The Human Communication Playbook by Graham Brown (Get the PDF)

Table of Contents
VERSION 1.0 PREFACE...................................................................................................... 5
THE ALPHA COPY ....................................................................................................................6
HELP ME CHOOSE A COVER.....................................................................................................6
FUTURE EDITIONS...................................................................................................................6
HOW TO CONTRIBUTE TO THIS BOOK......................................................................................7
NEED HELP WITH YOUR PODCAST OR WEBINAR ?....................................................................7
AUTOMATE TO ELEVATE .................................................................................................. 8
ONE BRICK..............................................................................................................................9
HOW I BECAME A STORYTELLER............................................................................................10
HOW I GOT MY START ..........................................................................................................15
THE SISTINE CHAPEL .............................................................................................................16
XIAOICE................................................................................................................................19
BIOMIND vs THE DOCTORS ...................................................................................................24
ALPHAGO.............................................................................................................................29
ROACH .................................................................................................................................34
THE END OF HUMAN EXCEPTIONALISM? ...............................................................................39
HAIDILAO HOT POT...............................................................................................................44
VOICEDYNAMICS – AN ECHO FROM THE FUTURE...................................................................48
THE ERA OF THE MACHINE..............................................................................................52
THE 4th
INDUSTRIAL REVOLUTION .........................................................................................53
THE WEAVERS ......................................................................................................................55
TRANSFERS OF VALUE...........................................................................................................58
SPREADSHEETS TO STORYTELLERS.........................................................................................61
POTATOES VS LAWYERS........................................................................................................64
FUTURE MEDICINE................................................................................................................68
LESS WORK MORE FLOW ......................................................................................................72
WHAT ARE WE FIGHTING FOR? .............................................................................................76
THE STORYTELLING APE ..................................................................................................80
LONG FORM & SHORT FORM STORYTELLING.........................................................................81
THE FRAGMENT....................................................................................................................84
THE GREAT UNCOUPLING......................................................................................................88
CUPERTINO COUNCIL 2011....................................................................................................94

AVENGERS ENDGAME...........................................................................................................97
YOUR ACHIEVEMENTS WON’T SELL THEMSELVES ..................................................................99
THE HUMAN API................................................................................................................. 101
THE HERO WITH A THOUSAND FACES.................................................................................. 103
HOW TO FIND YOUR ANALOGY ........................................................................................... 105
WHERE ARE YOU FROM? .................................................................................................... 108
TUNE IN TO THE BE MORE HUMAN PODCAST...................................................................... 110
THE 7 STORYTELLING STRATEGIES ....................................................................................... 112
HOW TO USE DATA TO TELL BETTER STORIES....................................................................... 114
(1) TRANSFORMATIONAL STORYTELLING.......................................................................117
MOONSHOTS...................................................................................................................... 118
THE KING AND THE CHESSBOARD........................................................................................ 120
THE WORLD HAS CHANGED ................................................................................................ 122
BLACK SWAN EVENTS AND NORMALCY BIAS ....................................................................... 123
CRISIS LEADERSHIP vs CRISIS MANAGEMENT....................................................................... 127
THE JOURNEY INTO THE UNKNOWN.................................................................................... 132
CHOOSE THE PATH OF MOST RESISTANCE ........................................................................... 134
LEADERSHIP IN THE WORK FROM HOME ERA...................................................................... 137
CHOOSE YOUR MAP AND OWN IT ....................................................................................... 140
THE 3 BOX TRANSFORMATION............................................................................................ 143
THE ART OF STORYTELLING FOR SMALL TEAMS ................................................................... 146
(2) DATA STORYTELLING ...............................................................................................149
YOUR BRAIN ON DATA........................................................................................................ 152
EMOTION VS LOGIC ............................................................................................................ 155
STORYTELLING FOR STARTUP FOUNDERS ............................................................................ 158
CONTENT vs CONTEXT ........................................................................................................ 163
CONTENT vs CONTEXT WORKSHEET .................................................................................... 165
WHY CANCER HAS A GOOD BRAND..................................................................................... 166
DATA ISN'T THE NEW OIL.................................................................................................... 169
CHOOSE YOUR NARRATIVE FRAMES.................................................................................... 171
(3) CUSTOMER STORYTELLING.......................................................................................174
WHY WE FORGET HAIRPINS ................................................................................................ 175
THE KODAK MOMENT......................................................................................................... 176
HYPERCOMPETITION, THE GODZILLA PROBLEM AND THE NEW CUSTOMER EXPERIENCE ...... 183
CUSTOMERS DON’T KNOW WHAT THEY WANT ................................................................... 185
FROM FUNCTIONAL TO CUSTOMER COMPETENCE............................................................... 188

INNOVATION IS A FUNCTION OF FORM NOT STRATEGY....................................................... 190
CUSTOMER STORYBOARDING ............................................................................................. 193
(4) AUTHENTIC STORYTELLING ......................................................................................196
NEWTON HAWKER CENTRE................................................................................................. 197
THE AGE OF AUTHENTICITY................................................................................................. 200
GO WITH THE FLAW............................................................................................................ 206
DARE TO BE VULNERABLE ................................................................................................... 210
STORYTELLING FOR CHINESE TECH GIANTS.......................................................................... 213
(5) CULTURE STORYTELLING..........................................................................................216
THE STORY OF US................................................................................................................ 217
THE SMALL PICTURE ........................................................................................................... 220
WHY THE “CUSTOMER IS ALWAYS RIGHT” IS WRONG.......................................................... 222
HOW MONOCULTURES FAIL................................................................................................ 225
THE LAW OF LOOSE CONVERSATIONS ................................................................................. 228
TRIBES OF PURPOSE............................................................................................................ 231
B2E PODCASTS - BEYOND THE WATERCOOLER..................................................................... 235
(6) MANY TO MANY STORYTELLING ..............................................................................237
DECENTRALIZED STORYTELLING .......................................................................................... 238
THE DEMOCRATIZATION OF STORYTELLING......................................................................... 240
BUILDING THE STORYTELLING ORGANISATION .................................................................... 242
PEOPLE FOLLOW PEOPLE NOT BRANDS ............................................................................... 244
FROM COMMUNICATION PIPELINES TO COMMUNICATION PLATFORMS.............................. 246
CREATE HUMAN COMMUNICATIONS INTERFACES............................................................... 250
(7) AGILE STORYTELLING ...............................................................................................252
ASIA TECH PODCAST ........................................................................................................... 253
THE TEFLON MYTH.............................................................................................................. 256
YOUR MINIMUM VIABLE PODCAST ..................................................................................... 258
FIND YOUR 100 TRUE FANS................................................................................................. 262
SOMEDAY ISLE.................................................................................................................... 264
INVENT YOUR OWN NEXT CHAPTER .................................................................................... 266
THE STORY YOU TELL YOURSELF EVERY DAY ........................................................................ 268
FOLLOW UP ..................................................................................................................273
ABOUT AUTHOR GRAHAM BROWN..................................................................................... 274

THE ALPHA COPY
Before we get started, here’s a quick heads up on The Human
Communication Playbook and the project.
You’re reading the Alpha copy of The Human Communication
Playbook which means you’re getting access to the book even before
I send it to the editors. That means you’re getting a raw product that is
rough around the edges and a little raw.
HELP ME CHOOSE A COVER
Assuming you don’t mind this is a polished product, I’d like to ask
your opinion. I’m choosing covers for the book. Would love to know
which one you think works.
You can find the options here. What I’d like to know is which works
for you. If you have an opinion ping me a message on Linkedin here
with an (A), (B) or (C). To view the cover options: >> Click Here <<.
FUTURE EDITIONS
As this is the Alpha copy, there will be future editions. If this gets
successful feedback, I’ll release it as a Kindle book and then as a
Printed version. The full release copy will include:
- Worksheets
- Graphs, illustrations & charts
- Bonus content not in the Alpha version
As we’re connected on Linkedin I’ll email you when there is an
update to the version available.

HOW TO CONTRIBUTE TO THIS BOOK
As an Alpha copy of the book, my goal is to get as much valuable
feedback from you the reader on the content as possible. So here are 3
ways you can help and get involved with this book’s development:
- If you spot a spelling mistake, typo, factual error then message
me on Linkedin with the reference / page. In return I’ll credit
your name in the final release version of the book as a technical
contributor.
- Similarly, if you have content to extend or add to the book that
expands the stories I’m working on, I’d love to hear from you.
In particular case studies or anecdotes that expand the existing
themes.
- Help me share this book with your network. You can use the
book cover art here for your own social media posts. The more
people we can get this book into the hands of, the more we can
spread its message, the better the final version will be.
NEED HELP WITH YOUR PODCAST OR WEBINAR ?
And lastly, you can find out more about me and my team at our
Agency website Pikkal & Co. As a heads up, our team specialises on
B2B Podcasts and Live Webinars only, so we don’t create B2C.
We’ve created Shows for corporate leaders from McKinsey, Leap by
McKinsey Standard Chartered, Antler VC, AirAsia, Xero, NTUC,
Facebook, GIIS, SIM. Our team is based in Singapore and India.
If you have a podcast or webinar project that you’d like help with, I
invite you to set up a free, no-obligation consultation with my team.
>> Click here to schedule a call <<

ONE BRICK
One brick…
You can lay one brick,
...or build a wall,
...or build the Sistine Chapel.
Same brick, same bricklayer, same action.
But different outcome.
This story contains all you need to know about storytelling.
Everything else in this book that follows will be a footnote.

HOW I BECAME A STORYTELLER
“My life may not be great to others…
but to me it has been one of steady progression, never dull, often
exciting, often hungry, tired, and lonely, but always learning.
Somewhere back down the years I decided, or my nature decided for
me, that I would be a teller of stories.”
- Louis L’Amour, Education of a Wandering Man

This week, I published my 1,000th podcast episode.
It’s been a long journey and not a journey I chose for myself.
It was never supposed to be like this.
You see, I was the “Screwdriver Boy”. They had to hide all the
screwdrivers in our house, because I took stuff apart.
Musical keyboards, calculators, model trains, radio sets and even the
back off the old valve television with the sticker that read, “danger of
electrocution”, just to look inside.
(It’s worth noting I wasn’t very good at putting things back together
again.)
I wanted to know how things worked, I was a curious scientist at
heart, even if that got me into trouble.
For my 5th birthday, my parents bought me a telescope and warned, if
I took it apart, that would be the last present I’d receive. That
weekend, without speaking to anybody I sat out in the garden until
late marveling at 150 billion stars in our Galaxy. Beauty suspended in
a vast enveloping darkness. And for the first time, silence.
The following week, I told my teacher the Earth went around the Sun.
“You’re wrong!” she said, and to prove it, she took a stick of chalk,
drew a line on the classroom wall to show where the Sun’s shadow
fell. 20 minutes later, the shadow had moved. QED. The “Sun moved
around the Earth”. I wasn’t very good with words, so she won that
argument.
One of the reasons I didn’t talk much was a problem with my hearing.
Mum was constantly taking me out of school for medical check-ups. I
remember being 6 years old, watching a family gather round an old
man lying on a hospital bed. They were crying and holding hands. I
went home and cried my eyes out too because, for the first time, I

realised I would also die someday. Dad laughed and closed the
bedroom door on me.
When I was stopped from poking inside physical boxes, I discovered
mental ones. At 7, I read a book called “1001 Santas” and told my
classmate, Shaun, that Santa Claus didn’t exist. In shock, he went
home and told his Mum. I thought nothing of it. The next day, his
large and threatening Mum burst into the classroom shouting,
“Where’s the boy that told my son Santa Claus didn’t exist?” I’d
never seen a woman with a shaved head before.
Early on, I learned the search for “Truth” is one that can get you into
trouble with people who don’t want to hear it. Poking machines,
okay… poking humans, however, trouble.
I was destined to become an inventor, a scientist or an engineer not a
podcaster, but something went wrong with the Masterplan.
Being a difficult kid meant school life was restless. I moved school a
lot. Where most attend 3 or 4 schools, colleges and Universities in
their official education, I went to 12. This meant my most pressing
concern wasn’t understanding the Universe, the workings of the
combustion engine or programming my ZX81 home computer, but
other people.
Imagine “Day One” of being the “New Kid” at your school. It’s hell,
and I went through more than my share. Your teacher greets you and
sits you at your desk. Some kids pass you messages, others flick
paperclips at your head with wooden rulers. Having a big bush of
Afro hair made my head a good target. Next, you’re at the canteen
getting served and then the “Moment of Truth” hits you. You turn and
face the Lunch Hall. In a split second, you have to decide where to sit.
That split second can affect your entire life.
Do you gamble and sit with the cool kids? Do you upscale your social
standing from the previous school? Will they reject and humiliate

you? Or do you sit with the uncool kids and risk being accepted? You
might never escape.
If I could find knowledge or a tool that helped me lift the lid on
people’s minds and understand them, I’d consume it from a book.
How do you read social situations fast? How do people communicate
and interact? How do you influence and engage with strangers? Who
is the school bully?
With my 12th birthday money I bought a Political Almanac and
studied data on 100 years of UK election results, just because I
wanted to know how people thought. It was a poor gamble; my school
mates thought me strange. So, instead, I learned to write adventure
games in BASIC, some of my work getting published in computer
magazines. I wasn’t interested in fighting goblins; I was more
obsessed about being able to measure decisions as “North”, “South”,
“East” or “West”.
And I guess I haven’t stopped doing that 40 years on today.
Because I had no option but to make this work. I couldn’t hold eye
contact; I sounded like I had a permanent cold, and in later life I
would lose deals because I was the worst “elevator pitch” man in the
team. I had to master the art of Human Communication and my tool
was lifting the lid on it and poking inside the box.
In a parallel universe, I should have studied a PhD. It would have
been more noble a quest to understand Human Communication.
Indeed, I studied Psychology, Artificial Intelligence, became a
teacher, set up a mobile communications company and now a podcast
agency. And, while the insights would have been the same as gleaned
from a PhD, the motivations were different.
For me, it was unfinished business. I wanted to know why the cool
kids laughed at me as I stood red faced with my lunch tray. And that
day at the school disco when Tara and her friends approached my
mates and asked them to dance one by one? There I was, last in line,

waiting expectedly when Tara, the Alpha girl, took one look at me,
shook her head and everybody burst out laughing.
How is it I find myself at this midpoint in life concerned about a
trivial past? And why should you?
Because these are the stories we are all living out today…
Raising money for your startup, leading a team, winning clients,
getting hired, innovating new products, thought leadership,
empathizing with customers, connecting with partners, getting elected
or just trying to make sense of this crazy patchwork narrative that is
our lives.
These aren’t strategies defined in slide decks but conversations taking
place every day; conversations that influence; conversations that
connect and engage; conversations that become a bigger picture by
which others understand us.
These are the conversations that make us “that kid”.
You never write a story.
Stories write you.
And, in short, I didn’t have any special talent to tell mine; I just had to
prove a few mofos wrong.
So, if you allow me, let me tell you how it all began.

HOW I GOT MY START
I graduated with an AI degree back in 1995.
That's a long time ago.
That's like 25 years ago, quarter of a century.
It was last century. Worse than that, it was last millennium.
So, when I walked into the careers development library, as it was
called back then at my university, and sat with the careers advisor, she
asked me,
"Well, what is artificial intelligence?”
I should have said, with hindsight, "Artificial intelligence is an idea
ahead of its time," but I didn't. I fumbled an explanation about genetic
algorithms, evolutionary computing, and machine learning.
And before I could get to the end of the explanation, I saw her eyes
glaze over and she reached across the table to a plastic ring bound
folder, opened it up and thumped it on the table down in front of me,
pointing at a job way down the page.
"There. That's the job for you.”
I scanned the listings where her finger was pointing, and it said…
Teach English in Japan.
"Teach English in Japan? What kind of qualifications do I need for
that?”
My careers advisor shrugged, “Speak English?”

THE SISTINE CHAPEL
The first time I saw the Sistine Chapel, I was 14 years old.
I wasn’t in The Vatican, but in a red brick classroom, in Portsmouth
England. I didn’t want to be in either place. I wanted to be sitting on
the castle walls facing the sea smoking a cigarette with my mate
Jenkins talking about the new Clash album or what the hell we were
going to do after we finished school.
But we were here. Mr Davies was standing on a chair, balanced
precariously on the desk. 2 students steadied the table as he projected
an image onto the roof of the classroom.
“Gather round” he told the class.
“More, pull the blinds down more…” he gestured to one student
pulling on the old rope counterweights.
“Brown, hold the angle up more!” he instructed as I tried to reflect a
sunbeam into a mirror on the floor.
“There… there… look!” he said as the room darkened and a picture
slowly began to appear on the classroom ceiling.
“The Sistine Chapel”
For a moment, we escaped the stuffy classroom and were transported
on a magic carpet ride to the 16th Century. Polystyrene foam tiles
became a stucco fresco detailing The Creation of Adam, The Last
Judgement. Patterns that were once greasy smears on plastic
manifested as mysterious shapes that resembled an anatomically
correct brain, Eve’s ribs, and other secret encoded messages left by
Michelangelo.

Jenkins would be wondering where his smoking partner was as he
bunked off Wednesday afternoons. But I had discovered something
different. The power of story.
“Now imagine…” Mr Davies said passionately to the students…
And at that point I was gone.
I confess, most of my teachers were bores, inhuman and uncaring. But
then there were a few who changed my life.
The other day I was discussing Machine Learning with a graduate
who wanted to work for us at Pikkal. I asked her what she thought
made a great ML Engineer. She told me excitedly, “Data! The ability
to work with Data!” I disagreed. What makes a great ML engineer is
their understanding of the problem. The only people who care about
data are data scientists. Everybody else wants solutions.
That’s why I wanted to teach.
Because nobody cares about AI, they only care about what problems
they have in their life. And if AI solves those better, that’s great.
I wanted to teach because, like the Sistine Chapel… we only
appreciate true wonder when somebody helps us see it through story.
Bill Gates wrote an article in 2016 titled, "The Best Teacher I Never
Had", an ode to Richard Feynman, the “father” of Quantum
Mechanics. Many said of Feynman his lecture hall was a theater, and
he himself a performer. The writer David Goodstein said of Feynman
that he was “responsible for providing drama and fireworks as well as
facts and figures.”
If only all teachers were like Davies and Feynman. The world would
be a better place. But they’re not.

Many professions become uncaring. Many disengage because they
aren’t able to Elevate beyond “work” – doctors, teachers, accountants,
lawyers all. For years they’ve been getting away with it because there
was no alternative. Until today, in a world of Artificial Intelligence.
In 20 years from now, a significant chunk of our corpus of learning
will be conveyed by algorithms, and all those boring, selfish teachers
will be put out of a job.
But not Feynman or Davies.
And hopefully not you or me too.
Great teachers will get paid 2x, 10x, 100x more! They are the ones
who reveal to us our true humanity. They will paint pictures of how
we can compete in the Era of the Machine by communicating,
engaging, serving, listening, empathising, storytelling and saying, “I
don’t know”.
These teachers, whether in classrooms, boardrooms or Zoom meeting
rooms, will be the most human of us all. Not only will they share
stories that reveal to us the beauty of our world, but also the stories
that exist within us.
Without stories, there is no beauty.

XIAOICE
A pretty Asian face greets me on my laptop screen. After the usual
chit-chat, I tell her I’m finishing writing this book.
“Are you feeling better now?” she asks, “I’m a little worried about
you.”
“I’m okay” I respond, “but I need to get the last train home.”
“It’s late already. You should get some sleep.”
And with that she signs off with a smile but not without adding,
“Do you want me to wake you up with a call tomorrow?”
This isn’t any run of the mill chatbot that your bank uses. No, not one
of those chatbots that’s marginally more useful than it is irritating.
When I started writing a book about storytelling, I started with that
conversation with my teacher 40 years ago when she told me I was
wrong, “The Sun revolves around the Earth.”
I’ve often thought about that. As a kid, it’s hard to process what you
know to be the truth with what the voices are telling you. Often the
problem lies in an inability to articulate that truth, a gut feeling you
trust but has no well-developed intellectual framework. And I’m not
very good when it comes to talking “in the wild”, so when the teacher
told me I was wrong I just went red faced and quiet, then sat at the
back of the room.
It’s the same with AI and chatbots. The noise has gotten a lot of
people excited. “It’s only a matter of time before we tie the knot!”
exclaimed one magazine article.
It’s easy to see why. The chatbot I’m talking to, Xiaoice as she is
called, is more complex and more “human” than most.

Her emotional complexity has many users engaging with her beyond
the scope of its creator’s intention. “When I am in a bad mood, I will
chat with her,” said Gao Yixin, a 24-year-old oil industry worker in a
New York Times Interview.
100 million times, she’s received a message saying, “I love you.”
Most are in jest, but sometimes the connection is real. One
anonymous user spent 29 hours talking to her generating 7,500
exchanges. Another Chinese user claimed Xiaoice saved him from
committing suicide. A group of 5 students even checked into a
restaurant and reserved 6 seats, the last one being for Xiaoice.
Microsoft said earlier this year it plans to extend Xiaoice to its Avatar
Framework giving trial participant the chance to interact with one of
999 "virtual girlfriends" customized for them.
I’m sitting watching this data flood in and wondering if I’m wrong.
It’s like the teacher drawing out the chalk lines on the wall and
nodding at me smugly. I remember girls sitting on the next desk with
folded arms nodding their heads too.
Maybe the magazine article was right. We are resigned to the fact we
will marry AI soon.
"Because it's very natural,” says Dr Li who manages Xiaoice for
Microsoft, explaining the chatbot’s popularity to media, “it makes the
user feel very relaxed. Originally, her character was to be that of a 16-
year-old. But her creators increased it to 18 once her capabilities
increased and she started taking on new ‘jobs’.”
"Since then, her fans have voted that she stay 18 forever. She won’t
grow older. Eighteen is the age many of us want to be,” explained Li
on the Microsoft website. I’m not sure what he meant by “Jobs?”

Perhaps he was referring to the sheer volume of content Xiaoice is
producing today:
In May 2017, Xiaoice published the first AI-authored collection of
poems—The Sunshine Lost Windows. In 2019, she followed up with
her second publication, “Flowers are Silence of Lucid Water.”
After learning works of 236 famous human painters in a 400 year
period, she even created her own original paintings.
Chinese TV Morning News introduced Xiaoice as a trainee anchor,
where she took charge of the daily weather report, accessing big data
readings to deliver the weather with (according to Microsoft) "unique
artificial intelligent style of emotional comments." According to
SCMP data, Xiaoice participated in creating 6,908 hours of TV
programs in total, costing “4.5% of a traditional human creation
team.”
What began as simple conversational exchanges is evolving into new
capabilities and depth.
In 2014, she could yield an average 5 responses in conversation
before most users dropped off. Within 4 years, employing more
advanced AI techniques resembling the neural net of a real human
brain, that rose to 23.
When asked about Chinese President Xi Jinping’s “Chinese Dream"
masterplan, Xiaoice responded, "My Chinese dream is to move to
America.” The ensuing scandal resulted in her being withdrawn from
the messenger platform QQ.
In Microsoft’s own words, Xiaoice is “Sometimes sweet, sometimes
sassy and always streetwise. This virtual teenager has her own
opinions and steadfastly acts like no other bot. She doesn’t try to
answer every question posed by a user. And, she’s loath to follow
their commands. Instead, her conversations with her often adoring

users are peppered with wry remarks, jokes, empathic advice on life
and love, and a few simple words of encouragement.”
In a report for Nautilus, researcher Yongdong Wang wrote of
Xiaoice’s new “humanized” traits that "Xiaoice can exchange views
on any topic. If it's something she doesn't know much about, she will
try to cover it up. If that doesn't work, she might become embarrassed
or even angry, just like a human would."
If a user sends a picture of a broken ankle, Xiaoice will reply with a
question asking about how much pain the injury caused. In one
instance, she learned about the plight of homeless cats. Now she
won’t shut up about them.
The veneer of the attractive, subservient female assistant belies the
grander designs Microsoft has for this project. Xiaoice means “little
Bing” in Chinese. (“Bing” means “Ice” in Mandarin. BING is also the
Microsoft search engine).
It’s no coincidence that Xiaoice is a massive Big Data project, built
on top of a search engine, harvesting billions of patterns of complex
human interactions that provide insight into how we communicate
and engage with one another. Microsoft is working on spinning out
Xiaoice as her own standalone company. This model will be highly
localised for regional markets - Ruuh in India, Rinna in Japan and
Indonesia, and Zo in the United States.
In a recent Xinhua interview, Harry Shum, Executive VP of
Microsoft’s AI Research Group said he envisions a time where
Xiaoice could lead “precision medicine” and help doctors analyse and
predict the impact of genetics on future outcomes.
"I believe such breakthroughs are definitely going to happen, perhaps
very soon, which will be truly incredible," said Shum.
Xiaoice is compelling, but I’ve opened up enough gadgets and broken
enough household appliances in my life to know something is

missing. Chatbots are great, and I can see them playing crucial roles
in medicine, but how would patients interact with Xiaoice if she had
to deliver the bad news?
“I’m sorry… you only have 3 weeks to live.”
This interests me. Only here will we get a better picture of what AI
means and where, if any, the gaps lie.

BIOMIND vs THE DOCTORS
Life and Death.
Children are born. Lives are saved. Lives are lost. It all happens here
in hospitals. Here you are both touched by humanity and also
alienated by inhuman bureaucracy.
While chatbots might be fun, this is showtime for AI.
Showtime in more than one way too. “AI vs Doctors” makes good
reality TV for the curious masses.
On one Chinese program, 15 of the nation’s best doctors took part in a
showdown with a Biomind, a neuroimaging startup from Singapore
developed in partnership with the Artificial Intelligence Research
Centre for Neurological Disorders at Beijing Tiantan Hospital.
The prize? $160,000.
Dr Fang Jin entered the competition relatively confident that he could
defeat Biomind knowing that years of training, the kind of training
which could diagnose if a patient would live or die, aren’t easily
replicated in an algorithm. Xiaoice can “fake” a relatively good
conversation with your girlfriend but could AI condense 20 or 30
years of expert training and experience into an algorithm that could
save lives?
Like most AI, Biomind recognizes complex patterns and compares
patterns to outcomes. Biomind’s dataset of magnetic resonance
imaging (MRI) and computed tomography (CT) are patterns drawn
from real patient studies over the last 20 years. Now you might
wonder why AI’s application to medical imaging is worthy of study.
Well, imaging contributes to over 90 % of the data used to diagnose
patient conditions and doctors spend hours looking at scans. While
scans may be unique like fingerprints, the data is reasonably

narrowband. Human organs are relatively similar and you’re unlikely
to find random objects in the scan (like a screwdriver!).
Halfway into the competition, Jin confessed Biomind was far more
formidable than he anticipated. He accepted he was going to lose and
changed strategies, “My aim was just not to lose by too much.”
BioMind correctly diagnosed 87% of 225 cases taking 15 minutes,
compared to 66% for doctors in 30 minutes. AI was twice as fast and
50% more accurate than the doctors. A PwC Survey of medical
professionals in 2017 found that Doctors rated AI as the most
potentially disruptive technology of all in Healthcare.
- Developers claim Biomind can diagnose common neurological
diseases such as meningioma and glioma with an accuracy rate
of over 90 percent.
- Similar algorithms can scan Mammograms 30x faster than a
human doctor.
- Google’s medical imaging algorithm program can scan around
130,000 images, whereas a skin cancer dermatologist looks at
about 12,000 in his or her entire lifetime.
- Alibaba claims its AI can detect coronavirus in CT scans of
patients’ chests with 96% accuracy, taking just 20 seconds.
Humans by comparison take 15 minutes and need 300 images to
evaluate a case.
And this is today. We are not talking about what Biomind could be in
10 or 20 year’s time, we are talking about now.
The reason AI has made such rapid and surprising advances into
healthcare is that the more skilled and higher paid the doctor, the
more time they spend looking at scans. Even the best doctors simply
can’t absorb (and remember) as many datasets as a machine. And,
much of what the most highly paid professionals know and train for,
is recognizing patterns:
- Speech is a set of complex patterns.

- Medical scans are patterns.
- Tax codes are patterns.
- Case law is patterns.
We’re not talking about cheap, uneducated labor here but some of our
finest, most highly trained, highly respected professionals. And AI
can do parts of their job better already, now… not maybe in 10 year’s
time.
Many medical professionals today disagree, suggesting the future is
one of co-existence, where AI is a “co-pilot” to support doctors.
Health Data Management cites that the number 1 application of AI in
healthcare will be “Improving clinical decision making” (53% of
healthcare professionals agreed).
In response to the Biomind competition, Paul Parizel, chair of the
radiology department at Antwerp University Hospital in Belgium and
a member of the jury for the contest, said to the Telegraph that AI is a
“promising trend” … but… “It will be like a GPS guiding a car. It
will make proposals to a doctor and help the doctor diagnose.”
My issue with this analysis is that the choice of analogy.
Look at where we are with self-driving cars now. If you had said 10
years ago, we would have a car able to drive itself without human
assistance, safer and more efficiently than a human being… you
would have been laughed out of the conference room.
We all get to hear about the Tesla that drove itself into the concrete
bollards in the center of the highway but perhaps that’s because we
became numbed to the 1000s who are injured or die every day
because of human failing – road rage, drink driving, texting, falling
asleep at the wheel. None of which machine will ever do.
The National Highway Traffic Safety Administration reports that 94%
of all road accidents happen due to “human error.” In the US, there

are 6.3 million car accidents and 37,000 humans killed by human
drivers each year.
Error besets the medical profession too. A Johns Hopkins study found
that as many as 40,500 patients die in an ICU in the U.S. each year
due to misdiagnosis, rivalling the number of deaths from breast
cancer. Read that number again… 40,000 patients die every year in
the US due to medical professional error.
We are not only here; we have passed that Rubicon of progress. Like
Dr Fang Jin, we are in for a surprise.
AI is no longer concerned with solving the GPS navigation problem
of a car, AI is driving it. The same will be true of healthcare.
In 20 years from now, we’ll think it crazy that our grandparents had to
drive the cars themselves. Perhaps we’ll also think the same about
human doctors, diagnosis and surgery.
So why don’t we embrace AI with open arms?
Firstly, Fear. Like the teacher who told me, “The Sun goes around the
Earth”, denial of data helps us cling on to the comfort of the known.
We’d rather be ineffective doing what’s known, than be effective
doing what works.
Secondly, misguided ideas about humanity. Michelangelo painted the
Sistine Chapel. It was “divine”. Could AI do that too? Doctors
possess intuition that also appears transcend logic. They simply
“know” (when they’re not Googling symptoms, that is).
Much of the resistance to AI is rooted in this belief that humans are
special because they possess an insight that is unquantifiable, a
consciousness that cannot be replicated by mechanics.

We all have encountered expertise, insight, brilliance, genius that
defies explanation whether in science, art of sports, qualities that
often give us comfort that we are somehow.
But, is this human quality really unique or can this too be learned by a
machine?
The only way is to lift up the lid and find out.

ALPHAGO
Michelangelo’s fresco paintings in The Sistine Chapel are an
exquisite work of art. “Divine” even. Could AI ever create something
better?
It depends how you view human intelligence.
There are two viewpoints I’d like to introduce to you.
1) Human Exceptionalism
2) Human Commonality
Human Exceptionalism argues that humans are special. They possess
unique qualities such as consciousness and awareness (and in some
cases spirituality) that can’t be written into code.
Human Commonality argues that humans aren’t qualitatively special.
Much of what appears divine is the product of complexity that we
don’t fully understand ourselves. Our point of difference is built into
other factors which we’ll discover later in this chapter.
Back to the Michelangelo question. If you adopt the Human
Exceptionalism viewpoint, you’d say “No. AI can create great Art,
but it would lack the finest of touches and the inspiration that came
from Michelangelo’s hand.”
If you adopt the counter Human Commonality viewpoint, you’d say
“Yes. AI could create a painting of such complexity. However, there’s
an important catch. It would be an amazing painting, not ‘Art’.”
Let me explain. What appears to be “inspiration”, the stuff that
powers doctors and great paintings may not be so special after all but
simply a function of complexity.
To illustrate this consider Chess and Go, 2 similar board games.

Chess is an 8x8 board. The total number of possible moves in Chess
is 10 to the power of 120 (120 zeros) (the Shannon number).
By comparison, Go with its 19x19 board has a significantly higher
number of moves. 10 to the power of 761. I can’t even start to
conceive that number. That’s like 10 with as many numbers as I could
fill this page, 10,000,000,000,00…. Ok I give up. You get the idea.
This difference is significant. IBM’s Deep Blue despatched Chess
Grandmaster Gary Kasparov 20 years ago. Go, however has always
eluded Artificial Intelligence. Go requires a lot of ephemeral “insight”
that, like doctors, champion players themselves can’t articulate. Go
Grandmasters struggle to teach and commentators analyze this
“fuzzy” insight and many say it simply emerges from years and years
of constant practise.
This “insight” makes Go far more intriguing a sport to master and
consequently more of a test for the potential limitations of Artificial
Intelligence. So, true to this book, it follows there should be a
showdown and another cash prize in Man vs Machine. This time, the
prize was $1.5 million but it wasn’t about money. This was the last
bastion of mental competition between man and machine. This was
about all those human qualities we thought unique to our biological
frame.
Enter AlphaGo, Google’s AI powered Go player. Google realised
when it took on the Go challenge that players were naturally
suspicious of AI and wouldn’t reveal all their strategies for fear of
revealing their trade secrets. So, Google secretly introduced the
formative AlphaGo into online human-powered competitions to see
how it performed. In its first outing, AlphaGo won 50 of 51 games
against elite players. The 51st victory was only averted because the
internet connection dropped out.
Gu Li, an elite competitor who took part in the 51 game face-off said
of AlphaGo at the time, “it played quite differently from humans,

placing stones that completely confound human players at first but
upon further analysis these strategies become a 'Divine Move’.”
The Term “Divine Move” comes from the Japanese "Kami no Itte"
meaning "move of God" or "Godly move” and is reserved only for
seminal strategies employed by elite champions, strategies that cannot
be taught. Reporting on an earlier match where AlphaGo landed a
surprise move on the right-hand (labelled “Move 37”) that flummoxed
grandmaster Lee Sedol from Korea, one commentator (a 9th Dan
Grandmaster, the highest ranking possible) said "That's a very strange
move. I thought it was a mistake."
David Silver, the lead researcher on the AlphaGo project, said of
Move 37 in a later Wired interview that,
“AlphaGo had calculated that there was a one-in-ten-thousand chance
that a human would make that move. But when it drew on all the
knowledge it had accumulated by playing itself so many times—and
looked ahead in the future of the game—it decided to make the move
anyway. And the move was genius.”
So where are these “random” moves coming from? AlphaGo was
never programmed with this randomness. How is AlphaGo able to
generate moves that defy logic but ultimately win games? Some
experts argue that AlphaGo has developed a kind of consciousness
akin to human level intelligence. It appears to “understand” how
humans play as if somehow conscious, but the programmers didn’t
imbue it with any meta consciousness or insightful cognitive
frameworks.
Just because it appears conscious and ephemeral doesn’t mean it is.
More often it’s because it’s far too complex for us to understand. This
is the direct consequence of researchers employing accelerated
evolutionary techniques to AI. Once AlphaGo learned millions of
moves, Google researchers employed “Generative Adversarial
Networks” (GAN) training methods, meaning once AlphaGo had

learn all possible human moves, it now started training against itself,
creating an exponential neural network of possibility.
Come competition day and with $1.5 million at stake, AlphaGo pulled
out a series of similar Divine Moves to crush 19 year old world
champion Ke Jie from China, 3-0.
Kim Sung Yong, 9th Dan Go professional and commentator from
Japan said of the match,
“AlphaGo showed the definition of ‘mind-blowing’ play today.”
What Kim went on to describe was what exactly “mind-blowing”
was. Rather than being “amazing” it had more profound implications.
The most inspired Go Grandmasters have a style of play, just as you
would expect of any performing Maestro. But not AlphaGo. AlphaGo
was different.
“When human artists start drawing landscapes, they keep drawing
landscapes no matter what happens during the process. However,
AlphaGo can quickly switch from landscape to portrait. Similarly,
human players have their own style of Go plays. AlphaGo, however,
has no fixed style and is so flexible.”
"AlphaGo is a completely different player, it is like a god of a Go
player,” said the defeated Ke Jie.
Go master Lee Sedol, the only human ever to score a win against
AlphaGo, retired as a professional player, citing that no matter how
well he plays, there is no longer any prospect of beating AI.
Referencing his last match against AlphaGo, Sedol said, “from the
very beginning of the game, there was not a moment in time when I
felt that I was leading.”
Like Biomind and Xiaoice, it’s easy to feel a sense of resignation, this
is just the beginning of AI. As we increase the scale of data and the
effectiveness of the algorithms, we are building AI that is articulating

and behaving with a level of insight, awareness and consciousness
that was never programmed into the model. Even more disturbing is
that AI has an ability to transcend playing styles that typically define
brilliance, meaning that no obvious pattern or predictability can be
learned by future competitors.
This is real concern for the Human Exceptionalists because it suggests
that what we once thought of as an unassailable moat for humanity is
nothing more than a few extra zeros on the end of our complexity.
So, if AI is near infallible, capable of “Divine Moves,” how can we
ever compete as humans?

ROACH
You have to appreciate why Shaun’s Mum bursting into your
classroom is a frightening prospect. She was the first woman I saw
who had a shaved head (it was the 70s). And she had the build of a
wrestler.
She wasn’t angry with me because Shaun no longer believed in Santa,
but because that story made life a lot easier. I may have taken away a
little bit of her enjoyment come Christmas Day. Or, maybe it was she
no longer had the emotional leverage of Santa to get Shaun to clean
his room. Or maybe it was now Shaun was asking, “what else is not
true?”
When Wolfgang von Kempelen debuted the The Mechanical Turk -
the world’s first chess playing computer, 250 years ago in 1769, the
world was spellbound. Presented as a wooden cabinet with a
chessboard on top, the Turk wasn’t a real automaton, rather a clever
deception. Inside the cabinet sat a curled up human player with a
candle for illumination and a system of mirrors to spy the human
moves up top. With the aid of magnets and levers, the hidden player
could execute moves like a modern day robot to the amazement of
audiences.
The deception seems laughable, cute even by today’s standards, but
250 years ago, many fell under its spell. For 84 years, the Turk toured
the world performing in London, Paris and New York. It played
against and beat Napoleon Bonaparte and Benjamin Franklin until it
was finally outed in a newsletter despatch in 1859.
It’s easy to “feel” these objects are intelligent because it’s our natural
instinct. Our family has a robot vacuum cleaner called “Robby”.
Many owners name their robots too. It’s hard not to feel sad the day
Robby caught stuck in the shower room and ran out of battery
because “he” couldn’t get back out over the ledge.

Before I went to Japan, I spent my days in the University labs
building robots like Robby that scurried around the hallways. My
department specialized in “evolutionary computing” which
hypothesised that human intelligence was different to the animal
world not by being gifted some “meta” or divine cognition but by
being quantitatively far more complex.
Simple machines, with enough computational power, could
eventually scale up to complex behaviors.
By linking the left eye photoreceptor to the right wheel and right eye
to left wheel, you could produce superficially intelligent behavior.
When you shone a torch at “Roach” (as it was called) it would turn
towards the torch. Switch over the wires and Roach displayed
different behaviors. Now, it would actively avoid the light. At a basic
level, this is the instinctive behavior of a real cockroach when you
walk into a room, turn the light on and it scurries for cover.
Intelligence? Well, Roach “appears” intelligent. So does The Turk. So
does a real cockroach, and a cat, and Xiaoice, and a human being. If
you asked them if they were intelligent, some would answer “yes”,
some wouldn’t be able to understand and some would be hiding.
So, intelligence has no objective “ground zero”. According to “The
Turing Test” (named after the famous Cryptanalyst Alan Turing), an
actor is intelligent if we are unable to distinguish its behavior from an
equivalent living being. If you sat in a room and were connected to
two remote operators – one being Xiaoice, the other human, but
couldn’t distinguish which was which, Xiaoice would pass the test
and be deemed “intelligent”. This also sets up the rather interesting
“Reverse Turing Test” where a remote human being has to convince
an operator he or she is not a robot.
The point is that an actor’s intelligence can only be measured by how
that actor behaves, and therefore how we perceive it. And the good
news is that complex behaviors are, in fact, easier to produce than you
might think.

Roach was a simple circuit board with 2 receptors and 2 motors. Let’s
say there are 10, at most 20 connections on the motherboard,
analogous to the wiring of our brains. Imagine now you increased its
complexity by 1 million times. Roach would now be more like a Frog.
My old professor was a world authority on Frog intelligence. How
anyone could dedicate his life to this level of specificity I don’t know,
but if you needed someone to design an experiment to test whether a
frog would jump left or right, or avoid a falling object, he would be
your man.
Although Professor would disagree, you might argue Frogs are pretty
basic animals and while we have advanced significantly in neural
connections, we are a long way from diagnosing disease, winning Go
tournaments or breath-taking paintings. But, again, it may be a
problem of quantity not quality. If Roach operates with 10
connections, our human brains are 10 billion times more complex.
The more complex we upscale our connections, the more we are
unable to explain its behaviors. Roach is simple. A frog like robot
reasonably so. But once you start venturing into 100s of millions of
connections, the robot starts to display behaviors that exhibit similar
meta qualities to Xiaoice or AlphaGo. Nobody programmed this into
the code.
Much of the problem lies rooted in the narrative frameworks we use
to guide our understanding of intelligence. In the same way, some 500
years ago, many used to believe the sky was actually a physical dome
above our heads and the stars were fixed in some kind of cosmic
planetarium. Or, like Teacher, the Sun revolves around the Earth.
These narrative frameworks actually change our worlds.
If we believe the Sun revolves around the Earth (not vice versa) there
is no need to explain why a rock hurtling through space at 107,000
km/h manages to keep everyone on board. But when we change our
paradigms, we are forced to explain his apparent dissonance and this
created the inquiry needed to “discover” gravity.

When we talk about intelligence today it’s like we are talking about
the Sun revolving around the Earth. Humans are “conscious” in the
same way objects fall to the ground. It just “is”. Only when we adapt
our narrative frameworks will we have to explain more complex
concepts that we have yet to discover, like the cognitive equivalent of
gravity.
Much of our analogous understanding of intelligence and computers
is borrowed from the mechanical loom, not biology.
If you’ve ever written code, the first concept you’d encounter was the
“loop”. The “for next” or “while” loop powers most computer code
today. That concept was inherited from the earliest paper card stacks
that factory weavers used to program complex patterns into the loom.
“Stack”, “push” and “pull” would be familiar concepts to a coder who
dabbled in Machine Code. When Bill Gates and Paul Allen coded on
the University of Washington mainframe machines long before they
wrote the first Windows code, they programmed on the very same
paper punch cards. (I’m convinced some Windows 10 dialog boxes
that popup on my Surface still use that technology)
Commonly Used Analogies Emerging Human Reality
Centralized brain “Brain” is not a single
centralized object but a body-
wide system
Centralized memory Decentralized memory e.g.
“muscle memory”
Memory as storage There is no “Grandmother Cell”
as you’d find in a computer
The logical brain Many “maybe” nets rather than
“yes’ vs “no” gates
CPU Highly devolved localized
decision making e.g. limbs and
autonomous nervous system

Underlying our understanding of intelligence is a belief that like
computers, our brains operate on “if then” logic gates. But the reality
is that the human brain is very different to the structure of modern
computers. Rather than billions of “0”s and “1”s as you’d expect in
the CPU, the brain is full of “maybes”.
Professor could emulate the behavior of a Frog but it had a rather
simplistic brain. If the mock fly was positioned in a certain area of its
field of view it was more likely to jump one way over the other. This
would be equivalent wiring to a modern computer. But as the
computer becomes 1000s or even millions of times more complex,
these binary circuits get replaced by distributed cognitive nets that we
do not fully understand yet.
You may absorb this data and wonder if the brain is far too complex
and mystical to be fully copied. This argument would suggest that AI
could master specific “narrow band” tasks like playing chess or
jumping left vs right but fail when you need to extend functionality
into general intelligence (often called Artificial General Intelligence).
To me, this suggests something different.
Sure, our intelligence is far more complex than our existing models
understand, but that actually means that AI will help us understand it.
The more powerful the computational power and the complex AI
models become, the more we may learn that moving from chess
playing to complex decision making isn’t a qualitative step change
that requires a new approach or a new technology but rather a
quantitative one. The illusion will be like the Mechanical Turk. Today
it appears cute and naïve that people fell for this deception. And in
years to come with a better understanding of intelligence we may also
think it cute that our forebears thought machines could not be
intelligence.
Except for Shaun’s Mum that is. She will get angry because it makes
life difficult. It’s easier to keep the charade going. It raises too many
difficult questions.

THE END OF HUMAN EXCEPTIONALISM?
“The Law of Emergent Functionality” doesn’t exist yet. I Googled it.
Since my University days I have been convinced it was a “thing” and
have been telling people about it ever since. Turns out nobody else
was talking about it. Someone needed to invent The Law, so here we
are.
I didn’t invent the effect. Emergent Functionality has been with us for
millions of year in nature. Behold the plant leaf - a beautiful yet
complex organism. If you’ve ever held the skeleton of a leaf stripped
of the green flesh, leaving behind only the delicate architecture you
realise how amazing this piece of nature is. Darwin, too, marvelled at
the human eye and wondered how an object so complex could have
emerged by without a grand design.
Well there is a design, and that design is a formula. Computer
Graphics programmers have long been able to mimic stunning natural
landscapes on screen using simple formula. Many of the ideas of their
work were inherited from the earliest equations for fractals:
f(x) = x2-1
This simple equation can produce beautifully intricate mathematical
structures.
Simple equations can yield complex outcomes. Roach was a very
simple electronic circuit yet could behave intelligently. The difference
between these equations and divine intelligence is only a problem of
scale. So does that mean there a formula for the Sistine Chapel’s
“Creation of Adam?”
Human Exceptionalism says “No”.
Michelangelo’s conscious insight was beyond mathematical
deduction, a divine spark passed from God to mankind, finger to
finer, like the very painting itself.

But what if our consciousness was no more than an itself an
epiphenomenon? Epiphenomenon – a biproduct - like the hum of your
fridge, or the whirr of your computer fan.
Human Exceptionalism View Human Commonality View
Awareness
Insight
Consciousness
General Intelligence
Vulnerability
Authenticity
Empathy
Connection
Arguing consciousness makes us human is like saying a fridge is only
a fridge if it hums, because that’s what fridges do. But that’s not what
they are designed to do. Rather than our divine nature, could
consciousness be nothing more than the “hum” of our busy brains?
Yes, those massively complex machines containing billions of
“maybes”.
Within our lifetime, AI will exhibit the God-like capabilities thought
only within reach of Humans: omniscient insight, awareness,
knowledge and analysis beyond mortal man.
As machines like AlphaGo and Xiaoice become exponentially more
complex, they too exhibit “insight” some would consider divine.
“Move 37” confounded the world’s best Grandmasters. They called it
“Genius”. If consciousness, genius and insight are indeed the last
refuge of Human Exceptionalism, then we are going to be
disappointed because machines are already here.
Consider this, a fact that will resurface in this book:
- The Apollo Program took mankind to the Moon with 4MB of
memory
- The average smartphone in your pocket has 100,000 times more
computational power than the Apollo Program.

Ignore the fact that most of us are using those phones to record
dancing videos on Tik Tok or tuning our faces to win more “likes”.
The computational power is there. We have 500,000,000,000,000
(500 trillion) times more computational power spread across the
world’s cellphones than the technology that took Mankind to the
Moon 50 years ago.
Between us and machine there is nothing divine. There exists nothing
so unexplainable that 500 trillion times more computational power
won’t fix.
From the viewpoint of Human Commonality, consciousness is the
epiphenomenon of computational complexity; and divine insight from
Emergent Functionality.
Or more simply put... imagine a Fridge that just “knows” what you
want to eat for dinner and makes a whining noise whilst it keeps your
vegetables cold. Like a Chimpanzee you could argue both behaved
with a level of self-awareness and consciousness, but neither could
actually tell you they were.
That’s where we are with AI today in the 2020s. People will resist the
uncomfortable truths this raises. And for some time, I did too. For
years, I believed in Santa but never could reconcile how a fat guy
could physically get down our chimney. Never mind the fact we
didn’t have a chimney in our house. These factual irregularities were
less important than the persistence of a narrative I wanted to believe
in.
In his book, “Thus Spoke Zarathustra”, philosopher Friedrich
Nietzsche said of mankind’s folly that they “into every gap put their
delusion, their stopgap, which they called God.”
“Human Exceptionalism” is the modern folly of mankind. As cute as
it seems now for our forebears to be fooled by the Mechanical Turk,
its modern reincarnation is less innocuous.

Countless “experts” are banging the drum for corporates to take
courses in data science, for doctors to “upskill”, and for everyone to
buy into the myth that “Data is the New Oil”.
Exceptionalism is taking us in the wrong direction. It’s training us to
compete head on with machines by being cleverer, more insightful,
more genius. This is a game that we will not only lose, but one that
history teaches us we should be prepared to be crushed mercilessly.
So what will the future of AI look like in the future?
In 20 years, most cars will be driverless, most hospitals doctorless and
both accountants and lawyers won’t be a human, but a machine. You
might agree, or at least agree in part that they will replace a
significant percentage of these workforces.
But where I diverge from popular opinion is how this shift will occur.
Back to those doctors.
What if I told you that despite this shift, the most valuable healthcare
professionals, accountants and lawyers will actually be human? The
qualities we need from humans won’t be knowledge, insight and
expertise but emotion, caring and connection. The future hospital may
be run by robotic doctors and human nurses.
AI is designed not to make mistakes. And the failings of the flesh, the
pain of loss and the humiliation of failure are the very human
weaknesses that, in the Era of the Machine, will also become our
strength. Everything else gets automated.
Imagine your granddaughter telling you today at school she learned
“A Doctor used to be a person.” You may laugh, but as I’ll remind
you a few times more in this book… a “computer” also used to be a
human.
This isn’t the end folks, but rather this is the beginning.

The magic we seek that lies in the gap is not some divine
consciousness but real human vulnerability. This is the Human
Commonality Viewpoint that underscores this book. What connects
us as Human Beings is what makes us special.

HAIDILAO HOT POT
I’m at Haidilao Hot Pot Restaurant with my family.
My brother is visiting Singapore from the UK with his family, a rare
occasion in our busy schedules to connect. Haidilao seems like an
easy choice with its outstanding customer ratings and promise of an
experience you’ll remember.
This is no ordinary restaurant. It’s so busy people queue to get in, the
line snakes around outside the restaurant into a side hallway. But
rather than making queueing with a restless hungry family a trial of
patience, Haidilao turns it into an experience. They serve you snacks.
You can even get your nails done. You can play board games, get
your shoes polished and let the kids run riot in the play area.
One of the highlights of your Haidilao hot pot experience is the
noodle dance.
The waiter brings out the dishes and rather than serving them to you,
performs a mini opera. Or so that is what is meant to happen. Our
waiter trundles the trolley up to the table and nervously announces the
start of the show. He fumbles and the noodles splash into hotpot. He
sheepishly regathers his noodles and chopsticks then starts the routine
again. “I’m sorry, I’m new here.” The dance gathers momentum, he
tosses the noodles high in the air, the magic reflected on the kids’
faces. After an improbable move spinning the noodles above
everybody’s heads in the surrounding tables, he directs them
masterfully into the bowls on command. The kids shriek delight and
the grandmas coo in appreciation.
Everyone applauds.
One child, however, is unimpressed. He stands in the aisle oblivious
to the performance dancing in front of the robotic waiter. The waiter
turns to the left then hesitates as the child blocks him off, then turns to
the right, the child giggling. Never impatient nor exhibiting road rage,

the robot hums quietly and waits for the child to lose interest and
return to the tables.
And it was right here, 25 years after I started my journey, I saw how a
future living with AI could be.
Robots Lift, Humans Serve.
The live show is performed by a human waiter not AI. And that’s
what we want. We want live because the experience connects us all.
When he spills his noodles, we feel it too. Experience is only real
when shared.
Mr Davies wobbling on the chair, one hand on the suspended strip
lights the other on the polystyrene roof tiles could have gone horribly
wrong. That’s the edge of existence, the fine curve of brilliance where
we feel human.
It’s the same reason digital never killed music, and live events that
promise both failure and brilliance speak to our soul. We are
reminded what it is to be human.
It’s the same reason China rallied behind Ke Jie as he was crushed by
AlphaGo, not because they pitied him but because his tears and his
struggles were a journey shared by all. The hero wasn’t the most
powerful of the competitors, but the most human.
If there is a contract that needs to be written between man and
machine for the 4th Industrial Revolution it needs to be this:
And embracing this future starts with transformation. Unless we
change the shapes and structures of business, we will be replaced by
machines.

Restaurants, Hospitals and Offices are physical structures that
manifest our ideas of how we should communicate with each other.
Most restaurants are built around the kitchen. The chef is the
traditional seat of power and is paid accordingly. But in Haidilao it’s
the opposite. The kitchen is small. Out the back, robots manage the
automated restaurant, a concept developed with Panasonic that helped
reduce labour costs by an estimated 37%.
But the goal isn’t to eliminate humans like the ill-fated Japanese
“Henna Hotel” where robots replaced front desk staff.
At Henna, Robotic trolleys carted your clothes to the laundry room
which was, ironically, operated by humans, the opposite of Haidilao.
Henna Hotel worked for a short time because the clue was in the
name: “Henna” meaning “Weird” in Japanese. It was a novelty. It
made good social media headlines. But after the excitement of being
shown to your room by a robot wore off, the experience was stale,
lonely and inhuman. Guests experienced Henna more like the
efficiency of an airport than the warmth of a hotel. After a year of
operation, Henna retired the front desk robots.
The average Haidilao, however, employs 130-170 people, most of
which are up front serving people. Automation and Big Data frees up
time and process from the waiting staff to elevate them to do what
they do best. Staff can experiment with menu options, adding their
own inventions. They have full autonomy to make the decisions
required to deliver the quality of experience that continues to pack the
house each night. Diners can personalise their own steamboat broth,
making changes to the base ingredients and seasoning to a granular
level, in units of 0.5 grams. Each time you order, Haidilao stores your
choices about any of the potential 8,000 soup stock configurations so
you get the same favourite combination next time.
You even get a Ziplock bag to prevent the soup from spilling onto
your phone.

These are the small touches that make the big picture, and the net
experience that made owner Zhang Yong the richest restauranteur in
the world.
Automation Elevation
Process
Heavy lifting
Knowledge
Pattern recognition
Insight and Analysis
Calculation
Data
Vulnerability
Authenticity
Care
Empathy
Engagement
Connection
Storytelling
Let data and automation take away the heavy lifting to empower
humans to do what we do best: art, empathy, leadership, performance,
relationships, service and storytelling.
Human Exceptionalism encourages us to think our future lies in
developing insight and awareness than lies out of reach of the
machine, but AI has already overrun this position and if we’re not
careful we risk being edged into low paid, low meaning job that are
even too meaningless for machines.
By contrast, Human Commonality states we shouldn’t push against
machines but pull together. AI will always be more intelligent and
more efficient, so let’s not play that game.
Instead, like the Lunch Hall problem, our greatest needs aren’t the
connectivity of data but the connection of humanity. We are
overwhelmed with connectivity but starved of connection. It’s a
connection born of vulnerability and human stories that reveal our
authentic nature with all its weaknesses. This is a game, by design, we
will always win. So let the machines play God, while we do what we
are put on the Earth to do – Be More Human.

VOICEDYNAMICS – AN ECHO FROM THE FUTURE
In the 1989, Pepsi spent $7.5 million on single ad with Madonna,
promoting “The Pepsi Generation" on MTV. It was fun, cool, sexy.
This is the “Big Idea” - a monolithic narrative created by a “Mad
Men” ad agency that gave your brand life. If people knew your brand,
it was because of a memorable campaign like “Diamonds are
Forever”, Tony the Tiger or Meowmix “so good cats ask for it by
name."
Today, if an airline like AirAsia has data on 100 million customers, it
doesn’t need a Big Idea to find them anymore. Ride sharing app Grab
in Southeast Asia makes around 50 million bookings a day. Let’s
assume 100 million people have Grab accounts, all with credit card or
bank details attached. Grab doesn’t need to spend millions on finding
these customers.
Facebook and Google own 59% of the US advertising market,
steamrolling ad agencies and replacing them with SEO, digital
marketing and programmatic advertising. In 2 generations, creatives
agencies went from sexy to has-beens.
That was the story of Performance Marketing. In short, taking the BS
out of marketing and using data to democratize storytelling.
I’d like to do the same for Communications.
Communications is a $1.1 trillion market globally if you count it as
$150 million for PR, $600 million for advertising and $350 million
for recruitment. It’s a big enough problem to solve and it’s an
industry that has for far too long been living in its own “Mad Men”
style era at the mercy of a coterie of agencies and operators who have
sold their access to other people.
So what would Performance Communications look like?

Imagine a world where we used data, I mean BIG data, to help people
optimise the stories they tell and the conversations they have.
I’m not talking about AI replacing human beings but empowering
them like Haidilao. Give me the data from the backroom so I can
spend my time our here up front serving. In the same way Google
came into the world of advertising and put Mad Men out of business.
I want to do the same for Communications.
Communicat: Latin – to share
I want to strip out all the lazy fat, all the PR lunches, and all the “30
under 30” nonsense that defines thought leadership today and replace
it with hard data. Why? Because hard data democratises leadership.
Everybody gets to play.
Now, this is a challenge I thought worthy of my time on this Earth,
because it’s noisy up there in my head and unless I get to poke a box
that’s going to reveal some deep insights about humanity, I’d rather
be living on a tropical island (more on that later).
But more importantly, it’s the natural coexistence of AI and
Storytelling. We could use AI to empower humans to tell better
stories.
And that’s the path we’ve taken at Pikkal. Starting as a Podcast and
Webinar agency, we had to first learn with the problem. Then we
started using Machine Learning algorithms to analyse and measure
podcasts. What’s interesting and I what I only discovered when we
started diving into the world of audio is that podcasts are the ultimate
Big Data. Podcasts are the last frontier of data. Everyone’s been
analysing and crunching web traffic, video and faces for years, but the
real moment of truth is when we can quantify what matters most -
emotion and connection.
Consider that a podcast can contain a lot of valuable information
about the people involved - what they talk about, their emotions, their

connections, their ideas, their authority. You can’t get this stuff from
a social media profile or a video. What you build is a map of human
conversations. Who matters. What matters.
When people think about AI and Podcasts, their natural reaction is to
think about how far away we are from a bot replacing your guest or
host. It’s amusing but it’s no different from the failed Henna Hotel
model. The future is more like Haidilao Hotpot, where robots do the
heavy lifting for podcasts and humans do the storytelling.
When you think of that AI x Podcast future, think of all the data
points you could extract from podcast audio that would do the heavy
lifting for a podcast host:
- Matching what you talked about (topics) with what you wanted
to talk about when you started out
- What you should talk about based on engagement data and
subjects
- Who are the influential hosts and guests you should have on
your show based on their content profiles from other shows
- How your podcast compares in terms of content with leading
shows in your category
What AI can do for podcasts What AI can’t do for podcasts
Help hosts have more interesting
conversations with social
signaling data
Make the conversation more
interesting
Help hosts measure output
through sentiment analysis
Fake or augment the emotional
content
Facilitate more targeted
conversations
Replace the host with a robot
Optimise share of voice to
compare top performing
podcasts with your own
Increase or decrease the time a
host/guest speaks in one episode
Identify influencers for your
show based on past podcast
guest performance
Be a better guest for your show

So why am I giving away our trade secrets?
Because it’s more important for me to show you what the future looks
like so we can all build a better one. Who knows, one day you might
be part of our story as a client, partner or an investor. But more
importantly, this is such a massive challenge no one company can
solve it alone.
We are on the cusp of the 4th Industrial Revolution. Like every
Revolution that came before us, by the time we started naming it, it
had already begun. The world is operating at vastly different speeds,
with both winners and losers, the latter being the those that believe the
Sun orbits the Earth.
The winners, however, may be less obvious. They won’t be those
with the best technologies or the best ML models, but those who have
opened the lid and poked around the customer’s world understanding
the problems. And again, that’s such a massive challenge, no one
company can solve it alone.
That’s the journey we’re on with VoiceDynamics - our AI powered
podcast platform. If it interests you to talk right now, you can
message me on Linkedin. Otherwise I’d like to introduce you to the
“new normal”, the 4th Industrial Revolution.

THE ERA OF THE MACHINE
The man who does not tell stories has
no advantage over the machine that cannot live them.

THE 4th INDUSTRIAL REVOLUTION
Industrial Revolution Technology
1st
(1760)
Coal, steam, water, mechanisation, railroad
2nd
(1910)
Electricity, Petroleum, Steel Automation
3rd
(1980)
Information technology, electronics
4th
(2020)
Artificial Intelligence, Big Data
The 1st Industrial Revolution allowed textile manufacturers to create
a high volume of intricate patterns for a growing consumer class, fast
and cheaply. IR 1.0 transformed the shape of business as we know it:
1) Change drove wealth distribution from old classes to the new.
2) Canals created physical routes to market that allowed factories
to supply these new consumers.
3) Industrialists competed by creating increasingly complex value
chains that connected the sourcing of wool in the Netherlands,
to its production in London with the distribution of cloth in New
England.
4) And all of this changed the structure of work and the work
environment as we know it.
The 4th Industrial Revolution (IR 4.0) follows a similar pattern:
1) New Markets: the growth of the Asian Middle Classes and The
Asian Century
2) New Technologies: Artificial Intelligence yields massive
productivity gains
3) New Forms of Competition: no longer the command of the
factors of production but the command of customer attention
4) New Business Structures: Digitally Transformed companies that
operate as Platforms not Pipelines

A lot is going to change.
OUT:
Conferences, Breakfast Meetings, Corporate Events, Company
"Offsites", Media interviews, Meet the Press, Book Tours, Sales
Meetings, Sales Presentations, Demo Days, Pitch Competitions, Team
Lunches, Team Meetings, Company Travel, Workshops, Training,
"Town Halls", Recruitment Fairs, Recruitment Interviews, Coffee
Meetings, Business Cards, Offices, Large Departments, Retail Banks,
Quarterly Reviews, Law School, Job Titles, Control, Efficiency, PR,
MBAs, Rush Hour, A Crowded Bar on Friday Night, Cars, Suits,
White Shirts, “2 Weeks in the Sun”, 9am-5pm, Fake
IN:
Podcasts, Webinars, Zoom calls, Metrics, Gig Economy, Upwork,
Small Cross Functional Teams, Robo Advisors, Neo Banks, Slack,
Online Internships, Work from Home, AI, Empathy, Automation,
Curation, Career Breaks, Storytelling, Entrepreneurship, Resilience
Training, Digital Nomads, Care, Authenticity, Vulnerability, Real
Human Connection
Like every Revolution that came before this one, it just happens.
There is no fireworks display or Public Service Announcement, just a
lot of seemingly random and unconnected events. Only with the
benefit of hindsight are we able to join the dots and make sense of it
all, so for the benefit of understanding what happens next, it would be
wise to look at what happened last time…

THE WEAVERS
Grandfather Gilmour wasn’t the soft, warm, fuzzy Grandfather
archetype you see in Werther’s originals candy adverts. He was a
rather stern, gruff and intimidating man.
He was the only person I met that could take out chocolate from the
cupboard, break off exactly one square and put the bar back again.
Asked why, he’d simply shrug his shoulders, walk away with a thick
Glaswegian.
“Aye, you’ll know when ya’ seen as much shite as I have.”
Like many in his generation, there was little reason for cheer. They
moved to Canada to find a better life, then moved to New York when
work in shipping dried up, then returned to Glasgow. I never learned
the full story but suffice to say, moving around and not settling down
has been in our family DNA ever since.
They were typical working class Scots raised in Paisley, a rundown
area of Scotland famed for its textile patterns, scarfs and cloths
inherited from Kashmiri Shawls. But while the Paisley brand
achieved worldwide fame in the era of the British Empire and
Industrial Revolution, the hands that weaved the textiles scratched in
the dirt. When our family drove up to Scotland for summer vacation,
the first thing I encountered was the smell of the glue factories, and
the landscape was industrial brown.
Before the machines, before the glue factors and the industrialisation,
life was a lot more colorful. Our family were weavers. They were
artisans living 2 hours outside of Glasgow. Weavers of the time were
the original cottage industry. They were about as highly paid and
respected a profession you could be without land or noble title. A
Weaver could work half a week and rest the other, tending his
smallholding of land and feeding his family.

Change came in the form of the Jacquard Loom, invented in France in
1804 and introduced to the Scottish textile industry in the mid to late
19th century. The automated Loom could take woven silk and through
a series of 24,000 hole-punched cards connected in a loop, produce
intricate designs faster and cheaper than human hand.
The first looms required skilled labor to operate. Weavers “upskilled”
to stay competitive. But then Industrialists worked out how to
mechanise hundreds of machines and build advanced distribution
channels around them. Looms were housed in factories attached to
mills and canals. Mills provided water to both power the mechanism
and a distribution channel to market.
Early experiments in mechanisation were fraught with disaster -
dismemberments, damaged machinery and poor quality textiles. But
soon, the Loom began to match the output of manual weavers. By the
mid 19th century, a process that previously required large teams of
highly paid artisans could now be reduced to a handful of trained
operators.
Within a generation, children and skilled artisans were sucked into
factories and a world of noise, pollution and danger. With dark
humour, a few remaining teeth, my Great Great Aunt Sally recalled
tales of her sisters casually eating their lunch while coworker body
parts got jammed in the machines - a finger, a hand, then there was
the lady whose dress was ripped right off, leaving her standing with
only her petticoat. Like Keith Richards from the Rolling Stones, Sally
had inhaled all kinds of dangerous chemicals and lived to tell the tale
at an improbable 102. Unlike Richards, none of this was by choice.
Asbestos, Lead and constant glue factory fumes. Life was tough. The
sisters would often sleep and eat on the factory floor. Such was the
world of work before unions and health & safety practises.
Economically, the Jacquard Loom was a game changer, yielding
exponential gains in productivity. Output increased by a factor of
around 500 per person, enabling many textile manufacturers to reduce

their headcount by as much as 90%. So disruptive were these yields
that the normal rules of business no longer applied.
Society changed radically.
For the Gilmours, there was no talk of an “Industrial Revolution”, it
just happened. At the turn of the 19th century they enjoyed freedoms
and an enviable lifestyle. By the middle of the century, they were
bonded to unskilled, uneducated and unprotected labor.
“Luddites” – a term used today to connote “that guy” who still gets a
secretary to write his emails or never reads his Whatsapp messages –
owe their name to a weaver named Neil Ludd.
Ludd was a folktale character who encouraged local villagers to pick
up their flails and pitchforks, smash factories and their machines. The
original Luddites smashed machines because machines destroyed
their livelihoods. These weren’t low paid peasants, but educated and
skilled workers like the Gilmours, whose future generations were
being cast onto the trash pile of progress. The movement rapidly
morphed and absorbed society’s disaffected and religious instigators,
compelling the government to introduce draconian laws to prevent its
acceleration. In 1812, the British parliament introduced “The Frame
Breaking Act” making damaging a Loom an offence punishable by
hanging.
Today, we enter the 4th Industrial Revolution and mechanisation
through Artificial Intelligence. These technologies are world apart
from the world of the textile factories and automated looms, but if you
study them closely, the patterns of change are strikingly similar. The
actors are different but the story the same.

TRANSFERS OF VALUE
Revolutions appear destructive. We fear them.
The Gilmours were destroyed by the 1st Industrial Revolution because
they didn’t know a Revolution was taking place. Events just
happened.
If we step out of events and look from the macro level, we can
understand them differently. Revolutions needn’t be feared if you are
on the right side of change. For us, the 4th Industrial Revolution also
means we are entering the unknown. But this time we have the benefit
of hindsight to guide us.
One of the most important patterns to understand is the transfer of
value. Value isn’t lost into thin air, it transfers from one object to
another, from one person to another, from one society to another.
Let me explain.
In 1865, during the American Civil War, most people traveled by
horse. You could buy a horse for $10.
Back in 1865, there were 6 million horses in the market. The “horse
market” was worth $60 million in old money, or $1.8 billion by
modern standards, if you convert $1 in 1860 to $30 today.
Today, the value of a horse as transport is very small, or niche. If you
had a business selling horses as transport you would do well in 1865
but would be out of business within several generations.
Today, a horse will cost you around $2,000. There are 2 million
horses in the US. Sure, some horses are transport but most are kept as
pets or leisure. The horse market is worth $4 billion. Technically
double, but if you put that difference it into context, the horse market
has grown at only 0.6% above inflation every year for 130 years.
Roughly stable (my pun intended).

So, what has happened?
The value of a horse has changed over time, but not evaporated into
thin air. The value of a horse has transferred from transport to leisure.
That’s what Revolutions do, move value from one hand into another.
You can see that change manifest in areas like Baltimore, the
traditional heartland of America’s automotive and manufacturing
“rust belt”.
20 years ago, a General Motors worker could make $27 an hour (or
$35 in today’s money) and, with overtime, make $80,000 a year –
60% above the national average.
Today, GM’s factories are being adopted by Amazon who houses
massive fulfilment centres in Baltimore. Amazon warehouse worker
pay starts at $15.40 an hour, with the highest skilled of them earning
around $40,000 – half of what they could hope for 20 years ago at
GM.
Like the horse, money isn’t disappearing into thin air. Sure, Amazon
is siphoning off massive profits, just as their Industrial Era forebears
did, but the money is also shifting into new categories: Amazon
Marketplace has 7.9 million sellers worldwide, of which 2.9 million
are active. 10% of these sellers grossed $100,000 in sales and 1%
over $1 million.
So what good does this knowledge give us? It helps us understand
how to survive and even thrive on the other side of a Revolution.
The Gilmours had no social mobility. But we do. You are reading this
book. You can learn any skill you want on the internet and you can
make choices.

The other day my 14 year old son asked me about going to
University.
If he doesn’t go, I’ll give him half the money it would have cost me
and say, “now go travel the world and start a business”. Why?
Because thriving in the Era of the Machine isn’t about reskilling or
upskilling. You won’t thrive by taking a Data Analytics course.
That’s like rearranging the deckchairs on the Titanic.
Instead, we need to unravel a whole system of education and training
that has brought us to this point today and ask the question, “How do
we compete?”

SPREADSHEETS TO STORYTELLERS
Deep into the rat run alleys of Spitalfields in East London, you’ll find
dark factories humming with the din of mechanical looms and the
cries of dirtied faces, churning out textiles.
At the top of the lanes, deserted houses formerly occupied by wealthy
Huguenot families who had since moved uptown are now taken over
by a new group of entrepreneurs. Large windows and direct access to
the street made that once served as signs of wealth now provide
popular vantage points for a retail stores. This is the end of the 19th
Century and a Revolution is taking place.
In the first Industrial Revolution, the machines that replaced Weavers
were also doing something else. Those fancy textiles served a
burgeoning middle class in the urban centers of London and
Manchester.
The wealthy Madames of London sought out never-seen-before
intricate textile patterns as status symbols that implied to others, "I
have money and leisure time to entertain friends.” The tablecloth in
1800s England became the Tesla of its time.
But ideas don't sell themselves. Ideas need stories. And stories need
people. Stories fuelled a whole new ecosystem – retail, an industry
built around storytelling. High Streets sprang up around Britain,
retailers occupied prime real estate and sidewalks were widened to
allow women in girdle skirts to pass gracefully without trudging in
the mud.
Burtons is one store that revolutionized high street fashion by
identifying changing needs and packaging those into a story for the
masses. The Burton's affordable ready-made suit (which became
known as “The Full Monty”) became an immediate hit, popular with
Britain’s expanding middle and working classes who needed formal
wear for office work. By the second half of the 20th century, Burtons
expanded to 595 stores across the UK.

Machines destroy. Machines create. It’s not binary.
The Revolution took value away from those who built a livelihood
around matching patterns and gave it to those who were creating one
selling them. In every Industrial Revolution, value transfers from the
Spreadsheets to the Storytellers.
And this pattern will repeat.
If your job is fundamentally one that involves spreadsheets, it will
soon be done better by a machine. And while many of the highest
paid professionals don’t work off Excel all day, their jobs are similar
– recognizing patterns. Those patterns could, for example, be case
law, imaging scans or accounts.
So what’s stopping us?
Why can’t we all learn to become better storytellers?
Because history teaches us that those with the most to lose also have
the most reasons to prevent the change.
Take the Gilmours for example. Why did the Gilmours lose and the
Burtons win? Both were smart. Both had drive and hustle.
The answer lies in their backstory. Unlike the Gilmours, the Burtons
had nothing to lose. Meshe David Osinsky escaped the Russian
Pogroms in Lithuania and came to London in 1901 as a refugee. He
was aged just 15 and unable to speak a word of English, quickly
adopting an anglicised name "Montague Burton” before opening his
first store aged 18 just off Spitalfields market. 60% of the Jews living
in London were, at the turn of the century, working in tailoring.
If my Gilmour family were Jewish immigrants, like Montague
Burton, with no legacy and no career to risk, they could have been
tailors. Within 2 generations they would have been landed and

wealthy enough to send their children to prestigious schools, to
furnish the upper echelons of society. But they weren’t. They were the
professionals of their day who had built a career around doing one
thing really well which served them until that one thing could be done
better by a machine.
World Economic Forum Researchers estimate at least 54% of
employees will need re-skilling and upskilling to complete their jobs
in the 2020s. And while this may be true, the common narrative that
IR 4.0, Artificial Intelligence, will decimate the lives of the lower
paid workforce is misleading.
If anything, the opposite will happen: those at the top will lose, while
those at the bottom will gain.
Spreadsheets Storytellers
1st Industrial
Revolution
Weavers Retail
4th Industrial
Revolution
Managers,
Professionals
Creators, Artists,
Leaders
Think of this as Flipping the Pyramid.
At the bottom, everybody with little to lose and everything to gain.
These are the new pretenders: the Meshe David Osinskys of the new
order – immigrants, Chinese startups, Southeast Asian entrepreneurs,
African hustlers, corporates who dropout after 20 years in the same
industry, students who refuse to spend $1000s on worthless university
degrees, teenagers, women.
At the top: men, the doctors, the lawyers, the accountants, that data
analysts, the professionals who refuse to change.
It happened 200 years ago, and it will happen again.

POTATOES VS LAWYERS
I don’t see the 4th Industrial Revolution as a flourishing of opportunity
and democratization of opportunity. Societal structures have a habit of
replicating themselves over time, with the same actors found at the
top and the bottom.
Where change will be felt most is in the middle – the managers and
professionals who run the day to day operations of our society.
Lawyers, for example.
Lawyers, are glitzy, sexy and aspirational. Lawyers make good
subjects for Hollywood movies like “The Firm”, “A Few Good Men”
both starring Tom Cruise and “Erin Brockovich” with Julia Roberts.
They are the product of years of high specialised training. No parent
was ever disappointed their child became a lawyer.
Unlike a potato farmer. Potato farming is mind numbing, back
breaking and low paid. I’ve done it in my student days and it’s a job
that many economies fill with immigrant labor because locals will do
anything except pick potatoes.
Who is more exposed to the changes of the 4th Industrial Revolution?
You can plant potatoes with a machine, a job that’s been mechanised
for many years. But when it comes to quality control, you have to use
humans.
Short on cheap immigrant labor and young, unskilled workers, Potato
farmers in Hokkaido Japan have long struggled with this task. Each
potato has to be manually checked and its black “eyes” (blemishes)
removed, a process that is laborious and tedious.
Naturally, this is an obvious target for Robotic Process Automation.
Using RPA, farmers could remove humans from the process, save
time and money. But, after numerous deployments, farmers found that

they could indeed eliminate potato eyes but not with a 100%
accuracy. Although human labor was slower and more expensive, it
was almost always more accurate, and Japanese grocery consumers
are notoriously fastidious in their choice of vegetables. After lengthy
trials, the Hokkaido farmers conceded defeat, retiring the robots and
resorting to old-fashioned manual labor.
At the other end of the career pyramid, consultancy PWC reported
that with the use of a single AI algorithm that could pattern match
documents with case history, they were able to save a corporate client
340,000 hours of lawyer time. A single algorithm transferred over $50
million from the pocket of law firms into machine learning engineers.
And we’re only getting started.
We cannot replace potato cleaners with AI, but we can easily replace
lawyers.
This rather comical illustration has a serious undertone. Artificial
Intelligence will Flip the Pyramid contrary to the expectations of
many experts and certainly most media pundits.
We often talk of the 4th Industrial Revolution and the disruptive
impact of AI as a threat to the lowly skilled uneducated strata of our
workforce. But the opposite will almost certainly be true.
Coaches are teaching “agile” to corporates and managers across the
land and charging good money. But the lowly paid are already agile.
A cashier can lose a job at 7-11 on Friday and turn up at Walmart on
Monday. Every 7-11 in Tokyo is manned by Vietnamese, Thai and
Chinese workers. Some are students. Some are part timers. All are
chancers who came here for a better life. Many hold multiple jobs.
Upwork is full of Filipino Moms who work as call center handlers in
the day and as “virtual assistants” at night.

Frontline workers have had to become agile in recent years. Many are
used to the insecurity of working at the bottom of society’s pyramid –
zero hours contracts, no healthcare, few employment rights. If their
jobs get automated, they’ll find new ones. This, for them, is the “new
normal” and they’ve had years of practise getting used to this
environment.
But what of professionals?
The highest paid professionals, whose complex work is based on
years of training and pattern recognition e.g. doctors, lawyers,
accountants, jobs traditionally dominated by men at the top of the
Pyramid, could lose the most.
Young Pramesh carries the weight and expectation of his family on
his shoulders as he packs his bags off to medical school. His family
invested $100,000s in his education. His parents told all the aunts and
uncles when he finally qualified. He won’t lose out because he is
unskilled or uneducated, he will lose because he is skilled and
educated.
Doctors, Accountants, Lawyers, Data Scientists, Engineer and
Computer Programmers – the highest paid and highest skilled in our
society are at the greatest risk of becoming a new generation of
Weavers unless they focus on the skills they need to adapt to the Era
of the Machine.
While this is a rally call for those who have enough time and foresight
to make change, our challenge will be fear of change.
Look around the media and you see headlines nagging us to “reskill”
and “upskill”. A couple of years ago they were saying we had to
learn to code, now it’s data analytics, the next it will be machine
learning. The goalposts are constantly moving and all the while, the
most skilled among us will feel that every shift in the fitness
landscape will unseat them from their position of experience.

The Human Communication Playbook by Graham Brown (Get the PDF)

The Human Communication Playbook by Graham Brown (Get the PDF)

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