July 7, 2022 • 26 mins
Shimon Elkabetz is the founder and CEO of Tomorrow.io.
His problem: How do you build a weather forecasting company from scratch? The company already sells weather intelligence to companies like JetBlue, Uber and the NFL.
Their next move: Send the first private constellation of weather satellites to space (without running out of money).
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Episode Transcript
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Speaker 1 (00:15):
Pushkin. Here's the thing I did not know about weather
forecasts until very recently. They basically all come from the government. Sure,
you may have your favorite weather app, your favorite TV
weather person, but their forecasts are almost entirely driven by
(00:35):
data that's collected and analyzed by government agencies. And you know,
it makes a certain kind of sense. Gathering the data
you need to make a useful forecast has traditionally been
a huge expensive undertaking, and having a reliable forecast is
really valuable for lots of people in lots of different settings.
So it's good that the government does the work and
(00:58):
makes forecasts freely available to everybody. But the government is
the government, and we shouldn't expect it to tailor forecasts
for different businesses, or even to build forecasts that are
really useful for people who live in other countries, in
countries where the government can't afford to produce its own forecasts. Now,
imagine what a private weather company could do. A company
(01:21):
that relied not only on government data, but that went
out and collected data on its own, A company that
came up with forecasts that would not have been possible before.
I'm Jacob Goldstein, and this is What's Your Problem, the
show where entrepreneurs and engineers talk about how they're going
to change the world once they solve a few problems.
(01:43):
My guest today is Shimon Alphabets, co founder and CEO
of tomorrow dot Io, a private company that plans to
put a constellation of weather satellites into orbit in the
next couple of years. Shimoon's problem, how do you build
a private weather company from scratch? We realized it's from
a jew political and cost effective way, and all kind
(02:05):
of the only way to solveet is to go to space.
Shimon and his co founders launched tomorrow dot Io in
twenty sixteen. The company hasn't launched its satellites yet, but
it already provides weather related advice for companies like Jet Blue,
Uber and the NFL. Even before he thought of founding
the company, weather was a big deal for Schimon When
(02:27):
he was in his twenties. He was an officer in
the Israeli Air Force. Whether it is obviously a huge
deal for pilots for planes, and Shimon was constantly on
the phone with meteorologists. The solution was, Hey, let's talk
to a meteorologist three, four or five times, a day, Hey,
what's going to happen here, what's going to happen there?
(02:47):
And then I take the data, I analyze what it
means for me, what it means for the organization. And
I have to do it several times because the weather
forecast is constantly changing, and I care about multiple locations
and I care about multiple parameters. So that was a
very archaic way of addressing challenges at scale. Calling the
meteorologist and deciding what each plane should do does not scale.
(03:08):
So in that universe you're not at scale yet, not scale,
not efficient, not automatic. When there's a human in the loop,
there will always be an error. Huh did you make mistakes?
Of course everyone makes mistakes. I made a few of them.
You know. I have colleagues that unfortunately lost their lives
due to whether they did accidents. It was very unfortunate.
(03:33):
It's just been there, you know. But I didn't think
I'm going to start a company around it. Shimon moved
to the US to go to business school, and one
day a few of his friends, military veterans who like
shimone wanted to start a company, started talking about the weather,
and we started talking about past experiences, and everybody We're like,
oh you also feel this way, Oh you also experienced that.
(03:54):
And when we started looking at it, we said, okay,
there is something here. We need to start looking into this.
And let's try and understand whether you know how the
technology how forecast is being generated, why is it limited
in accuracy? And now let's look at how businesses make decisions.
Do they do it in the same way we did
it in the past, or is there a better way
(04:16):
to do it? And what we found out led us
to start a company. Well, what did you find out
that led you to start a company? All right, that's
where it's becoming interesting. So the first thing we found
out is that climate change is here. That was in
twenty sixteen. It wasn't cool to speak to speak about
climate change back then. I mean I think it was cool.
(04:36):
I think it was cool to me in twenty sixteen.
Trust me, when I spoke to investors back at the
time and you spoke about climate change, they were like,
give me some sass solution. Don't talk to me about
climate change. Okay, fair sass software as a service keep going.
So we understood that the problem of managing whatever the
challenges are is going to get bigger climate change equals
(04:58):
weather events become more frequent and more volatile in any
given ear in every part of the world. More hurricanes,
more wildfires, more heat waves. Doesn't really matter where you are,
there's some extreme phenomen it is going to happen more frequently. Okay,
So that's one thing we'll learn. The second thing we'll
learn is that the technology of forecasting weather, meaning what's
(05:19):
responsible for the accuracy, is generated and dominated by government agencies.
And as folks who served in the government for many years,
we understood that there must be a way to privatize
and innovate faster. And just to give an example, you know,
(05:41):
NASA for decades innovated and paved the way to space right,
But today you have SpaceX, who's augmenting the capability of
a private company doing what the government has done for decades. Yeah,
maybe not inventing the rocket from from scratch, but definitely
taking all the decades of research and adjusting it to
a commercial use case. What we found out is that
(06:03):
there is an opportunity to create a SpaceX of weather.
So SpaceX built rockets. You want to build weather forecasts.
How do you do that? What do you need to
make a weather forecast, you need three ingredients. On a
very high level. You need observations that describe the atmospheric
conditions in real time, the temperature, the wind pressure. Yeah,
(06:26):
then you have a good real time description, right. The
next thing you need is a model, an equation, a
set of equations. Physical models doesn't really matter. The point
is that you take the observations and you assimilate them
into a model, and then the last thing you need
is a computing power on which you process the model.
The output of the model is a weather forecast. And
(06:49):
what we found out is that there is an industry
of weather forecasting companies, you know, big brands. You know,
a blue logo, an orange logo, Acy Weather. Right. The
point is that these guys are here since the sixties, seventies,
maybe eighties. They just repackaged the forecast that the government
agency or the government agencies publish every day, every hour whatever.
(07:16):
So I'll say, I know, the weather forecast is like
a classic thing to complain about. Oh the weather man
said it would be Sunday and we had a picnic
and it rain. I do feel like weather forecasts are
pretty good, and clearly they've gotten better. Was there a
particular weakness or failure or set of weaknesses or failures
that you really thought you could improve. First of all,
(07:39):
you set out to do this. If I may ask,
where do you live, I live in New York. I
live in New York City. Okay, so you're privileged because
I am privileged. I'll be the first to say I'm
in Boston. I'm as privileged as you are. Most of
the world doesn't have Noah, the US government agency, the
big rich country government agencies that do a pretty good
job of forecasting exactly. And if you're a private company
(08:01):
and you try to provide equally accurate forecast for the
rest of the world, you're limited. You cannot provide it.
So there is a global problem. So one thing you
want to do better is provide better forecasts for people
businesses who don't live in rich countries that have big,
fancy weather agencies like NOAH in the US. That's one thing.
(08:22):
The second thing is that even within the US, you know,
the agency's main job is to save people's lives, Okay,
it is not to optimize businesses. Right. That also seems reasonable,
like absolutely, that's what I want them to be optimized for. Absolutely,
we're on the same page here. But with some scientific
improvement you can help businesses have better outcome, improve their
(08:46):
top line, their bottom line, their safety, their efficiency. So
there is a lot of room for improvement. The other
element of it is that remember that I said, you
know it's it's one thing to handle the forecast. The
second thing is that once you improve either on the observation,
on the modeling, on the computing power, and you get
(09:06):
to more accurate forecasts, there is the are part of it,
which is how you make decisions and how do you
do it at scale? Right, So this is something that
the government almost doesn't address at all, except maybe like
when you need to evacuate a city for a hurricane
or something, right, very rare circumstances, exactly exactly. Now, I'll
(09:28):
give an example. A lot of company like utilities or airlines.
They work in a very similar way to the way
that I described in my military service. Some guy calling
some other guy on the phone and being like, what
should we do. You go to a metrologist, You speak
to that meteorologist or get a report of road data,
and then you have to do a full analysis of
what it means and make a decision it's not scalable.
(09:50):
Or if I have thousands of trucks driving in the country,
or if I have thousands of miles of railways track,
or if I have many airplanes in the air and
I care about hundreds of airports globally, it's very hard
to rely on five or ten even meteorologists on staff. Right.
I've heard you say that a mistake you made early
(10:13):
on was optimizing for accuracy, and that's really interesting to me,
and I want you to tell me what that means.
So at the beginning, we thought that if we just
create a more accurate forecast, that's it. It's done. Deal
with serve like that's a hugely valuable thing, right, even
(10:34):
if you're a little bit more accurate, that's worth a
ton of money to an airline or the NFL or
any any number of really big companies. But what we
found out, but we learned that most of the businesses
that are impacted by weather do not know what to
do with a weather forecast or with a weather data
(10:55):
and they need the full loop, the translation to insights
and decisions. And that's what helped us design our platform
and the way we're operating today. So nobody understands hands
how to read a weather forecast. Basically, how do you
think from a business perspective? Yeah, so you realize from
(11:15):
that that like, providing these people with a better weather
forecast isn't actually going to help them solve their problem
because they don't because they're not experts in analyzing the
meaning of a weather forecast. Yeah. I mean you've named
a lot of your clients publicly, right, I mean whatever,
Delta and jet Blue and what Uber and the NFL.
I guess the NFL isn't playing now, Like what do
(11:36):
you telling JetBlue today? Like what do they want to
know today? So you know, it's almost summertime. In the
summer time, as you know, in the biggest hubs like
JFK or Boston Logan, you have disruptions related to lightning
strikes thunderstorms. Well, well exactly, they'll shut the airport down
for hours and everything will be a total mess exactly.
(11:59):
So instead of someone looking at a model and whatever,
we're just basically providing a weekly calendar that says, expect
the disruptions between dead time to dead time. Here are
the recommendations to do. ABC staff more people here, staffless
people there. So we actually go into the operational recommendations
as a result of the expected disruption as a result
(12:21):
of the weather forecast. Now, listen carefully to what I'm saying.
It's as a result to the weather forecast. So if
you're not relying on an accurate forecast, the business insight
is useless. It's actually damaging. So there's no way to
get around the need to improve the accuracy. Specifically, what
(12:41):
are you better at forecasting than anybody else right now?
Precipitation data. We provide global real time and now casting
data that is providing a kind of like minute by
minute forecast for a range of about six hours on
every point on Earth, which is quite useful. For example,
(13:06):
you have some sixty minutes minute by minute works that
you have on some phones, but it's only in the
US and in the UK. Yes, I do find I
have that on my phone and I find it's pretty good.
Dark Skies. I have Dark Skies on my phone and
it's good. But you're saying, if I if I left
the US, if I went on vacation to Mexico or something,
it just wouldn't work. It's not available. It's just not available.
(13:30):
And we created this thing on a global scale with
longer time horizon. That's one example. And the other example
is like quind, we forecast twind in higher accuracy for
the next day, two days, three days, which is very
useful for farms. I think, so okay, and are you
just better at that because you're more focused on it
and you've trained the models more than and it's more
(13:50):
important to your clients than it is to say, a
government agency, so you have an incentive to figure it
out exactly that. I'm sure that if Noah wanted to
double down on that, specifically Noah the government agency, they
would have been able to do that. But they have
no incentive, and you know, the pace of making a
decision in a large organization, it's just not enabling them
(14:13):
to move fast enough. The next problem Shimona's colleagues are
trying to solve, how do you predict the weather for
people who live in countries that can't afford a big
national weather service like Noah to do that. Tomorrow, dot
Io is going to go to space. That's the end
(14:36):
of the ads. Now we're going back to the show. Tomorrow.
Dot Io's next big project is putting a constellation of
weather satellites into space. And there are two big questions
I had about that. What problem will it solve and
what's it going to take to make it happen. So
the first thing I'll say, what motivated us to get
to space. The main motivation was how do we optimize
(15:00):
forecast and make it more accurate? And when we looked
at the blend between okay, we have observations, we have models,
we found out that the lack of observations on a
global scale are the main reason why we cannot improve
whether focussing significantly on a global scale. So the problem
wasn't the models. The problem wasn't the computing power. The
(15:21):
problem is just there's just not enough data when you
get outside of what outside of the US, Europe, Japan,
basically the data quality falls off. Yeah, okay. And the
most important weather sensor that we identified and I think
is agreed on all the community from NOAH to NASA
to others, is Doppler radar. A Toppler radar, just to
(15:44):
be clear, is it the one where you see a
color like if it's raining really hard, it's red or
something that stopple? Correct? Okay, Now, radars are looking out
in the sky and they help us know where is
the training in real time, how the cloud formation looks like.
It gives you some kind of three D description of
the atmosphere. Okay, now what we found out is that
(16:06):
five billion people leave outside of radar coverage. Five billion.
He goes out of the border to Mexico, all the
way to southern South America, and basically you don't know
where it's raining in real time, say for Africa, India
and many other places. That is surprising to me. Maybe
I'm naive, but like, were you surprised when you learned that, No,
(16:29):
because I came from a place where it was not
Oh you didn't have it either. You didn't have it either. Yeah,
it was pretty broken most of the time. And it's
not a new technology, right, it's a decades old. It's
not a new technology. But the implication of not having
it is huge. You cannot provide flood alerts. Pilots when
they fly, for example, to Cancun, they don't know the
(16:51):
weather in the route. It's a huge problem for the economy.
The next point is that the oceans and the seas
are not covered with radars, and every time, for example,
a hurricane is formed over the Atlantic. The US government
is flying airplanes over the eye of the storm to
scan it with a radar so we can send it
back to the model that as an understand if it's
(17:12):
going to be category one, two or three, when and
where it's going to eat, and whether we should evacuate
Miami or New Orleans. So rest assured, nobody's flying any
airplane over a typhoon or a cyclone in the East.
So this is a huge in Asia. In Asia, they're
not going out in Asia to get a really accurate
forecast of where it's going to go. They can't afford
to do that. But you can do it from space.
(17:33):
Is that where this is going so exactly, So we
realize that from a geopolitical and cost effective way and
all kind of the only way to solve it is
to go to space. The problem is that radars are
pretty big. We actually the world has one radar in
space today. It is called the GPM. It's a program
by NASA with the collaboration of the Japanese agency. It's
(17:57):
more than a billion dollar program that created one radar
in space, a very sophisticated one. We have one radar
in space today. That radar cost about a billion dollar
if not more, and it samples every point on Earth
every three days, So it's not very useful for hurricane
forecasting because imagine you just sample the hurricanes moving too fast,
(18:19):
or general weather forecasting. So what we were trying to
do was to say, how can we take this huge
radar and minimize it so we can put many of them.
But we are a small company, we don't have a
billion dollar How can we actually do it in a
way that will be cost effective? And the goal is,
(18:39):
of course, to monitor every point on Earth with a
radar in almost real time, because when you do that,
you are going to improve weather forecasting dramatically. You're going
to improve hurricane cyclone typhoons, you are going to be
able to provide flood alerts for every point on Earth.
And it will improve also climate science because now climate
(19:02):
scientists will have better understanding of what actually happen. No,
I'm sold on why it would be useful. It seems
like the hard thing is how do you do it exactly?
So how do we do that? The first thing we
did was to focus on the sensor. How can we
build a sensor that we'll keep most, if not all,
the characteristics of the radar. We looked at and how
(19:23):
can we make it small enough so we can launch
it a not a nano or micro satellite, but something
smaller than you know, the stationary satellites, a low orbit.
And bottom line, we've finished the development of the radar
and in a few months we're going to launch the
first satellite out of a constellation of about thirty And
(19:46):
our constellation is going to have two types of sensors.
One is the radar, the second is a microwave sounder.
The combination of the two is going to provide a
very good scientific result for every point on Earth. You
sound very confident, like are you at a point where
you know it's going to work or is it the
kind of thing that you hope is going to work. No,
we know it's going to work. The question is, okay,
(20:07):
will it take us more time? Will we fail in
the first lunch? Will we need to reiterate between one
lunch to a number? But it is feasible, it is working.
It is And how much is it going to cost
you to get roughly thirty satellites up and monitoring the weather.
Our early estimations, which so far given the inflation, are
(20:29):
still are still in the same ballpark. We're looking at
around one hundred million dollars for the entire constellation. So
that's a big cost reduction. That's compared to what is
it saying a billion for an existing one that only
does once every three days? Yeah, what are the things
that might go wrong? I mean, it seems like a
quite hard thing that you're trying to do. I feel like,
(20:50):
as you're describing it, it's like, oh, yeah, now, all
we got to do is get these thirty satellites up
into space and we're going to go But so imagine
it's still going to be quite hard and lots of
things can go wrong, of course, So you want me
to give you examples of things that can go wrong? Yeah,
what are you worried about? Okay, the rocket can explode
in lunch. Sure. Classic second thing is that you know,
(21:10):
we may have some communication malfunction. We may have some
when we build our satellites and the radars. We may
have to wait for longer than expected for chips to
arrive or all kinds of chips. Radiance like supply chain,
supply chain, supply chain issues is something pretty big right now.
There are so many things that can happen. But are
(21:34):
you sure the thing you built is going to work?
All the things you've described as like, oh yeah, the
rocket could blow up, that's not really our faulter, the
chip oncome, that's not really our fault. Like, is it
is it at the point where it's like, oh, yes,
this will definitely work. Is it like that or is
it possible that Okay, it's gonna work. It's gonna work.
The question is is it going to be more expensive
than we thought, It's going to take longer, and there
(21:55):
might be you know, business implications on tomorrow. But it
is going to work. It's not a question of science
business like like might you run out of money before
you can get it going? When you say business, everything
can happen in that context. But this thing is working.
Pending one hundred million to put a fleet of satellites
(22:16):
into space, is it's still a lot for your business?
It is a lot. And the market is very bad.
It's probably the toughest market in the last twenty years
for tech companies. The market for raising funding you mean, yeah, yeah.
The investors are not very happy to see businesses that
(22:36):
waste money or spend money or invest money, depending on
how you look at it to build a solution, and
it's definitely a challenge, and I just hope that, you know,
the investment community will keep supporting us. We'll get to
the lightning round in a minute, but before we do,
I just want to say that what Shimone talked about
(22:58):
in this episode is actually a really good example of
a big idea that came up in an earlier episode
of the show. It was the episode where I interviewed
the founder of the company Rocket Left, and I was
going on about how making rockets and satellites cheaper was
a big deal, and he made the point that the
big breakthrough is not just that they're cheaper. It's that
(23:19):
cheaper rockets and satellites enable people to do big new things,
things that just did not get done before. And Shimone's
plan tomorrow dot Io's plan is a perfect example of
that idea. Even a decade ago, it would have been
prohibitively expensive, but today it's possible to put a constellation
of satellites into orbit to improve forecasts everywhere on the
(23:42):
globe for a price that is affordable for a startup.
As long as they can get a few more years
of funding, we'll have the lightning round with Shimone in
just a minute. Now, let's get back to the show. Okay,
I know you have to go soon, but let's do
(24:03):
a quick lightning round. What is one piece of advice
you'd give to someone trying to solve a hard problem.
Focus on the problem and not on a solution. The
solution will be obsolete. There are many kinds of solutions,
but if you're focused on the problem, you're going to
objectively look at what's right, what's wrong, and you'll be
able to ditch something that doesn't work and find out
(24:25):
something that is better. Focus on the problem. What do
you prefer really hot weather or really cold weather? Hot?
Okay hot? Could there really be a shark nado like
in the movie Shark Nado? I don't know. What's the
most underrated weather hazard? Most underrated heatwave? Lots of people
(24:48):
die from heatwave annually, strokes, health issues, heart attacks. I
actually testified in front of the Congress in the summer
of twenty one on this topic, specifically, other than weather,
what's the domain where people should use probabilistic thinking more finance,
for sure? I mean, how do you manage your investments?
(25:09):
Although I feel like bad. Use of probabilistic thinking was
a major problem in the run up to the financial
crisis of two thousand and eight. I don't know if
you remember, but people kept saying like this is a
one in ten thousand year move in whatever you know race, Like,
clearly it's not your model is wrong? Right, yeah? Yeah,
(25:30):
I'm thinking more in a personal level. On a personal level,
like a household, how can a household manage their risk
and everything? I think they should think about all the scenarios,
all the probabilities, and I think people don't do that enough.
So whether it's like this classic way to make small talk,
you know, when you don't want to talk about work, right,
So what do you talk about when you want to
(25:52):
make small talk and don't want to talk about work? Oh? Football,
I mean soccer? I guess that's the other classic, right sports? Yeah? Yeah,
but I'm very passionate about it for real. I mean
I can we could talk about it for an hour.
Who's your team? What do you say? Who's your club?
Who's your club? My club is in Israelity team called
mac It's uh, how's doing and learning? Will if everything
(26:16):
goes well, what's a problem You'll be trying to solve
in five years how to reduce carbon emission with our solution.
That will be probably the next step and will be
the most impactful thing we can do. But we'll try, okay.
Simon Alphabets is the co founder and CEO of tomorrow
dot Ido. Today's show was produced by Edith Russelo, edited
(26:39):
by Robert Smith, and engineered by Amanda ka Wong. I'm
Jacob Goldstein, and I'll be back next week with another
episode of What's Your Problem.
Pushkin. Here's the thing I did not know about weather
forecasts until very recently. They basically all come from the government. Sure,
you may have your favorite weather app, your favorite TV
weather person, but their forecasts are almost entirely driven by
(00:35):
data that's collected and analyzed by government agencies. And you know,
it makes a certain kind of sense. Gathering the data
you need to make a useful forecast has traditionally been
a huge expensive undertaking, and having a reliable forecast is
really valuable for lots of people in lots of different settings.
So it's good that the government does the work and
(00:58):
makes forecasts freely available to everybody. But the government is
the government, and we shouldn't expect it to tailor forecasts
for different businesses, or even to build forecasts that are
really useful for people who live in other countries, in
countries where the government can't afford to produce its own forecasts. Now,
imagine what a private weather company could do. A company
(01:21):
that relied not only on government data, but that went
out and collected data on its own, A company that
came up with forecasts that would not have been possible before.
I'm Jacob Goldstein, and this is What's Your Problem, the
show where entrepreneurs and engineers talk about how they're going
to change the world once they solve a few problems.
(01:43):
My guest today is Shimon Alphabets, co founder and CEO
of tomorrow dot Io, a private company that plans to
put a constellation of weather satellites into orbit in the
next couple of years. Shimoon's problem, how do you build
a private weather company from scratch? We realized it's from
a jew political and cost effective way, and all kind
(02:05):
of the only way to solveet is to go to space.
Shimon and his co founders launched tomorrow dot Io in
twenty sixteen. The company hasn't launched its satellites yet, but
it already provides weather related advice for companies like Jet Blue,
Uber and the NFL. Even before he thought of founding
the company, weather was a big deal for Schimon When
(02:27):
he was in his twenties. He was an officer in
the Israeli Air Force. Whether it is obviously a huge
deal for pilots for planes, and Shimon was constantly on
the phone with meteorologists. The solution was, Hey, let's talk
to a meteorologist three, four or five times, a day, Hey,
what's going to happen here, what's going to happen there?
(02:47):
And then I take the data, I analyze what it
means for me, what it means for the organization. And
I have to do it several times because the weather
forecast is constantly changing, and I care about multiple locations
and I care about multiple parameters. So that was a
very archaic way of addressing challenges at scale. Calling the
meteorologist and deciding what each plane should do does not scale.
(03:08):
So in that universe you're not at scale yet, not scale,
not efficient, not automatic. When there's a human in the loop,
there will always be an error. Huh did you make mistakes?
Of course everyone makes mistakes. I made a few of them.
You know. I have colleagues that unfortunately lost their lives
due to whether they did accidents. It was very unfortunate.
(03:33):
It's just been there, you know. But I didn't think
I'm going to start a company around it. Shimon moved
to the US to go to business school, and one
day a few of his friends, military veterans who like
shimone wanted to start a company, started talking about the weather,
and we started talking about past experiences, and everybody We're like,
oh you also feel this way, Oh you also experienced that.
(03:54):
And when we started looking at it, we said, okay,
there is something here. We need to start looking into this.
And let's try and understand whether you know how the
technology how forecast is being generated, why is it limited
in accuracy? And now let's look at how businesses make decisions.
Do they do it in the same way we did
it in the past, or is there a better way
(04:16):
to do it? And what we found out led us
to start a company. Well, what did you find out
that led you to start a company? All right, that's
where it's becoming interesting. So the first thing we found
out is that climate change is here. That was in
twenty sixteen. It wasn't cool to speak to speak about
climate change back then. I mean I think it was cool.
(04:36):
I think it was cool to me in twenty sixteen.
Trust me, when I spoke to investors back at the
time and you spoke about climate change, they were like,
give me some sass solution. Don't talk to me about
climate change. Okay, fair sass software as a service keep going.
So we understood that the problem of managing whatever the
challenges are is going to get bigger climate change equals
(04:58):
weather events become more frequent and more volatile in any
given ear in every part of the world. More hurricanes,
more wildfires, more heat waves. Doesn't really matter where you are,
there's some extreme phenomen it is going to happen more frequently. Okay,
So that's one thing we'll learn. The second thing we'll
learn is that the technology of forecasting weather, meaning what's
(05:19):
responsible for the accuracy, is generated and dominated by government agencies.
And as folks who served in the government for many years,
we understood that there must be a way to privatize
and innovate faster. And just to give an example, you know,
(05:41):
NASA for decades innovated and paved the way to space right,
But today you have SpaceX, who's augmenting the capability of
a private company doing what the government has done for decades. Yeah,
maybe not inventing the rocket from from scratch, but definitely
taking all the decades of research and adjusting it to
a commercial use case. What we found out is that
(06:03):
there is an opportunity to create a SpaceX of weather.
So SpaceX built rockets. You want to build weather forecasts.
How do you do that? What do you need to
make a weather forecast, you need three ingredients. On a
very high level. You need observations that describe the atmospheric
conditions in real time, the temperature, the wind pressure. Yeah,
(06:26):
then you have a good real time description, right. The
next thing you need is a model, an equation, a
set of equations. Physical models doesn't really matter. The point
is that you take the observations and you assimilate them
into a model, and then the last thing you need
is a computing power on which you process the model.
The output of the model is a weather forecast. And
(06:49):
what we found out is that there is an industry
of weather forecasting companies, you know, big brands. You know,
a blue logo, an orange logo, Acy Weather. Right. The
point is that these guys are here since the sixties, seventies,
maybe eighties. They just repackaged the forecast that the government
agency or the government agencies publish every day, every hour whatever.
(07:16):
So I'll say, I know, the weather forecast is like
a classic thing to complain about. Oh the weather man
said it would be Sunday and we had a picnic
and it rain. I do feel like weather forecasts are
pretty good, and clearly they've gotten better. Was there a
particular weakness or failure or set of weaknesses or failures
that you really thought you could improve. First of all,
(07:39):
you set out to do this. If I may ask,
where do you live, I live in New York. I
live in New York City. Okay, so you're privileged because
I am privileged. I'll be the first to say I'm
in Boston. I'm as privileged as you are. Most of
the world doesn't have Noah, the US government agency, the
big rich country government agencies that do a pretty good
job of forecasting exactly. And if you're a private company
(08:01):
and you try to provide equally accurate forecast for the
rest of the world, you're limited. You cannot provide it.
So there is a global problem. So one thing you
want to do better is provide better forecasts for people
businesses who don't live in rich countries that have big,
fancy weather agencies like NOAH in the US. That's one thing.
(08:22):
The second thing is that even within the US, you know,
the agency's main job is to save people's lives, Okay,
it is not to optimize businesses. Right. That also seems reasonable,
like absolutely, that's what I want them to be optimized for. Absolutely,
we're on the same page here. But with some scientific
improvement you can help businesses have better outcome, improve their
(08:46):
top line, their bottom line, their safety, their efficiency. So
there is a lot of room for improvement. The other
element of it is that remember that I said, you
know it's it's one thing to handle the forecast. The
second thing is that once you improve either on the observation,
on the modeling, on the computing power, and you get
(09:06):
to more accurate forecasts, there is the are part of it,
which is how you make decisions and how do you
do it at scale? Right, So this is something that
the government almost doesn't address at all, except maybe like
when you need to evacuate a city for a hurricane
or something, right, very rare circumstances, exactly exactly. Now, I'll
(09:28):
give an example. A lot of company like utilities or airlines.
They work in a very similar way to the way
that I described in my military service. Some guy calling
some other guy on the phone and being like, what
should we do. You go to a metrologist, You speak
to that meteorologist or get a report of road data,
and then you have to do a full analysis of
what it means and make a decision it's not scalable.
(09:50):
Or if I have thousands of trucks driving in the country,
or if I have thousands of miles of railways track,
or if I have many airplanes in the air and
I care about hundreds of airports globally, it's very hard
to rely on five or ten even meteorologists on staff. Right.
I've heard you say that a mistake you made early
(10:13):
on was optimizing for accuracy, and that's really interesting to me,
and I want you to tell me what that means.
So at the beginning, we thought that if we just
create a more accurate forecast, that's it. It's done. Deal
with serve like that's a hugely valuable thing, right, even
(10:34):
if you're a little bit more accurate, that's worth a
ton of money to an airline or the NFL or
any any number of really big companies. But what we
found out, but we learned that most of the businesses
that are impacted by weather do not know what to
do with a weather forecast or with a weather data
(10:55):
and they need the full loop, the translation to insights
and decisions. And that's what helped us design our platform
and the way we're operating today. So nobody understands hands
how to read a weather forecast. Basically, how do you
think from a business perspective? Yeah, so you realize from
(11:15):
that that like, providing these people with a better weather
forecast isn't actually going to help them solve their problem
because they don't because they're not experts in analyzing the
meaning of a weather forecast. Yeah. I mean you've named
a lot of your clients publicly, right, I mean whatever,
Delta and jet Blue and what Uber and the NFL.
I guess the NFL isn't playing now, Like what do
(11:36):
you telling JetBlue today? Like what do they want to
know today? So you know, it's almost summertime. In the
summer time, as you know, in the biggest hubs like
JFK or Boston Logan, you have disruptions related to lightning
strikes thunderstorms. Well, well exactly, they'll shut the airport down
for hours and everything will be a total mess exactly.
(11:59):
So instead of someone looking at a model and whatever,
we're just basically providing a weekly calendar that says, expect
the disruptions between dead time to dead time. Here are
the recommendations to do. ABC staff more people here, staffless
people there. So we actually go into the operational recommendations
as a result of the expected disruption as a result
(12:21):
of the weather forecast. Now, listen carefully to what I'm saying.
It's as a result to the weather forecast. So if
you're not relying on an accurate forecast, the business insight
is useless. It's actually damaging. So there's no way to
get around the need to improve the accuracy. Specifically, what
(12:41):
are you better at forecasting than anybody else right now?
Precipitation data. We provide global real time and now casting
data that is providing a kind of like minute by
minute forecast for a range of about six hours on
every point on Earth, which is quite useful. For example,
(13:06):
you have some sixty minutes minute by minute works that
you have on some phones, but it's only in the
US and in the UK. Yes, I do find I
have that on my phone and I find it's pretty good.
Dark Skies. I have Dark Skies on my phone and
it's good. But you're saying, if I if I left
the US, if I went on vacation to Mexico or something,
it just wouldn't work. It's not available. It's just not available.
(13:30):
And we created this thing on a global scale with
longer time horizon. That's one example. And the other example
is like quind, we forecast twind in higher accuracy for
the next day, two days, three days, which is very
useful for farms. I think, so okay, and are you
just better at that because you're more focused on it
and you've trained the models more than and it's more
(13:50):
important to your clients than it is to say, a
government agency, so you have an incentive to figure it
out exactly that. I'm sure that if Noah wanted to
double down on that, specifically Noah the government agency, they
would have been able to do that. But they have
no incentive, and you know, the pace of making a
decision in a large organization, it's just not enabling them
(14:13):
to move fast enough. The next problem Shimona's colleagues are
trying to solve, how do you predict the weather for
people who live in countries that can't afford a big
national weather service like Noah to do that. Tomorrow, dot
Io is going to go to space. That's the end
(14:36):
of the ads. Now we're going back to the show. Tomorrow.
Dot Io's next big project is putting a constellation of
weather satellites into space. And there are two big questions
I had about that. What problem will it solve and
what's it going to take to make it happen. So
the first thing I'll say, what motivated us to get
to space. The main motivation was how do we optimize
(15:00):
forecast and make it more accurate? And when we looked
at the blend between okay, we have observations, we have models,
we found out that the lack of observations on a
global scale are the main reason why we cannot improve
whether focussing significantly on a global scale. So the problem
wasn't the models. The problem wasn't the computing power. The
(15:21):
problem is just there's just not enough data when you
get outside of what outside of the US, Europe, Japan,
basically the data quality falls off. Yeah, okay. And the
most important weather sensor that we identified and I think
is agreed on all the community from NOAH to NASA
to others, is Doppler radar. A Toppler radar, just to
(15:44):
be clear, is it the one where you see a
color like if it's raining really hard, it's red or
something that stopple? Correct? Okay, Now, radars are looking out
in the sky and they help us know where is
the training in real time, how the cloud formation looks like.
It gives you some kind of three D description of
the atmosphere. Okay, now what we found out is that
(16:06):
five billion people leave outside of radar coverage. Five billion.
He goes out of the border to Mexico, all the
way to southern South America, and basically you don't know
where it's raining in real time, say for Africa, India
and many other places. That is surprising to me. Maybe
I'm naive, but like, were you surprised when you learned that, No,
(16:29):
because I came from a place where it was not
Oh you didn't have it either. You didn't have it either. Yeah,
it was pretty broken most of the time. And it's
not a new technology, right, it's a decades old. It's
not a new technology. But the implication of not having
it is huge. You cannot provide flood alerts. Pilots when
they fly, for example, to Cancun, they don't know the
(16:51):
weather in the route. It's a huge problem for the economy.
The next point is that the oceans and the seas
are not covered with radars, and every time, for example,
a hurricane is formed over the Atlantic. The US government
is flying airplanes over the eye of the storm to
scan it with a radar so we can send it
back to the model that as an understand if it's
(17:12):
going to be category one, two or three, when and
where it's going to eat, and whether we should evacuate
Miami or New Orleans. So rest assured, nobody's flying any
airplane over a typhoon or a cyclone in the East.
So this is a huge in Asia. In Asia, they're
not going out in Asia to get a really accurate
forecast of where it's going to go. They can't afford
to do that. But you can do it from space.
(17:33):
Is that where this is going so exactly, So we
realize that from a geopolitical and cost effective way and
all kind of the only way to solve it is
to go to space. The problem is that radars are
pretty big. We actually the world has one radar in
space today. It is called the GPM. It's a program
by NASA with the collaboration of the Japanese agency. It's
(17:57):
more than a billion dollar program that created one radar
in space, a very sophisticated one. We have one radar
in space today. That radar cost about a billion dollar
if not more, and it samples every point on Earth
every three days, So it's not very useful for hurricane
forecasting because imagine you just sample the hurricanes moving too fast,
(18:19):
or general weather forecasting. So what we were trying to
do was to say, how can we take this huge
radar and minimize it so we can put many of them.
But we are a small company, we don't have a
billion dollar How can we actually do it in a
way that will be cost effective? And the goal is,
(18:39):
of course, to monitor every point on Earth with a
radar in almost real time, because when you do that,
you are going to improve weather forecasting dramatically. You're going
to improve hurricane cyclone typhoons, you are going to be
able to provide flood alerts for every point on Earth.
And it will improve also climate science because now climate
(19:02):
scientists will have better understanding of what actually happen. No,
I'm sold on why it would be useful. It seems
like the hard thing is how do you do it exactly?
So how do we do that? The first thing we
did was to focus on the sensor. How can we
build a sensor that we'll keep most, if not all,
the characteristics of the radar. We looked at and how
(19:23):
can we make it small enough so we can launch
it a not a nano or micro satellite, but something
smaller than you know, the stationary satellites, a low orbit.
And bottom line, we've finished the development of the radar
and in a few months we're going to launch the
first satellite out of a constellation of about thirty And
(19:46):
our constellation is going to have two types of sensors.
One is the radar, the second is a microwave sounder.
The combination of the two is going to provide a
very good scientific result for every point on Earth. You
sound very confident, like are you at a point where
you know it's going to work or is it the
kind of thing that you hope is going to work. No,
we know it's going to work. The question is, okay,
(20:07):
will it take us more time? Will we fail in
the first lunch? Will we need to reiterate between one
lunch to a number? But it is feasible, it is working.
It is And how much is it going to cost
you to get roughly thirty satellites up and monitoring the weather.
Our early estimations, which so far given the inflation, are
(20:29):
still are still in the same ballpark. We're looking at
around one hundred million dollars for the entire constellation. So
that's a big cost reduction. That's compared to what is
it saying a billion for an existing one that only
does once every three days? Yeah, what are the things
that might go wrong? I mean, it seems like a
quite hard thing that you're trying to do. I feel like,
(20:50):
as you're describing it, it's like, oh, yeah, now, all
we got to do is get these thirty satellites up
into space and we're going to go But so imagine
it's still going to be quite hard and lots of
things can go wrong, of course, So you want me
to give you examples of things that can go wrong? Yeah,
what are you worried about? Okay, the rocket can explode
in lunch. Sure. Classic second thing is that you know,
(21:10):
we may have some communication malfunction. We may have some
when we build our satellites and the radars. We may
have to wait for longer than expected for chips to
arrive or all kinds of chips. Radiance like supply chain,
supply chain, supply chain issues is something pretty big right now.
There are so many things that can happen. But are
(21:34):
you sure the thing you built is going to work?
All the things you've described as like, oh yeah, the
rocket could blow up, that's not really our faulter, the
chip oncome, that's not really our fault. Like, is it
is it at the point where it's like, oh, yes,
this will definitely work. Is it like that or is
it possible that Okay, it's gonna work. It's gonna work.
The question is is it going to be more expensive
than we thought, It's going to take longer, and there
(21:55):
might be you know, business implications on tomorrow. But it
is going to work. It's not a question of science
business like like might you run out of money before
you can get it going? When you say business, everything
can happen in that context. But this thing is working.
Pending one hundred million to put a fleet of satellites
(22:16):
into space, is it's still a lot for your business?
It is a lot. And the market is very bad.
It's probably the toughest market in the last twenty years
for tech companies. The market for raising funding you mean, yeah, yeah.
The investors are not very happy to see businesses that
(22:36):
waste money or spend money or invest money, depending on
how you look at it to build a solution, and
it's definitely a challenge, and I just hope that, you know,
the investment community will keep supporting us. We'll get to
the lightning round in a minute, but before we do,
I just want to say that what Shimone talked about
(22:58):
in this episode is actually a really good example of
a big idea that came up in an earlier episode
of the show. It was the episode where I interviewed
the founder of the company Rocket Left, and I was
going on about how making rockets and satellites cheaper was
a big deal, and he made the point that the
big breakthrough is not just that they're cheaper. It's that
(23:19):
cheaper rockets and satellites enable people to do big new things,
things that just did not get done before. And Shimone's
plan tomorrow dot Io's plan is a perfect example of
that idea. Even a decade ago, it would have been
prohibitively expensive, but today it's possible to put a constellation
of satellites into orbit to improve forecasts everywhere on the
(23:42):
globe for a price that is affordable for a startup.
As long as they can get a few more years
of funding, we'll have the lightning round with Shimone in
just a minute. Now, let's get back to the show. Okay,
I know you have to go soon, but let's do
(24:03):
a quick lightning round. What is one piece of advice
you'd give to someone trying to solve a hard problem.
Focus on the problem and not on a solution. The
solution will be obsolete. There are many kinds of solutions,
but if you're focused on the problem, you're going to
objectively look at what's right, what's wrong, and you'll be
able to ditch something that doesn't work and find out
(24:25):
something that is better. Focus on the problem. What do
you prefer really hot weather or really cold weather? Hot?
Okay hot? Could there really be a shark nado like
in the movie Shark Nado? I don't know. What's the
most underrated weather hazard? Most underrated heatwave? Lots of people
(24:48):
die from heatwave annually, strokes, health issues, heart attacks. I
actually testified in front of the Congress in the summer
of twenty one on this topic, specifically, other than weather,
what's the domain where people should use probabilistic thinking more finance,
for sure? I mean, how do you manage your investments?
(25:09):
Although I feel like bad. Use of probabilistic thinking was
a major problem in the run up to the financial
crisis of two thousand and eight. I don't know if
you remember, but people kept saying like this is a
one in ten thousand year move in whatever you know race, Like,
clearly it's not your model is wrong? Right, yeah? Yeah,
(25:30):
I'm thinking more in a personal level. On a personal level,
like a household, how can a household manage their risk
and everything? I think they should think about all the scenarios,
all the probabilities, and I think people don't do that enough.
So whether it's like this classic way to make small talk,
you know, when you don't want to talk about work, right,
So what do you talk about when you want to
(25:52):
make small talk and don't want to talk about work? Oh? Football,
I mean soccer? I guess that's the other classic, right sports? Yeah? Yeah,
but I'm very passionate about it for real. I mean
I can we could talk about it for an hour.
Who's your team? What do you say? Who's your club?
Who's your club? My club is in Israelity team called
mac It's uh, how's doing and learning? Will if everything
(26:16):
goes well, what's a problem You'll be trying to solve
in five years how to reduce carbon emission with our solution.
That will be probably the next step and will be
the most impactful thing we can do. But we'll try, okay.
Simon Alphabets is the co founder and CEO of tomorrow
dot Ido. Today's show was produced by Edith Russelo, edited
(26:39):
by Robert Smith, and engineered by Amanda ka Wong. I'm
Jacob Goldstein, and I'll be back next week with another
episode of What's Your Problem.
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