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Problems Solved: Drones, Bananas and Real Estate* · What's Your Problem?

0:15
Speaker 1: Pushkin. It's been a year since we started this show,
0:22
Speaker 1: and we started it because I wanted to talk to
0:25
Speaker 1: people who are trying to make technological progress, people who
0:29
Speaker 1: are struggling with big, interesting problems, who go to work
0:33
Speaker 1: every day and try to figure out how to do
0:35
Speaker 1: things that no one in the world knows how to do.
0:39
Speaker 1: And now enough time has passed since we started the
0:41
Speaker 1: show that some of the people we've talked to have
0:44
Speaker 1: in fact figured some things out, they have solved some problems.
0:52
Speaker 1: I'm Jacob Goldstein, and this is What's Your Problem? On
0:55
Speaker 1: today's show, we're following up with three people who we've
0:58
Speaker 1: spoken to over the past year. First Keenan Wirobek. He
1:02
Speaker 1: may have solved the key problem that has been preventing
1:05
Speaker 1: drone delivery from taking off in the United States. Then
1:10
Speaker 1: Katherine Sizov, who seems to have figured out how to
1:13
Speaker 1: get ripe bananas onto grocery store shelves at the right time.
1:17
Speaker 1: And finally Glenn Kelman. He is the CEO of the
1:20
Speaker 1: real estate company Redfinn and since we talked to him
1:24
Speaker 1: last he, like a lot of people in the real
1:26
Speaker 1: estate industry, has had something of a tough time. What
1:29
Speaker 1: I've been through just personally has sometimes felt like a
1:32
Speaker 1: spiritual death of the past six months, but he has
1:36
Speaker 1: come through it with some really illuminating insights. We'll hear
1:40
Speaker 1: those later in the show. First up is Keenan Wirobek,
1:46
Speaker 1: co founder and CTO of Zipline. Zip Line is a
1:50
Speaker 1: drone delivery company. They uperated in a bunch of countries
1:53
Speaker 1: around the world. They've done hundreds of thousands of flights
1:55
Speaker 1: in Rwanda, where their drones deliver medicines across the entire country.
2:00
Speaker 1: But when I talked with Keenan last year, he was
2:03
Speaker 1: up against this kind of combination technical slash regulatory problem
2:07
Speaker 1: that was making it hard for him and other drone
2:09
Speaker 1: delivery companies to expand in the United States. Here's what
2:13
Speaker 1: the problem was. In other countries, there are strict rules
2:17
Speaker 1: that require planes to have transponders, these devices that allow
2:21
Speaker 1: drones and planes to detect and avoid each other. But
2:24
Speaker 1: rules about transponders are more lax in the US, and
2:28
Speaker 1: as a result, commercial drone companies have to figure out
2:32
Speaker 1: how to detect and avoid planes that are flying without transponders. Today,
2:38
Speaker 1: drone delivery companies like zip Line in the US solve
2:42
Speaker 1: for this in a kind of ridiculously low tech and
2:46
Speaker 1: labor intensive way. They actually pay people to sit on
2:50
Speaker 1: the ground and stare at the sky watching for planes.
2:54
Speaker 1: This obviously is not going to scale. We're not going
2:57
Speaker 1: to have a drone delivery business when you have to
2:59
Speaker 1: pay people to sit on the ground and stare at
3:01
Speaker 1: the sky for drone delivery to be a real economically
3:05
Speaker 1: viable service in this country, someone needs to figure out
3:09
Speaker 1: how drones can automatically detect and avoid planes. When I
3:13
Speaker 1: talked to Keenan last year, that was ziplines big problem. Now,
3:18
Speaker 1: after years of trial and error, he thinks they've solved it.
3:21
Speaker 1: Keenan told me the story of how they figured it out.
3:24
Speaker 1: So we went on a journey here. Um we started.
3:28
Speaker 1: We actually started with radar because radar was the thing
3:30
Speaker 1: that sort of the holy grail of FA regulators understand radar.
3:35
Speaker 1: It has the benefit of being able to see through
3:37
Speaker 1: clouds and things like this. Okay, so why didn't radar work.
3:40
Speaker 1: The regulators want drones to be able to sense aircraft
3:44
Speaker 1: coming from any direction and avoid them, and this is
3:47
Speaker 1: a big challenge. And so if you put enough radar
3:49
Speaker 1: on an aircraft, to see in all directions up, down,
3:52
Speaker 1: you know, left, right, front, back. It weighs something like
3:56
Speaker 1: five times more than our drone does. Okay, it's a
3:59
Speaker 1: massive radar system, and it's it's basically there's just no
4:03
Speaker 1: way to fly it. Okay, So it's too heavy. The
4:06
Speaker 1: simple answer is too heavy. Won't work. It's too heavy,
4:09
Speaker 1: too heavy, And so we went from there to cameras.
4:11
Speaker 1: It's like, okay, can you just look now? Cameras have
4:14
Speaker 1: some really interesting challenges, and I think the first challenge
4:16
Speaker 1: is easy to If you ever tried to take out
4:18
Speaker 1: your cell phone and like look for a plane in
4:21
Speaker 1: the sky, you've seen the first challenge. You almost it's
4:23
Speaker 1: almost impossible to see it on your phone. Right to
4:26
Speaker 1: our eyes, we can kind of figure out, oh, there's
4:27
Speaker 1: a plane there, but it's literally a couple of pixels
4:30
Speaker 1: to a camera, even a very high resolution camera, and
4:32
Speaker 1: so being able to tell the difference between you know,
4:35
Speaker 1: smudge on the lens a bird and a plane very
4:38
Speaker 1: difficult to do that reliably. Okay, So radar won't work,
4:42
Speaker 1: cameras won't work. What do you try? So this is
4:48
Speaker 1: sort of a back to the drawing board moment of
4:49
Speaker 1: like what on Earth could work. So if you remember
4:52
Speaker 1: the early hearing aids, that were these cones that you
4:54
Speaker 1: would stick in your ear and use still hear something,
4:57
Speaker 1: So personally remember them. But I have seen photos. Yes,
5:00
Speaker 1: you see photos. Okay, yes, the So before radar was
5:05
Speaker 1: invented the Brits in the United States, we built each
5:09
Speaker 1: huge cones, like twenty foot size cones that humans would
5:13
Speaker 1: stick their ear onto the end of the twenty foot
5:15
Speaker 1: size cone and this they point the cone at the
5:18
Speaker 1: horizon to try to hear aircraft coming before you could
5:21
Speaker 1: see them. Wow. So when was that in use? World
5:24
Speaker 1: War One and then early World War Two until radar
5:27
Speaker 1: was invented, which was mid World War two. So just
5:30
Speaker 1: be a dude with his ear on a giant cone
5:32
Speaker 1: and if he heard a plane, he'd say whatever, everybody
5:35
Speaker 1: into the bunker, planes coming, scramble the planes intercept this
5:40
Speaker 1: kind of thing. Yeah, and you know it kind of
5:43
Speaker 1: makes sense, right, Like as humans were pretty good, we're
5:45
Speaker 1: walking around, you hear a plane, you look up, you're like, oh,
5:47
Speaker 1: there's the plane. Right, Um, we don't see a plane first.
5:50
Speaker 1: We hear a plane first. And that was the inspiration
5:54
Speaker 1: and it was like, okay, well, we've tried the other
5:56
Speaker 1: the things that seemed more obvious, let's try this. And
5:59
Speaker 1: that this was one in turn was working with me,
6:02
Speaker 1: and it was basically saying, hey, let's you know, the
6:04
Speaker 1: philosophy is fail fast, right, It's basically, let's try to
6:07
Speaker 1: understand the technical reasons why this is impossible as quickly
6:11
Speaker 1: as possible. Let's rule it out. Yeah. So the number
6:14
Speaker 1: one reason to be skeptical is it you've ever been, like,
6:16
Speaker 1: you know, on a bike or you know, in a
6:18
Speaker 1: motorcycle or a car and it's quiet in the car,
6:22
Speaker 1: But on a motorcycle you put your head, you're out
6:23
Speaker 1: the window, and you hear all this noise. You can
6:25
Speaker 1: barely hear the person, you know, biking next to you.
6:28
Speaker 1: That's called aero acoustic noise. It's basically the noise made
6:31
Speaker 1: by the rush of air over your well. Yeah, when
6:34
Speaker 1: you roll down the window in the car when you're
6:35
Speaker 1: on the freeway, it's super loud. It's super loud, right,
6:39
Speaker 1: And so these planes, the microphones are in the rushing
6:41
Speaker 1: wind created by the plane in the sky, by the
6:43
Speaker 1: drone in the sky exactly, So is there Basically the
6:46
Speaker 1: question was, with all that noise from that rushing wind,
6:48
Speaker 1: would we still hear those aircraft far enough away to
6:52
Speaker 1: be able to avoid them? That was the biggest thing
6:54
Speaker 1: that we were worried about. Um, but we we we
6:58
Speaker 1: found a way using a microphone array. So basically, you know,
7:02
Speaker 1: a set of microphones on the aircraft to be able
7:04
Speaker 1: to literally listen for where aircraft are and use that
7:07
Speaker 1: to avoid them. So so you're saying it works, You
7:10
Speaker 1: figured it out, Tell me how it works. Yeah, So
7:13
Speaker 1: so the final system across the wing, um, there's eight
7:18
Speaker 1: microphones and they're on. They're on, and they're on little
7:20
Speaker 1: they're on little polls. They look like basically like a
7:22
Speaker 1: you know, I don't know the length of a straw,
7:24
Speaker 1: like a drinking straw coming out the front of the
7:27
Speaker 1: of the micro of the wing. There's eight microphones and
7:31
Speaker 1: now you need a bunch of microphones to do basically
7:33
Speaker 1: what your ears do, which is beam form, which is
7:36
Speaker 1: basically you figure out how the sound hits one microphone
7:39
Speaker 1: a little sooner than the other one, and it helps
7:41
Speaker 1: you understand, oh, the noise is coming from over there
7:43
Speaker 1: versus you know, coming from your right side versus your
7:45
Speaker 1: left side, so that that's what it looks like on
7:47
Speaker 1: the aircraft. And then okay, so this is the hardware piece.
7:52
Speaker 1: Is there a sort of software layer. I've got the
7:54
Speaker 1: hardware piece in my mind. You're gathering this sound. I mean,
7:59
Speaker 1: I mean, I know everything sounds like an AI problem now,
8:01
Speaker 1: but this one really does sound like an A problem. Right.
8:04
Speaker 1: You're pattern matching because you want to say it, does
8:07
Speaker 1: this sound like a plane? Let's test it against a
8:09
Speaker 1: billion other sounds? And I don't know how what's the
8:12
Speaker 1: software piece work? Like, Yeah, there's a big software piece.
8:15
Speaker 1: The software has been far and away the biggest effort here.
8:18
Speaker 1: There's a there's a tiny team of hardware engineers and
8:21
Speaker 1: a big team of software engineers working on this. So
8:23
Speaker 1: how does this software work? So the software has layers,
8:27
Speaker 1: and the first layer is a layer called beam forming,
8:29
Speaker 1: and beam forming is this technique very similar to what
8:32
Speaker 1: our ears used to figure out? Okay, where are the
8:35
Speaker 1: sound sources coming from? Right now? That's the first layer. Okay,
8:38
Speaker 1: that's cool. So now you know where the sound is
8:40
Speaker 1: coming from, but you don't know what it is yet
8:43
Speaker 1: exactly so, and then the next layer is an AI
8:45
Speaker 1: layer where we've we've done thousands of flights with different aircraft,
8:50
Speaker 1: different sound sources, and we've used that data to train
8:53
Speaker 1: AI model to actually figure out basically it reinforces where
8:58
Speaker 1: it's coming from and to figure out, hey, this is
9:00
Speaker 1: probably an aircraft, this is probably not an aircraft. And
9:03
Speaker 1: then the AI just says, yes, it's an aircraft. I've
9:06
Speaker 1: detected and now avoid avoid avoid or what what's the
9:09
Speaker 1: last So there's two there's two more layers here. So
9:12
Speaker 1: the next layer is it's called the tracking layer, and
9:15
Speaker 1: the tracking layer is you kind of think of that
9:17
Speaker 1: that layer is the layer that remembers over time, right,
9:20
Speaker 1: So this takes the information from this AI layer and
9:23
Speaker 1: over time says, oh, you think something's kind of over
9:25
Speaker 1: in that direction, I'm gonna I'm gonna keep track of that,
9:27
Speaker 1: and every every you know, every fraction of a second,
9:30
Speaker 1: it gets an update and it kind of updates that
9:32
Speaker 1: track of sort of where it thinks that sound is
9:34
Speaker 1: coming from. And then the final layer takes those tracks
9:37
Speaker 1: of where uh these these and these tracks are estimates,
9:40
Speaker 1: all right, you kind of think of them as like, hey,
9:42
Speaker 1: I'm there's you know, there's an there's an aircraft in
9:44
Speaker 1: that direction kind of over there. And the next layer
9:47
Speaker 1: is the layer the planning layer, the layer that takes
9:49
Speaker 1: that information and says, okay, there's an aircraft over there,
9:51
Speaker 1: I better go over here to say away, I better
9:55
Speaker 1: go in the other direction, exactly exactly. So okay. So
10:01
Speaker 1: so now you have solved this non trivial technical problem.
10:06
Speaker 1: There is also a regulatory problem, right, you have to
10:09
Speaker 1: get approval to fly your commercial drones without people on
10:13
Speaker 1: the ground looking at the drone the whole way. The
10:15
Speaker 1: FAA has to say, yes, we believe that this works
10:18
Speaker 1: and you can fly your drones. Do you have that approval? Yes,
10:23
Speaker 1: But let me explain it because, as in all things
10:25
Speaker 1: with from a regulatory perspective, it's subtle. So what's really
10:28
Speaker 1: exciting is we just got yeah, yes, Asterius. So there,
10:32
Speaker 1: basically we're about to start this walk crawl run sort
10:36
Speaker 1: of progression, which is how you should start a new
10:39
Speaker 1: technology like this. So we just got approved to fly
10:42
Speaker 1: this in operations in the United States. So in the
10:45
Speaker 1: crawl walk run, the crawl phase which we're about to start,
10:47
Speaker 1: which is basically this technology turned on, so doing the
10:51
Speaker 1: avoid les, sensing and avoidance, but with still these visual
10:54
Speaker 1: observers standing on the ground staring at the sky and
10:56
Speaker 1: then later this year we go through a process with
10:59
Speaker 1: the FA to remove the visual observers and keep operating.
11:01
Speaker 1: And that's the holy grail moment that's coming soon. So
11:06
Speaker 1: the holy grail moment is flying a commercial drone flight
11:09
Speaker 1: in the United States without someone watching from the ground exactly.
11:15
Speaker 1: And when do you think that will happen? If I'm
11:19
Speaker 1: being optimistic, it's about three months from now. If I'm
11:21
Speaker 1: being more realistic, three to six months from now. It
11:24
Speaker 1: seems like this has been the big underappreciated by the
11:28
Speaker 1: general public problem that has prevented commercial drone delivery from
11:33
Speaker 1: taking off. Excuse my fun in this country? Is that right?
11:38
Speaker 1: And like you think you've solved it, and the FA
11:40
Speaker 1: thinks you've probably solved it. Yes, this is Yes, it
11:45
Speaker 1: has been a long few years to get through this journey,
11:48
Speaker 1: but I am over the moon excited about this because
11:51
Speaker 1: this is this is the big unlock. Great, good to
11:55
Speaker 1: talk to you. Can we talk in a year, Let's
11:58
Speaker 1: do it, okay, twenty twenty four, back to you then, excellent.
12:05
Speaker 1: One other piece of zip Line news that Tenan told
12:07
Speaker 1: me about. The company is working on this new kind
12:11
Speaker 1: of drone. It's this wild two part system where the
12:14
Speaker 1: drone itself hovers like three hundred feet above the delivery site,
12:18
Speaker 1: and then it lowers down this little box on some
12:21
Speaker 1: kind of wire. The box goes all the way down
12:23
Speaker 1: to the ground, opens up, delivers the package, and goes
12:26
Speaker 1: back up into the drone. They hope to have that
12:28
Speaker 1: in operation by early twenty twenty four, so that'll be
12:32
Speaker 1: one more thing for me and Keenan to talk about
12:34
Speaker 1: next year. Still to come on today's very special episode
12:39
Speaker 1: of What's Your Problem, The Banana Ripening Frontier also how
12:44
Speaker 1: higher interest rates are profoundly changing not only the real
12:48
Speaker 1: estate business, but lots and lots of businesses. Next up,
13:00
Speaker 1: Katherine says Off, founder and CEO of a company called
13:04
Speaker 1: Strella Biotech. Catherine and Strella, they developed these sensors that
13:10
Speaker 1: detect when apples and pears are getting ripe. They work
13:14
Speaker 1: not like in the grocery store in your house, but
13:16
Speaker 1: in these giant storage rooms where apples and pears sit
13:20
Speaker 1: and ripen. The idea behind the product was to reduce waste,
13:24
Speaker 1: increase efficiency, that kind of thing, and the sensors are
13:28
Speaker 1: based on this thing that happens in nature. There are
13:31
Speaker 1: lots of kinds of fruit where when they get ripe,
13:34
Speaker 1: they emit ethylene gas, and that gas is like a
13:37
Speaker 1: signal that causes nearby pieces of fruit to ripen more quickly.
13:42
Speaker 1: When Catherine and I talked last year, she was trying
13:45
Speaker 1: to solve ripening problems for other kinds of fruit besides
13:48
Speaker 1: apples and pears. When I followed up with her earlier
13:51
Speaker 1: this month, I asked her, is there some new kind
13:53
Speaker 1: of fruit that you've cracked since we last talked? Bananas?
13:57
Speaker 1: Actually bananas. Yeah, it's a big one. Yeah, it is
14:01
Speaker 1: a one. Tell me about bananas. What happened and when
14:04
Speaker 1: did it happen? Yeah? I guess my first question is
14:07
Speaker 1: how do you like your bananas? Because, says a heated topic,
14:11
Speaker 1: I prefer them a little too ripe as opposed to
14:14
Speaker 1: a little bit unripe, Like if it's still kind of green,
14:17
Speaker 1: if it's still hard to peel, if when you peel
14:19
Speaker 1: it it's hard to peel, and then you eat it
14:22
Speaker 1: it's a little chalky. That's that's not my favorite banana.
14:25
Speaker 1: I'm one of those freaks that like, say, a little
14:27
Speaker 1: bit underripe, actually, But I think you're in the majority.
14:30
Speaker 1: So what have you figured out about bananas, Like what
14:34
Speaker 1: was the problem and then what did you figure out,
14:37
Speaker 1: Like what was the status quo whatever a year ago?
14:40
Speaker 1: What was the banana problem? So the banana problem is
14:43
Speaker 1: that every time we go to the grocery store, the
14:44
Speaker 1: bananas tend to be a little bit on the green side.
14:47
Speaker 1: I don't know if you have this from personal experience,
14:49
Speaker 1: but they tend to actually edge towards green and then
14:52
Speaker 1: sometimes it goes to the other direction where they're way
14:54
Speaker 1: too ripe and no one wants to buy them. Right.
14:56
Speaker 1: But basically what happens is that they're brought into North
15:00
Speaker 1: America totally under ripe, so they haven't they're not mature whatsoever,
15:04
Speaker 1: and they're green. When you peel them, it's very hard
15:07
Speaker 1: to peel them. They ooze latex, so they're like if
15:11
Speaker 1: you try to peel it, it's it's kind of almost
15:13
Speaker 1: like bleeding latex. And then what happens is these bananas
15:17
Speaker 1: are put into what are called ripening rooms. So basically
15:21
Speaker 1: bananas go into these rooms and then they're dosed with
15:24
Speaker 1: ethylene gas, which is what makes them ripen. And this
15:28
Speaker 1: is just to restate when we talked about last time,
15:31
Speaker 1: like in the wild a ripening piece of fruit or
15:34
Speaker 1: ripening banana emits ethylene gas and that is a signal
15:37
Speaker 1: to other surrounding bananas that they should also ripen. Right,
15:40
Speaker 1: So the dosing with ethylene gas is just doing what
15:43
Speaker 1: happens in nature. That's exactly right. So they're put into
15:46
Speaker 1: what's called a ripening room and they're dosed with ethylene gas.
15:49
Speaker 1: And then comes the art of it, which is that
15:52
Speaker 1: every day a ripener that's a job will go into
15:56
Speaker 1: that room and say, hey, or these bananas more green
15:58
Speaker 1: or less green than what I was expecting, And they
16:01
Speaker 1: just look at them visually and they say, mm, doesn't
16:05
Speaker 1: look quite yellow enough, and then they adjust the conditions
16:08
Speaker 1: of the ripening room base on that. So it's a superhuman,
16:12
Speaker 1: super subjective way to do it. And we thought, you
16:16
Speaker 1: know what, this is probably the cause of a lot
16:18
Speaker 1: of inconsistencies. Every single ripener has a different idea of
16:22
Speaker 1: what a stage seven banana looks like or stage four
16:25
Speaker 1: banana looks like. And so let's take the human out
16:27
Speaker 1: of this, let's take the art out of it. We
16:30
Speaker 1: know about fruit physiology, let's turn this into a science.
16:34
Speaker 1: So what we did was we put our sensors inside
16:36
Speaker 1: these ripening rooms, and we can tell how the bananas
16:39
Speaker 1: are reacting to their conditions and how they're ripening. And
16:42
Speaker 1: then we throw some machine learning on top of that
16:45
Speaker 1: and we say, hey, this banana is going to be
16:47
Speaker 1: under ripe if you keep going the way that you're going.
16:50
Speaker 1: So we're basically going to the retailer and helping them
16:53
Speaker 1: ripen their bananas for the store shelf. Oh huh, so
16:57
Speaker 1: how does that work? So a grocery store can you
16:59
Speaker 1: name it? Are you working with some grocery store chain?
17:01
Speaker 1: I would have heard of a big one in Canada. Okay,
17:05
Speaker 1: can you say the name? I can't, sorry, like hundreds
17:10
Speaker 1: of stores across Canada or something. Yes, So, so how
17:13
Speaker 1: do you go into the grocery store? So you go
17:15
Speaker 1: talk to a ripener and they say, you know what,
17:18
Speaker 1: You're totally right. This is a huge problem for me
17:20
Speaker 1: and I have a million other things to do, and
17:22
Speaker 1: I will Oh. I wouldn't have expect that. I would
17:25
Speaker 1: have expected them to say I'm a genius at ripening.
17:28
Speaker 1: Go away. That happens too sometimes. But I think the
17:33
Speaker 1: ones that you know in general, when you're when you're
17:36
Speaker 1: good at your job and a new tool comes along,
17:38
Speaker 1: You're like, hell, yeah, that's awesome. So I think most
17:42
Speaker 1: people are really interested. So okay, so you go, you
17:47
Speaker 1: talk to the ripeners. Keep going. So we talk to
17:50
Speaker 1: the ripeners. They're like, I have a million things on
17:52
Speaker 1: my plate. Ripening in bananas is one of them. I
17:54
Speaker 1: don't know if I'm doing a good job. Would love
17:56
Speaker 1: to see what's up. So then we put up We
17:58
Speaker 1: set up shop in a couple of ripening rooms. We
18:01
Speaker 1: work with the ripener to understand what they're doing, why
18:04
Speaker 1: they're doing it, and train our model on it. And
18:06
Speaker 1: then we expand and how long did it take you
18:10
Speaker 1: for your sensors to be better than a ripener? It's
18:14
Speaker 1: we're still going. So we're kind of well, let me
18:17
Speaker 1: ask you, are you better than a human ripener? Now? Well,
18:20
Speaker 1: it depends, right, So if what we can do is
18:23
Speaker 1: we can basically give a tablet to a person off
18:25
Speaker 1: the street and you can go ripen a bunch of
18:27
Speaker 1: bananas and not do a terrible job at it. So
18:30
Speaker 1: that's pretty important because it usually it takes three to
18:32
Speaker 1: five years to train a ripener, yeah, in the art
18:36
Speaker 1: of banana ripening, But now we can Now we can
18:39
Speaker 1: kind of nagate all that in terms of like the
18:41
Speaker 1: ogs of banana ripening, I don't necessarily think so, but
18:46
Speaker 1: with a little bit more time than I think, we'll
18:48
Speaker 1: get there. So basically you can make anybody as good
18:53
Speaker 1: as the sort of average five years of training banana ripener.
18:57
Speaker 1: That's right now, yep. How long did it take you
18:59
Speaker 1: to figure to do that? Oh? My god, well, I
19:01
Speaker 1: mean I have. I didn't talk to you too long ago,
19:03
Speaker 1: but to me it feels like basically an eternity. It
19:06
Speaker 1: was a year. It was a lot of year for you.
19:08
Speaker 1: You did a lot of very long year. When you
19:12
Speaker 1: think you'll be doing it in the US, when do
19:13
Speaker 1: you think some US grocery store will be buying your service?
19:17
Speaker 1: We're in trials with several grocery stores and distributors, now, okay,
19:22
Speaker 1: in the US, yes, anybody you can name. No, okay.
19:30
Speaker 1: Automating the task of figuring out how to ripen bananas
19:35
Speaker 1: is not some world changing technology, But you can zoom
19:39
Speaker 1: out a little bit here. Creating machines to automate one
19:43
Speaker 1: task or another has been at the very core of
19:46
Speaker 1: technological progress for hundreds of years. In the long run,
19:51
Speaker 1: little automations like this are like the fundamental efficiency game,
19:55
Speaker 1: getting more and more efficient at lots and lots of
19:57
Speaker 1: little things. That is how over the long run we
20:00
Speaker 1: have raised living standards for most of the people on Earth.
20:06
Speaker 1: In a minute, we'll talk tech and real estate giant
20:10
Speaker 1: macro economic fluctuations with Glenn Kelman, the CEO of Redfinn.
20:23
Speaker 1: About a decade ago, this new kind of thing popped
20:27
Speaker 1: up in the real estate world. It was called ie buying.
20:30
Speaker 1: If you wanted to sell your house, you could go
20:33
Speaker 1: on to a few sites on the internet, enter some
20:35
Speaker 1: information about your house, and a big company like say Zillo,
20:39
Speaker 1: would actually buy your house for cash. Then they would
20:43
Speaker 1: fix it up a little turn around, and try and
20:45
Speaker 1: sell it for a profit. Zillo famously lost a ton
20:50
Speaker 1: of money on this business and they got out in
20:52
Speaker 1: late twenty twenty one. Another big eyebuyer was Redfinn. Redfinn
20:58
Speaker 1: is a big brokerage operates all around the country. And
21:01
Speaker 1: when I talked to Glenn Kelman, the company CEO, last year,
21:04
Speaker 1: Redfinn was still in the eye buying business. But Glenn
21:08
Speaker 1: knew that if house prices started falling, it would be
21:10
Speaker 1: tough to stay in that business, and he told me,
21:12
Speaker 1: so in very dramatic terms. I brought this up with
21:16
Speaker 1: him when I talked to him again earlier this year. So, look,
21:19
Speaker 1: the last time we talked was in the first half
21:23
Speaker 1: of last year, and we talked about iBuying, and you
21:27
Speaker 1: said that I buying would be good on the way
21:30
Speaker 1: up and on the way down you said, in your words,
21:34
Speaker 1: it would be a roller coaster ride to hell. Did
21:39
Speaker 1: say that? Remarkable prescience, remarkable prescience. So tell me what
21:47
Speaker 1: happened with Redfin's I buying business since the last time
21:51
Speaker 1: we talked in the first half of last year. Actually,
21:54
Speaker 1: we closed the business, and we did that for two reasons.
21:57
Speaker 1: One is very intuitive, which is just that home prices
22:01
Speaker 1: fell dramatically between May and the end of twenty twenty two,
22:06
Speaker 1: and we were left holding the bags of homes that
22:10
Speaker 1: we had to sell by hook or by crook. But
22:14
Speaker 1: the other factor is just the increasing cost of capital.
22:16
Speaker 1: So you're basically fronting the money to people who own
22:20
Speaker 1: a home so that they can move on. And when
22:24
Speaker 1: capital was nearly free, we were able to front that
22:27
Speaker 1: money at very low costs, so the offers we were
22:29
Speaker 1: making to people were very competitive. The idea being that
22:34
Speaker 1: that very low interest that almost free money you were
22:37
Speaker 1: able to borrow was core to that I buying business.
22:40
Speaker 1: It is because you're giving someone five hundred thousand dollars
22:44
Speaker 1: for the three or four months that it takes you
22:46
Speaker 1: to rehabilitate the house, put it back on the market,
22:49
Speaker 1: and get someone else to move in. It's a wildly
22:51
Speaker 1: capital intensive business. It's a wildly capital intensive business. To
22:57
Speaker 1: actually be the principle in a transaction where you're the
22:59
Speaker 1: source of the money yourself, that was something that was new.
23:03
Speaker 1: We saw that with we Work, we saw it with ibuy,
23:08
Speaker 1: we saw it with all used car marketplaces. And so
23:11
Speaker 1: that was all driven by this ultra low interest rate
23:15
Speaker 1: environment that drove so much of the tech boom right correct.
23:18
Speaker 1: And so we felt that even if housing prices stabilized
23:22
Speaker 1: again where we could have a reasonable expectation that we
23:25
Speaker 1: could buy a house for five hundred thousand dollars and
23:28
Speaker 1: sell it for five hundred and thirty thousand dollars, what
23:30
Speaker 1: had really changed in a permanent way is we had
23:33
Speaker 1: gone back to a world where capital would be expensive
23:36
Speaker 1: and the cost of fronting that money would be so
23:39
Speaker 1: high that we would have to offer much less for
23:44
Speaker 1: the house to offset the borrowing cost, and that has
23:48
Speaker 1: been the way most businesses operated for most of time,
23:52
Speaker 1: that if you're fronting money and taking risk, you're going
23:54
Speaker 1: to charge the consumer a hefty premium for that. And
23:56
Speaker 1: that suddenly was an offer that I wouldn't take if
24:01
Speaker 1: I myself were the consumer. And so if you're selling
24:05
Speaker 1: a product you don't believe is good for your customer,
24:08
Speaker 1: you've got to stop. Ellen, I want to talk sort
24:11
Speaker 1: of beyond housing, Like you were the one who told
24:16
Speaker 1: me a long time ago that I buying existed in
24:19
Speaker 1: part because companies like Redfinn had suddenly had access to
24:24
Speaker 1: just an incredible kind of unlimited amount of really cheap capital.
24:27
Speaker 1: And that was one of those things that when you
24:29
Speaker 1: told me that, it helped me understand the whole economic
24:32
Speaker 1: era we were living through. It really did, and I
24:34
Speaker 1: thought about it a lot. And now you're telling me
24:37
Speaker 1: that that era is over, and you know, obviously the
24:40
Speaker 1: world is telling me that as well. And so I'm
24:43
Speaker 1: curious what you think it means, not just for housing,
24:46
Speaker 1: but for the broader economy, for you know, technology companies,
24:49
Speaker 1: for innovation, for the economy more gendure. What's it mean? Well,
24:54
Speaker 1: what I've been through just personally has sometimes felt like
24:58
Speaker 1: a spiritual death of the past six months, because if
25:02
Speaker 1: you pride yourself in part on your creativity, as many
25:07
Speaker 1: people in technology do, your basic attitude toward almost every
25:12
Speaker 1: idea is yes, And even if that idea takes ten
25:15
Speaker 1: or twenty years to pay off, if the cost capital
25:19
Speaker 1: is nearly free, a dollar twenty years from now is
25:22
Speaker 1: almost worth the same as a dollar today, so you
25:25
Speaker 1: might as well make the investment. And so I've had
25:30
Speaker 1: to shift my own approach to running Redfin from one
25:33
Speaker 1: that's very entrepreneurial I hope that's always still part of Redfin,
25:38
Speaker 1: to something that's a more bead eyed allocation of capital,
25:43
Speaker 1: where people come up with a hundred ideas, all of
25:46
Speaker 1: them are very exciting, but if it were your own money,
25:51
Speaker 1: you'd mostly say no. And even though I always prided
25:55
Speaker 1: myself on spending Redfence money as if it were my own,
25:58
Speaker 1: there was such a premium on growth that you took
26:03
Speaker 1: more risks. And sometimes I took the risk because I
26:05
Speaker 1: wanted to do it, and at other times I took
26:09
Speaker 1: the risk because I felt like I had to. It
26:11
Speaker 1: was like one am at a casino table. You've got
26:14
Speaker 1: a lot of chips, you think maybe I should just
26:17
Speaker 1: stand up and walk away, and just everyone in the
26:21
Speaker 1: room is yelling at you to keep pushing the money
26:23
Speaker 1: out to the middle of the table. That wasn't just
26:25
Speaker 1: with eye buying. It was a whole bunch of different initiatives,
26:28
Speaker 1: and so that can be a pretty stressful way to live.
26:31
Speaker 1: But it's also creative and exciting and exhilarating. And now
26:39
Speaker 1: I just think this is a good way to live too.
26:42
Speaker 1: I love my job more than ever, even though we've
26:45
Speaker 1: been through hell over the past twelve months. But it's
26:51
Speaker 1: more important for me to say no than to say yes.
26:56
Speaker 1: And that isn't always creative, but it's sober and responsible.
27:01
Speaker 1: It's about time. I'm fifty two years old, Jacob, It's
27:04
Speaker 1: time to grow up. Yeah. So, if the metaphor of
27:09
Speaker 1: the teens when money was free was, you know, be
27:12
Speaker 1: at being at the table at the casino at midnight,
27:15
Speaker 1: the pile follow chips and everybody's yelling at you to
27:19
Speaker 1: go all in, keep keep betting, what's the metaphor. Now
27:23
Speaker 1: you're in your hotel room. Only you have the code
27:27
Speaker 1: to the safe. Nobody uses that safe, But now your
27:31
Speaker 1: balance sheet that's safe really matters, like how did you
27:35
Speaker 1: do in the end on I buying, Well, we can't
27:38
Speaker 1: call it a win. It was it a profitable business,
27:42
Speaker 1: but we didn't get our fanny handed. Two is either
27:45
Speaker 1: you own any houses. We've got like a few houses
27:49
Speaker 1: to sell, but not many, and obviously there's always a
27:51
Speaker 1: sting in the tail. So the last house you sell
27:54
Speaker 1: is the ugliest one. So you know, never count yourself
27:59
Speaker 1: lucky until you're dead, I think, is some saying of
28:02
Speaker 1: the ancient Romans. We'll see how we do on that
28:05
Speaker 1: last property, but we think we've mostly gotten out alive
28:14
Speaker 1: to wisdom of Glenn Kellman, CEO of Rickfin. To everyone
28:20
Speaker 1: who has listened to this show over the past year,
28:23
Speaker 1: thank you, thank you very much. If you want to
28:26
Speaker 1: help us grow into our second year of What's Your
28:29
Speaker 1: Problem as I see it, you have three options. Option one,
28:34
Speaker 1: recommend the show to a friend, Option two, leave us
28:38
Speaker 1: a nice review on your podcast s app or option three.
28:42
Speaker 1: So just a guest for the coming year by emailing
28:45
Speaker 1: us at problem at Pushkin dot fm, or by letting
28:49
Speaker 1: me know on Twitter. I'm at Jacob Goldstein. By the way,
28:53
Speaker 1: options one through three are not mutually exclusive have at It.
28:58
Speaker 1: Today's show was produced by Edith Russlo, edited by Sarah Knicks,
29:03
Speaker 1: and engineered by Amanda k Wong. I'm Jacob Goldstein, and
29:07
Speaker 1: we'll be back next week with another episode of What's
29:09
Speaker 1: Your Problem.