Hello, and welcome to Power of Ten, a show about design operating at

many levels of Zoom, from thoughtful detail through to transformation in

organizations, society, and the world.

My name is Andy Polaine.

I'm a design leadership coach, service design consultant, educator, and author.

Sometimes I like to change things up a little, and my guest today comes

from a very different field to design.

Dr.

Hansi Singh is professor of physical climate science at the

University of Victoria, a U.

S.

Department of Energy Office of Science fellow and awardee.

Specialist in earth system modeling and high performance computing,

working group co chair of the Community Earth System Model funded by the NSF.

And she also uses all those amazing skills as CEO of Planet, a company specializing

in AI accelerated environmental forecasts to help inform decision making.

Hansi, welcome to Power of Ten.

Thank you so much for having me, Andy.

Super exciting to be here.

So you're unusual as you, as you know, the show is mostly around design.

Although I do talk about kind of design operating at different levels of zoom and

thinking about that systems thinking and how small things make a big difference.

Before we get onto Planette can you just tell us a little bit about your

background and maybe the sort of pivot?

I don't know if it's a full pivot or that kind of your current role,

how you're getting into that.

Yeah.

Yeah.

I have a very sort of non linear life trajectory in many ways.

So.

Yeah, it's super strange.

I did my undergrad in physics.

And then after that, I actually spent some time as a modern dancer.

So was a modern dancer.

And then from there, yeah had a child and then was like, Oh, I

love the topology of knitting.

I'm going to like make crazy things with it.

And so I spent some time as a knitting or craft at that time it wasn't even

called influencer, but I realized now that's sort of what it was.

You know, so doing that, I kind of created a very successful

small business with that.

And then I was like, Oh, math is so fun.

I'm going to go to grad school.

And so then suddenly I ended up in grad school in math.

And from there I transitioned into climate because it was such a cool use of math.

And then from there, you know, yeah, did all of these other sorts of things.

you know, things that you, you list there.

So became worked at the Department of Energy for a while.

They actually are the ones that sponsored my, my graduate program.

I was a DOE CSGF fellow, which is basically computational science.

And so, yeah, so they sort of really try to, I'm steep you in national lab

research culture so that you know, you're coming in with this very specific science

subject matter expertise, but you know a lot about high performance computing.

So there's all the computing stuff coming in as well.

And then from there, yeah, transitioned into being a prof and actually you

listed that I'm currently a prof.

I actually just resigned.

Oh, okay.

Well, congratulations.

Commiserations.

Not sure how that works.

Yeah, no, it was, it was a good kind of resign because this is, you

know, really wanted to devote myself full time to what we do at Planette.

There's a little step there where you said on modern dance and topology of knitting.

And then I went to grad school and did all this computer, the hardcore

kind of maths and computer stuff.

Presumably you were also already good at that.

And it wasn't just like, you know, waltzed in.

Yeah.

Yeah.

I mean, you know, my undergrad was in physics for sure.

So, you know, did the math and everything, right?

But I think at some point kind of thinking through what it meant to

be sort of a professional artist.

It was, I don't know, at that time and probably even now,

not necessarily what I wanted.

So I think that's where that transition came in.

And I think also at that same time I was, I was still doing other

things that were keeping me close to, you know, physics and science.

So I did a lot of like tutoring.

And so it wasn't as though I just had this, you know, 10 year break

and hadn't done any math and then suddenly went back to it.

So, but . I guess there's kind of two themes there, right?

I mean, one of them is just like that kind of underlying theme of, of just

science and technology and computing.

But I think there's also this idea of what can you make out of that

when you can be creative with it?

Yeah.

Okay.

Right.

So like that, the creativity is like the part that's.

I don't know, adds some spark to that and makes it exciting and where you're

sort of thinking about possibilities.

And, you know, I definitely spent a lot of time getting that subject matter

expertise, but now I feel like I'm in full creative mode, which is really fun.

All right.

Well, that's good.

That's very good.

We'll get on to it in a second.

I cannot let the topology of knitting go though, so I, what

Okay, we're going to talk about the topology.

Yeah, just tell me, just tell me briefly what that is, yeah.

Yeah, so, so basically at that time I think it was like 2007, 2006,

there was kind of a lot of online chatter about this, there was this

one mathematician over at Cornell.

Her name was Diana Taimiņa I hope I said her last name right, but basically she

was doing like kind of teaching students about higher order topology using crochet.

So essentially, you can create these sort of really cool.

I guess, what is the word for hyperbolic surfaces using crochet and you see

them a lot in the natural world.

Like imagine anything that's like super crenulated that is

actually a hyperbolic surface.

And so she was doing that and, you know, the big place that

you see those crenulations is often in coral reefs, right?

Is she the woman behind that?

Yes.

I know what you're about to say.

Go on.

Yeah.

So then that like really just kind of got me thinking because these people

are doing crochet in the context of these like beautiful coral reefs.

And so then, you know, there's these big displays going up in, you know,

You know, various institutions.

I think Cornell probably had one at that time, but I, I don't know.

They were, they were going up all over, all over the world.

You know, which was basically a bunch of crochet folks making these hyperbolic

surfaces and, you know, they have these beautiful kind of coral reef

shapes and they then, you know, are putting them in these kind of displays.

It's like you know an art setting that they're, they're doing almost

like a diorama, but it's like giant.

And so I was just like, well, but they're doing crochet.

Can I do the same thing with knitting?

What can I do with knitting?

Yes.

So beautiful.

Yeah.

What can I do with knitting?

And so it was sort of from there that I was

there is so much beautiful underwater imagery.

And so, why just knit a sweater?

Why not knit like an octopus or a nudibranch or, I

don't know a squid, right?

Why keep yourself to sort of the usual types of things that people like to make?

Yeah, those are so beautiful.

Yeah, I've seen that exhibition in the flesh, actually.

It was in Germany and, went, went to see it is, it is amazing.

Yeah.

It's beautiful.

And it's just this beautiful community thing because all these

people kind of send stuff in as well.

Yeah.

Yeah, exactly.

So you know, I started to make, like, it actually first started

with an octopus, started making an octopus and, you know, posted it.

I think I posted it on Flickr and then I actually posted

the octopus itself on Etsy.

And then I suddenly got all these like questions of like, Hey you know,

I'd love to make this for myself.

Like, can you share the pattern?

And yeah, that was like, Oh, well we can sell patterns.

Right.

And yeah, so that's where this whole thing, I had this little

brand called Hansi Garumi.

And, you know, basically just created all sorts of crazy

patterns for various stuffies.

And so, yeah, did that for a few years and wrote a book.

And then from there, I was suddenly like grad school.

I don't know where that came from.

So...

Amazing.

Well, that's quite a pivot.

So so well, so tell us tell us about Planette then what what was the...

you know, there's been obviously weather and climate forecasts for a long time.

So you know, what were you seeing where you were thinking, well, I should tell

us about what Planette does actually, I gave the sort of one liner, but you should

maybe tell us a little bit more about it.

Yeah.

So as you know, we have reached some really, I think

important planetary milestones.

milestones, maybe not good milestones.

So for example, we are now at the point where we are 1.

5 degrees Celsius warmer than we were in 1850.

And so, you know, that might not seem like much, but I think when you put

it in the perspective of the fact that during the last glacial maximum,

when there were ice sheets covering huge parts of North America and Europe

and Eurasia in general, at that time, the earth was only about three and a

half degrees cooler than it is today.

So thinking about that, and then thinking that now we've, we're at 1.

5 degrees warmer, that is huge.

And I know that that is also, you know, like we have the IPCC and you know,

in Paris with the COP agreement, we were like, yeah, we're shooting for 1.

5, but.

1.

5 is still a very different world, right?

And yes, we're shooting for 1.

5 because we know that it's much worse further, right?

And so, yeah, I think that that is where Planet comes in, because we recognize that

because the world is very different than it was you know just over the last century

that is going to require adaptation.

And so here you get into this kind of big issue, right?

Where we as a species are trying to figure out, first of all, we have to

decrease emissions, but at the same time, this world that we have, this 1.

5 degree world is something that we are stuck with.

Right.

Like we can't change this 1.

5 degree world within my lifetime, your lifetime, even,

you know, our grandkids lifetime.

And that's just because carbon cycles are really slow.

Even if we find ways to, you know, kind of speed them up where, you know, I mean,

sure, there's a lot of like kind of ideas of how we can better pull carbon back

into the ground, but honestly, we haven't even figured out how to stop emitting.

So this is the world we're stuck with and it might get worse and so

adaptation is a huge and important area where we need to put in resources

and this is where Planette comes in.

So, today If I want to know what's going to happen tomorrow or in the next few

days, I look at a weather forecast, right?

If I want to like get really depressed and hear about how bad things

are going to be in 2050, right?

Yeah.

Yeah.

If I want to cry, if I want to go catatonic in my bedroom then I will

go read the IPCC report or some other kind of climate timescale Projection.

And those are like, you know, 30 years into the future and beyond.

And so what that means is that I have a few days versus 30 years.

What about the in between time where people actually need information?

And this is an area where there's like, very little information currently

available for the public, for businesses to be able to adjust to make decisions.

And yet as climate change is increasing, volatility is increasing, you know,

extreme weather event magnitude.

This is information that people really need.

You need this in between timescale information.

And so this is what we do at Planette.

So why has it been, why is it missing?

So now I get, cause I was going to ask you this question, but you, you talk

about on, on the website, you talk about the data and the, and the forecast being

actionable, and I guess this is kind of what you're saying in the, you know,

if I, if I really want, you know, if I want to know the weather now, I look

out the window, if I want to know the weather in a few days, you look at where

the forecast and for some things I can know I, I need, you know, to put stuff

under cover or whatever it is, right.

And down the other end, it's kind of, well, you know, in 10

years time, don't know, right.

Our business might not even be around.

So you know, the actionable window is this sort of near, near to midterm,

I guess, you know, of ducks in a row.

I'm sort of nonplussed that that doesn't exist already.

So, so first of all, why, why is there this gap?

Yeah.

So a few things, first of all, it's been, some of it is

science and technology, right?

So in order to do forecasting at these timescales, you can't

use a weather model, right?

You have to use a large scale global climate model and that's

because predictability over these longer time horizons completely

depends on the state of the ocean.

So you have to be able to predict the state of the ocean.

And then that state of the ocean sort of you know, creates what

we call teleconnections to land.

And then that is what we then experience as a particular climate state for that

particular month or that particular family of weather events that could

occur based on that ocean state.

So, so you need to run these different models the technology for being able

to do this and the science, even it's only been around for approximately

the last 10 years or so, and this is because you have to take these big

models, and these are very similar to the models that like the IPCC uses.

And so, you know, all of the major climate modeling centers in the world

use, and then you have to initialize them very particularly with the state of

the ocean, but it's not straightforward how that initialization happens.

So, you know, for these last 10 years, 10 or 15 years, scientists incrementally

have been figuring all of that out.

And now this kind of shorter term forecasting is more or less like, you

know, production ready, but it's mostly stuck within research and academia.

Right.

So what?

Okay.

Exactly.

So part of this issue too, that we get to is tech transfer.

So if you're at an engineering school anywhere, like literally the first

thing, like people are constantly thinking about tech transfer.

They're like, patent transfer, commercialize, patent

transfer, commercialize.

And that is just, you know, like bread and butter for what any engineering

school around the world does.

But for climate science, people have like conventionally thought, yeah, this

is just something that we study for fun.

And now suddenly it's being thrust into this area where the planet, every single

human on this planet is our stakeholder, and yet the way that that discipline has

been functioning has not caught up, in my opinion, to, you know, the magnitude

of global need for this information.

We, when you talked before you know, before this recording talked

about the side of the, kind of the storytelling, if you like, of that

data too, and I'm interested kind of about how you go about this.

Because one of the things that's been, I think, an Achilles heel of

scientists is they tend to think, well, if people just knew the data,

then they'd change their behavior.

So we just need to tell them more data and they still don't get it.

Let's tell them some more data.

And of course, what happens is people get in that sort of helplessness mode.

And then it's like, oh, it's just all too much.

It doesn't really matter.

I'm just going to carry on how things are.

Someone else will sort this out.

I'm interested kind of how you go from that, what I imagine is a lot

of very heavy number crunching and actually kind of tell the story of

that because step changes in public awareness have been, you know, Al Gore

David Attenborough of a polar bear on a shard of ice, a seahorse with its

tail wrapped around a plastic earbud.

Those are those kinds of things that really stick with people

and then make them actually, well, if they make them act, they

certainly stir some kind of action.

So I'm interested how you take this thing, which I imagine is very

heavily computational and turn it into something that your, your clients,

your customers, your businesses and other organizations can look

at and make sense of without being, well, without being people like you.

Yes.

So that is kind of a huge area of figuring out how to take this kind of, you know,

midterm climate information, right?

So midterm forecasting.

So somewhere between what's going to happen next month versus all

the way to what's going to happen, say, five years into the future.

That is the window that we occupy.

And it's a little bit beyond what people conventionally call S2S sub

seasonal to seasonal forecasting.

Usually S2S is up to a year or maybe two.

I think NOAA defines it as two.

But yeah, we can go even further just because there is predictability

in the system and for some people that's useful to know.

But.

So in terms of thinking about how to make the data actually useful.

So from a business perspective if I just give a random business that data,

like they won't know what to do with it.

And so what people have to do, what we have to do is figure out a way to

translate that information into what does that mean for that particular business?

And so in the process of that translation, right you know,

there are so many possibilities.

So first of all, there is some certain classes of businesses, for

example, that are very data savvy.

They can just take the data and they can write the story themselves.

So for example, one example of that is insurance companies.

They have amazing ways in which they model risk.

And so our data is kind of one of the factors that they might use

to say, you know, model risk for underwriting for next year, for example.

So that is relatively straightforward, but for some other verticals, right?

It is like so much more complicated in terms of they might not be data savvy,

and you know, they might have sort of very specific things that they want to look at.

And so some of it might be, you know, like us actually having to provide

some sort of a dashboard where we input some of their information, take our

forecast, say of extreme weather or of, you know, various other weather average

environmental variables, and then use that to pull out the intelligence and

then have that visible on the dashboard.

So that's one of those areas we're still kind of working on figuring out

what the scalable kind of solution is there for, for different areas.

So like, for example, think about energy, right?

So in this new world, we're going to have like so much renewable energy,

which is wonderful and great, but renewable energy, it's not the same

as having your coal fired plant that can just be burning all the time.

Right.

It's intermittent.

It's peaky.

Yeah.

And it's weather dependent, right?

Yeah.

Totally.

Totally.

It's totally weather dependent.

And so because it's weather dependent, right, what that means too is that

sometimes when you have high demand events, you have low production.

So like imagine, for example you have a heat wave.

And at the same time, most heat waves are accompanied by

something called a thermal high.

And so because of that, you have like this high pressure

that's kind of sitting there.

Usually with high pressures, like you don't have much wind, right?

So suddenly all your wind turbines are off.

And the temperatures say if they're really high, even the solar panels,

their production will be down.

And so you could have a case where you're not actually meeting demand.

Right, because everyone's aircon is on.

Yes.

Which is the, yeah, okay.

Yeah.

Exactly.

Exactly.

You're not meeting demand.

And literally there's places, right, where people are alive

because of the air conditioning.

Like think about Phoenix in the summer.

You know, just kind of thinking about that, I'm like, I'm not

sure if that place is really habitable without air conditioning.

I mean, maybe it is.

I, I, I don't know.

So essentially the information that we can provide is this kind of longer

timescale information so people can actually be prepared for events like that.

So, for example, we can forecast, Hey, you know, it's going to be low production,

but high demand this particular month.

And so, you know, y'all should make sure that you have other energy sources on the

grid or that you are prepared to purchase energy from neighboring electricity grids.

Right.

Okay.

And so for real estate, this is also, so I've got a little story here for

you actually, which you might enjoy.

There is a a place, I think it's called Yulara.

It's near Uluru, which is, is known as Ayer's Rock, but Uluru is the, this

big rock in the center of Australia.

That everyone knows as a sort of tourist place to go to is that

famous kind of red kind of plateau.

And when they were doing the surveying for Yulara, which is a kind of village, it's

been a few kilometers away where there were the sort of tourists and there's this

kind of like, well, there's a campsite, but there's two other hotels there.

And the indigenous Australians said, well, you know, don't build a hotel

there because it's a watering hole and the surveyors were like, well,

there hasn't been water here for a hundred years and they were like, and

sure enough, of course, you know in indigenous knowledge, the kind of cycles

of seasons are things like, you know, there's this one thing that lasts for

a month and it comes every 10 years.

And and sure enough, you know, when it, there was a downpour the, the luxury

hotel started to flood because, you know, that's what happens at the watering hole.

So I guess, I'm guessing with real estate, I know in Miami, there's a

massive kind of issue about this, right?

In the States, I imagine there's other places where rising sea

levels and all the rest of it.

But how else does real estate get involved in your world?

Yeah.

So, you know, you were kind of talking about how to translate that data, right?

So for energy, you would translate it in terms of energy production and

demand and helping people figure out like, you know, how to plan for those

Times where load was unequal, right?

For real estate.

So, you know, it could be something like this year is going to be a

really, you know, bad hurricane year.

And so for that year, make sure that you are taking the proper precautions

in terms of your real estate in terms of say insurance coverage, or in terms of

various types of retrofitting, like this might be the time to do that retrofit.

The other interesting thing about real estate, I have to say, Andy,

is like, I don't know if those markets have caught up to reality.

I was about to suggest that.

Yeah.

Yeah.

I don't think they have.

No.

No.

There was a, there was a really good, I think it was like an NPR thing where some

guy was was thinking about moving to Miami and, and he was then asking realtors,

you know, so, you know I, I noticed, you know, is there any, you know, what are

you doing about kind of flooding here?

You know, how's this condo kind of built for that?

And, but you know, it's fine.

Everything's fine.

And he, you know, this person obviously knew that it absolutely wasn't.

And I imagine it, that's, that's tomorrow's problem, right?

Which is the fundamental issue.

I think that is the fundamental issue, and I think some of it too is

because, for example as scientists, how do we tell those stories, right?

There's the issue of stories, but I think there's also the issue

of how humans function, right?

And so all of those things, I think they kind of are at odds with each other.

So, you know, the storytelling is particularly about the fact that this

land is subsiding, literally the land is subsiding while at the same time sea

levels are rising, while at the same time you have changes in weather patterns.

So for example, like the Atlantic this year, it's an El Nino year.

Usually you expect El Nino years to be, you know, pretty low

on the hurricane totem pole in the Atlantic, but guess what?

They're not going to be this year because the Atlantic is also really warm, right?

And so, you know, all of these different factors that come into play.

How do you communicate that to people?

And how do you even communicate that to the market?

I think the market does not know because people don't know.

But it's hard to say that they don't know because honestly, like who doesn't

know unless you were living in a hole, but I, I, yeah, this is, this is hard.

Real estate is one of those areas where I don't view that as being one of our

first markets because that's an area where people are, yeah, I don't know.

People.

People are people.

People are people.

You said the markets thing.

You hear that on the news, the markets reacted in this way to something.

And of course, you know, it's people.

And we tend to think, I think, because those people are dealing

with numbers and sometimes very large numbers, that somehow it's kind of

all very, very logical, but it's it's terribly instinctual, all of that.

And as we know, you know, you get kind of you know, runs on things

and you get all sorts of bubbles and, and all the rest of it.

Yep.

I wanted to switch, you, you talk about this the work you're

doing or the, the sort of what you're creating is powered by AI.

And of course, right now is a little bit of like, oh God, you know, well,

everything, my shoes are powered by AI.

This feels like an area where it's like legitimate.

So can you tell me a little bit about what role that plays and how that's meant you

can do things that you couldn't do before?

Yeah.

So in this case Let me tell you about how climate scientists run large models.

So our very large models are run on high performance computing systems using

thousands of processors running at once.

And sometimes individual experiments can take months to run.

And if you might wonder, well, what does she mean by a global climate model?

It is literally a couple of million lines of code that is often in Fortran and

usually pieces of it are in Fortran 77.

Right?

Because there's a lot of legacy code in there.

And so, you know, you can kind of think of it as a, a sort of layered

ball that people have just been like building on top of, right?

And so, yeah, there's Fortran 77 deep in there.

And then all of these other layers, you know, Fortran 95, right?

Oh, so, so modern.

And, and so, yeah, so these are the kinds of things that we run.

In order to, you know, produce say climate projections for the IPCC.

But as I said, this is also what we have to run in order to produce these

types of short or midterm projections, which is what we do at Planette.

So one month to five years.

And, and so in order to do that in a way that is efficient, operationalizable,

and actually useful to humans rather than just being like, Oh, look at

this cool thing that this model did.

We actually have to use the AI in there.

So the AI, we use it to speed up, right?

So we can actually use it to like for example do something called boosting.

So what we do, right, is that we can run our Earth system model,

and then we can create a whole bunch more ensemble realizations

of that model using an AI emulator.

So that's kind of, yeah.

So that's, so that's sort of variations on the, on the theme.

Okay.

And I guess people who have been using kind of image generators will,

will know that kind of idea of like, you start with one and then you can

kind of do variations on it, right?

Yeah, exactly.

But they're like physics based variations.

Cause the emulator that we use is based on a, what we call the hybrid AI model.

So meaning that there's physics in there.

Okay.

But it's AI, so it's really fast and efficient.

Yeah, so you're not having to recalculate recrunch everything

from scratch each time.

Yes, exactly.

And, and so, yeah, so that's part of the acceleration.

But the other thing too that AI actually allows, and you might have been hearing

all of this stuff about how AI can do weather better than a weather model.

Partly that's like, I mean, a lot of these AI models, they're just

quote unquote, dumb models, right?

Because it's just taking all of the statistics and just crunching it.

And then kind of producing an emulator that can emulate the weather.

Like that's, that's, that's pretty much what these things are.

The next level up is when the model actually has some physics

in it, but it still has the statistics that AI specializes in.

And so from there, then, you know, you can calculate things that potentially

are outside of the models training data.

That's kind of one of the challenges here as well, when it comes to,

you know, say a weather model.

If the weather model has only seen a particular type of weather,

right from the past, then as our climate changes, yeah, yeah.

I get it.

Yeah, is it able to actually do that?

And this is an issue both with numerical models as well as real life models

as you know, or or AI models sorry.

Because of the fact that, you know, everyone is kind of working with

the same training data in terms of AI or with the same physics, right?

And so, And not only that, but interestingly, like, technically, a

numerical weather model should be good for any type of weather, but practically,

all of these models are tuned, they are tuned to a particular climate state.

And so, you know, this is one of those things where this hurricane Otis

intensified really rapidly over 24 hours.

How come our models didn't capture it?

First of all, they only ran 10 ensemble members, right?

That's usually how people do these types of simulations.

And then on top of that even if you were using an AI model, if the AI had not seen

enough examples of rapid intensification and the factors that cause it, then

it's not going to produce an ensemble member that has rapid intensification.

So I think we're getting into all sorts of interesting things where you have

AI, it's powerful, but it's powerful in the context of training data.

How do you get it to behave outside of that training data envelope and

the way that you do that is through putting some physics into there as well?

A couple of last questions.

One is what are your hopes for Planette?

Obviously you hope it's going to be successful, but what do

you hope it's impact will be?

Yeah.

So we're a mission driven company and yet we just raised a bunch

of money from venture capital.

So we just raised 2.

4 million.

Congratulations.

Thank you so much.

Thank you so much.

And so what that means, right, is that people are betting on us as a high

growth company, high growth, scalable company, which is great because one thing

that's going to Good about that model is that it assumes that everyone wants

your, whatever you're offering, right?

In this case, our data or the intelligence that comes from that data.

So that's great.

So what that means is that, you know, we are shooting for high

reach, but the impact part is that look not everybody can pay.

So there are a lot of people that can pay.

Insurance companies can pay you know, the military can pay

I'm trying to think like...

There's a lot of businesses or sectors where I guess it's highly lucrative

for them to understand what you offer.

Absolutely.

Yes.

This kind of midterm prediction.

Absolutely.

So those folks can pay, but then for example, thinking about impact,

what about say disaster preparation in the developing world, right?

There are so many places where, for example, if you knew that next month

or in three months, there was going to be the possibility of really extreme

precipitation there are measures that you can take to save lives.

Usually with extreme precipitation, what kills people is actually not the flooding

itself, but it's actually the fact that the sewage gets into the drinking water

and then people die from cholera, right?

So if you can get that clean water there.

You know, well in advance of when these events happen, rather than

having to figure out how to get it there really fast once people are

already drinking contaminated water, you can save so many lives, right?

So even just thinking about those kinds of impacts, there are so many use

cases like this where, by being able to deploy this data really widely, we can

really make a difference in the world.

And I mean, one thing that I often think about is when you think about

the developing world, they're not the ones that caused the climate change

and caused the sewers are flooding all the time kind of situations, right?

And yet they're the ones that are going to, In general, be the most afflicted.

And so by being able to provide this information, we are trying to do sort of

the right thing in terms of allowing folks to be prepared in however way possible.

I mean, when we think adaptation in our world, think about adaptation

in the developing world, that's even harder because you don't

even have the money to do it.

So there is one final question.

Maybe, maybe you've already answered it.

Ray and Charles Eames, they made a film called Power of 10.

My favorite.

Yeah.

Classic.

I remember watching that at the San Francisco Exploratorium when I was four.

There you go.

So my story is my dad had the flip book of it that I used to flip through as a child.

And it really kind of that, that levels of zoom thing.

That's what, you know, why I talk about it because I, I'm always very fascinated

by how one small thing can kind of ripple up and have a massive effect.

And the sort of systems thinking thing of also how the larger system,

if there's a slight shift in it, how it ripples all the way down.

I used to find it very, very difficult to find examples of this until COVID happened

and then everyone's like, Oh, yeah.

Okay.

And I'll get how that works now.

So it's a very useful way of explaining my particular sort of area of design of

service design, where we kind of look at that relationship between those two things

and some of what you're talking about just now about getting clean drinking water to

to areas that will be hit by a disaster.

I know all the sort of systems thinkers and services and people,

they're kind of radars we'll be going want to get involved in that.

So the, the final question is what one small thing is either overlooked

or could be redesigned that would have an outsized effect on the world?

That's a hard question.

Yeah.

Yeah.

I mean, I could bring it back to climate adaptation, because to be honest, that is

one area where I think there is not enough emphasis put in, and yet it's going to be

such a huge part of how we stay civilized as we move into this warmer, wetter, more

volatile world, right, that we've created.

That is like sort of one of my hopes that as we think about, okay,

okay, we have to decrease emissions.

We have to decrease emissions.

How do we do that?

How do we actually make this possible?

Given the fact that there are so many countries that still need to develop

and industrialize and lift its, you know, citizens out of poverty.

And I think the other side of that, of course, is that this

world is different, right?

This is a different world than the one that our parents grew up in.

I often think about this in the context of how kids experience summers.

Think about this whole idea of, Oh yeah, in the summer you just go to camp

and you hang out for several months and you, you know, you swim in the

lake and kids this last summer, they didn't get to do any of that because

either it was smoky or something was burning or it was just too hot.

It is a different world.

And so this whole idea of this large scale transformation that has to

happen with adaptation, adapting to this new world, that to me is such a

crucial piece of I think how we have to think about the climate story.

Yeah.

It's interesting as a species, you know, obviously our success has been

that we're incredibly adaptable.

Yeah.

But as sort of individuals, we're kind of really rubbish

at thinking about it sometimes.

Yeah, that is so interesting, actually, because you're

right, like, we are adaptable.

We have brains.

I mean, to some extent, that's almost one of those areas where you're like,

you wish you weren't quite so adaptable.

Because I think that's where you get into the frog in the frying

pan analogy, where everyone is just like, Oh, yeah, it's all fine.

Sure.

This is the new world.

I think that if we were a little bit more like, hey, we need to change things.

I, I think that, yeah, I, I don't like the direction this is going.

People can find you on, at planet with an extra E or double T E dot AI.

Where else can people find you linkedIn and things.

Yeah.

So I'm on LinkedIn.

I'm on Twitter.

Usual places.

It's been fascinating.

Thank you so much for being my guest on Power of Ten.

Thank you, Andy.

It's really great to be here.

Thank you.

You have been listening to and watching a Power of Ten.

You can find more about the show on polaine.com where you can also

check out my leadership coaching practice, online courses, and

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If you have any thoughts, put them in the comments or get in touch, you

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Thanks for listening and see you next time.