around

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<v Speaker 2>towards our delicate electronics, and so I have to hurl

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<v Speaker 2>myself onto the sprinklers, getting soaked, just to save our electronics.

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<v Speaker 1>It's like the nerd version of throwing yourself on a

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<v Speaker 1>grenade exactly.

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<v Speaker 2>I really felt like I was in an action movie sequence.

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<v Speaker 2>Ah good, But that sacrifice really paid off because with

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<v Speaker 2>that flight, we managed to set the world record for

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<v Speaker 2>weather balloon endurance.

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<v Speaker 1>How long did it stay up?

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<v Speaker 2>It stayed up for seventy six hours?

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<v Speaker 1>Wow, and your previous best.

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<v Speaker 2>Was fourteen minutes exactly.

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<v Speaker 1>So you're in college, how do you build weather balloons

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<v Speaker 1>that are better than whatever the federal government? Noah's launching

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<v Speaker 1>our you know, state of the art weather balloon.

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<v Speaker 2>People. So the first big thing is that some new

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<v Speaker 2>consumer electronics have come out, in particular, things like micro

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<v Speaker 2>controllers and lightweight, low power satellite communications, and we realize

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<v Speaker 2>that we can use these innovations to make a balloon

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<v Speaker 2>smarter and to control its altitude.

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<v Speaker 1>What's a micro controller.

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<v Speaker 2>Micro controller is a super small computer that you can

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<v Speaker 2>put in any device to give it a brain.

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<v Speaker 1>And they're cheap.

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<v Speaker 2>They're dirt cheap. Anytime you hear the word Internet of things,

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<v Speaker 2>that means micro controlers.

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<v Speaker 1>And nobody else was doing this at the time, Like

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<v Speaker 1>all the big, you know, well funded weather agencies weren't

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<v Speaker 1>sticking my micro controllers on their on their weather balloons.

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<v Speaker 2>Well, with these big agencies, they are really incentivized to

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<v Speaker 2>keep things stable because weather is this really important thing

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<v Speaker 2>that billions of people depend on, and so they're not

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<v Speaker 2>going to go and say, hey, what crazy experiments can

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<v Speaker 2>we run.

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<v Speaker 1>Their incentive is just like, don't screw it up exactly

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<v Speaker 1>the way we did it yesterday. Don't screw it up

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<v Speaker 1>and you won't get fired exactly. And so specifically, what

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<v Speaker 1>was it that you were doing that no one had

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<v Speaker 1>done before.

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<v Speaker 2>Yeah, So it's a very simple concept that is really

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<v Speaker 2>hard to make work and practice. The way we control

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<v Speaker 2>our altitude is when it gets too high up, we

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<v Speaker 2>vent some of our gas so it has less lift.

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<v Speaker 2>When we fall down too far, we drop some ballast

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<v Speaker 2>so it stops falling. But the hard part about this

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<v Speaker 2>is it has to function at negative seventy degrees celsius.

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<v Speaker 2>That's the temperature of dry ice. You have to have

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<v Speaker 2>a lot of on board software. That's where the micro

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<v Speaker 2>controllers come in to do things like understand is this

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<v Speaker 2>just turbulence that you're hitting or is it actually a

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<v Speaker 2>real change in lift that we need to drop ballast

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<v Speaker 2>or vent gas to account for. And in fact, we

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<v Speaker 2>stayed up twenty four to seven. Every single time it

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<v Speaker 2>talked to us. Every five minutes, we had it play

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<v Speaker 2>an airhorn sound because we were, of course falling asleep

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<v Speaker 2>in the middle of them to see what it was doing.

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<v Speaker 1>So, okay, so you set the record, you stay up

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<v Speaker 1>for what three days watching it? How far did it go?

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<v Speaker 2>It flew all the way across the country and landed

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<v Speaker 2>out in the Atlantic Ocean. But the real reason why

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<v Speaker 2>we decided to form a company around this was because

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<v Speaker 2>we realized the impact this could have. We got into

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<v Speaker 2>this from the engineering side. The lands are fun, but

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<v Speaker 2>we realized, wait a secd eighty five percent of the

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<v Speaker 2>Earth's atmosphere is invisible to humanity. Weather is this crazy,

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<v Speaker 2>unpredictable thing, and we can solve that. And weather is

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<v Speaker 2>so much more than just do you bring an umbrella

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<v Speaker 2>to work tomorrow is the single most immediately destructive aspect

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<v Speaker 2>of climate change, and improving the weather forecast it of

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<v Speaker 2>course helps with adapting to climate change, things like predicting

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<v Speaker 2>where a hurricane will make landfall, but it can also

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<v Speaker 2>help with preventing climate change in the first place. If

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<v Speaker 2>you have a better weather forecast, you can better route

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<v Speaker 2>ships and planes to save a huge amount of fuel.

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<v Speaker 2>You can accelerate the transition to renewables because you know

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<v Speaker 2>when the wind will be blowing or the sun will

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<v Speaker 2>be shining. And we looked at this and said, no

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<v Speaker 2>one else has our balloon technology. If we don't do this,

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<v Speaker 2>no one will. And if we want away from this opportunity,

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<v Speaker 2>we can't live with ourselves.

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<v Speaker 1>I mean, I buy that the stakes are high. I

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<v Speaker 1>buy that it could be very helpful, but like, why

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<v Speaker 1>wouldn't anybody else do it?

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<v Speaker 2>Yeah, a lot of people are trying to improve the

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<v Speaker 2>weather forecast. But what's unique about Windborne is the combination

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<v Speaker 2>of our balloon technology with our AI weather forecasts, because

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<v Speaker 2>really those are the two levers to pull to increase

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<v Speaker 2>the accuracy of a weather forecast.

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<v Speaker 1>You mentioned that eighty five percent of the world lacks

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<v Speaker 1>good weather data, which was surprising to me the first

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<v Speaker 1>time I heard it. But I would think, like, get

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<v Speaker 1>satellites do it. That's kind of I guess my naive

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<v Speaker 1>ish thought. It's like, I know there's a lot of

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<v Speaker 1>satellites looking down at the Earth all the time.

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<v Speaker 2>I'm actually a big fan of the space industry. I

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<v Speaker 2>used to work at SpaceX. But the problem is the

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<v Speaker 2>laws of physics fundamental limit what satellites can measure. Take

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<v Speaker 2>for example, pressure. It turns out pressure is extremely important

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<v Speaker 2>for predicting the weather because it determines where the winds

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<v Speaker 2>are blowing, how the weather systems are moving. But a

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<v Speaker 2>satellite fundamentally cannot measure pressure from space because it's not

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<v Speaker 2>in the atmosphere. It can't see what's going on.

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<v Speaker 1>So you start the company you're good at making weather

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<v Speaker 1>balloons that go up and stay up for a long time,

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<v Speaker 1>and that you can steer. I also read that you

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<v Speaker 1>launched thinking that you could collect data for a tenth

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<v Speaker 1>the cost of existing alternatives, which sounds compelling, but as

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<v Speaker 1>I understand was not compelling enough. Is that right?

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<v Speaker 2>It is. We're now at one hundred and fifty times

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<v Speaker 2>more data per dollar than alternatives.

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<v Speaker 1>Okay, was there some reason one tenth the cost was

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<v Speaker 1>not cheap enough.

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<v Speaker 2>Yeah. When we started the company, we thought it would

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<v Speaker 2>be cheap enough. But it turns out when you really

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<v Speaker 2>look at the scale of weather data collection, you need

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<v Speaker 2>to really understand the atmosphere. You want ten thousand balloons

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<v Speaker 2>aloft concurrently, that's a balloon every sixty miles in the atmosphere.

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<v Speaker 2>And in order to get to that level of scale,

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<v Speaker 2>it's the difference between your company spending one hundred million

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<v Speaker 2>dollars a year and spending a billion dollars a year.

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<v Speaker 2>One was a lot more in reach.

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<v Speaker 1>How do you drive down the cost to one one

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<v Speaker 1>hundred and fiftieth the cost? I mean, I'm sure it's

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<v Speaker 1>many many, many incremental you know, efficiency gains. But like,

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<v Speaker 1>what's an example of one.

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<v Speaker 2>Yeah, Well, one of the big pieces is improving the

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<v Speaker 2>software that flies on it so that the balloon can

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<v Speaker 2>fly for longer. I talked about in the student group

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<v Speaker 2>that first flight lasting seventy six hours. Now our longest

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<v Speaker 2>flight can fly for over four days.

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<v Speaker 1>Wow.

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<v Speaker 2>And the longer you fly, well, the hardware costs stays

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<v Speaker 2>the same, so that means you're collecting a lot more data.

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<v Speaker 1>Yeah. Yeah. So basically, make the balloon stamp for longer

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<v Speaker 1>is the fundamental way that you make it cheaper exactly,

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<v Speaker 1>And how do you make it fly longer?

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<v Speaker 2>Yeah, Well, one of the big things is improving the

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<v Speaker 2>software to better decide when do you vent gas, when

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<v Speaker 2>do you drop ballast or really one of the other

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<v Speaker 2>big things is just making everything smaller and lighter, because

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<v Speaker 2>the smaller it is, the lighter it is, the less

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<v Speaker 2>chance of a leak, the less ballast you have to use.

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<v Speaker 2>And things have just shrunk down so far. It really

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<v Speaker 2>surprises me sometimes when I'm like, wait, that tiny thing

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<v Speaker 2>the size of a dime replaces the thing that was

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<v Speaker 2>the size of a dinner plate before.

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<v Speaker 1>Yeah, that's amazing. So okay, so let's talk about where

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<v Speaker 1>you are today. Let's talk about sort of how it works.

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<v Speaker 1>How big are the balloons?

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<v Speaker 2>So the balloon is two pieces. There's the envelope that's

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<v Speaker 2>the bag that holds all parts the balloon part of

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<v Speaker 2>the balloon, and then there's the main unit, which is

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<v Speaker 2>the electronics and the ballast.

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<v Speaker 1>The stuff exactly, the balloon and the stuff the balloon part.

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<v Speaker 2>Of the balloon. The balloon part of the balloon is

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<v Speaker 2>five and a half meters tall. So that's two and

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<v Speaker 2>a half three times the height of person, depending on

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<v Speaker 2>how tall you are.

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<v Speaker 1>Yeah, so tall. Yeah, it's a big balloon. And how

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<v Speaker 1>how big is the stuff? How big is are the

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<v Speaker 1>sensors in the part that's not the.

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<v Speaker 2>Yeah, that stuff is. I guess this is an audio thing,

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<v Speaker 2>so I would say about this big.

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<v Speaker 1>The size of a basketball.

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<v Speaker 2>Yeah, it's a roughly the size of a basketball. It

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<v Speaker 2>weighs just over four pounds, so about the weight of

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<v Speaker 2>a large duck. Okay, and is kind of long and skinny.

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<v Speaker 2>It kind of looks like a fish. I think of

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<v Speaker 2>it as a trout attached to a giant bag.

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<v Speaker 1>How many of your balloons are in the sky right now? About?

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<v Speaker 2>Yeah, there are a few dozen aloft right now.

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<v Speaker 1>Okay.

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<v Speaker 2>We launch around one hundred a month and are quickly

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<v Speaker 2>ramping that up.

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<v Speaker 1>So where do you launch your balloons from?

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<v Speaker 2>We launch them every day from three continents, South Korea,

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<v Speaker 2>paloelto New York and Cabo Verde.

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<v Speaker 1>Cabo Verde is just off off the west coast of Africa.

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<v Speaker 1>Is that right?

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<v Speaker 2>You got it. One of the reasons why we have

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<v Speaker 2>launch site in Cabo Verde right now. That's right where

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<v Speaker 2>a lot of hurricanes are forming, and so the fact

0:14:02.356 --> 0:14:06.116
<v Speaker 2>that we're collecting data around there is going to be

0:14:06.316 --> 0:14:10.716
<v Speaker 2>really impactful for better predicting the path of these hurricanes. Huh.

0:14:10.756 --> 0:14:13.876
<v Speaker 1>So they start there and then they essentially travel across

0:14:13.916 --> 0:14:16.396
<v Speaker 1>the Atlantic and into the Americas, and if you can

0:14:16.516 --> 0:14:19.876
<v Speaker 1>understand what's going on there, ideally you can sort of

0:14:19.996 --> 0:14:22.716
<v Speaker 1>understand the hurricane where it's going to go in the

0:14:22.796 --> 0:14:26.276
<v Speaker 1>dream scenario, even just as it's becoming a tropical storm.

0:14:26.356 --> 0:14:30.476
<v Speaker 2>Exactly. Yeah, Noah did an analysis of the Impactor data

0:14:30.516 --> 0:14:35.756
<v Speaker 2>had on the twenty twenty two hurricane season, and it

0:14:36.436 --> 0:14:40.836
<v Speaker 2>made the forecasts for Hurricane Fiona about twenty percent better.

0:14:43.876 --> 0:14:46.316
<v Speaker 1>Now it's time for a few ads. After the ads,

0:14:46.556 --> 0:14:50.716
<v Speaker 1>Kai and I will talk about AI. Of course, we'll

0:14:50.716 --> 0:14:54.156
<v Speaker 1>also talk about Winborne's business, what they're selling, and we'll

0:14:54.156 --> 0:14:56.876
<v Speaker 1>talk about the company's quest to build balloons that can

0:14:56.916 --> 0:14:59.476
<v Speaker 1>stay in the air for months at a time and

0:14:59.476 --> 0:15:16.916
<v Speaker 1>make multiple trips around the world. What is your business

0:15:16.956 --> 0:15:18.636
<v Speaker 1>right now, like, what are you selling and who you're

0:15:18.636 --> 0:15:19.036
<v Speaker 1>selling it.

0:15:18.996 --> 0:15:22.236
<v Speaker 2>To right now. Our business has two pieces to it.

0:15:22.596 --> 0:15:26.276
<v Speaker 2>We are selling the observations of the atmosphere we collect

0:15:26.516 --> 0:15:30.356
<v Speaker 2>to the government right now, so that those governments can

0:15:31.436 --> 0:15:36.236
<v Speaker 2>use this data to improve your weather forecast. And just

0:15:36.276 --> 0:15:40.116
<v Speaker 2>a couple of weeks ago, we announced our AI based

0:15:40.156 --> 0:15:43.836
<v Speaker 2>weather model, which is the world's most accurate global weather

0:15:43.916 --> 0:15:44.996
<v Speaker 2>model bar none.

0:15:45.036 --> 0:15:46.836
<v Speaker 1>I feel like you need a little TM when you

0:15:46.876 --> 0:15:50.116
<v Speaker 1>say the world's most accurate global weather model bar none.

0:15:50.276 --> 0:15:51.916
<v Speaker 2>We really do, we really do.

0:15:52.076 --> 0:15:54.596
<v Speaker 1>So let's talk about that. Is that claim validated? I mean,

0:15:54.836 --> 0:15:57.196
<v Speaker 1>I know there was news about that just this year,

0:15:57.236 --> 0:16:01.476
<v Speaker 1>and congratulations, but also, how do I know that's true? Respectfully?

0:16:01.636 --> 0:16:06.116
<v Speaker 2>Yeah, I love that question because anytime a weather company

0:16:06.156 --> 0:16:10.636
<v Speaker 2>makes a claim, you should look at it closely. So,

0:16:10.996 --> 0:16:15.116
<v Speaker 2>first off, we've published our results on our website and

0:16:15.196 --> 0:16:18.876
<v Speaker 2>so you can dig into some of the raw numbers there. Second,

0:16:18.996 --> 0:16:23.076
<v Speaker 2>we're in the process of submitting to weather bench which

0:16:23.156 --> 0:16:28.836
<v Speaker 2>is these benchmarks run by Google, which were the previous

0:16:29.116 --> 0:16:33.916
<v Speaker 2>holders of this record. So we're actively talking with them

0:16:33.996 --> 0:16:38.956
<v Speaker 2>right now about submitting our results getting them validated into

0:16:38.996 --> 0:16:41.956
<v Speaker 2>live up there. It's just a process that takes a

0:16:41.996 --> 0:16:42.996
<v Speaker 2>little while, so.

0:16:43.036 --> 0:16:45.676
<v Speaker 1>Let's talk about the model you built. So I mean

0:16:46.676 --> 0:16:49.236
<v Speaker 1>generally when I talk to people who are working on AI,

0:16:49.356 --> 0:16:51.516
<v Speaker 1>they talk a lot about data, right, And it seems

0:16:51.596 --> 0:16:57.156
<v Speaker 1>like to some significant degree the models are kind of commoditized,

0:16:57.196 --> 0:17:00.316
<v Speaker 1>not quite, but like models are pretty similar, it seems

0:17:00.356 --> 0:17:02.676
<v Speaker 1>in many settings at least one to the other, and

0:17:02.756 --> 0:17:06.476
<v Speaker 1>the differentiator often ends up being the data. Is that

0:17:06.516 --> 0:17:08.076
<v Speaker 1>the case in this instance? I mean, do you think

0:17:08.116 --> 0:17:10.596
<v Speaker 1>you're the best. If you're the best because you have

0:17:10.836 --> 0:17:13.596
<v Speaker 1>all this data from all your balloons.

0:17:14.316 --> 0:17:16.556
<v Speaker 2>I think that you're spot on. Data is the real

0:17:16.596 --> 0:17:20.436
<v Speaker 2>mote here, because what really sets us apart is the

0:17:20.476 --> 0:17:24.356
<v Speaker 2>ability to have all of this data that no one

0:17:24.356 --> 0:17:28.716
<v Speaker 2>else has and to use that both for training the

0:17:28.756 --> 0:17:32.236
<v Speaker 2>model but then also for running the model in real time.

0:17:32.476 --> 0:17:35.956
<v Speaker 2>Data isn't just about training. It's about essentially giving your

0:17:35.956 --> 0:17:40.436
<v Speaker 2>model the prompt of what's going to happen next, and

0:17:40.676 --> 0:17:43.076
<v Speaker 2>you need new data every single day for that.

0:17:44.396 --> 0:17:48.676
<v Speaker 1>Just one thing to clarify. So, if you're selling the

0:17:48.796 --> 0:17:53.556
<v Speaker 1>data to governments, are they not sharing it widely? Like

0:17:53.596 --> 0:17:56.076
<v Speaker 1>they're paying for the data but they're not. But not

0:17:56.156 --> 0:17:58.156
<v Speaker 1>everybody can get at the data? Is that why it's

0:17:58.156 --> 0:17:58.756
<v Speaker 1>a mode for you?

0:17:59.396 --> 0:18:03.236
<v Speaker 2>Yeah, so what they're using the data we sell them

0:18:03.436 --> 0:18:09.236
<v Speaker 2>for is putting it into conventional physics based weather model

0:18:09.596 --> 0:18:14.796
<v Speaker 2>that they then release. So they can't redistribute the data itself,

0:18:15.756 --> 0:18:20.116
<v Speaker 2>but the general public can benefit from its use in

0:18:20.156 --> 0:18:21.116
<v Speaker 2>these weather models.

0:18:21.436 --> 0:18:24.356
<v Speaker 1>And so just to distinguish and this is a distinction

0:18:24.516 --> 0:18:27.636
<v Speaker 1>that's not particular to your company, but you mentioned the

0:18:27.676 --> 0:18:30.676
<v Speaker 1>sort of traditional physics based weather models. I mean, I

0:18:30.676 --> 0:18:33.516
<v Speaker 1>think it's worth spending just a moment here to distinguish, right,

0:18:33.596 --> 0:18:39.476
<v Speaker 1>like between the classic weather model, you know, the sort

0:18:39.516 --> 0:18:42.436
<v Speaker 1>of pre AI weather model, and the AI weather model. Like,

0:18:43.316 --> 0:18:44.996
<v Speaker 1>what's the basic difference between those two?

0:18:45.436 --> 0:18:49.516
<v Speaker 2>Yeah, So a conventional physics based weather model, it takes

0:18:49.516 --> 0:18:52.956
<v Speaker 2>the initial state of the atmosphere and then runs a

0:18:53.156 --> 0:18:59.116
<v Speaker 2>bunch of fluid dynamics on it to simulate what's going

0:18:59.156 --> 0:19:01.196
<v Speaker 2>to happen to all of these different fluids.

0:19:01.676 --> 0:19:04.036
<v Speaker 1>There's a sort of kind of classic sort of feels

0:19:04.036 --> 0:19:08.036
<v Speaker 1>like kind of nineteenth century deterministic. Give me the initial

0:19:08.796 --> 0:19:10.876
<v Speaker 1>condition and I'll tell you what's going to happen in

0:19:10.876 --> 0:19:13.516
<v Speaker 1>the future. It's that right, it's sort of rules based.

0:19:13.836 --> 0:19:21.076
<v Speaker 2>Exactly exactly, and that is nice because you can see

0:19:21.116 --> 0:19:25.236
<v Speaker 2>the exact physics that is being used, but you need

0:19:26.316 --> 0:19:29.996
<v Speaker 2>compute clusters. They cost hundreds of millions of dollars in

0:19:30.076 --> 0:19:33.076
<v Speaker 2>order to run this because the atmosphere is so big.

0:19:34.396 --> 0:19:39.076
<v Speaker 2>And by contrast, a AI based weather model can run

0:19:39.236 --> 0:19:41.396
<v Speaker 2>on just a gaming laptop.

0:19:42.316 --> 0:19:42.516
<v Speaker 1>Huh.

0:19:43.356 --> 0:19:48.956
<v Speaker 2>And what it does, by contrast, is effectively picking out statistics.

0:19:49.116 --> 0:19:51.196
<v Speaker 1>I mean, it's just it's machine learning. I presume it's

0:19:51.236 --> 0:19:52.596
<v Speaker 1>pattern matching exactly.

0:19:53.316 --> 0:19:57.276
<v Speaker 2>It's our model is a transformer based architecture, the same

0:19:57.316 --> 0:19:58.676
<v Speaker 2>thing that powers chat GPT.

0:19:59.556 --> 0:20:02.516
<v Speaker 1>Our A models in general better at this point than

0:20:02.556 --> 0:20:04.316
<v Speaker 1>the physics based traditional models.

0:20:04.916 --> 0:20:07.276
<v Speaker 2>It depends on the use case. But if you're talking

0:20:07.276 --> 0:20:12.876
<v Speaker 2>about hurricanes, yeah, we ran a case study on Hurricane

0:20:12.916 --> 0:20:19.476
<v Speaker 2>Ian and our model would have predicted landfall by two

0:20:19.596 --> 0:20:21.556
<v Speaker 2>hundred kilometers closer.

0:20:21.876 --> 0:20:24.956
<v Speaker 1>I mean, that's another one where like I'm intrigued by

0:20:24.956 --> 0:20:27.676
<v Speaker 1>what you said, but I'm very eager for I'm not

0:20:27.796 --> 0:20:31.356
<v Speaker 1>eager for this hurricane season to come, but like it

0:20:31.396 --> 0:20:34.676
<v Speaker 1>will resonate more with me when you do that prospectively, right,

0:20:34.756 --> 0:20:39.156
<v Speaker 1>of course, I mean, will you this year be predicting

0:20:39.196 --> 0:20:40.676
<v Speaker 1>where hurricanes are going to make landfall?

0:20:41.276 --> 0:20:45.236
<v Speaker 2>We will be predicting that for public safety reasons. We

0:20:45.436 --> 0:20:47.956
<v Speaker 2>aren't going to be you won't tell me.

0:20:48.116 --> 0:20:49.756
<v Speaker 1>Yeah you will, but you won't tell me we.

0:20:49.796 --> 0:20:55.236
<v Speaker 2>Will know a hurricane. Okay, but fair, you of course

0:20:55.276 --> 0:20:58.956
<v Speaker 2>don't want to say, hey, don't listen to Noah about

0:20:58.956 --> 0:21:00.396
<v Speaker 2>these evacuation orders.

0:21:01.076 --> 0:21:05.236
<v Speaker 1>No, no, fair that that is very responsible. So basically,

0:21:05.396 --> 0:21:09.916
<v Speaker 1>you're selling data. You are likely soon to be selling forecasts.

0:21:10.116 --> 0:21:14.956
<v Speaker 1>That's sort of the business. On the technical side, what

0:21:15.636 --> 0:21:17.596
<v Speaker 1>is the frontier? What are you trying to figure out

0:21:17.596 --> 0:21:18.876
<v Speaker 1>that you haven't figured out yet?

0:21:19.636 --> 0:21:23.316
<v Speaker 2>Yeah, well, one of the big areas for innovation is

0:21:23.996 --> 0:21:28.596
<v Speaker 2>on the AI modeling side. We were kind of surprised

0:21:28.716 --> 0:21:31.716
<v Speaker 2>that our models did as well as they did because

0:21:32.356 --> 0:21:35.836
<v Speaker 2>there are a lot of quite frankly obvious things that

0:21:36.116 --> 0:21:40.596
<v Speaker 2>we haven't done, things like just increasing the amount of

0:21:40.676 --> 0:21:44.676
<v Speaker 2>compute we're using to train these models. We use something

0:21:44.756 --> 0:21:49.396
<v Speaker 2>like a fifteenth as much as Google did. So what

0:21:49.516 --> 0:21:51.676
<v Speaker 2>happens when you train it for longer?

0:21:51.956 --> 0:21:54.076
<v Speaker 1>I mean, I'll say, on the AI side, it's not

0:21:54.396 --> 0:21:57.556
<v Speaker 1>obvious that it's optimal for you to be doing the

0:21:57.596 --> 0:22:00.316
<v Speaker 1>AI as well. Right, there's a universe where you are

0:22:00.756 --> 0:22:04.236
<v Speaker 1>optimizing the balloons and the data, and then someone else

0:22:05.276 --> 0:22:08.156
<v Speaker 1>like Google, who has all the computers and all the

0:22:08.196 --> 0:22:12.276
<v Speaker 1>AI knows what to do with doing the AI side.

0:22:12.036 --> 0:22:17.116
<v Speaker 2>Of it, right, Yes, and no, I think that where

0:22:17.156 --> 0:22:21.396
<v Speaker 2>that falls apart is coupling our data with the models

0:22:21.516 --> 0:22:24.676
<v Speaker 2>much more closely. In that there's so much to be

0:22:24.836 --> 0:22:28.156
<v Speaker 2>done in terms of figuring out how to better take

0:22:28.196 --> 0:22:33.516
<v Speaker 2>advantage of our data into particular and also things like saying,

0:22:34.556 --> 0:22:37.316
<v Speaker 2>based on this weather model, where should we be flying

0:22:37.316 --> 0:22:41.236
<v Speaker 2>our balloons to improve the forecast? And we're already using

0:22:41.436 --> 0:22:45.356
<v Speaker 2>our own AI weather forecasts to do that flight plan

0:22:45.476 --> 0:22:48.876
<v Speaker 2>optimization to figure out where our balloons are going to fly.

0:22:49.276 --> 0:22:52.796
<v Speaker 2>So it's really this beneficial effect where the better our

0:22:52.876 --> 0:22:56.116
<v Speaker 2>weather forecast, the better we can fly our balloons. And

0:22:56.836 --> 0:23:00.076
<v Speaker 2>we can then target our balloons to fly to the

0:23:00.076 --> 0:23:02.716
<v Speaker 2>places that will most improve the weather forecast.

0:23:03.396 --> 0:23:04.516
<v Speaker 1>That's a good feedback loop.

0:23:04.676 --> 0:23:06.716
<v Speaker 2>There's also a lot to do on the balloon side

0:23:06.716 --> 0:23:10.796
<v Speaker 2>of things. I wish I could tell you about our

0:23:11.476 --> 0:23:16.436
<v Speaker 2>project that will increase flight time by another factor.

0:23:16.076 --> 0:23:18.876
<v Speaker 1>Of ten, another factor of ten.

0:23:19.076 --> 0:23:21.676
<v Speaker 2>Yeah, so what what are you at now?

0:23:21.716 --> 0:23:23.716
<v Speaker 1>What's the like median flight time?

0:23:23.996 --> 0:23:29.076
<v Speaker 2>Medium flight time is seven to ten days depending on

0:23:29.356 --> 0:23:30.236
<v Speaker 2>how we're targeting it.

0:23:30.396 --> 0:23:33.996
<v Speaker 1>And so you think you're going to get to ninety days. Yep,

0:23:34.276 --> 0:23:38.956
<v Speaker 1>we around the world in eighty days. Yeah, you say

0:23:38.956 --> 0:23:40.356
<v Speaker 1>you wish you could tell me like you feel like

0:23:40.476 --> 0:23:43.076
<v Speaker 1>you're about to do it. Yeah, tell me, tell me

0:23:43.116 --> 0:23:44.716
<v Speaker 1>without telling me what you can't tell me.

0:23:44.996 --> 0:23:49.756
<v Speaker 2>Okay, I'll do my best. So we have had some

0:23:50.356 --> 0:23:56.836
<v Speaker 2>very successful flight tests where we have demonstrated the ability

0:23:57.076 --> 0:24:03.116
<v Speaker 2>to fly without using any finite resources.

0:24:03.356 --> 0:24:06.636
<v Speaker 1>So what what is the key finite resource? The the

0:24:06.676 --> 0:24:08.876
<v Speaker 1>lift gas, the what is it helium or hydrogen?

0:24:08.916 --> 0:24:13.236
<v Speaker 2>What you used to keep Our balloons are compatible with

0:24:13.276 --> 0:24:16.556
<v Speaker 2>both helium and hydrogen, So which we use is dependent

0:24:16.636 --> 0:24:17.276
<v Speaker 2>on the country.

0:24:17.676 --> 0:24:20.316
<v Speaker 1>When you say flying without any finite resource, that's what

0:24:20.356 --> 0:24:21.556
<v Speaker 1>I think of, yep.

0:24:22.196 --> 0:24:25.036
<v Speaker 2>Okay, yeah, that and ballast.

0:24:25.716 --> 0:24:30.836
<v Speaker 1>Oh right, So so you want to you can? I should?

0:24:30.876 --> 0:24:31.316
<v Speaker 1>I guess.

0:24:31.596 --> 0:24:35.476
<v Speaker 2>I mean, there's heat there, leave it there and just say.

0:24:37.156 --> 0:24:39.076
<v Speaker 1>If you get that, could you fly forever?

0:24:40.036 --> 0:24:45.636
<v Speaker 2>That the dream someday, someday we're we're definitely ninety.

0:24:45.396 --> 0:24:47.996
<v Speaker 1>Days for a weather balloon is sort of forever, right.

0:24:47.996 --> 0:24:51.436
<v Speaker 2>It really is. And so, yeah, we've had some very

0:24:51.476 --> 0:24:52.876
<v Speaker 2>successful flight tests.

0:24:53.196 --> 0:24:55.596
<v Speaker 1>What's the longest flight you've had to this point?

0:24:55.796 --> 0:24:57.236
<v Speaker 2>Forty days?

0:24:57.276 --> 0:24:58.316
<v Speaker 1>Forty days? Okay?

0:24:58.436 --> 0:24:58.716
<v Speaker 2>Long?

0:24:58.836 --> 0:24:59.156
<v Speaker 1>Yeah?

0:24:59.236 --> 0:25:02.356
<v Speaker 2>Long? And that was without using this technology, was that

0:25:02.436 --> 0:25:07.316
<v Speaker 2>like a fluke. It was definitely on the side of

0:25:07.356 --> 0:25:10.956
<v Speaker 2>the bell curve. But we've had multiple month long flights.

0:25:11.756 --> 0:25:16.236
<v Speaker 1>What why do your balloons usually what do you say fall?

0:25:16.316 --> 0:25:19.076
<v Speaker 1>Crash sounds a little extreme crash fall.

0:25:19.956 --> 0:25:23.996
<v Speaker 2>Yeah, it's that they run out of ballast and lifting gas,

0:25:24.236 --> 0:25:29.156
<v Speaker 2>and so these finite resources have been used up. You

0:25:29.196 --> 0:25:32.156
<v Speaker 2>don't have a more ballast to drop, you don't have

0:25:32.196 --> 0:25:35.796
<v Speaker 2>a more gas event, and so you can no longer

0:25:35.876 --> 0:25:36.916
<v Speaker 2>control your altitude.

0:25:37.316 --> 0:25:40.836
<v Speaker 1>What happens when it falls crashes comes down.

0:25:41.636 --> 0:25:44.756
<v Speaker 2>So by the time it falls, it has used up

0:25:44.996 --> 0:25:49.956
<v Speaker 2>all of the ballast. So instead of weighing four pounds,

0:25:50.036 --> 0:25:55.476
<v Speaker 2>it weighs about two hundred grams.

0:25:56.036 --> 0:25:58.516
<v Speaker 1>Two hundred grams is what half a pound?

0:25:58.676 --> 0:26:02.996
<v Speaker 2>Ish? Yes, I'd looked it up. Zero point four to

0:26:03.036 --> 0:26:03.756
<v Speaker 2>four pounds.

0:26:04.076 --> 0:26:06.796
<v Speaker 1>Okay. By the way, when you're getting rid of ballast,

0:26:06.876 --> 0:26:08.676
<v Speaker 1>is it just like sand or something? You just like

0:26:08.716 --> 0:26:14.836
<v Speaker 1>sprinkle sand out exactly? Okay, So it's half a pound,

0:26:14.916 --> 0:26:17.556
<v Speaker 1>so it wouldn't hurt if it hit me on the head?

0:26:17.636 --> 0:26:19.316
<v Speaker 1>Is that is that part of the reason half a

0:26:19.316 --> 0:26:21.156
<v Speaker 1>pound is important? Would it if it hit me on that?

0:26:21.396 --> 0:26:25.196
<v Speaker 2>Is it? Anybody on that it's never hit anybody. We

0:26:25.276 --> 0:26:29.516
<v Speaker 2>do landing simulations and control where it lands to direct

0:26:29.556 --> 0:26:36.236
<v Speaker 2>it towards unpopulated areas. But remember it also has this

0:26:36.836 --> 0:26:40.356
<v Speaker 2>envelope attached to it, which acts like a parachute, so

0:26:40.556 --> 0:26:45.516
<v Speaker 2>it actually falls very slowly. It's very light and not

0:26:45.636 --> 0:26:46.356
<v Speaker 2>an issue.

0:26:46.516 --> 0:26:49.156
<v Speaker 1>And then what happens. Then you have this big deflated

0:26:49.156 --> 0:26:51.756
<v Speaker 1>balloon sitting on the ground or floating in the ocean,

0:26:51.836 --> 0:26:54.196
<v Speaker 1>and is it just what happens to you?

0:26:54.316 --> 0:26:58.316
<v Speaker 2>Yeah? So I love that question because it's a chance

0:26:58.396 --> 0:27:02.236
<v Speaker 2>to talk about how our balloons can reduce the amount

0:27:02.316 --> 0:27:05.076
<v Speaker 2>of waste going out into the world compared to the

0:27:05.116 --> 0:27:07.996
<v Speaker 2>half a million that are launched every year, of which

0:27:08.036 --> 0:27:09.476
<v Speaker 2>only a fifth are covered.

0:27:10.156 --> 0:27:12.756
<v Speaker 1>Just get answer directly. I appreciate that you want to

0:27:12.796 --> 0:27:15.956
<v Speaker 1>contextualize it, but like, first, let's talk about what happens

0:27:15.956 --> 0:27:18.476
<v Speaker 1>with your balloons and then feel free to provide the bigger.

0:27:18.156 --> 0:27:22.996
<v Speaker 2>Cart sounds good, sounds good? Yeah, yeah, So our balloons

0:27:23.076 --> 0:27:26.996
<v Speaker 2>are out in the world, and we recover as many

0:27:27.036 --> 0:27:30.516
<v Speaker 2>of them as we can. In the long term, we're

0:27:30.636 --> 0:27:34.596
<v Speaker 2>aiming to recover essentially all of them because we'll direct

0:27:34.636 --> 0:27:36.276
<v Speaker 2>them to specific landing sites.

0:27:36.716 --> 0:27:39.996
<v Speaker 1>I mean, the ocean has got to be tough to write.

0:27:40.036 --> 0:27:41.636
<v Speaker 1>I presume when they fall in the ocean, for the

0:27:41.676 --> 0:27:42.836
<v Speaker 1>most part, you don't recover them.

0:27:42.876 --> 0:27:45.196
<v Speaker 2>Is that right, exactly? And that's one of the reasons

0:27:45.236 --> 0:27:49.636
<v Speaker 2>why improving endurance is so exciting, because if you circumnavigate

0:27:49.716 --> 0:27:52.836
<v Speaker 2>multiple times, well, on the last leg, you bring it

0:27:52.916 --> 0:27:55.996
<v Speaker 2>down in a field, have somebody drive around and collect

0:27:56.036 --> 0:27:59.476
<v Speaker 2>all of them. They are about half million weather balloons

0:27:59.556 --> 0:28:04.316
<v Speaker 2>launched each year, and only a fifth of those are recovered.

0:28:04.636 --> 0:28:09.196
<v Speaker 2>So when each balloon flies for only two hours, well,

0:28:09.356 --> 0:28:12.196
<v Speaker 2>you have to launch a lot more balloons to collect

0:28:12.276 --> 0:28:14.996
<v Speaker 2>the same amount of data. So we really see this

0:28:15.036 --> 0:28:18.196
<v Speaker 2>as an opportunity to reduce the amount of stuff that's

0:28:18.236 --> 0:28:19.556
<v Speaker 2>going out there in the first place.

0:28:20.916 --> 0:28:25.236
<v Speaker 1>Right, So there will actually be fewer balloons going into

0:28:25.276 --> 0:28:26.596
<v Speaker 1>the world if you succeed.

0:28:26.956 --> 0:28:27.516
<v Speaker 2>Exactly.

0:28:28.516 --> 0:28:30.596
<v Speaker 1>So, you're at this point where you've learned a lot.

0:28:30.676 --> 0:28:32.356
<v Speaker 1>You sort of feels like you're kind of on the

0:28:32.396 --> 0:28:35.356
<v Speaker 1>precipice of a lot more. And I want to talk

0:28:35.356 --> 0:28:37.996
<v Speaker 1>about sort of two futures. Right. One is a future

0:28:38.036 --> 0:28:42.076
<v Speaker 1>where where your company doesn't succeed for any number of

0:28:42.076 --> 0:28:45.756
<v Speaker 1>reasons with which you're truly more familiar than I. And

0:28:45.796 --> 0:28:47.916
<v Speaker 1>then the other is if you do succeed. Right, So,

0:28:48.596 --> 0:28:50.476
<v Speaker 1>in the sort of sad version where you don't succeed,

0:28:50.516 --> 0:28:52.316
<v Speaker 1>what are some reasons it might not work out?

0:28:52.956 --> 0:28:56.916
<v Speaker 2>Yeah, I think that probably the biggest reason is that

0:28:57.356 --> 0:29:02.676
<v Speaker 2>scaling up hardware is hard. We need to increase manufacturing

0:29:02.836 --> 0:29:06.196
<v Speaker 2>by a factor of one hundred effectively, and.

0:29:06.116 --> 0:29:08.956
<v Speaker 1>So it's scaling up that final assembly by a factor

0:29:08.956 --> 0:29:12.076
<v Speaker 1>of one hundred is complicated and hard and just kind

0:29:12.116 --> 0:29:17.956
<v Speaker 1>of classic hard building. A business gets more capital intensive exactly,

0:29:18.076 --> 0:29:21.796
<v Speaker 1>and the money before you can get the money exactly. Okay,

0:29:21.916 --> 0:29:26.516
<v Speaker 1>So that's an easy to imagine sad outcome. Let's talk

0:29:26.516 --> 0:29:30.356
<v Speaker 1>about the happy outcome. Like it works. You scale up

0:29:31.156 --> 0:29:35.396
<v Speaker 1>your balloons, stay up for months at a time. What's

0:29:35.436 --> 0:29:37.996
<v Speaker 1>the world look like? What are you doing in that scenario.

0:29:38.276 --> 0:29:41.516
<v Speaker 2>We want everybody to see twice as far into the

0:29:41.556 --> 0:29:44.516
<v Speaker 2>future when it comes to weather. So making the ten

0:29:44.596 --> 0:29:47.996
<v Speaker 2>day forecast is accurate is the current five day forecast.

0:29:48.156 --> 0:29:50.596
<v Speaker 2>Making the twenty day forecast as accurate as the ten

0:29:50.676 --> 0:29:54.156
<v Speaker 2>day forecast, so we want people to see twice as

0:29:54.196 --> 0:29:57.516
<v Speaker 2>far into the future. We want to pinpoint where hurricanes

0:29:57.556 --> 0:30:01.956
<v Speaker 2>are going to make landfall a week in advance, and

0:30:02.436 --> 0:30:07.276
<v Speaker 2>we want day to day weather that businesses rely on

0:30:07.756 --> 0:30:15.356
<v Speaker 2>to be really accurate, things like never having to cancel

0:30:15.396 --> 0:30:18.796
<v Speaker 2>a flight last minute because a bomb cyclone popped up,

0:30:20.556 --> 0:30:24.156
<v Speaker 2>saving fuel with all of your shipping because you know

0:30:24.436 --> 0:30:28.996
<v Speaker 2>where there will be headwinds. We want to accelerate the

0:30:29.036 --> 0:30:33.276
<v Speaker 2>transition to renewables, and we want weather to go from

0:30:33.316 --> 0:30:39.036
<v Speaker 2>this crazy, unpredictable source of uncertainty to something that humans

0:30:39.116 --> 0:30:39.836
<v Speaker 2>just know about.

0:30:42.916 --> 0:30:56.076
<v Speaker 1>We'll be back in a minute with the lightning round. Okay,

0:30:56.116 --> 0:31:01.636
<v Speaker 1>let's do the lightning round. Would you rather have it

0:31:01.676 --> 0:31:03.036
<v Speaker 1>be too hot or too cold?

0:31:03.276 --> 0:31:04.516
<v Speaker 2>Too cold any day?

0:31:06.556 --> 0:31:10.836
<v Speaker 1>Okay? The next one is multiple choice. What is your

0:31:10.836 --> 0:31:15.756
<v Speaker 1>favorite movie prominently featuring balloons? Wizard of Oz around the

0:31:15.756 --> 0:31:19.556
<v Speaker 1>World in eighty days, up the Red Balloon, or none

0:31:19.596 --> 0:31:20.036
<v Speaker 1>of the above.

0:31:20.196 --> 0:31:22.476
<v Speaker 2>It's got to be up brilliant.

0:31:21.996 --> 0:31:27.196
<v Speaker 1>Movie, Blimps, overrated or underrated?

0:31:27.476 --> 0:31:31.116
<v Speaker 2>Ooh, so here, I've got to make a distinction between

0:31:31.236 --> 0:31:34.316
<v Speaker 2>blimps and Zeppelins because I actually used to work at

0:31:34.476 --> 0:31:35.916
<v Speaker 2>a zeppelin company.

0:31:37.676 --> 0:31:40.996
<v Speaker 1>Is that the Larry Page Zeppelin company? It sure is

0:31:41.596 --> 0:31:45.996
<v Speaker 1>perhaps the only zeppelin company. Yeah, and so is the

0:31:46.076 --> 0:31:48.996
<v Speaker 1>distinction that a zeppelin has a rigid frame?

0:31:49.436 --> 0:31:50.156
<v Speaker 2>Got it in? Won?

0:31:51.036 --> 0:31:53.596
<v Speaker 1>Okay? So how about this. Let me reformulate it to

0:31:54.396 --> 0:31:59.076
<v Speaker 1>see if I get it this time. Airships overrated or underrated?

0:31:59.276 --> 0:32:06.556
<v Speaker 2>Underrated? I thought that zeppelins are so cool. I don't

0:32:06.636 --> 0:32:10.716
<v Speaker 2>have a great mission driven answer here other than you know,

0:32:11.436 --> 0:32:14.396
<v Speaker 2>I think it's amazing. I read too many books growing

0:32:14.476 --> 0:32:18.236
<v Speaker 2>up which prominently featured airships.

0:32:18.596 --> 0:32:22.556
<v Speaker 1>Isn't the isn't the Larry Page airship company dreams some

0:32:22.716 --> 0:32:25.436
<v Speaker 1>kind of cargo, like the idea that, for like really

0:32:25.476 --> 0:32:28.076
<v Speaker 1>heavy things, giant airships would be an efficient way to

0:32:28.476 --> 0:32:29.116
<v Speaker 1>move cargo.

0:32:29.636 --> 0:32:32.756
<v Speaker 2>Full disclosure, It's been a long time since I worked there,

0:32:32.796 --> 0:32:37.356
<v Speaker 2>so I can't speak to that company in particular. But yeah,

0:32:38.036 --> 0:32:43.116
<v Speaker 2>faster deliveries than a ship, more efficient deliveries than a plane.

0:32:43.556 --> 0:32:49.956
<v Speaker 1>Aha, It's like a niche an Until what's something besides

0:32:49.996 --> 0:32:52.516
<v Speaker 1>the weather that AI will be really good at predicting?

0:32:53.236 --> 0:33:02.076
<v Speaker 2>Oh? Great question, let's see. Uh. I don't have a

0:33:02.076 --> 0:33:05.316
<v Speaker 2>good answer off the top of my head. I think

0:33:05.396 --> 0:33:10.956
<v Speaker 2>that it will be really interesting for economics in terms

0:33:11.036 --> 0:33:18.276
<v Speaker 2>of taking short term data, various real time indicators, and

0:33:18.476 --> 0:33:22.436
<v Speaker 2>making predictions about what the final readings are going to be.

0:33:24.196 --> 0:33:27.036
<v Speaker 1>I'm sure a lot of very smart people are working

0:33:27.076 --> 0:33:29.076
<v Speaker 1>on that and they may in fact already have good

0:33:29.116 --> 0:33:31.116
<v Speaker 1>models and they're not telling us.

0:33:31.276 --> 0:33:32.276
<v Speaker 2>I think that's likely.

0:33:32.356 --> 0:33:33.676
<v Speaker 1>Is there anything else you want to say?

0:33:35.076 --> 0:33:37.516
<v Speaker 2>I think that covers it. Yeah, great to be on

0:33:37.556 --> 0:33:37.876
<v Speaker 2>the show.

0:33:39.236 --> 0:33:40.996
<v Speaker 1>Thanks for your time. It was lovely to talk with you.

0:33:41.116 --> 0:33:41.596
<v Speaker 1>Good luck.

0:33:41.716 --> 0:33:46.156
<v Speaker 2>Thanks and if you ever are in Paloelto, you're welcome

0:33:46.156 --> 0:33:47.316
<v Speaker 2>to come see a balloon launch.

0:33:48.436 --> 0:33:52.996
<v Speaker 1>Great. So do you still you launch from where you launch?

0:33:53.036 --> 0:33:55.876
<v Speaker 1>From that Air Force base or like or just from.

0:33:56.196 --> 0:33:56.996
<v Speaker 2>From our part part?

0:33:57.036 --> 0:33:59.676
<v Speaker 1>Are they easy? Yeah? From the parking lot, that's cool.

0:33:59.836 --> 0:34:00.156
<v Speaker 2>Yeah.

0:34:00.196 --> 0:34:02.236
<v Speaker 1>How much space do you need to launch a weather balloon?

0:34:03.756 --> 0:34:08.076
<v Speaker 2>Not that much? About fifty by fifty feet. It's really

0:34:08.156 --> 0:34:10.596
<v Speaker 2>just a matter of making sure there's nothing that the

0:34:10.596 --> 0:34:12.996
<v Speaker 2>balloon will blow into right after releasing it.

0:34:13.556 --> 0:34:17.196
<v Speaker 1>Uh huh. Seems like it'll be fun to see. Yeah,

0:34:17.236 --> 0:34:22.996
<v Speaker 1>I like a balloon. Kai Marshland is the co founder

0:34:23.036 --> 0:34:27.716
<v Speaker 1>and chief product officer of Windborne Systems. Today's show was

0:34:27.756 --> 0:34:31.436
<v Speaker 1>produced by Gabriel Hunter Chang and edited by Lydia Jean Kott,

0:34:31.596 --> 0:34:34.796
<v Speaker 1>who was engineered by Sarah Buguer. You can email us

0:34:34.836 --> 0:34:38.716
<v Speaker 1>at problem at Pushkin dot FM. I'm Jacob Goldstein and

0:34:38.756 --> 0:34:41.116
<v Speaker 1>we'll be back next week with another episode of What's

0:34:41.116 --> 0:34:50.596
<v Speaker 1>Your Problem,