6:00

bomber gets to Kokura, there's briefly cloud cover. And

6:02

they don't want to accidentally drop the bomb somewhere

6:04

that's not the city, because of course that would

6:06

not have the same effect. So they

6:08

decide to go to the secondary target, which

6:10

is Nagasaki. They literally do a loop to

6:12

see, hey, maybe it clears up. Yes. It

6:16

doesn't. Yeah. And on

6:18

to Nagasaki. Exactly. They actually, I think, do loops

6:20

until they're running low on fuel and they're starting

6:22

to think, okay, we're not going to make it

6:24

to the secondary target. So they finally pull the

6:26

plug on Kokura, drop the bomb on Nagasaki. So

6:28

hundreds of thousands of people live or die in

6:30

these cities based on a 19 year

6:32

old vacation and a cloud. And the point

6:34

that I think is important to realize here

6:36

is that if you were modeling this,

6:38

if you were trying to say, how is the US government

6:41

going to determine where to drop the atomic bomb? You

6:43

would not put in your model the vacation

6:45

histories of American government officials or cloud

6:48

cover. You would come up with these very

6:50

obvious big things like where are the places

6:52

that have strategic importance or propaganda value? And

6:54

if you did that, you probably would put

6:56

Kyoto on top of the list and you

6:58

get the wrong answer and you wouldn't get

7:00

the wrong answer because you were stupid. You'd

7:02

get the wrong answer because sometimes things that

7:04

don't seem to be important actually end up

7:06

being the most important factor in an outcome.

7:09

And the Japanese actually have an expression Kokura's

7:11

luck. Tell us what that means to the

7:13

Japanese. Yeah. I think this is a very

7:15

useful thing to think about. It's Kokura's luck

7:17

refers to when you unknowingly escape disaster. So

7:20

it was a long time before the US

7:22

government acknowledged that they were planning to drop

7:24

the bomb on Kokura. So hundreds

7:26

of thousands of people in that city had no

7:28

idea there was an airplane over them that but

7:30

for a cloud would have incinerated the entire city

7:33

and killed most of them. And so

7:35

I think this is the kind of thing where

7:37

one of the ideas that is central to the

7:39

argument in Fluke is that these sorts of things,

7:41

this Kokura's luck is happening to us all the

7:43

time. We're completely oblivious to the

7:45

diversions in our lives and our societies,

7:48

the alternative possible history, simply

7:50

because we can only experience one reality. And what

7:52

we do is we then stitch a narrative back

7:55

where it's A to B. This makes complete sense.

7:57

Here are the five reasons why this happened. I

8:00

think this is a way that we end up

8:02

deluding ourselves into a neater and tidier version of

8:04

the real world So so you describe why we

8:06

can't know what matters Most

8:08

because we can't see the alternative universes.

8:11

I love this quote We ignore

8:14

the invisible pivots the

8:16

moments that we will never realize were

8:18

consequential The near misses and near hits

8:20

that are unknown to us because we

8:22

have never seen and will never see

8:24

our Alternative possible lives

8:26

that that's really very

8:29

chilling to know that we're just walking through

8:31

life Unaware that hey

8:33

atomic bomb over our head better.

8:36

Hope the clouds don't clear up. Yeah I have this

8:38

saying that I refer to a lot in the book

8:40

Which is that we control nothing but we influence everything

8:42

and this is when you think about this in our

8:44

own lives I think this is

8:46

something where you realize that there are these diversions happening

8:49

constantly There's a film in the 1990s

8:51

with gun of Peltra called sliding doors And

8:53

it has this idea and I sort of riff

8:55

on that with this concept I coined called the

8:57

snooze button effect where I you imagine

9:00

that you know, it's Tuesday morning You're a little

9:02

bit groggy wake up the snooze button beckons to

9:04

you You slap it and you get delayed by

9:06

five minutes You imagine you're now your life rewinds

9:08

by 30 seconds and you say no,

9:11

I won't hit the snooze button I'll get out of bed now.

9:13

I think that has changed your life Now

9:15

the question is how much has it changed your life and

9:17

under some short time scales Maybe things sort of get ironed

9:19

out in the end, but you you're gonna have different conversations

9:22

that day You're gonna talk to different people you might get

9:24

in a car accident in some days, right? I mean, these

9:26

are the kinds of things that we sort of are

9:28

oblivious to and I think when you think about them

9:31

with social Change it's happening all the time, too I

9:33

mean, there's just so many ways that

9:35

the world could have unfolded differently but for a few

9:37

small changes I mean, you know you think about even

9:39

like 9-11 We

9:41

think about all the variables that go into 9-11 one of them

9:43

that people don't talk about was the weather It

9:45

was incredibly blue blue sky day. Yes. Yeah,

9:48

and if you had if you had a

9:51

You know very very cloudy day or storm some of the

9:53

planes wouldn't have taken off on time They might have had

9:55

a chance to foil some of the plots or if you

9:57

had had a different slate of passengers on flight 93 So

10:00

if it had gone September 10th or September 12th,

10:03

maybe those passengers don't take down the plane, maybe

10:05

the White House or the Capitol is destroyed, and

10:08

then the world's different. I mean, can you

10:10

imagine how it would change America or geopolitics

10:12

if there was no White House anymore? So

10:14

I think these are the kinds of things

10:16

where you just imagine that there's this straight

10:19

line of cause and effect. And of course, when

10:21

we experience the world, we then explain it. But

10:24

these small changes could really reshape the future.

10:26

Some of them are going to be more

10:29

consequential, like the Kyoto story. Others

10:31

are going to be a little bit less consequential,

10:33

at least on human timescales. But the

10:35

point is, we can't know. And I think that's something that

10:37

is bewildering to think about. So can

10:40

we actually identify cause and effect?

10:42

We tell ourselves stories. We

10:45

have not only narrative fallacy in

10:47

everything we do, because we love

10:49

a good plot line, but there's

10:51

also hindsight bias where we imagine,

10:53

oh, I knew this was coming all

10:55

along. And

10:57

can we really, truly know the

10:59

impact of how A

11:02

leads to B, or

11:04

how something that we think is completely

11:06

meaningless actually has deep

11:08

significance? Yeah, so I very much

11:11

subscribe to this view that all models are wrong, but

11:13

some are useful. George Box. Yes, exactly. But

11:15

I think that one of the things that has been lost on us is

11:17

I think there's so much of the world that runs on

11:19

models that we sometimes forget

11:22

that they are extremely simplified abstractions of

11:24

reality and that we actually don't understand

11:26

how the causation works. And

11:28

I think that creates hubris that's dangerous. So when

11:30

you think about why the atomic bomb ended up

11:32

getting dropped on Hiroshima, there are an infinite number

11:34

of causes. And there are things that we would

11:36

not think about. Geological

11:39

forces forging uranium millions of years ago is part

11:41

of that story. Einstein being born

11:43

is part of that story. The Battle of

11:45

Midway pivoting on a fluke event where the

11:47

US wins because they just happened to stumble

11:49

upon the Japanese fleet at the right moment.

11:52

If any of these things have been different, there's an

11:54

almost infinite number of them where little tweak would have

11:56

been different, a different outcome would have happened. Now, for

11:59

the useful navigation, of society, we have to simplify

12:01

reality, because we can't build a model that has

12:03

900,000 variables. So

12:06

what you instead do is you sort of

12:08

say, OK, this is a crude version of

12:10

reality. And I think one of

12:12

the things that is really useful about some models,

12:14

like Google Maps, for example, we know

12:17

that's not the world. We know the map is not

12:19

the territory. You look at Google Maps, and you're not

12:21

like, oh, well, I imagine that that's what the real

12:23

world looks like. It's a clear abstraction. I think when

12:25

we start to get into forecasting and

12:28

other modeling of social change, I

12:30

think we lose sight of the fact that

12:32

we have a Google Maps distortion, and that

12:34

we're actually looking at something that is potentially

12:36

useful to navigate, but is very, very different

12:38

from the real world. Really

12:41

interesting. So let's talk

12:43

about the way different

12:45

schools of thought perceive

12:49

and manage these philosophical

12:51

differences. You point out

12:54

Eastern and Western thinking have

12:57

a very different set of precepts

13:00

because of just the

13:02

nature of each society. In

13:05

the Bible, in Genesis, God proclaims, let us

13:07

make man in our image after

13:09

our likeness, and

13:11

let them have dominion over the fishes, the fowl, the

13:14

cattle, et cetera. Eastern culture takes

13:16

a whole lot more of

13:18

a collectivist approach where you're part

13:20

of a group, not you

13:23

were made in God's images. Tell

13:25

us a little bit about how this schism

13:28

developed, and what

13:30

is the relationship of chaos theory

13:32

to each? Yeah, so this

13:34

is a speculative theory, but it's a

13:36

theory that suggests that the reason why

13:38

Eastern cultures have much more relational concepts

13:40

of interconnectivity between humans and the rest

13:42

of the world and

13:44

human society as well is

13:46

derived from the differences, or proximity

13:48

rather, that humans have to primates,

13:51

for example, in their own cultures.

13:54

So there's lots of monkey gods and so on, and

13:56

there's also, of course, lots of monkeys in many of

13:58

these cultures that are developing. And... The

14:00

idea is, the hypothesis, is

14:03

that this meant that people could not

14:05

avoid the commonality that we have with

14:07

the rest of the world, right? Whereas

14:09

if you think about biblical societies, if you look

14:11

at animals and you see camels, you

14:14

think like, hey, we are super different. We are separate

14:16

from the rest of the world. So

14:18

the argument is that over the

14:20

long stretch of civilization, that this

14:22

created a slightly different mentality that

14:24

then manifests in what's called relational

14:26

versus atomistic thinking. And

14:28

Western society is atomistic thinking on steroids, which

14:31

is to say, you know, I

14:33

mean, the American dream is very atomistic, individualist.

14:35

It's like, you know, if you just want

14:37

to succeed, then you have to do everything.

14:40

Whereas the relational concepts are much more

14:42

about the interconnections that people have. And

14:45

so I think that also tells you how you think about

14:47

society, right? Social change is either driven by individuals or it's

14:49

driven by systems. And I think

14:51

that there is a way in which Western

14:54

culture, I think, can learn to actually

14:56

appreciate some of the complexity of social

14:58

change more with a healthy

15:00

increased dose of relational thinking. And

15:03

you kind of bring the Eastern

15:05

and Western philosophies together, where you

15:07

discuss the overview effects. And

15:10

it really begins with the

15:12

United States, Western society

15:14

sends astronauts to the moon,

15:16

sends astronauts around around the

15:19

earth. And these astronauts are

15:21

chosen out of often

15:23

out of the military, out of the

15:25

Air Force, they're pilots, they're, they're logical,

15:28

they're unfeeling, they're supposed to be

15:32

essentially soldiers. And

15:34

yet all of them have this impact

15:36

when they see the blue green earth

15:38

in its entirety from space. They

15:41

all describe it as being

15:43

overwhelmed by a life

15:46

shattering epiphany on the interconnection

15:49

of everything. That doesn't

15:51

sound very Western. That sounds more like an Eastern

15:53

philosophy. But this has been time and time again,

15:55

lots of astronauts have had this. Yeah, there's, you

15:57

know, it's funny because There's been like 9,500 generations

15:59

of modern humans and 9,497 of them have not

16:01

seen the earth,

16:08

right? So, when

16:10

people do see the earth, they have this profound

16:12

epiphany. And as you say, they were worried about

16:14

sending up philosophers and poets because they

16:16

figured they'd be overwhelmed by the sort of existential awe

16:19

and like, you know, forget to hit the right buttons

16:21

or whatever. So they picked these people who are supposed

16:23

to be robots effectively in their personality. And all of

16:25

them still have this incredible sort of

16:27

epiphany about the interconnection of the world because you

16:29

look at the single planet and you think, okay,

16:32

this is one structure. This is not something where

16:34

I'm this distinct bit. You're like, this is all

16:36

together, right? Now I think what's

16:38

really striking about that is that those

16:40

world views do shape your thinking around social

16:42

change. And I think when you

16:44

start to think that you are in control rather than

16:47

an agent of influence, you have a different worldview. When

16:49

you start to think that you're individual rather than relational,

16:51

you have a different worldview. And all these

16:53

things feed into the ways that we set up models that we sort of

16:56

interact with our conceptions of social

16:58

change and so on. And also the degree

17:00

to which we have hubris that we can control things. And

17:03

I think this is where the danger comes in, right? It's not

17:05

that you shouldn't model. It's not that you shouldn't have abstractions of

17:07

systems. It's that when you start to

17:09

get hubristic about it and think you have top-down

17:11

individualist control, you start to get

17:14

overconfident in ways that you try to tame

17:16

something that I think is untamable. And

17:18

this is where we get shocks more often because you

17:20

try to impose this sort of control on

17:23

a system that is so complex that

17:25

it resists control. And so

17:27

there's some of these things where I think the

17:29

insights, the philosophy behind this, it's

17:31

sort of lurking there invisibly where

17:33

no one says this when they build a model, but

17:36

it's obviously shaping the way they think about it

17:38

and their sort of assumptions before they go into

17:40

trying to determine how to navigate risk and uncertainty.

17:43

Along those lines, you have a great quote in

17:46

the book. God may have created

17:48

the clock, but it was Newton's laws

17:50

that kept it ticking. So

17:52

how do you resolve

17:55

that inherent tension between

17:57

big, driving

18:00

things or random elements affecting

18:03

it, or is there no

18:05

resolving them, they both matter? Yeah,

18:08

so I think it's a question of time scales. And

18:11

I think one of the big problems, and this

18:13

is something that I, you know, it's such a nuanced concept

18:15

that it's sometimes difficult to explain, but I

18:17

think there's a really important point about whether

18:20

ideas that happen for a long

18:22

time seem to be validated by what goes

18:25

on, the patterns that we see, right? Whether

18:27

you can actually falsify a theory when you're talking

18:29

about social change. So my favorite example of this

18:32

is the Arab Spring. In political science, my

18:34

own realm, there is a lot of stuff

18:36

written in sort of 2008, 2009, even

18:39

into 2010 that says, here's

18:42

why Middle Eastern dictatorships are extremely resilient. And there's

18:44

all this data showing this, the longevity, et cetera,

18:46

et cetera. And then like within six months of

18:48

some of these books coming out, you know, all

18:50

of them are on fire. I mean, I saw

18:52

a political risk map when I was in grad

18:54

school where like every single country that was on

18:56

fire was green on the political risk map from

18:58

the previous year, right? Now, there's

19:00

two ways of thinking about that. The first

19:02

way is to say the theory has been

19:04

falsified. They were wrong, right? The second

19:07

way of thinking about it is, hold on, maybe

19:09

the world changed. Maybe the patterns of cause and

19:11

effect have actually shifted, right? And I

19:13

think this is something that people don't appreciate that much,

19:15

is they assume that the patterns of the past are

19:17

going to be predictive of the patterns of the future.

19:19

I mean, David Hume came up with this idea hundreds

19:21

of years ago, but it is something that I think

19:24

is particularly important for our world, because

19:26

the patterns of the past being indicative

19:28

of the patterns of the future has never

19:30

before been as flawed of an assumption

19:32

because our world is changing faster than

19:34

ever before. So I think one of

19:36

the issues that we have is when we think about these sort

19:38

of clockwork models, where we say, oh yes, you know, these are

19:40

the ways that things have worked in the past, our

19:43

world is very, very different year to year. And that

19:45

didn't used to happen. I mean, I was talking before

19:47

about these, you know, 9,500 generations of humans. If

19:51

you think about the sort of entirety

19:53

of human history as a 24 hour day, 23

19:56

hours in like 10 minutes is

19:59

hunter-gatherer period. And then you

20:01

get into farming, which is another 30 minutes, and

20:03

then you got a few minutes for the Industrial

20:06

Revolution. And you get to the information age, which

20:08

we're in now, which is 11 seconds in this

20:10

one day clock. And I think the

20:12

point that's important here is that if

20:14

we base almost all of our decision making

20:16

and almost all of our models on causal

20:19

inference from past patterns of behavior, but

20:21

the world is changing year to year, then

20:24

the assumptions we're making are becoming more

20:26

and more short-lived. And I think that's

20:28

where we're embedding risk into our thinking

20:30

because we have no other way

20:32

of inferring cause and effect other than past patterns. There's no

20:35

alternative. That's what Hume says. He's like, this is the only

20:37

way we can understand the world. This is to look at

20:39

what happened in the past. We can't look into the future.

20:42

So I think this is something that I do

20:44

worry about when I see a lot of decision

20:46

making built on this sort of mentality

20:48

of the clockwork model that like, oh yes, well, it's

20:50

just gonna keep ticking along. And there's

20:52

a lot of very smart thinkers who have thought about black swans

20:54

and so on. I just think that we've made a system where

20:57

the black swans are actually gonna be more

20:59

frequent. I think we've designed a system that

21:02

is more prone to systemic risk than before.

21:04

Especially given not only does information move faster

21:06

than ever, but we're more

21:09

interconnected, we're more related, and

21:11

it becomes increasingly difficult if

21:13

not impossible to figure out

21:15

what are the unanticipated results,

21:18

consequences, side effects

21:20

of anything that we do. Yeah,

21:23

and this is one of those things where

21:25

I think there's some pretty good examples from

21:27

history of when somebody tries to control a

21:29

system that is uncontrollable and it

21:31

backfires catastrophically. And my favorite example is,

21:33

I shouldn't say favorite, it's a horrible

21:35

tragedy, but the best illustration of this

21:38

is Mao has this idea in communist China. He

21:40

has this idea, he says, we're gonna

21:42

eradicate disease, and the way we're gonna do this is

21:45

massive four pests campaign, so we're gonna kill all these

21:47

pests. So he basically tells

21:49

everyone to go out and kill

21:51

all these various things that potentially are vectors

21:54

of disease. And what it

21:56

ultimately does, it leads to one of the worst

21:58

famines in human history because they've... disrupted the ecosystem

22:00

and they figure, oh, as long as we just

22:02

get rid of these pests, it will be fine.

22:04

What they actually have done is they've made it

22:06

so the crops fail. And

22:09

so this is the kind of stuff where I

22:11

think it's the parable that warns us of

22:14

assuming that simply because we

22:16

have either have had some success in the past or

22:18

because our model seems to guide us in this way

22:21

that we can therefore insert ourselves into a system

22:23

and not worry about the unintended consequences. I think

22:25

that's the kind of thing where a lot of

22:27

the people who are the doomers in AI are

22:29

talking about this. There are some things where when

22:32

you have AI-based decision making, the

22:35

training data is the past. So there

22:37

are some things that I think are

22:39

getting worse in this front. And we

22:41

are also, as you said,

22:43

the interconnectivity. One

22:45

of my favorite examples of this is the Suez

22:47

Canal boat, the infamous Suez Canal boat. You

22:50

have a gust of wind that hits a boat

22:52

and twists it sideways. It gets lodged in the

22:54

canal. And the best estimate I've seen is

22:56

that it created $54 billion of economic

22:58

damage. And they said it was something like 0.2% to 0.4%

23:00

of global GDP could have been

23:02

wiped off by this one

23:04

boat. Now, the question is, is there ever

23:06

another moment in human history where one boat

23:09

could do that? And I think the answer

23:11

is quite clearly no. So

23:13

maybe the one that brought the plague. But

23:16

I mean, this is the kind of stuff where

23:18

I think one of the lessons that I think

23:20

is important is that there's a trade-off very

23:23

often between optimization and resilience. And

23:26

I think we're told all the time

23:28

efficiency and optimization are the guiding

23:31

principles of so many of our systems. But

23:34

they come at a cost. They do create less resilience.

23:36

And I think there are some things where the

23:39

long-term planning that we can do is to put a

23:41

little bit more into resilience and a little bit less

23:43

into optimization. It will cost us money in the short

23:45

term, but it will probably save us a hell of

23:47

a lot of money in the long term. Really, really

23:50

interesting. And

23:55

more of the planet's environmental

23:57

leaders and problem solvers for

24:00

the Bloomberg Green Festival. in

24:02

Seattle July 10th to the

24:05

13th title sponsor Amazon official

24:07

airline Alaska Airlines supporting sponsor

24:10

Providence contributing sponsor warehouse er

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get 40% off using

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24:17

Bloomberg live.com/green festival so

24:21

I found the book fascinating and

24:23

I really enjoyed where you will

24:25

you go down the evolutionary biology

24:28

rabbit hole starting with convergence

24:31

is the everything happens for

24:33

a reason school of evolutionary

24:36

biology contingency is

24:38

the the G rated

24:40

version is stuff happens theory explain

24:43

the difference between the two yes

24:45

I I think that evolutionary biology has a

24:47

lot to teach us about understanding change it's

24:49

a historical science and they're trying to understand

24:51

you know the origin story of species and

24:54

they're thinking about cause and effect just as people

24:56

in economics and politics are as well and

24:59

so these two ideas they're very simple

25:01

to understand with two examples the first example

25:03

of contingency is the asteroid that wipes out

25:05

the dinosaurs now if this asteroid

25:07

which was by the way was produced by

25:09

an oscillation in a place called the Oort

25:11

cloud in the distant reaches of space absolute

25:14

outer ring of assorted

25:18

detritus that surrounds the entire solar

25:20

system beyond Pluto yeah so this

25:22

this oscillation flings the space rock

25:25

towards earth and it hits

25:27

in the most destructive way possible it hits in

25:29

the ocean in a way that brings up a

25:31

lot of toxic gas and effectively incinerates

25:33

the dinosaurs because the surface temperature went up to

25:35

about the same level as a broiled chicken I

25:38

mean it was it was deadly right

25:40

now the reason this is important is because if it

25:42

had hit a slightly different place on the earth the

25:45

dinosaurs probably wouldn't have died out and let me just

25:47

point out and you mentioned this in the book it's

25:49

not like if it hits a different continent five

25:52

seconds earlier five seconds later it

25:55

completely misses that sulfur rich

25:58

if miss at the in the

26:00

Yucatan Peninsula. Yeah, so

26:02

I mean, this is the kind of stuff

26:04

where you think about it and it is

26:07

very unsettling because you can imagine everything that

26:09

humans have done. I mean,

26:11

you have a second difference in this asteroid. There's

26:13

no humans. Because the extinction of the dinosaurs is

26:15

what led to the rise of mammals and eventually

26:17

the evolution of us. And so

26:20

this is contingency. It's where this small

26:22

change could radically reshape the future. Now,

26:24

convergence is the alternative hypothesis and they

26:26

both exist, right? The sort of order

26:28

and disorder. And convergence

26:30

says, okay, yeah, there's a lot of noise. There's

26:32

a lot of fluctuations and flukes. But

26:35

eventually things that work win, right? So my

26:37

favorite example of this is that if you

26:39

look at, if you were to take out

26:41

a human eye and you were

26:43

to look at it and you were to compare

26:45

it next to an octopus's eye, they're actually extremely

26:47

similar, which is bizarre because there's about 600 million

26:49

years of separate evolutionary

26:52

pathways for the two branches of

26:54

life. And the reason this

26:56

happened isn't because, you know, we

26:58

just got super lucky. It's because evolution came up

27:00

with a strategy by random experimentation

27:02

that simply worked. It made the species

27:04

navigate the world effectively long enough to

27:07

survive to have offspring, which is the

27:09

engine of evolution, right? So this

27:11

is the kind of stuff where, yeah, there was like

27:13

a lot of very profound differences. I mean, we do

27:15

not look like octopus, thank goodness. But it's something where,

27:17

as a result of that, the eye is basically the

27:20

same. And so the question

27:22

here, I think, is can

27:24

we apply these frameworks to our own change, right?

27:26

In our own societies. And so what I try

27:28

to say is, okay, there's some stuff that is

27:30

ordered. There's lots of regularity. There's lots of patterns

27:32

in our lives. That's the convergence stuff.

27:34

At some point, you know, you go on

27:36

the highway, there might be an accident sometimes,

27:38

but like most of the time, you know,

27:40

the cars drive around the same speed. They

27:43

have space between them. It's about the same distance,

27:45

right? And like, there's all these patterns, but every

27:47

so often there's a car accident and that's contingency,

27:50

right? So this is the kind of stuff where

27:52

what I say is that the way that

27:54

social change happens and also our lives unfold

27:56

is what I call contingent convergence. It's not

27:59

the most beautiful. phrase, but it's I

28:01

think very accurate in saying, okay, so there's

28:03

these contingencies that change the path you're on.

28:06

And then once you're on that path, the sort of

28:09

forces of order do constrain the outcomes

28:11

that are possible. They say, look, this

28:13

stuff's going to work, that stuff's not

28:16

going to work. And the sort of

28:18

survivors bias produces the stuff that does

28:20

work. So I think this is a

28:22

useful framework that I'm borrowing from evolutionary

28:24

biology to help us better understand social

28:26

change. So before I get to contingents

28:29

convergence, I want to stay

28:31

with the difference between contingents, which

28:34

is the meteor killing the dinosaurs

28:36

and allowing them out mammals to

28:38

arrive to rise and convergence. A

28:40

couple of other examples that

28:43

you give in the book of convergence. Crab

28:46

like bodies, keep evolving

28:48

time and again, there are

28:51

five separate instances that

28:53

that shape somehow seems to provide a useful,

28:55

adaptive way to navigating the world. Yeah. So

28:57

this is, I mean, this is one of

28:59

those things where evolutionary biologists joke about that

29:01

and they always say, you know, eventually we're

29:03

going to have pincers like we're, we're all

29:05

going to end up as crabs because like

29:07

evolution, if you know, and some of them

29:09

say, if there, if there is a God,

29:11

he really likes crabs. And this is actually,

29:13

you know, I actually heard that about Beatles,

29:15

but there's actually a word for

29:18

this. Carsonization is the process of

29:20

evolving towards a crab like shape.

29:23

Similarly, flight. I never thought about this

29:25

until I read it in the book,

29:27

flight evolved four separate times. It's insects,

29:30

it's bats, it's birds, and it's pterosaurs.

29:32

That that's amazing. Yeah. I mean, this

29:35

is the stuff where, you know, evolution

29:37

is the, it's a

29:39

really powerful lesson of the value

29:41

of undirected experimentation because every strange thing that

29:44

we see around us, every, you know, organism,

29:46

every plant, et cetera, is just the byproduct

29:48

of this undirected experimentation, navigating uncertainty, right? I

29:51

mean, that the world is changing all the

29:53

time. There's different concentrations of oxygen. They sometimes

29:55

have to be in the ocean, sometimes have

29:57

to be on land. And the, this

30:00

sort of diverse array of life is

30:02

just undirected experimentation. But the thing is

30:05

that these forces do end

30:07

up constraining the possibilities. Now, when

30:09

we talk about carcinization, there's a really interesting thing that I

30:11

don't go into much depth in the book, but it's called

30:13

the Burgess Shale up in Canada and the Canadian Rockies. And

30:16

it's basically like this fossilized

30:19

museum of all these really wild body

30:21

plans that used to exist hundreds of

30:23

millions of years ago before a mass

30:25

extinction event. And what happened

30:27

is they all got obliterated, so you can't

30:29

have any sort of convergence from those body

30:31

plans because they don't exist anymore. Whereas

30:33

the ones that survived, all of us are

30:36

derived from them, right? So the contingency is

30:38

like, okay, which body plans exist? Which sort

30:40

of ways could you set up life with

30:42

spines or not spines, whatever it is. And

30:45

then once you have that contingent event where

30:47

there's the extinction, within that there's

30:49

this sort of constrained evolution that is, okay,

30:52

well, when this happens, the animal dies. So

30:54

it doesn't exist very long. And when

30:56

this happens, the animal survives, so it does

30:58

exist. And this is where carcinization, you need

31:00

to have a term because the crabs

31:03

are very much survivors. And

31:05

it turns out that unless you were on the other

31:07

side of the planet from where the

31:10

meteor hit, if you're

31:12

a burrower, if you get underground, you

31:14

could survive those fires in that heat

31:18

and then come out and continue

31:20

the evolutionary process. Yeah, I mean, this is

31:22

the thing, I find this really fascinating to

31:24

think about, but also unsettling is that, all

31:28

the life that exists now is basically offspring

31:30

of either something that could dig when

31:32

the asteroid hit or that lived in

31:34

the ocean. And that's it, right? Because

31:36

everything else died. Now, the really

31:39

strange thing to think about as well is that I

31:41

told the story about my great grandfather's first wife

31:43

and then there's this murder and so on, but

31:45

you keep tracing these things back, right? So my

31:48

great grandfather's ancestors had to meet in just the

31:50

right way. And their great grandfather, they had to

31:52

meet. But you go back then six million years,

31:54

this chimpanzee-like creature had to meet another chimpanzee-like creature

31:57

and the two of them mating is part of

31:59

the story of human. existence. You go back further,

32:01

you know, there's a worm-like creature hundreds of millions

32:03

of years ago. It dies, we probably don't exist.

32:06

Or my favorite example I think in the book

32:08

is, this is a finding from Modern Science

32:11

about a year ago, was they

32:13

found out that the reason why mammals

32:15

don't lay eggs, right, why we don't

32:17

have eggs and we instead have live

32:19

births, is they believed based

32:21

on genetic testing that a single shrew-like

32:23

creature got infected by a virus a

32:25

hundred million years ago which caused a

32:27

mutation which led to placenta and the

32:30

rise of mammals. And you think

32:32

of, I mean, to me that is just so

32:34

utterly bizarre to imagine that our existence, like everything

32:36

in humans, you know, ancient Rome, all this stuff,

32:38

you know, Donald Trump, whatever it is, all of

32:41

it is completely contingent on a shrew-like creature a

32:43

hundred million years ago getting sick. It's

32:45

like when you think about this stuff, I think

32:47

evolutionary biology tell, you know, they have encountered black

32:49

swans throughout hundreds of millions of years. It's basically

32:51

the origin story of complex

32:54

life. So let's talk about one

32:56

of those black swans and the

32:58

specific concept of contingent

33:01

convergence. I love

33:03

the example you use of the

33:05

long-term evolution experiment using

33:07

E. coli, 12 identical

33:10

flasks of E.

33:13

coli and in separate,

33:15

separate environment,

33:17

separate but identical environments, run

33:20

10 million years worth of

33:22

human evolution through it. What's

33:24

the results of that? Yeah, this one, this one,

33:26

making E. coli sexy in a book is pretty

33:29

hard, I must say, but I think this is

33:31

such a powerful lesson for change. So I had

33:33

to include it. I flew out to Michigan State

33:35

to meet with the people running the long-term evolution

33:37

experiment and the simple idea they had, the genius

33:40

idea was they said, let's see

33:42

what happens if we take 12 identical

33:44

populations of E. coli. So they're genetically

33:46

identical. We put them in

33:48

12 flasks and we just evolve them

33:50

for decades, right? And because E. coli

33:53

life cycles are so short, it's basically

33:55

the equivalent of millions of years of

33:57

human evolution. Like multiple lifespans a day,

33:59

generations. Exactly. So it's

34:01

like, it's the equivalent of it. If you went

34:03

through like great, great, great grandparents each day, right?

34:06

Now the beauty of this experiment is they controlled

34:08

everything. So there's nothing in these flasks except

34:10

for a glucose and citrate

34:13

mix because the glucose is food

34:15

for the E. coli and the citrate is like a

34:17

stabilizer. Okay? Now what happens they

34:19

figure, okay, let's test contingency or convergence

34:22

and for like the first 15 years or so

34:24

the experiment The lesson was okay.

34:26

It's it's convergence because all 12 of the

34:28

lines were evolving in slightly different ways. There's

34:30

noise, right? There's little differences. The genome is

34:32

not the same, but they're

34:34

basically all getting fitter at eating

34:37

glucose so they're getting better at surviving and

34:40

then one day a researcher comes in and one of

34:42

the flasks is Cloudy and this is not supposed to

34:44

be the way it is It looks like a little

34:46

bit of milk has been dropped into it instead of

34:48

this really clear substance that the rest the other 11

34:50

are So they sort of think

34:52

oh, this is a mistake and they throw it out They

34:55

restart because they've frozen the equalize they restart

34:57

it like every the equivalent of every 500

34:59

years Yeah, five. So they could

35:02

reset the clock anytime. They won't exactly 12 flasks.

35:04

Yes, they're all frozen They all this sort

35:06

of fossil record. They can restart at any point

35:08

So they restart the experiment in this flask just

35:10

backing up a little bit and about

35:12

two weeks later I think it is or something like that. They

35:15

The flask turns cloudy again and like okay, this was

35:18

not an accident. There's something going on here So they

35:20

actually paid to sequence the genome very expensive at the

35:22

time a lot cheaper today But

35:24

they paid it paid to sequence it and

35:26

the amazing finding this is the thing when I read

35:28

this I was like this is a central way of

35:31

capturing my idea is That

35:33

when they looked at the genome there

35:35

were four totally random mutations that

35:37

did not matter at all for

35:39

the survivability of the E.

35:41

coli That proceeded in just

35:44

the right chain that when the fifth

35:46

mutation happened all of the sudden

35:48

that population could now eat the citrate Which was

35:50

not supposed to happen right? It was supposed to only

35:52

eat the glucose the citrate was there as a

35:54

stabilizer But as a result

35:56

of this they became way more fit way more

35:58

survivable than the other population because they

36:00

could eat something the others couldn't, right? And

36:03

what happened then is that since then, and this has

36:05

now been going on for 20 plus years or so,

36:08

since then, the citrate population has an advantage

36:10

over all of the other 11, and none

36:13

of the others have developed that mutation because

36:15

it's sort of like a house of cards.

36:17

You had to have these exact four accidents

36:19

in exactly the right order. If they'd reached, if they'd changed the

36:21

order, it wouldn't have happened. And then they had

36:24

to finally, on top of that four, those four accidents, they had

36:26

to have the fifth accident, which gives them the ability to eat

36:28

citrate. And so this is the

36:30

idea of contingent convergence, right? It's like for

36:32

that population that evolved the ability to eat

36:34

citrate, that one mutation

36:37

has changed everything forever. It will never go

36:39

back to eating glucose the same way as

36:41

the others. But for

36:43

the others that didn't develop that change, they

36:46

are all still evolving in relatively predictable ways.

36:48

So I think this is the capturing

36:51

of the sort of paradox of our

36:53

lives is that we exist somewhere between

36:55

order and disorder. Complete disorder would destroy

36:57

humans, we couldn't exist and our societies

36:59

couldn't function. Complete order also wouldn't work

37:01

because there'd be no change, there'd be

37:03

no innovation and so on. And

37:06

so I think this is where contingent convergence

37:08

really shines, but I will admit that trying

37:10

to do a sound bite version of the

37:12

long-term evolution experiment is something that in writing

37:14

the book was probably the

37:16

greatest challenge of making something about bacteria interesting.

37:18

But it's really fascinating because if you stop

37:20

and think about that, first of all, the

37:22

genius of doing this over 20 years when

37:25

you have no idea what the outcome is and

37:28

hey, maybe we're wasting our lives and our career doing this,

37:30

number one. But number two, you come

37:32

in and you see that it's cloudy. Is

37:35

it, I'm assuming it's cloudy because

37:37

they're reproducing in greater numbers, they're

37:39

processing the citrate, a whole bunch

37:41

of different stuff is going on

37:44

than the other 11 environments.

37:46

And one has to imagine that

37:49

if this wasn't taking place in an experiment,

37:51

but this was a big

37:54

natural scenario, the

37:57

citrate consuming E. coli would. eventually take over

37:59

the population because they have twice as much

38:02

food available or more than just the plain

38:04

old glucose eating E. coli. Yeah, and this

38:06

is, I mean, what I was talking to,

38:08

so one of the researchers named Richard Lenski,

38:11

the other one, Zach Blount, and I was

38:13

talking to them about this. And they said,

38:15

look, we tried to control everything. We tried

38:17

to control every single, you know, you pipette

38:20

the exact same amount of solution into the

38:22

beakers each day and so on. But

38:24

what they said was that, you know, well, what

38:27

if one day, you know, when we

38:29

were washing the flask, just a

38:31

tiny microscopic amount of soap stayed

38:33

on there, right? That could affect the evolution.

38:36

And so there's, I mean, even, even in this experiment,

38:38

there's contingency they couldn't control, which is, I mean, it's

38:41

the most controlled evolutionary experiment that's ever been done. But

38:43

it's still like, you know, these little tiny bits, if

38:46

you just have, you know, a microscopic bit of soap,

38:48

well, that's going to kill some of the bacteria and

38:50

then the evolutionary pathway is going to be slightly changed.

38:52

And I think this is the stuff where, you know,

38:55

had they been a different researcher, had a grant run

38:57

out, they might've just said, okay, we've solved it.

38:59

It's all convergence because they could have

39:01

shut down the experiment after 15 years. So there's just

39:04

all these things that are like layered on top of

39:06

each other. And I think, you know, a lot of

39:08

scientists, especially in the world of evolutionary biology, understands

39:10

that this is something that we really

39:13

have to take seriously. And I

39:15

think the way that we are

39:17

set up in human society is to ignore

39:19

the contingency, because those are not useful things

39:21

to think about. They're the noise, they're the

39:23

aberrations, they're the outliers, you know, you delete

39:25

them from the data, whatever. And I think

39:27

this is the kind of stuff where the

39:29

lesson here is that those are actually central

39:32

to the question of how change happens. I

39:34

love this quote from the book, I

39:36

began to wonder whether the

39:39

history of humanity is just

39:41

an endless but futile struggle

39:43

to impose order, certainty and

39:45

rationality onto a world defined

39:47

by disorder, chance and chaos.

39:50

Yeah, I mean, I think this is where I became

39:53

a bit of a disillusioned social scientist, to be honest,

39:55

was that I think that the

39:57

way that I was taught to present change

39:59

to people. was to come

40:01

up with a really elegant model, you know, a

40:03

really beautiful equation, and that

40:05

has statistical significance and has like the smallest

40:08

number of variables possible to explain the entire

40:10

world. And the reason that I

40:12

ended up, you know, having that mentality that

40:14

I think we're trying to cram complexity into these

40:16

neat and tidy sort of straight jack models is

40:18

because my PhD dissertation

40:21

and so on, I was looking at the origin story

40:23

of coups and civil wars, that

40:25

was part of my research. And

40:27

these are black swan events. I mean, you know,

40:29

there's only a few coup attempts that happen every

40:31

year, and they're so hard to

40:34

predict. I mean, because, you know, one of

40:36

the coup plots that I studied was

40:38

where this guy, you know, who's a sort of

40:40

mid-level officer in the army, just on a whim,

40:43

decides to try to overthrow the government. And

40:45

he's got like 50 guys in his command, this is in 1997

40:47

in Zambia, and

40:51

his plan is to kidnap the army commander and

40:53

force the army commander to announce the coup on the radio.

40:55

It's not a stupid plan, it's actually, it probably would have

40:57

worked. But the group of soldiers that

40:59

were dispatched to the house, I interviewed some

41:02

of them when I went to Zambia, and

41:04

they said, look, you know, we ran

41:06

in, the army commander's in his pajamas, he runs out the back

41:08

because he sees these soldiers coming to kidnap him, and

41:10

he climbs up the compound wall, and

41:13

you know, it's like in a film where like they

41:15

grab his pant leg, he's pulling up, they're pulling down,

41:17

and they just, he slips through their fingers. And

41:20

he then goes to the government HQ

41:22

and announces that there's a coup plot

41:24

underway, and so the soldiers go to

41:26

the radio station, they capture the coup

41:28

ringleader, who's at this point literally hiding

41:30

in a trash can, okay? Three

41:33

hours after the coup plot has been hatched. Now,

41:35

the problem is I was reading all this stuff

41:37

about like Zambia's democracy, and it was, oh, Zambia's

41:39

a resilient democracy, it's one of the beacons of

41:41

African democracy in the 1990s. And

41:44

I'm trying to reconcile this with the fact that in

41:47

my own research, I'm finding this story where the soldier says

41:49

like, yeah, I think if I was like one second faster,

41:52

I probably would have gotten the government

41:54

overthrown. And on top of this, the other contingency

41:57

was they didn't chase him. And I said, why didn't you

41:59

chase him? said, well, the

42:01

army commander's wife was really attractive and we

42:03

wanted to talk to her and also we

42:05

opened the fridge and there's

42:07

Namibian import beer in the fridge and

42:10

we hadn't had Namibian beer for a long time. So we said,

42:12

you know, screw this, we're going to, we're going to drink some

42:14

beer and talk to the wife. And I'm

42:16

thinking, you know, like, like, how do I put this

42:18

in my model? Like, you know, I mean, like, like

42:20

what is my quantitative analysis going to show me about

42:22

this? And I think that's the stuff where

42:24

those little pivot points and

42:27

studying really rare events that are highly consequential

42:29

makes you think differently about the nature of

42:31

social change. And I would go to these like political

42:33

science conferences and I was just like,

42:35

I don't, I don't believe this is how the world works.

42:37

I think there are times where these can be useful models,

42:40

but I don't think we're capturing reality accurately. And that's

42:42

where, you know, some of the origin story professionally of

42:44

the book comes from. You

42:47

have to build in attractive women

42:49

and imported beer into your models

42:51

or, or more accurately

42:53

just completely random events. There's

42:55

a research note in the

42:57

book from an

43:00

evolutionary biologist. 78% of

43:02

new species were triggered by

43:04

a single event, typically

43:08

a random mistake or genetic error.

43:10

Yeah. My favorite, my favorite example of this

43:13

is something called the bottleneck effect. And it's

43:15

actually, I think it's actually an important idea

43:17

for economics as well. So I'll start with

43:19

the, the biology, the bottleneck is where a

43:21

population arbitrarily gets reduced to a very small

43:23

number. And the number of people in

43:25

that population could be, you know, it could be 10, it could

43:27

be a hundred, whatever it is, but who

43:30

those 10 or a hundred people are

43:32

really, really matters. So there's, there's, there's

43:34

one Island, for example, where half the

43:36

population has asthma because it was populated

43:38

initially by this bottleneck of a very small number

43:40

of people who disproportionately had more asthma than the

43:42

rest of the population. There's

43:44

elephant seals, for example, who got whittled down

43:47

through hunting and so on to something like,

43:49

I think it was 50 breeding pairs or

43:51

something like that. But which exact seals lived

43:54

or died completely changed the trajectory of

43:56

that species. Now I sort of say

43:59

this because human society has had bottlenecks

44:01

at various times. We don't know exactly how small

44:03

they've been, but the hypothesis is

44:05

perhaps that it may have been as few

44:07

as a few thousand humans at one point.

44:10

And which humans were in that group, that

44:12

determined everything for who's alive now, right? So

44:14

if you swap out, you know, one person

44:16

for a different person, you've changed the trajectory

44:19

of the species. Now I think this is

44:21

also true when you think about economics, you

44:23

think about innovation. Every so

44:25

often shocks go through industries and they whittle

44:27

down the competition. And who survives in that

44:30

moment is potentially somewhat arbitrary. It could be

44:32

based on some pressures, it could be a

44:34

smart CEO, but the sort

44:36

of survivors in that bottleneck then will dictate how

44:38

the industry might unfold in the future. I mean,

44:41

you know, Apple has this outsized effect on the

44:43

tech industry, but you know, maybe the timing's a

44:45

little bit different and Apple dies. I mean, it's

44:47

not implausible. Hey, but for Microsoft giving them a

44:50

loan in what was it, 98? But

44:53

for the antitrust case, which

44:55

gave Microsoft an incentive to have

44:58

another survivable operating system, who

45:00

knows? Yeah. And so this, you know, when

45:02

you think about, I think bottlenecks are a

45:04

useful way of thinking about this partly because

45:06

they affect trajectories very, very profoundly, but

45:09

also because they can be arbitrary. And I think this

45:11

is something where what we do

45:13

in human society is we write history

45:15

backwards. So we look at who is successful

45:17

and we say, I mean, hindsight bias, you know,

45:20

many people, I'm sure, have talked to you about

45:22

this, but it's very important to underline that, like,

45:24

when these arbitrary things happen, if

45:26

you then infer causality, that's a neat and

45:28

tidy story, you actually are learning

45:30

exactly the wrong lesson. I mean, the

45:33

reason these particular elephant seals survived is

45:35

probably arbitrary. It just happened to depend

45:37

on who the people who are poaching

45:39

them, you know, happened

45:41

to stumble upon. And then,

45:43

of course, the evolutionary history of that animal is completely changed.

45:46

So I think that lesson is that, you

45:48

know, sometimes when bottlenecks happen, it reshapes the

45:50

trajectory of the future, but it also is

45:53

inescapably arbitrary at times. And

45:56

we don't like that. I mean, the entire world

45:58

of self help in the entire world of sort

46:00

of business advice is, oh,

46:03

these people were successful, here's how you replicate it.

46:05

And the replication is always just do what they

46:07

did, right? But I mean, of course, the world's

46:09

different now. I mean, if you do what they

46:12

did, you're just making something that's not truly innovative.

46:14

Right, you can't invent an iPhone today. Exactly. So

46:17

it's fascinating when you talk about

46:19

bottlenecks, I read a book some

46:21

years ago called Last Ape Standing,

46:23

and it talks about all the

46:26

various proto-human

46:28

species, from Cro-Magnum to

46:30

Neanderthal to Homo sapiens.

46:33

And the theory is that in

46:36

the last Ice Age, maybe

46:38

it's 20 or 40,000 years ago, we

46:41

were down to a few thousand

46:43

humans. And but

46:46

for the Ice Age ending when

46:48

it did, another year, again,

46:51

we may not be having this conversation. There may be

46:54

no humans around. Yeah, I mean, this is the, this

46:57

is the stuff also where I think that the sort of

46:59

predictable patterns that people try to impose on the world are

47:02

also subject to whims of timing,

47:04

right? And your example

47:07

is completely apt, and I think it's a very important

47:09

one. And I think it also speaks to the question

47:11

when you say when the Ice Age ends, right? The

47:13

timing issue is so important. Now,

47:15

one of my examples of this that I

47:17

think is so fascinating is you

47:20

think about like our daily lives. And

47:22

our daily lives are basically set up

47:24

in groups of seven. We've got a

47:26

seven day week. Why is that? So

47:28

I start looking into this. And effectively

47:30

what happens is there's this period in

47:33

ancient Rome where they have

47:35

this superstition that says the planets are

47:37

really important for being auspicious and so

47:39

on. And they can see, because

47:41

they don't have telescopes, five planets with

47:43

the naked eye and the sun and the moon. You

47:46

add them up, that's seven. They set up a

47:48

seven day week because of that. That's why we

47:50

divide our lives in seven. And it's because of

47:52

this thing that I also talk about in Fluke,

47:55

which is this concept of lock-in, where an arbitrary

47:57

thing can happen. And then sometimes it persists doesn't

48:00

and that's often very random. So my

48:02

other example of this is everything that we write, everything

48:04

that we say is derived from

48:06

English being locked in when the printing press was

48:08

invented. If the printing press had been invented six

48:11

decades earlier, six decades later, there'd be a different

48:13

language because the language was in flux and

48:15

all of a sudden it became really important to have a

48:17

standardized system. So a lot of people used to write the

48:20

word had, H-A-D-D-E. Now

48:23

that was expensive because they figured, okay, we've got

48:25

to typeset this with a bunch of letters. Why

48:27

don't we just do H-A-D? Boom,

48:29

all of a sudden the language changes. There's

48:31

a series of things that happen really, really quickly

48:33

but they basically produce modern English. And

48:35

so I think this sort of concept of the

48:37

arbitrary experimentation and superstition of the Romans

48:40

and then getting locked in and the empire sort of

48:42

sets it up and then it spreads and all that.

48:44

And then you think, okay, why do we have a

48:46

five day working? I mean, it's partly tied to the

48:49

superstition about the auspicious nature

48:51

of the visible planets which themselves are an

48:53

arbitrary byproduct of how our eyes evolved. So

48:55

I mean, it's just sort of everything

48:58

you think about has got these sort of tentacles where

49:00

they could have been slightly different and then our

49:02

lives would be radically changed. One

49:05

of the things that's so fascinating

49:07

with us as narrative storytellers, right?

49:09

We think about, okay, we've had the spoken

49:12

language for tens

49:14

of thousands of years, maybe a hundred thousand

49:16

years and we think about the cuneiform

49:18

and the written language going back to the

49:21

Egyptians and the Greeks but

49:24

that's history and

49:26

99% of the people who

49:28

lived during that period were illiterate. In

49:31

fact, species

49:33

wide literacy, which we arguably still

49:36

don't have but are closer to,

49:38

this is like a century old.

49:40

Like for a hundred years, people

49:42

could read and write and

49:45

meaning most people but go back

49:48

beyond the century and the vast majority

49:50

of people either couldn't read,

49:52

couldn't write, never went to school. They

49:55

had to get up and work the land. They

49:57

didn't have time to mess around with this

49:59

silly stuff. Yeah, you know,

50:01

I think there's a lot of things where we

50:03

are blinded to the fact that

50:05

we have lives that are unlike any humans who

50:07

have come before us, right? And I

50:10

think there's some really big superstructure events that

50:12

are related to this that really do affect

50:14

our lives. So my favorite way of thinking

50:16

about this is that I think

50:18

that every human who came before the modern period, most,

50:20

you know, at least, you know, maybe the last 200

50:22

years or so, what they experienced was

50:25

uncertainty in their day to day life. There was almost

50:27

no regularity, no patterns in their day to day life.

50:29

They didn't know where their next meal would come from.

50:31

They didn't know, you know, whether they would get eaten

50:33

by an animal, etc. The crops might fail, you know,

50:35

etc. But they had what

50:37

I call global stability, which is to say like the

50:39

parents and the children lived in the same kind of

50:41

world, you're a hunter-gatherer, your kids a hunter-gatherer, you know,

50:44

and this means that the parents teach the kids how to

50:46

use technology. There's basically regularity from

50:48

generation to generation for thousands of years. Yeah,

50:50

we have flipped that, right? So what we

50:52

have is local stability and global instability. So

50:55

we have extreme regularity, like no human has

50:57

ever experienced before, where we can know to

50:59

almost the minute when something we order off

51:01

the internet is going to arrive at our

51:03

house, and we go to Starbucks anywhere in

51:05

the world, and we can have the same

51:07

drink, and it's going to taste basically the

51:09

same thing. And we're really angry if somebody

51:11

messes up, you know, an order because that

51:13

expectation of regularity is so high. But

51:16

we have global instability. I mean, you know, I

51:18

grew up in a world where the internet didn't

51:20

exist really for ordinary people. And now it's impossible

51:22

to live without it. You know,

51:24

you think about the ways that children teach parents how

51:26

to use technology, that's never been possible before. And

51:29

on top of this, you have the sort of AI, you know,

51:32

rise where the world's going to profoundly change in

51:34

a very short period of time, there has never

51:36

been a generation

51:39

of our species, where not just

51:41

the global dynamics have changed generation to

51:43

generation, but within generations, I mean, we're

51:45

going to live in a world where,

51:48

you know, the way that we understand

51:50

and navigate systems and our

51:52

lives is going to change multiple times

51:54

in one lifetime. And you think about,

51:56

you know, Hunter Gathers, the average

51:59

human generation is about 20 years old, 26.9

52:01

years in the long stretch of our

52:03

species, you can go 27 years over

52:05

and over and over, it's pretty much

52:07

the same world for pretty much the entirety of

52:09

our species until I would say the last maybe

52:11

100 years or so. And that's the thing, you

52:14

think about this, the more you think about this, the

52:16

more of these examples you find. I mean, one of them

52:18

is, you know, jet lag, I flew in from London,

52:21

and there's been three generations of people who

52:23

could ever move fast enough to knock out

52:25

their biology in a way that they have

52:27

jet lag. So, I mean, there's just a

52:29

million things that we experience as routine that

52:31

no humans before us have ever been able to

52:34

experience. You could never outrun your circadian rhythm until

52:37

you could travel at a few hundred

52:39

miles an hour and go from country

52:41

to country. You

52:43

couldn't even change time zones until,

52:45

what is it, 75 years ago? Yeah,

52:48

I mean, there's an amazing map. I

52:50

don't know the exact name of it. It's

52:52

an ISO chrome map or something like that, but

52:54

it's a map of London from 100

52:56

plus years ago, and

52:58

it's showing the world based on how long

53:01

it takes you to get anywhere. And

53:03

you see that like Western Europe is,

53:05

you know, the closest and it's like five

53:07

plus days or whatever, right? Now, somebody

53:09

made a renewed version of that map a couple of

53:12

years ago, and the furthest reach you can

53:14

go is like 36 plus hours, whereas

53:16

in the old map, it was like three plus

53:18

months. And, you know, that's the

53:20

stuff as well, where we just, we've sped up the

53:22

world so much, and I think this is embedded a

53:24

lot of the dynamics where flukes and sort

53:27

of chance events become more common. 36

53:29

hours, I think you get to the moon in 36 hours. That's

53:32

right, I mean, it's true. That's how much it's changed.

53:36

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53:58

Singapore. That's Bloomberg Live. So

56:00

that's one of those things where if a different set of

56:02

people have been in the room with Cameron then maybe

56:04

they don't hold the referendum and then that's a very different world

56:06

we live in. So you're over

56:09

in the UK looking at the United States as

56:11

a political science. The

56:14

election of Donald Trump in 2016 by 40 or

56:16

50,000 votes and a handful of swing states,

56:22

fascinating question. Was

56:24

that a random contingency

56:27

or was the convergence and the arc

56:29

of history moving towards a

56:31

populist in the United States? Yes,

56:34

so there's sort of precursor factors that Trump

56:36

tacked into and this is the convergence, right?

56:39

This is the stuff that's the trends. I do think

56:41

there's some pretty big contingencies around Trump. I mean, there's

56:43

one hypothesis which I can't, I don't know

56:45

Donald Trump's thinking, but there's speculation by people

56:47

who are close to him that the

56:49

moment he decided he would definitely run for the 2016 race

56:52

was in 2011 when there was the White

56:54

House Correspondents Dinner and

56:57

he was publicly humiliated by Barack Obama with

56:59

a joke that basically said something to the

57:01

effect of, I really sympathize with

57:03

you Donald because I couldn't handle the hard choices

57:05

that you have to make on Celebrity Apprentice whereas

57:08

I have to make the easy choices in the

57:10

Situation Room and everyone's sort of laughing at Donald

57:12

Trump and so on. And the

57:14

question is, if the joke writer had not

57:16

come up with that idea or Obama said,

57:18

let's just can that joke, does Trump run?

57:20

I mean, that's question one. Then

57:23

there's the questions around the election, right? And this

57:25

is something where without

57:27

going into too much detail, the reopening of

57:29

the FBI investigation, which happens because of a

57:31

congressman in New York and his

57:33

inability to sort of control himself. Sending

57:37

naked genital pictures to underage women. Thank

57:39

you for saying it for me. So

57:41

this is the thing where this

57:43

causes the reopening of the FBI investigation. Did this cause

57:46

a shift in votes in those three critical states? I

57:48

don't know, but possibly, right? Could be. And

57:50

on top of that, you have one

57:52

of my things that I do talk about in the book. I have a

57:55

chapter called The Lottery of Earth and

57:58

this is the strangest example of US politics uncertainty

1:22:00

can be a really wonderful thing and

1:22:03

you just have to sometimes accept it and

1:22:05

then navigate based on the understanding that

1:22:07

there is radical uncertainty that we can't

1:22:10

eliminate and that is where

1:22:12

some of the best flukes in life

1:22:15

come from. Really very fascinating. Thank You

1:22:17

Brian for being so generous with your

1:22:19

time. We have been speaking with Brian

1:22:21

Klass, Professor of Global Politics at University

1:22:24

College London and author of the

1:22:26

new book Fluke, Chance, Chaos

1:22:28

and Why Everything We Do Matters.

1:22:31

If you enjoy this conversation well be sure and check

1:22:33

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1:22:38

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1:23:24

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