Latest / What's Your Problem? / Building Bespoke Weather Forecasts

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Building Bespoke Weather Forecasts · What's Your Problem?

0:15
Speaker 1: Pushkin. Here's the thing I did not know about weather
0:22
Speaker 1: forecasts until very recently. They basically all come from the government. Sure,
0:28
Speaker 1: you may have your favorite weather app, your favorite TV
0:30
Speaker 1: weather person, but their forecasts are almost entirely driven by
0:35
Speaker 1: data that's collected and analyzed by government agencies. And you know,
0:40
Speaker 1: it makes a certain kind of sense. Gathering the data
0:43
Speaker 1: you need to make a useful forecast has traditionally been
0:47
Speaker 1: a huge expensive undertaking, and having a reliable forecast is
0:51
Speaker 1: really valuable for lots of people in lots of different settings.
0:55
Speaker 1: So it's good that the government does the work and
0:58
Speaker 1: makes forecasts freely available to everybody. But the government is
1:03
Speaker 1: the government, and we shouldn't expect it to tailor forecasts
1:06
Speaker 1: for different businesses, or even to build forecasts that are
1:09
Speaker 1: really useful for people who live in other countries, in
1:12
Speaker 1: countries where the government can't afford to produce its own forecasts. Now,
1:17
Speaker 1: imagine what a private weather company could do. A company
1:21
Speaker 1: that relied not only on government data, but that went
1:23
Speaker 1: out and collected data on its own, A company that
1:26
Speaker 1: came up with forecasts that would not have been possible before.
1:34
Speaker 1: I'm Jacob Goldstein, and this is What's Your Problem, the
1:37
Speaker 1: show where entrepreneurs and engineers talk about how they're going
1:40
Speaker 1: to change the world once they solve a few problems.
1:43
Speaker 1: My guest today is Shimon Alphabets, co founder and CEO
1:48
Speaker 1: of tomorrow dot Io, a private company that plans to
1:51
Speaker 1: put a constellation of weather satellites into orbit in the
1:54
Speaker 1: next couple of years. Shimoon's problem, how do you build
1:58
Speaker 1: a private weather company from scratch? We realized it's from
2:02
Speaker 1: a jew political and cost effective way, and all kind
2:05
Speaker 1: of the only way to solveet is to go to space.
2:09
Speaker 1: Shimon and his co founders launched tomorrow dot Io in
2:12
Speaker 1: twenty sixteen. The company hasn't launched its satellites yet, but
2:16
Speaker 1: it already provides weather related advice for companies like Jet Blue,
2:20
Speaker 1: Uber and the NFL. Even before he thought of founding
2:24
Speaker 1: the company, weather was a big deal for Schimon When
2:27
Speaker 1: he was in his twenties. He was an officer in
2:29
Speaker 1: the Israeli Air Force. Whether it is obviously a huge
2:32
Speaker 1: deal for pilots for planes, and Shimon was constantly on
2:36
Speaker 1: the phone with meteorologists. The solution was, Hey, let's talk
2:41
Speaker 1: to a meteorologist three, four or five times, a day, Hey,
2:45
Speaker 1: what's going to happen here, what's going to happen there?
2:47
Speaker 1: And then I take the data, I analyze what it
2:49
Speaker 1: means for me, what it means for the organization. And
2:52
Speaker 1: I have to do it several times because the weather
2:54
Speaker 1: forecast is constantly changing, and I care about multiple locations
2:58
Speaker 1: and I care about multiple parameters. So that was a
3:01
Speaker 1: very archaic way of addressing challenges at scale. Calling the
3:05
Speaker 1: meteorologist and deciding what each plane should do does not scale.
3:08
Speaker 1: So in that universe you're not at scale yet, not scale,
3:11
Speaker 1: not efficient, not automatic. When there's a human in the loop,
3:14
Speaker 1: there will always be an error. Huh did you make mistakes?
3:19
Speaker 1: Of course everyone makes mistakes. I made a few of them.
3:24
Speaker 1: You know. I have colleagues that unfortunately lost their lives
3:28
Speaker 1: due to whether they did accidents. It was very unfortunate.
3:33
Speaker 1: It's just been there, you know. But I didn't think
3:35
Speaker 1: I'm going to start a company around it. Shimon moved
3:38
Speaker 1: to the US to go to business school, and one
3:40
Speaker 1: day a few of his friends, military veterans who like
3:43
Speaker 1: shimone wanted to start a company, started talking about the weather,
3:47
Speaker 1: and we started talking about past experiences, and everybody We're like,
3:52
Speaker 1: oh you also feel this way, Oh you also experienced that.
3:54
Speaker 1: And when we started looking at it, we said, okay,
3:57
Speaker 1: there is something here. We need to start looking into this.
3:59
Speaker 1: And let's try and understand whether you know how the
4:03
Speaker 1: technology how forecast is being generated, why is it limited
4:07
Speaker 1: in accuracy? And now let's look at how businesses make decisions.
4:12
Speaker 1: Do they do it in the same way we did
4:14
Speaker 1: it in the past, or is there a better way
4:16
Speaker 1: to do it? And what we found out led us
4:19
Speaker 1: to start a company. Well, what did you find out
4:22
Speaker 1: that led you to start a company? All right, that's
4:24
Speaker 1: where it's becoming interesting. So the first thing we found
4:27
Speaker 1: out is that climate change is here. That was in
4:31
Speaker 1: twenty sixteen. It wasn't cool to speak to speak about
4:33
Speaker 1: climate change back then. I mean I think it was cool.
4:36
Speaker 1: I think it was cool to me in twenty sixteen.
4:39
Speaker 1: Trust me, when I spoke to investors back at the
4:41
Speaker 1: time and you spoke about climate change, they were like,
4:44
Speaker 1: give me some sass solution. Don't talk to me about
4:46
Speaker 1: climate change. Okay, fair sass software as a service keep going.
4:49
Speaker 1: So we understood that the problem of managing whatever the
4:54
Speaker 1: challenges are is going to get bigger climate change equals
4:58
Speaker 1: weather events become more frequent and more volatile in any
5:01
Speaker 1: given ear in every part of the world. More hurricanes,
5:04
Speaker 1: more wildfires, more heat waves. Doesn't really matter where you are,
5:07
Speaker 1: there's some extreme phenomen it is going to happen more frequently. Okay,
5:13
Speaker 1: So that's one thing we'll learn. The second thing we'll
5:15
Speaker 1: learn is that the technology of forecasting weather, meaning what's
5:19
Speaker 1: responsible for the accuracy, is generated and dominated by government agencies.
5:27
Speaker 1: And as folks who served in the government for many years,
5:32
Speaker 1: we understood that there must be a way to privatize
5:36
Speaker 1: and innovate faster. And just to give an example, you know,
5:41
Speaker 1: NASA for decades innovated and paved the way to space right,
5:45
Speaker 1: But today you have SpaceX, who's augmenting the capability of
5:50
Speaker 1: a private company doing what the government has done for decades. Yeah,
5:54
Speaker 1: maybe not inventing the rocket from from scratch, but definitely
5:57
Speaker 1: taking all the decades of research and adjusting it to
6:00
Speaker 1: a commercial use case. What we found out is that
6:03
Speaker 1: there is an opportunity to create a SpaceX of weather.
6:07
Speaker 1: So SpaceX built rockets. You want to build weather forecasts.
6:11
Speaker 1: How do you do that? What do you need to
6:13
Speaker 1: make a weather forecast, you need three ingredients. On a
6:17
Speaker 1: very high level. You need observations that describe the atmospheric
6:23
Speaker 1: conditions in real time, the temperature, the wind pressure. Yeah,
6:26
Speaker 1: then you have a good real time description, right. The
6:28
Speaker 1: next thing you need is a model, an equation, a
6:31
Speaker 1: set of equations. Physical models doesn't really matter. The point
6:35
Speaker 1: is that you take the observations and you assimilate them
6:37
Speaker 1: into a model, and then the last thing you need
6:40
Speaker 1: is a computing power on which you process the model.
6:44
Speaker 1: The output of the model is a weather forecast. And
6:49
Speaker 1: what we found out is that there is an industry
6:52
Speaker 1: of weather forecasting companies, you know, big brands. You know,
6:56
Speaker 1: a blue logo, an orange logo, Acy Weather. Right. The
7:00
Speaker 1: point is that these guys are here since the sixties, seventies,
7:04
Speaker 1: maybe eighties. They just repackaged the forecast that the government
7:08
Speaker 1: agency or the government agencies publish every day, every hour whatever.
7:16
Speaker 1: So I'll say, I know, the weather forecast is like
7:18
Speaker 1: a classic thing to complain about. Oh the weather man
7:20
Speaker 1: said it would be Sunday and we had a picnic
7:22
Speaker 1: and it rain. I do feel like weather forecasts are
7:25
Speaker 1: pretty good, and clearly they've gotten better. Was there a
7:29
Speaker 1: particular weakness or failure or set of weaknesses or failures
7:35
Speaker 1: that you really thought you could improve. First of all,
7:39
Speaker 1: you set out to do this. If I may ask,
7:41
Speaker 1: where do you live, I live in New York. I
7:44
Speaker 1: live in New York City. Okay, so you're privileged because
7:47
Speaker 1: I am privileged. I'll be the first to say I'm
7:49
Speaker 1: in Boston. I'm as privileged as you are. Most of
7:52
Speaker 1: the world doesn't have Noah, the US government agency, the
7:56
Speaker 1: big rich country government agencies that do a pretty good
7:58
Speaker 1: job of forecasting exactly. And if you're a private company
8:01
Speaker 1: and you try to provide equally accurate forecast for the
8:06
Speaker 1: rest of the world, you're limited. You cannot provide it.
8:09
Speaker 1: So there is a global problem. So one thing you
8:12
Speaker 1: want to do better is provide better forecasts for people
8:16
Speaker 1: businesses who don't live in rich countries that have big,
8:18
Speaker 1: fancy weather agencies like NOAH in the US. That's one thing.
8:22
Speaker 1: The second thing is that even within the US, you know,
8:25
Speaker 1: the agency's main job is to save people's lives, Okay,
8:30
Speaker 1: it is not to optimize businesses. Right. That also seems reasonable,
8:34
Speaker 1: like absolutely, that's what I want them to be optimized for. Absolutely,
8:38
Speaker 1: we're on the same page here. But with some scientific
8:41
Speaker 1: improvement you can help businesses have better outcome, improve their
8:46
Speaker 1: top line, their bottom line, their safety, their efficiency. So
8:51
Speaker 1: there is a lot of room for improvement. The other
8:53
Speaker 1: element of it is that remember that I said, you
8:56
Speaker 1: know it's it's one thing to handle the forecast. The
8:59
Speaker 1: second thing is that once you improve either on the observation,
9:04
Speaker 1: on the modeling, on the computing power, and you get
9:06
Speaker 1: to more accurate forecasts, there is the are part of it,
9:10
Speaker 1: which is how you make decisions and how do you
9:12
Speaker 1: do it at scale? Right, So this is something that
9:16
Speaker 1: the government almost doesn't address at all, except maybe like
9:20
Speaker 1: when you need to evacuate a city for a hurricane
9:22
Speaker 1: or something, right, very rare circumstances, exactly exactly. Now, I'll
9:28
Speaker 1: give an example. A lot of company like utilities or airlines.
9:32
Speaker 1: They work in a very similar way to the way
9:35
Speaker 1: that I described in my military service. Some guy calling
9:38
Speaker 1: some other guy on the phone and being like, what
9:40
Speaker 1: should we do. You go to a metrologist, You speak
9:42
Speaker 1: to that meteorologist or get a report of road data,
9:45
Speaker 1: and then you have to do a full analysis of
9:47
Speaker 1: what it means and make a decision it's not scalable.
9:50
Speaker 1: Or if I have thousands of trucks driving in the country,
9:53
Speaker 1: or if I have thousands of miles of railways track,
9:56
Speaker 1: or if I have many airplanes in the air and
9:59
Speaker 1: I care about hundreds of airports globally, it's very hard
10:02
Speaker 1: to rely on five or ten even meteorologists on staff. Right.
10:09
Speaker 1: I've heard you say that a mistake you made early
10:13
Speaker 1: on was optimizing for accuracy, and that's really interesting to me,
10:18
Speaker 1: and I want you to tell me what that means.
10:22
Speaker 1: So at the beginning, we thought that if we just
10:26
Speaker 1: create a more accurate forecast, that's it. It's done. Deal
10:32
Speaker 1: with serve like that's a hugely valuable thing, right, even
10:34
Speaker 1: if you're a little bit more accurate, that's worth a
10:36
Speaker 1: ton of money to an airline or the NFL or
10:39
Speaker 1: any any number of really big companies. But what we
10:43
Speaker 1: found out, but we learned that most of the businesses
10:47
Speaker 1: that are impacted by weather do not know what to
10:51
Speaker 1: do with a weather forecast or with a weather data
10:55
Speaker 1: and they need the full loop, the translation to insights
11:00
Speaker 1: and decisions. And that's what helped us design our platform
11:05
Speaker 1: and the way we're operating today. So nobody understands hands
11:09
Speaker 1: how to read a weather forecast. Basically, how do you
11:12
Speaker 1: think from a business perspective? Yeah, so you realize from
11:15
Speaker 1: that that like, providing these people with a better weather
11:19
Speaker 1: forecast isn't actually going to help them solve their problem
11:21
Speaker 1: because they don't because they're not experts in analyzing the
11:25
Speaker 1: meaning of a weather forecast. Yeah. I mean you've named
11:28
Speaker 1: a lot of your clients publicly, right, I mean whatever,
11:31
Speaker 1: Delta and jet Blue and what Uber and the NFL.
11:34
Speaker 1: I guess the NFL isn't playing now, Like what do
11:36
Speaker 1: you telling JetBlue today? Like what do they want to
11:39
Speaker 1: know today? So you know, it's almost summertime. In the
11:43
Speaker 1: summer time, as you know, in the biggest hubs like
11:46
Speaker 1: JFK or Boston Logan, you have disruptions related to lightning
11:52
Speaker 1: strikes thunderstorms. Well, well exactly, they'll shut the airport down
11:56
Speaker 1: for hours and everything will be a total mess exactly.
11:59
Speaker 1: So instead of someone looking at a model and whatever,
12:02
Speaker 1: we're just basically providing a weekly calendar that says, expect
12:06
Speaker 1: the disruptions between dead time to dead time. Here are
12:09
Speaker 1: the recommendations to do. ABC staff more people here, staffless
12:13
Speaker 1: people there. So we actually go into the operational recommendations
12:18
Speaker 1: as a result of the expected disruption as a result
12:21
Speaker 1: of the weather forecast. Now, listen carefully to what I'm saying.
12:24
Speaker 1: It's as a result to the weather forecast. So if
12:26
Speaker 1: you're not relying on an accurate forecast, the business insight
12:31
Speaker 1: is useless. It's actually damaging. So there's no way to
12:35
Speaker 1: get around the need to improve the accuracy. Specifically, what
12:41
Speaker 1: are you better at forecasting than anybody else right now?
12:47
Speaker 1: Precipitation data. We provide global real time and now casting
12:54
Speaker 1: data that is providing a kind of like minute by
12:58
Speaker 1: minute forecast for a range of about six hours on
13:01
Speaker 1: every point on Earth, which is quite useful. For example,
13:06
Speaker 1: you have some sixty minutes minute by minute works that
13:09
Speaker 1: you have on some phones, but it's only in the
13:12
Speaker 1: US and in the UK. Yes, I do find I
13:15
Speaker 1: have that on my phone and I find it's pretty good.
13:17
Speaker 1: Dark Skies. I have Dark Skies on my phone and
13:20
Speaker 1: it's good. But you're saying, if I if I left
13:22
Speaker 1: the US, if I went on vacation to Mexico or something,
13:25
Speaker 1: it just wouldn't work. It's not available. It's just not available.
13:30
Speaker 1: And we created this thing on a global scale with
13:33
Speaker 1: longer time horizon. That's one example. And the other example
13:36
Speaker 1: is like quind, we forecast twind in higher accuracy for
13:40
Speaker 1: the next day, two days, three days, which is very
13:41
Speaker 1: useful for farms. I think, so okay, and are you
13:44
Speaker 1: just better at that because you're more focused on it
13:48
Speaker 1: and you've trained the models more than and it's more
13:50
Speaker 1: important to your clients than it is to say, a
13:52
Speaker 1: government agency, so you have an incentive to figure it
13:55
Speaker 1: out exactly that. I'm sure that if Noah wanted to
13:59
Speaker 1: double down on that, specifically Noah the government agency, they
14:03
Speaker 1: would have been able to do that. But they have
14:05
Speaker 1: no incentive, and you know, the pace of making a
14:08
Speaker 1: decision in a large organization, it's just not enabling them
14:13
Speaker 1: to move fast enough. The next problem Shimona's colleagues are
14:17
Speaker 1: trying to solve, how do you predict the weather for
14:20
Speaker 1: people who live in countries that can't afford a big
14:23
Speaker 1: national weather service like Noah to do that. Tomorrow, dot
14:27
Speaker 1: Io is going to go to space. That's the end
14:36
Speaker 1: of the ads. Now we're going back to the show. Tomorrow.
14:39
Speaker 1: Dot Io's next big project is putting a constellation of
14:44
Speaker 1: weather satellites into space. And there are two big questions
14:47
Speaker 1: I had about that. What problem will it solve and
14:51
Speaker 1: what's it going to take to make it happen. So
14:54
Speaker 1: the first thing I'll say, what motivated us to get
14:56
Speaker 1: to space. The main motivation was how do we optimize
15:00
Speaker 1: forecast and make it more accurate? And when we looked
15:03
Speaker 1: at the blend between okay, we have observations, we have models,
15:08
Speaker 1: we found out that the lack of observations on a
15:11
Speaker 1: global scale are the main reason why we cannot improve
15:15
Speaker 1: whether focussing significantly on a global scale. So the problem
15:19
Speaker 1: wasn't the models. The problem wasn't the computing power. The
15:21
Speaker 1: problem is just there's just not enough data when you
15:24
Speaker 1: get outside of what outside of the US, Europe, Japan,
15:27
Speaker 1: basically the data quality falls off. Yeah, okay. And the
15:31
Speaker 1: most important weather sensor that we identified and I think
15:36
Speaker 1: is agreed on all the community from NOAH to NASA
15:40
Speaker 1: to others, is Doppler radar. A Toppler radar, just to
15:44
Speaker 1: be clear, is it the one where you see a
15:45
Speaker 1: color like if it's raining really hard, it's red or
15:47
Speaker 1: something that stopple? Correct? Okay, Now, radars are looking out
15:52
Speaker 1: in the sky and they help us know where is
15:54
Speaker 1: the training in real time, how the cloud formation looks like.
15:58
Speaker 1: It gives you some kind of three D description of
16:01
Speaker 1: the atmosphere. Okay, now what we found out is that
16:06
Speaker 1: five billion people leave outside of radar coverage. Five billion.
16:11
Speaker 1: He goes out of the border to Mexico, all the
16:14
Speaker 1: way to southern South America, and basically you don't know
16:19
Speaker 1: where it's raining in real time, say for Africa, India
16:22
Speaker 1: and many other places. That is surprising to me. Maybe
16:24
Speaker 1: I'm naive, but like, were you surprised when you learned that, No,
16:29
Speaker 1: because I came from a place where it was not
16:31
Speaker 1: Oh you didn't have it either. You didn't have it either. Yeah,
16:34
Speaker 1: it was pretty broken most of the time. And it's
16:37
Speaker 1: not a new technology, right, it's a decades old. It's
16:40
Speaker 1: not a new technology. But the implication of not having
16:43
Speaker 1: it is huge. You cannot provide flood alerts. Pilots when
16:48
Speaker 1: they fly, for example, to Cancun, they don't know the
16:51
Speaker 1: weather in the route. It's a huge problem for the economy.
16:54
Speaker 1: The next point is that the oceans and the seas
16:58
Speaker 1: are not covered with radars, and every time, for example,
17:02
Speaker 1: a hurricane is formed over the Atlantic. The US government
17:05
Speaker 1: is flying airplanes over the eye of the storm to
17:08
Speaker 1: scan it with a radar so we can send it
17:10
Speaker 1: back to the model that as an understand if it's
17:12
Speaker 1: going to be category one, two or three, when and
17:14
Speaker 1: where it's going to eat, and whether we should evacuate
17:16
Speaker 1: Miami or New Orleans. So rest assured, nobody's flying any
17:21
Speaker 1: airplane over a typhoon or a cyclone in the East.
17:24
Speaker 1: So this is a huge in Asia. In Asia, they're
17:26
Speaker 1: not going out in Asia to get a really accurate
17:28
Speaker 1: forecast of where it's going to go. They can't afford
17:31
Speaker 1: to do that. But you can do it from space.
17:33
Speaker 1: Is that where this is going so exactly, So we
17:36
Speaker 1: realize that from a geopolitical and cost effective way and
17:40
Speaker 1: all kind of the only way to solve it is
17:42
Speaker 1: to go to space. The problem is that radars are
17:45
Speaker 1: pretty big. We actually the world has one radar in
17:49
Speaker 1: space today. It is called the GPM. It's a program
17:53
Speaker 1: by NASA with the collaboration of the Japanese agency. It's
17:57
Speaker 1: more than a billion dollar program that created one radar
18:00
Speaker 1: in space, a very sophisticated one. We have one radar
18:04
Speaker 1: in space today. That radar cost about a billion dollar
18:08
Speaker 1: if not more, and it samples every point on Earth
18:12
Speaker 1: every three days, So it's not very useful for hurricane
18:15
Speaker 1: forecasting because imagine you just sample the hurricanes moving too fast,
18:19
Speaker 1: or general weather forecasting. So what we were trying to
18:22
Speaker 1: do was to say, how can we take this huge
18:26
Speaker 1: radar and minimize it so we can put many of them.
18:30
Speaker 1: But we are a small company, we don't have a
18:32
Speaker 1: billion dollar How can we actually do it in a
18:35
Speaker 1: way that will be cost effective? And the goal is,
18:39
Speaker 1: of course, to monitor every point on Earth with a
18:42
Speaker 1: radar in almost real time, because when you do that,
18:47
Speaker 1: you are going to improve weather forecasting dramatically. You're going
18:51
Speaker 1: to improve hurricane cyclone typhoons, you are going to be
18:55
Speaker 1: able to provide flood alerts for every point on Earth.
18:59
Speaker 1: And it will improve also climate science because now climate
19:02
Speaker 1: scientists will have better understanding of what actually happen. No,
19:05
Speaker 1: I'm sold on why it would be useful. It seems
19:07
Speaker 1: like the hard thing is how do you do it exactly?
19:09
Speaker 1: So how do we do that? The first thing we
19:13
Speaker 1: did was to focus on the sensor. How can we
19:15
Speaker 1: build a sensor that we'll keep most, if not all,
19:19
Speaker 1: the characteristics of the radar. We looked at and how
19:23
Speaker 1: can we make it small enough so we can launch
19:27
Speaker 1: it a not a nano or micro satellite, but something
19:32
Speaker 1: smaller than you know, the stationary satellites, a low orbit.
19:36
Speaker 1: And bottom line, we've finished the development of the radar
19:40
Speaker 1: and in a few months we're going to launch the
19:42
Speaker 1: first satellite out of a constellation of about thirty And
19:46
Speaker 1: our constellation is going to have two types of sensors.
19:49
Speaker 1: One is the radar, the second is a microwave sounder.
19:52
Speaker 1: The combination of the two is going to provide a
19:55
Speaker 1: very good scientific result for every point on Earth. You
19:59
Speaker 1: sound very confident, like are you at a point where
20:02
Speaker 1: you know it's going to work or is it the
20:03
Speaker 1: kind of thing that you hope is going to work. No,
20:05
Speaker 1: we know it's going to work. The question is, okay,
20:07
Speaker 1: will it take us more time? Will we fail in
20:10
Speaker 1: the first lunch? Will we need to reiterate between one
20:12
Speaker 1: lunch to a number? But it is feasible, it is working.
20:15
Speaker 1: It is And how much is it going to cost
20:17
Speaker 1: you to get roughly thirty satellites up and monitoring the weather.
20:21
Speaker 1: Our early estimations, which so far given the inflation, are
20:29
Speaker 1: still are still in the same ballpark. We're looking at
20:33
Speaker 1: around one hundred million dollars for the entire constellation. So
20:36
Speaker 1: that's a big cost reduction. That's compared to what is
20:39
Speaker 1: it saying a billion for an existing one that only
20:41
Speaker 1: does once every three days? Yeah, what are the things
20:45
Speaker 1: that might go wrong? I mean, it seems like a
20:47
Speaker 1: quite hard thing that you're trying to do. I feel like,
20:50
Speaker 1: as you're describing it, it's like, oh, yeah, now, all
20:51
Speaker 1: we got to do is get these thirty satellites up
20:54
Speaker 1: into space and we're going to go But so imagine
20:56
Speaker 1: it's still going to be quite hard and lots of
20:58
Speaker 1: things can go wrong, of course, So you want me
21:00
Speaker 1: to give you examples of things that can go wrong? Yeah,
21:03
Speaker 1: what are you worried about? Okay, the rocket can explode
21:06
Speaker 1: in lunch. Sure. Classic second thing is that you know,
21:10
Speaker 1: we may have some communication malfunction. We may have some
21:16
Speaker 1: when we build our satellites and the radars. We may
21:19
Speaker 1: have to wait for longer than expected for chips to
21:23
Speaker 1: arrive or all kinds of chips. Radiance like supply chain,
21:26
Speaker 1: supply chain, supply chain issues is something pretty big right now.
21:30
Speaker 1: There are so many things that can happen. But are
21:34
Speaker 1: you sure the thing you built is going to work?
21:36
Speaker 1: All the things you've described as like, oh yeah, the
21:38
Speaker 1: rocket could blow up, that's not really our faulter, the
21:40
Speaker 1: chip oncome, that's not really our fault. Like, is it
21:43
Speaker 1: is it at the point where it's like, oh, yes,
21:44
Speaker 1: this will definitely work. Is it like that or is
21:46
Speaker 1: it possible that Okay, it's gonna work. It's gonna work.
21:50
Speaker 1: The question is is it going to be more expensive
21:53
Speaker 1: than we thought, It's going to take longer, and there
21:55
Speaker 1: might be you know, business implications on tomorrow. But it
22:01
Speaker 1: is going to work. It's not a question of science
22:04
Speaker 1: business like like might you run out of money before
22:07
Speaker 1: you can get it going? When you say business, everything
22:09
Speaker 1: can happen in that context. But this thing is working.
22:13
Speaker 1: Pending one hundred million to put a fleet of satellites
22:16
Speaker 1: into space, is it's still a lot for your business?
22:20
Speaker 1: It is a lot. And the market is very bad.
22:23
Speaker 1: It's probably the toughest market in the last twenty years
22:26
Speaker 1: for tech companies. The market for raising funding you mean, yeah, yeah.
22:31
Speaker 1: The investors are not very happy to see businesses that
22:36
Speaker 1: waste money or spend money or invest money, depending on
22:39
Speaker 1: how you look at it to build a solution, and
22:44
Speaker 1: it's definitely a challenge, and I just hope that, you know,
22:49
Speaker 1: the investment community will keep supporting us. We'll get to
22:53
Speaker 1: the lightning round in a minute, but before we do,
22:56
Speaker 1: I just want to say that what Shimone talked about
22:58
Speaker 1: in this episode is actually a really good example of
23:01
Speaker 1: a big idea that came up in an earlier episode
23:04
Speaker 1: of the show. It was the episode where I interviewed
23:07
Speaker 1: the founder of the company Rocket Left, and I was
23:10
Speaker 1: going on about how making rockets and satellites cheaper was
23:13
Speaker 1: a big deal, and he made the point that the
23:15
Speaker 1: big breakthrough is not just that they're cheaper. It's that
23:19
Speaker 1: cheaper rockets and satellites enable people to do big new things,
23:23
Speaker 1: things that just did not get done before. And Shimone's
23:27
Speaker 1: plan tomorrow dot Io's plan is a perfect example of
23:31
Speaker 1: that idea. Even a decade ago, it would have been
23:34
Speaker 1: prohibitively expensive, but today it's possible to put a constellation
23:38
Speaker 1: of satellites into orbit to improve forecasts everywhere on the
23:42
Speaker 1: globe for a price that is affordable for a startup.
23:45
Speaker 1: As long as they can get a few more years
23:47
Speaker 1: of funding, we'll have the lightning round with Shimone in
23:50
Speaker 1: just a minute. Now, let's get back to the show. Okay,
24:01
Speaker 1: I know you have to go soon, but let's do
24:03
Speaker 1: a quick lightning round. What is one piece of advice
24:06
Speaker 1: you'd give to someone trying to solve a hard problem.
24:09
Speaker 1: Focus on the problem and not on a solution. The
24:13
Speaker 1: solution will be obsolete. There are many kinds of solutions,
24:16
Speaker 1: but if you're focused on the problem, you're going to
24:18
Speaker 1: objectively look at what's right, what's wrong, and you'll be
24:22
Speaker 1: able to ditch something that doesn't work and find out
24:25
Speaker 1: something that is better. Focus on the problem. What do
24:28
Speaker 1: you prefer really hot weather or really cold weather? Hot?
24:31
Speaker 1: Okay hot? Could there really be a shark nado like
24:36
Speaker 1: in the movie Shark Nado? I don't know. What's the
24:41
Speaker 1: most underrated weather hazard? Most underrated heatwave? Lots of people
24:48
Speaker 1: die from heatwave annually, strokes, health issues, heart attacks. I
24:52
Speaker 1: actually testified in front of the Congress in the summer
24:55
Speaker 1: of twenty one on this topic, specifically, other than weather,
25:00
Speaker 1: what's the domain where people should use probabilistic thinking more finance,
25:05
Speaker 1: for sure? I mean, how do you manage your investments?
25:09
Speaker 1: Although I feel like bad. Use of probabilistic thinking was
25:13
Speaker 1: a major problem in the run up to the financial
25:16
Speaker 1: crisis of two thousand and eight. I don't know if
25:18
Speaker 1: you remember, but people kept saying like this is a
25:21
Speaker 1: one in ten thousand year move in whatever you know race, Like,
25:27
Speaker 1: clearly it's not your model is wrong? Right, yeah? Yeah,
25:30
Speaker 1: I'm thinking more in a personal level. On a personal level,
25:34
Speaker 1: like a household, how can a household manage their risk
25:37
Speaker 1: and everything? I think they should think about all the scenarios,
25:40
Speaker 1: all the probabilities, and I think people don't do that enough.
25:43
Speaker 1: So whether it's like this classic way to make small talk,
25:47
Speaker 1: you know, when you don't want to talk about work, right,
25:49
Speaker 1: So what do you talk about when you want to
25:52
Speaker 1: make small talk and don't want to talk about work? Oh? Football,
25:54
Speaker 1: I mean soccer? I guess that's the other classic, right sports? Yeah? Yeah,
25:59
Speaker 1: but I'm very passionate about it for real. I mean
26:02
Speaker 1: I can we could talk about it for an hour.
26:04
Speaker 1: Who's your team? What do you say? Who's your club?
26:05
Speaker 1: Who's your club? My club is in Israelity team called
26:09
Speaker 1: mac It's uh, how's doing and learning? Will if everything
26:16
Speaker 1: goes well, what's a problem You'll be trying to solve
26:18
Speaker 1: in five years how to reduce carbon emission with our solution.
26:24
Speaker 1: That will be probably the next step and will be
26:27
Speaker 1: the most impactful thing we can do. But we'll try, okay.
26:32
Speaker 1: Simon Alphabets is the co founder and CEO of tomorrow
26:35
Speaker 1: dot Ido. Today's show was produced by Edith Russelo, edited
26:39
Speaker 1: by Robert Smith, and engineered by Amanda ka Wong. I'm
26:43
Speaker 1: Jacob Goldstein, and I'll be back next week with another
26:45
Speaker 1: episode of What's Your Problem.