RECENT READING Tom Peters/11 July 2013. FILTER BUBBLE

RECENT RECENT READINGREADING

Tom Peters/11 July 2013Tom Peters/11 July 2013

FILTER FILTER BUBBLEBUBBLE

The Filter Bubble: The Filter Bubble: How the New, How the New,

Personalized Web Is Personalized Web Is Changing What We Changing What We Read and How We Read and How We

ThinkThink —Eli Pariser—Eli Pariser

““bonding capital”bonding capital”vs.vs.

““bridging capital”bridging capital”

——Eli Pariser, Eli Pariser, The Filter Bubble: How the New, Personalized Web The Filter Bubble: How the New, Personalized Web Is Changing What We Read and How We ThinkIs Changing What We Read and How We Think

““If you’re not If you’re not paying for paying for

something, ysomething, youou are are the product being the product being sold.”sold.” —Andrew Lewis, MetaFilter.com (from Eli —Andrew Lewis, MetaFilter.com (from Eli

Pariser, Pariser, The Filter Bubble: How the New, Personalized Web Is The Filter Bubble: How the New, Personalized Web Is Changing What We Read and How We Think)Changing What We Read and How We Think)

““How much time you take How much time you take between the moment you between the moment you enter your query and the enter your query and the

moment you click on a result moment you click on a result sheds light sheds light [for Google][for Google] on your on your

personality.”personality.” —Eli Pariser, —Eli Pariser, The Filter Bubble: The Filter Bubble:

How the New, Personalized Web Is Changing What We Read How the New, Personalized Web Is Changing What We Read and How We Thinkand How We Think

““It is hardly possible to overrate the It is hardly possible to overrate the value of placing human beings in value of placing human beings in

contact with persons dis-similar to contact with persons dis-similar to themselves, and with modes of themselves, and with modes of thought and action unlike those thought and action unlike those

with which they are familiar. with which they are familiar. Such Such communication has alwacommunication has alwayys been, s been,

and is peculiarland is peculiarlyy in the in the ppresent aresent agge, e, one of the one of the pprimarrimaryy sources of sources of

pproroggressress.”.” —John Stuart Mill (1806-1873)—John Stuart Mill (1806-1873)

““I believe this isI believe this is the quest for whatthe quest for what

a personal computer a personal computer really is. really is. It isIt is

to cato cappture one’s ture one’s entire lifeentire life.”.” —Gordon Bell—Gordon Bell

““Psychologists have a name for Psychologists have a name for

this fallacy: this fallacy: fundamental fundamental attribution errorattribution error. We . We

tend to attribute peoples’ tend to attribute peoples’ behavior to their inner traits behavior to their inner traits

and personality rather than to and personality rather than to the situations in which they’re the situations in which they’re placed.”placed.” —Eli Pariser, —Eli Pariser, The Filter Bubble: How the New, The Filter Bubble: How the New,

Personalized Web Is Changing What We Read and How We ThinkPersonalized Web Is Changing What We Read and How We Think

““Some people rush for a deal, Some people rush for a deal, others think that the deal means others think that the deal means

the merchandise is subpar. Just by the merchandise is subpar. Just by eliminating the persuasion styles eliminating the persuasion styles that rub people the wrong way that rub people the wrong way [as [as

deduced from prior Web behavior patterns], [the deduced from prior Web behavior patterns], [the

marketer]marketer] found he could increase the found he could increase the effectiveness of marketing materials effectiveness of marketing materials

from 30 to 40 percent.” from 30 to 40 percent.” —Eli Pariser, —Eli Pariser, The The Filter Bubble: How the New, Personalized Web Is Changing What Filter Bubble: How the New, Personalized Web Is Changing What

We Read and How We ThinkWe Read and How We Think

““With new forms of With new forms of

‘sentiment ‘sentiment analysis’analysis’ it’s now it’s now

possible to guess what mood one’s possible to guess what mood one’s in. People use substantially more in. People use substantially more

positive words when they’re up …” positive words when they’re up …” ——Eli Pariser, Eli Pariser, The Filter Bubble: How the New, Personalized The Filter Bubble: How the New, Personalized

Web Is Changing What We Read and How We ThinkWeb Is Changing What We Read and How We Think

““LinkedIn offers a career trajectory prediction LinkedIn offers a career trajectory prediction by comparing your resume to other peoples’ by comparing your resume to other peoples’

who are in your field but further along. LinkedIn who are in your field but further along. LinkedIn can forecast where you’ll be in five years. … As can forecast where you’ll be in five years. … As a service to customers, it’s pretty useful. But a service to customers, it’s pretty useful. But

imagine if LinkedIn offered the data to corporate imagine if LinkedIn offered the data to corporate clients to weed out people who are forecast to clients to weed out people who are forecast to

be losers. … It seems unfair for banks to be losers. … It seems unfair for banks to discriminate against you because your high discriminate against you because your high

school buddy is bad at paying his bills or school buddy is bad at paying his bills or because you like something that a lot of loan because you like something that a lot of loan defaulters also like. And that points to a basic defaulters also like. And that points to a basic problem with induction, the logical method by problem with induction, the logical method by

which algorithms use data to make predictions.”which algorithms use data to make predictions.” —Eli Pariser, —Eli Pariser, The Filter Bubble: How the New, Personalized Web Is Changing What The Filter Bubble: How the New, Personalized Web Is Changing What

We Read and How We ThinkWe Read and How We Think

““Technodeterminism is Technodeterminism is alluring and convenient alluring and convenient

for newly powerful for newly powerful entrepreneurs because entrepreneurs because

it absolves them of it absolves them of responsibility for what responsibility for what they do.” they do.” —Eli Pariser, —Eli Pariser, The Filter Bubble: How The Filter Bubble: How

the New, Personalized Web Is Changing What We Read and How the New, Personalized Web Is Changing What We Read and How We ThinkWe Think

ROBOT ROBOT FUTURESFUTURES

Robot Robot FuturesFutures

——Illah Reza Nourbakhsh, Illah Reza Nourbakhsh, Professor of Robotics, Carnegie MellonProfessor of Robotics, Carnegie Mellon

““Analytics can yield literally hundreds of Analytics can yield literally hundreds of millions of data points—far too many for millions of data points—far too many for

human intuition to make any sense of the human intuition to make any sense of the data. So in conjunction with the ability to data. So in conjunction with the ability to

store very big data about online store very big data about online behavior, researchers have developed behavior, researchers have developed

strong tools for data mining, statistically strong tools for data mining, statistically evaluating correlations between many evaluating correlations between many types and sources of data to expose types and sources of data to expose

hidden patterns and connections. The hidden patterns and connections. The patterns predict human behavior—and patterns predict human behavior—and

even hidden human motivations.” even hidden human motivations.” —Illah Reza —Illah Reza Nourbakhsh, Nourbakhsh,

Professor of Robotics, Carnegie Mellon,Professor of Robotics, Carnegie Mellon, Robot FuturesRobot Futures

““[Very successful websites send 99% of [Very successful websites send 99% of their traffic to tried-and-true designs, but their traffic to tried-and-true designs, but risk 1% of their traffic on new variations risk 1% of their traffic on new variations to discover ever better conversion rates to discover ever better conversion rates from visits to dollars. When Google was from visits to dollars. When Google was choosing the right shade of blue for a choosing the right shade of blue for a navigation bar, the company famously navigation bar, the company famously performed A/B split testing across 41 performed A/B split testing across 41 shades of blue. … When numbers are shades of blue. … When numbers are

large and hundreds of millions of people large and hundreds of millions of people are in play, the tiniest improvements are in play, the tiniest improvements translate into breathtaking levels of translate into breathtaking levels of

profit improvement.”profit improvement.” —Illah Reza Nourbakhsh, —Illah Reza Nourbakhsh, Professor of Robotics, Carnegie Mellon,Professor of Robotics, Carnegie Mellon, Robot FuturesRobot Futures

““Robotics will drive this very Robotics will drive this very innovation. Landing page tuning will innovation. Landing page tuning will bust out of the Internet and become bust out of the Internet and become ‘interaction tuning.’ Companies will ‘interaction tuning.’ Companies will apply their analytics engines to all apply their analytics engines to all

interaction opportunities with interaction opportunities with people everywhere: online, in the people everywhere: online, in the car, in a supermarket aisle, on the car, in a supermarket aisle, on the

sidewalk, and of course in your sidewalk, and of course in your home.” home.” —Illah Reza Nourbakhsh, Professor of Robotics, —Illah Reza Nourbakhsh, Professor of Robotics,

Carnegie Mellon,Carnegie Mellon, Robot Futures Robot Futures

““Human level Human level capability has not capability has not turned out to be a turned out to be a special stopping special stopping

point from an point from an engineering engineering

perspective. ….”perspective. ….”

Source: Illah Reza Nourbakhsh, Professor of Robotics, Carnegie Mellon, Source: Illah Reza Nourbakhsh, Professor of Robotics, Carnegie Mellon, Robot Robot FuturesFutures

BIGBIGDATADATA

Big Data: A Big Data: A Revolution That Revolution That Will Transform Will Transform How We Live, How We Live,

Work, and ThinkWork, and Think——Viktor Mayer-Schonberger and Kenneth CukierViktor Mayer-Schonberger and Kenneth Cukier

““As humans, we have been conditioned to look for As humans, we have been conditioned to look for causes, even though searching for causality is often causes, even though searching for causality is often difficult and may lead us down the wrong paths. In a difficult and may lead us down the wrong paths. In a

big data world, by contrast, we won’t have to be big data world, by contrast, we won’t have to be fixated on causality; instead, we can discover patterns fixated on causality; instead, we can discover patterns

and correlations in the data that offer us novel and and correlations in the data that offer us novel and invaluable insights. The correlations may not tell us invaluable insights. The correlations may not tell us

precisely why something is happening, but they alert precisely why something is happening, but they alert us that it is happening. And in many situations, this is us that it is happening. And in many situations, this is good enough. If millions of electronic medical records good enough. If millions of electronic medical records

reveal that cancer sufferers who take a certain reveal that cancer sufferers who take a certain combination of aspirin and orange juice see their combination of aspirin and orange juice see their

disease go into remission, then the exact cause for the disease go into remission, then the exact cause for the remissionremission

in health may be less important than the fact thatin health may be less important than the fact that they lived.”they lived.”

Source:Source: Big Data: A Revolution That Will Transform How We Live, Big Data: A Revolution That Will Transform How We Live, Work, and Think, Work, and Think, by Viktor Mayer-Schonberger and Kenneth Cukierby Viktor Mayer-Schonberger and Kenneth Cukier

““Correlations let us Correlations let us analyze a phenomenon not analyze a phenomenon not

by shedding light on its by shedding light on its inner workings, but by inner workings, but by

identifying a useful proxy identifying a useful proxy for it.”for it.”


““Predictions Predictions based on based on

correlationscorrelations lie lie at the heart of at the heart of

big data.”big data.”Source:Source: Big Data: A Revolution That Will Transform How We Live, Big Data: A Revolution That Will Transform How We Live,

Work, and Think, Work, and Think, by Viktor Mayer-Schonberger and Kenneth Cukierby Viktor Mayer-Schonberger and Kenneth Cukier

““There is a There is a philosophical debate philosophical debate going back centuries going back centuries

over whether causality over whether causality even exists.”even exists.”


““Unfortunately, Kahneman argues Unfortunately, Kahneman argues [Nobel laureate Daniel Kahneman’s [Nobel laureate Daniel Kahneman’s

masterpiece masterpiece Thinking, Fast and SlowThinking, Fast and Slow],], very very often our brain is too lazy to think often our brain is too lazy to think slowly and methodically. Instead, slowly and methodically. Instead,

we let the fast way of thinking we let the fast way of thinking take over. As a consequence, we take over. As a consequence, we often ‘see’ imaginary causalities, often ‘see’ imaginary causalities,

and thus fundamentally and thus fundamentally misunderstand the world.”misunderstand the world.”


Walmart: “[Using big data], Walmart: “[Using big data], the company noticed that the company noticed that

prior to a hurricane, not only prior to a hurricane, not only did sales of flashlights did sales of flashlights

increase, but so did sales of increase, but so did sales of Pop-Tarts. … Walmart stocked Pop-Tarts. … Walmart stocked

boxes of Pop-Tarts at the boxes of Pop-Tarts at the front of the store [and front of the store [and

dramatically boosted sales].”dramatically boosted sales].”


““Aviva, a large insurance firm, has studied the Aviva, a large insurance firm, has studied the idea of using credit reports and consumer-idea of using credit reports and consumer-

marketing data as proxies for the analysis of marketing data as proxies for the analysis of blood and urine samples for certain blood and urine samples for certain

applicants. The intent is to identify those who applicants. The intent is to identify those who may be at higher risk of illnesses like high may be at higher risk of illnesses like high

blood pressure, diabetes, or depression. The blood pressure, diabetes, or depression. The method uses lifestyle data that includes method uses lifestyle data that includes

hundreds of variables such as hobbies, the hundreds of variables such as hobbies, the websites people visit, and the amount of websites people visit, and the amount of

television they watch, as well as estimates of television they watch, as well as estimates of their income. Aviva’s predictive model, their income. Aviva’s predictive model, developed by Deloitte Consulting, was developed by Deloitte Consulting, was

considered successful at identifying health considered successful at identifying health risks.”risks.”

Source:Source: Big Data: A Revolution That Will Transform How We Live, Big Data: A Revolution That Will Transform How We Live, Work, and Think, by Viktor Mayer-Schonberger and Kenneth CukierWork, and Think, by Viktor Mayer-Schonberger and Kenneth Cukier

Editor-in-chief Chris Anderson authored Editor-in-chief Chris Anderson authored a a WiredWired cover story titled “The Petabyte cover story titled “The Petabyte

Age.” The use of “big data” (more or Age.” The use of “big data” (more or less everything, not a sample) and the less everything, not a sample) and the attendant primacy of correlation over attendant primacy of correlation over

causation as the basis for discovery was causation as the basis for discovery was described thusly: “The data deluge described thusly: “The data deluge

makes the scientific method obsolete.” makes the scientific method obsolete.” He also called the phenomenon “the He also called the phenomenon “the

end of theory.”end of theory.”


AUTOMATE THIS: AUTOMATE THIS: HOW ALGORITHMS HOW ALGORITHMS CAME TO RULE THE CAME TO RULE THE

WORLDWORLD

Automate This: Automate This: How Algorithms How Algorithms

Came to Rule Came to Rule Our WorldOur World —Christopher —Christopher

SteinerSteiner

April 2011. Prof Michael Eisen goes to April 2011. Prof Michael Eisen goes to Amazon to buy book Amazon to buy book The Making of a FlyThe Making of a Fly. .

Expects price to be $35-$40.Expects price to be $35-$40. Follows Follows bid war for 3 days: Price hits bid war for 3 days: Price hits

$23,698,655.93$23,698,655.93. . Culprit: “Unsupervised [pricing] Culprit: “Unsupervised [pricing]

algorithm.” (Parallels 5/6/10 Wall algorithm.” (Parallels 5/6/10 Wall Street flash crash: Market dropped Street flash crash: Market dropped

1K points in about 5 minutes.)1K points in about 5 minutes.)From: Christopher Steiner, From: Christopher Steiner, Automate This: How Algorithms Came to Rule Our WorldAutomate This: How Algorithms Came to Rule Our World

““Algorithms have already written symphonies Algorithms have already written symphonies as moving as those composed by as moving as those composed by

Beethoven, picked through legalese , picked through legalese

with the deftness of a senior with the deftness of a senior law partner, diagnosed patients with more , diagnosed patients with more

accuracy than a accuracy than a doctor, written news , written news

articles with the smooth hand of a seasonedarticles with the smooth hand of a seasoned reporter, and driven vehicles on urban , and driven vehicles on urban

highways with far better control than a human highways with far better control than a human

driver.”.”

——Christopher Steiner,Christopher Steiner, Automate This: How Algorithms Came to Rule Our World

““When you ask [Cloudera founder Jeffrey] When you ask [Cloudera founder Jeffrey] Hammerbacher what he sees as the most promising Hammerbacher what he sees as the most promising field that could be hacked by people like himself, he field that could be hacked by people like himself, he responds with two words: ‘Medical diagnostics.’ And responds with two words: ‘Medical diagnostics.’ And clearly doctors should be watching their backs, but clearly doctors should be watching their backs, but

they should be extra vigilant knowing that the they should be extra vigilant knowing that the smartest guys of our generation—people like smartest guys of our generation—people like

Hammerbacher---are gunning for them. The targets on Hammerbacher---are gunning for them. The targets on their backs will only grow larger as their complication their backs will only grow larger as their complication

rates, their test results and their practicesare rates, their test results and their practicesare scrutinized by the unyielding eyeof algorithms built by scrutinized by the unyielding eyeof algorithms built by smart engineers. smart engineers. Doctors aren’t going away, but Doctors aren’t going away, but those who want to ensure their employment those who want to ensure their employment

in the future should find ways to be in the future should find ways to be exceptional. Bots can handle the grunt work, exceptional. Bots can handle the grunt work,

the work that falls to our average the work that falls to our average practitioners.”practitioners.” —Christopher Steiner, —Christopher Steiner, Automate This: How Automate This: How

Algorithms Came to Rule Our WorldAlgorithms Came to Rule Our World

Shades of Ned Ludd …Shades of Ned Ludd …

““When Emmy [algorithm] When Emmy [algorithm] produced orchestral pieces so produced orchestral pieces so impressive that some music impressive that some music

scholars failed to identify them as scholars failed to identify them as the work of a machine, [Prof. the work of a machine, [Prof. David] Cope instantly created David] Cope instantly created legions of enemies. … At an legions of enemies. … At an

academic conference in Germany, academic conference in Germany, one ofone of

his peers walked up to him and his peers walked up to him and whacked him on the nose. …”whacked him on the nose. …”

——Christopher Steiner, Christopher Steiner, Automate This: How Algorithms Came to Rule Our WorldAutomate This: How Algorithms Came to Rule Our World

“ … “ … The audience then voted on The audience then voted on the identity of each composition.* the identity of each composition.*

[Music theory professor and [Music theory professor and contest organizer] Larson’s pride contest organizer] Larson’s pride took a ding when his piece was took a ding when his piece was

fingered as that belonging to the fingered as that belonging to the computer. When the crowd computer. When the crowd

decided that [algorithm] Emmy’s decided that [algorithm] Emmy’s piece was the true product of the piece was the true product of the

late musician [Bach], Larson late musician [Bach], Larson winced.” winced.” —Christopher Steiner, —Christopher Steiner,

Automate This: How Algorithms Came to Rule Our WorldAutomate This: How Algorithms Came to Rule Our World

*There were three: Bach/Larson/Emmy-the-algorithm.*There were three: Bach/Larson/Emmy-the-algorithm.

“ … “ … Which haiku are human writing and which Which haiku are human writing and which are from a group of bits? Sampling centuries of are from a group of bits? Sampling centuries of

haiku, devising rules, spotting patterns, and haiku, devising rules, spotting patterns, and inventing ways to inject originality, Annie inventing ways to inject originality, Annie

[algorithm] took to the short Japanese sets of [algorithm] took to the short Japanese sets of prose the same way all of [Prof David] Cope’s. prose the same way all of [Prof David] Cope’s. algorithms tackled classical music. ‘In the end, algorithms tackled classical music. ‘In the end,

it’s just layers and layers of binary math, he it’s just layers and layers of binary math, he says. … Cope says says. … Cope says Annie’sAnnie’s penchantpenchant forfor tastefultasteful

oriorigginalitinality could push her past most human y could push her past most human composers who simply build on work of the composers who simply build on work of the

past., which, in turn, was built on older works. past., which, in turn, was built on older works. …” …” —Christopher Steiner, —Christopher Steiner, Automate This: How Algorithms Automate This: How Algorithms

Came to Rule Our WorldCame to Rule Our World

Legal industry/Pattern Legal industry/Pattern Recognition/Discovery (e-Recognition/Discovery (e-

discovery algorithms):discovery algorithms):

500500 lawyers to … lawyers to …

ONEONESource:Source: Race AGAINST the Machine, Race AGAINST the Machine, Erik Brynjolfsson and Andrew McAfeeErik Brynjolfsson and Andrew McAfee

Lionbridge/IBM: GeoFluentLionbridge/IBM: GeoFluentEvaluated as successfulEvaluated as successful

in customer-service in customer-service transactions; medical diagnosis transactions; medical diagnosis Medical knowledge from labs, Medical knowledge from labs,

descriptions, via pattern descriptions, via pattern recognition/intuitionrecognition/intuition

Watson/IBM: Beats human Watson/IBM: Beats human Jeopardy players w/ puns, other Jeopardy players w/ puns, other

idiosyncratic word playidiosyncratic word play

Source:Source: Race AGAINST the Machine, Race AGAINST the Machine, Erik Brynjolfsson and Andrew McAfeeErik Brynjolfsson and Andrew McAfee

StatsMonkey: Sports StatsMonkey: Sports writing (Readers writing (Readers

cannot tell difference)cannot tell difference)


REALITY IS BROKEN: REALITY IS BROKEN: WHY GAMES MAKEWHY GAMES MAKE

US BETTER AND HOW US BETTER AND HOW THEY CAN CHANGETHEY CAN CHANGE

THE WORLDTHE WORLD

Reality Is Broken: Reality Is Broken: Why Games MakeWhy Games Make

Us Better and How Us Better and How They Can ChangeThey Can Change

the World the World —Jane McGonigal—Jane McGonigal

MMORPG/Massively MMORPG/Massively Multiplayer Online Multiplayer Online Role-Playing GameRole-Playing GameSource: Source: Jane McGonigal, Jane McGonigal, Reality Is Broken: Why Games MakeReality Is Broken: Why Games Make

Us Better and How They Can Change the World Us Better and How They Can Change the World

““Why exactly are we competing with each other to do Why exactly are we competing with each other to do the dirty work? We’re playing a free online game the dirty work? We’re playing a free online game

called called Chore WarsChore Wars —and it just —and it just

so happens that ridding our real-world kingdom of so happens that ridding our real-world kingdom of toilet stains is worth more experience points, or XP, toilet stains is worth more experience points, or XP, than any other chore in our apartment. … A mom in than any other chore in our apartment. … A mom in

Texas describes a typical Chore Wars experience: ‘We Texas describes a typical Chore Wars experience: ‘We have three kids, ages 9, 8, and 7. I sat down with the have three kids, ages 9, 8, and 7. I sat down with the

kids, showed them their characters and the kids, showed them their characters and the adventures, and they literally jumped up and ran off adventures, and they literally jumped up and ran off to complete their chosen task. I’ve never seen my 8-to complete their chosen task. I’ve never seen my 8-year-old son make his bed. I nearly fainted when my year-old son make his bed. I nearly fainted when my

husband cleaned out the toaster oven.’ …”husband cleaned out the toaster oven.’ …”

——Jane McGonigal, Jane McGonigal, Reality Is Broken: Why Games Make Us BetterReality Is Broken: Why Games Make Us Better

and How They Can Change the Worldand How They Can Change the World

““You get a sense of the scale and You get a sense of the scale and intricacy of the task by considering intricacy of the task by considering the sound effects alone: The game the sound effects alone: The game

contains 54,000 pieces of audio and contains 54,000 pieces of audio and 40,000 lines of dialogue. There are 40,000 lines of dialogue. There are 2,700 different noises for footsteps 2,700 different noises for footsteps alone depending on whose foot is alone depending on whose foot is stepping on what.” stepping on what.” ——Sam LeithSam Leith onon Halo 3, Halo 3,

from from Jane McGonigal, Jane McGonigal, Reality Is Broken: Why Games MakeReality Is Broken: Why Games Make Us Better and How They Can Change the World Us Better and How They Can Change the World

““The popularity of an unwinnable game The popularity of an unwinnable game like Tetris completely upends the like Tetris completely upends the stereotype that gamers are highly stereotype that gamers are highly

competitive people who care more about competitive people who care more about winning than anything else. Competition winning than anything else. Competition

and winning are and winning are notnot defining traits of defining traits of games—nor are they defining interests of games—nor are they defining interests of the people who love to play them. Many the people who love to play them. Many gamers would rather keep playing than gamers would rather keep playing than

win. In high-feedback games, the state of win. In high-feedback games, the state of being intensely engaged may ultimately be being intensely engaged may ultimately be more pleasurable than the satisfaction of more pleasurable than the satisfaction of winning.” winning.” —Jane McGonigal, —Jane McGonigal, Reality Is Broken: Why Games Make Reality Is Broken: Why Games Make

Us Better and How They Can Change the World Us Better and How They Can Change the World

““When we are playing a When we are playing a well-designed game, failure well-designed game, failure

doesn’t disappoint us. It doesn’t disappoint us. It makes us happy in a very makes us happy in a very

peculiar way: excited, peculiar way: excited, interested, and most of all interested, and most of all optimistic.”optimistic.” —Studies from M.I.N.D. Lab, Helsinki, in —Studies from M.I.N.D. Lab, Helsinki, in

Jane McGonigal, Jane McGonigal, Reality Is Broken: Why Games Make Us Better and How Reality Is Broken: Why Games Make Us Better and How They Can Change the World They Can Change the World

““It may have once been true It may have once been true that computer games that computer games

encouraged us to act more with encouraged us to act more with machines than with each other. machines than with each other. But if you still think of gamers But if you still think of gamers

as loners, then you’re not as loners, then you’re not playing games.”playing games.” —Jane McGonigal, —Jane McGonigal, Reality Is Broken: Reality Is Broken:

Why Games Make Us Better and How They Can Change the World Why Games Make Us Better and How They Can Change the World

““World of Warcraft is the World of Warcraft is the singlemost powerful IV singlemost powerful IV

drip of productivity ever drip of productivity ever created.”created.” —Brian, friend, in—Brian, friend, in Jane McGonigal, Jane McGonigal,

Reality Is Broken: Why Games Make Us Better and How They Can Reality Is Broken: Why Games Make Us Better and How They Can Change the World Change the World

3-D 3-D PRINTING/ PRINTING/ FAB LABSFAB LABS

Fab Labs/Fabrication Fab Labs/Fabrication Labs/Fabulous Labs/digital Labs/Fabulous Labs/digital fabrication machine/parts fabrication machine/parts

themselves are digitalized/3-D themselves are digitalized/3-D printer/MIT Center for Bits and printer/MIT Center for Bits and Atoms/ Prof Neil Gershenfeld/ Atoms/ Prof Neil Gershenfeld/ $5K: “large-format computer-$5K: “large-format computer-controlled milling machine can controlled milling machine can make all the parts in an IKEA make all the parts in an IKEA flat-pack box” customized for flat-pack box” customized for

the individual/Etc./Etc.the individual/Etc./Etc.

Source: “How to Make Almost Anything,” Beil Gershenfeld, Source: “How to Make Almost Anything,” Beil Gershenfeld, Foreign AffairsForeign Affairs/11-12.2012/11-12.2012

It’s Getting a Little Weird OutIt’s Getting a Little Weird Out**

Bradesco’s biometric ATM Bradesco’s biometric ATM sensors/blood flowsensors/blood flow ((Economist Economist 0519)0519)

Oscar Pistorius’ sprinting Oscar Pistorius’ sprinting acumen/approved foracumen/approved for

London London ((WSJWSJ 0602) 0602)

DelFly/lighter than your DelFly/lighter than your wedding ringwedding ring ((EconomistEconomist 0602) 0602)

*Kurzweil’s Singularity is nigh?!*Kurzweil’s Singularity is nigh?!

RACE RACE AGAINST AGAINST

THE THE MACHINEMACHINE

Race AGAINST The Machine: Race AGAINST The Machine: How the Digital Revolution Is How the Digital Revolution Is

Accelerating Innovation, Accelerating Innovation, Driving Productivity, and Driving Productivity, and Irreversibly Transforming Irreversibly Transforming

Employment and the Employment and the EconomyEconomy —Erik Brynjolfsson and Andrew McAfee —Erik Brynjolfsson and Andrew McAfee

““The root of our problem is notThe root of our problem is not that we’re in a Great Recessionthat we’re in a Great Recession

or a Great Stagnation, but ratheror a Great Stagnation, but rather that we are in the early that we are in the early

throes of a throes of a Great Restructuring. Our . Our

technologies are racing ahead,technologies are racing ahead, but our skills and organizationsbut our skills and organizations

are lagging behind.”are lagging behind.”


Explanations for Slow RecoveryExplanations for Slow Recovery

CyclicalCyclicalStagnationStagnation

Rise of BRICS+Rise of BRICS+““End of Work”/ End of Work”/

Accelerated Pace* of Accelerated Pace* of Technological ChangeTechnological Change

*The “second half of the chessboard”*The “second half of the chessboard”


Worst in 30 Years!Worst in 30 Years!

““The number of Americans in the labor force — The number of Americans in the labor force — those who have a job or are looking for one — those who have a job or are looking for one —

fell by nearly half a million people from fell by nearly half a million people from February to March [2013], the government February to March [2013], the government said Friday. And the percentage of working-said Friday. And the percentage of working-age adults in the labor force — what's called age adults in the labor force — what's called the participation rate — fell to 63.3 percent the participation rate — fell to 63.3 percent

last monthlast month. It's the lowest such figure since May

1979.”

Source: AP/0407.13Source: AP/0407.13

+400,000+400,000--

2,000,0002,000,000

+400,000+400,000*/*/-2,000,000-2,000,000****

“new computing technologies “new computing technologies that destroy middle-class that destroy middle-class

[white-collar] jobs even as they [white-collar] jobs even as they create jobs for highly skilled create jobs for highly skilled

workers who can exploit them”workers who can exploit them”

**ManufacturinManufacturingg jobs jobs addedadded USA 2007-2012 USA 2007-2012

****WhiteWhite--collarcollar jobs jobs llostost USA 2007-2012USA 2007-2012

Source: Source: Financial TimesFinancial Times, page 1, 0402.13 , page 1, 0402.13 (“Clerical Staff Bears Brunt of US Jobs Crisis”)(“Clerical Staff Bears Brunt of US Jobs Crisis”)

3 million jobs unfilled/6% 3 million jobs unfilled/6% unemployment per se/50% unemployment per se/50% companies with shortfall in companies with shortfall in

skilled people/college degree not skilled people/college degree not required: “The numbers of the required: “The numbers of the

undertrained are undertrained are staggering.”/MA: 100K jobs @ staggering.”/MA: 100K jobs @

$75K; 40% SMEs report $75K; 40% SMEs report “difficulty finding skilled “difficulty finding skilled

craftsmen to replace retirees”craftsmen to replace retirees”

Source:Source: Nina EastonNina Easton/ Fortune// Fortune/11.201211.2012

22ndnd Half of the Chessboard Half of the Chessboard

Squares 1-32; 4B grains = Squares 1-32; 4B grains = 1 large field1 large field

Squares 33-64; pile bigger thanSquares 33-64; pile bigger than Mt EverestMt Everest


““The median The median worker is worker is

losing the race losing the race against the against the

machine.”machine.” —Erik Brynjolfsson —Erik Brynjolfsson

and Andrew McAfee, and Andrew McAfee, The Race Against the MachineThe Race Against the Machine

““A bureaucrat A bureaucrat is an expensive is an expensive

microchip.”microchip.”

——Dan Sullivan, consultant and executive coachDan Sullivan, consultant and executive coach

“The median worker is losing the race against the machine.” —Erik Brynjolfsson and —Erik Brynjolfsson and

Andrew McAfee, The Race Against the MachineAndrew McAfee, The Race Against the Machine

“A bureaucrat is an expensive microchip.”

——Dan Sullivan, consultant and executive coachDan Sullivan, consultant and executive coach

”… ”… breakage of the historic link breakage of the historic link between value creation and job between value creation and job

creation”:creation”: “The median “The median worker is losing the race worker is losing the race against the machine.”/ against the machine.”/

Great Recession: “lack of Great Recession: “lack of hiring rather than hiring rather than

increase in layoffs”increase in layoffs”


40 Years: Median inflation adjusted wages, 40 Years: Median inflation adjusted wages, men 30-50 with jobs, 1969-2009: $33K,men 30-50 with jobs, 1969-2009: $33K,

-27%

Source: “The Slow Disappearance of the American Source: “The Slow Disappearance of the American Working Man,” Working Man,” Bloomberg BusinessweekBloomberg Businessweek/08.11/08.11

Post-Great Recession: Post-Great Recession: Equipment Equipment expenditures +26%; payrolls flat/expenditures +26%; payrolls flat/““Great Recession … lack of hiring Great Recession … lack of hiring

rather than increase in layoffs”/“… rather than increase in layoffs”/“… breakage of the historic link between breakage of the historic link between

value creation and job creation”value creation and job creation”

The “U-shaped Curve” The “U-shaped Curve” Phenomenon:Phenomenon:

High-skilled Waaaaay Up!!!High-skilled Waaaaay Up!!!Low-skilled: Stable/UpLow-skilled: Stable/Up

Middle: Middle: Down/Down/DownSource:Source: Race AGAINST the Machine, Race AGAINST the Machine, Erik Brynjolfsson and Andrew McAfeeErik Brynjolfsson and Andrew McAfee

Q3 2011/BLSQ3 2011/BLS+3.1/+3.1/Non-farm productivity growthNon-farm productivity growth

+3.8/+3.8/Non-farm outputNon-farm output

+0.6/+0.6/Non-farm hours workedNon-farm hours worked

+5.4/+5.4/Manufacturing productivityManufacturing productivity

++4.74.7//Manufacturing outputManufacturing output

-0.6 / /Manufacturing hours Manufacturing hours

workedworked

Source: Bureau of Labor Statistics/03 November 2011Source: Bureau of Labor Statistics/03 November 2011

China China tootoo/Foxconn:/Foxconn:

1,000,000 1,000,000 robots in next robots in next

3 years3 yearsSource:Source: Race AGAINST the Machine, Race AGAINST the Machine, Erik Brynjolfsson and Andrew McAfeeErik Brynjolfsson and Andrew McAfee

SBTC/Skill-Biased SBTC/Skill-Biased Technical Change: Technical Change:

“race between “race between education and education and technology”technology”


Fab Labs/Fabrication Labs/Fabulous Fab Labs/Fabrication Labs/Fabulous Labs/digital fabrication machine/ Labs/digital fabrication machine/

parts themselves are digitalized/ parts themselves are digitalized/ 3-3-D printerD printer /MIT Center for /MIT Center for

Bits and Atoms/ Prof Neil Bits and Atoms/ Prof Neil Gershenfeld/ $5K: “large-format Gershenfeld/ $5K: “large-format

computer-controlled milling machine computer-controlled milling machine can make all the parts in an IKEA flat-can make all the parts in an IKEA flat-

pack box” customized for the pack box” customized for the individual/Etc./Etc.individual/Etc./Etc.

Source: “How to Make Almost Anything,” Beil Gershenfeld, Source: “How to Make Almost Anything,” Beil Gershenfeld, Foreign AffairsForeign Affairs/11-12.2012/11-12.2012

Night to Day = 6 YearsNight to Day = 6 Years

DARPA Grand Challenge DARPA Grand Challenge 2004: No dice2004: No dice

Google 2010: 140K miles in Google 2010: 140K miles in driverless carsdriverless cars


Lionbridge/IBM: GeoFluentLionbridge/IBM: GeoFluentEvaluated as successfulEvaluated as successful

in customer-service in customer-service transactions; medical diagnosis transactions; medical diagnosis Medical knowledge from labs, Medical knowledge from labs,

descriptions, via pattern descriptions, via pattern recognition/intuitionrecognition/intuition

Watson/IBM: Beats human Watson/IBM: Beats human Jeopardy players w/ puns, other Jeopardy players w/ puns, other

idiosyncratic word playidiosyncratic word play


Legal industry/Pattern Legal industry/Pattern Recognition/Discovery (e-Recognition/Discovery (e-

discovery algorithms):discovery algorithms):

500500 lawyers to … lawyers to …

ONEONESource:Source: Race AGAINST the Machine, Race AGAINST the Machine, Erik Brynjolfsson and Andrew McAfeeErik Brynjolfsson and Andrew McAfee

Bachelor’s degree, age Bachelor’s degree, age 25-34: 40% F; 30% M25-34: 40% F; 30% M

Graduate degree Graduate degree students: 60% F; 40% Mstudents: 60% F; 40% M

Source: Source: Sydney Morning HeraldSydney Morning Herald /26.03.12 /26.03.12

StatsMonkey: Sports StatsMonkey: Sports writing (Readers writing (Readers

cannot tell difference)cannot tell difference)


Standard optimization Standard optimization problem, 1998-2003: problem, 1998-2003:

43,000,000-fold speed 43,000,000-fold speed improvement; 1,000X improvement; 1,000X

processor speed; 43,000X processor speed; 43,000X algorithms betteralgorithms better


China China tootoo/Foxconn:/Foxconn:

1,000,000 1,000,000 robots in next robots in next

3 years3 yearsSource:Source: Race AGAINST the Machine, Race AGAINST the Machine, Erik Brynjolfsson and Andrew McAfeeErik Brynjolfsson and Andrew McAfee

Post-Great Recession: Post-Great Recession:

Equipment Equipment expendituresexpenditures

+26%; payrolls flat+26%; payrolls flat


USA/AgricultureUSA/Agriculture

1800: 90%1800: 90%1900: 41%1900: 41%2000: 2%2000: 2%


The “U-shaped Curve” Phenomenon:The “U-shaped Curve” Phenomenon:

High-skilled Waaaaay Up!!!High-skilled Waaaaay Up!!!Low-skilled: Stable/UpLow-skilled: Stable/Up

Middle: Down/Down/DownMiddle: Down/Down/Down


SMEs!SMEs!Source:Source: Race AGAINST the Machine, Race AGAINST the Machine, Erik Brynjolfsson and Andrew McAfeeErik Brynjolfsson and Andrew McAfee

““In the wake of the 2012 presidential election, some political commentators have In the wake of the 2012 presidential election, some political commentators have written political obituaries of the "red" or conservative-leaning states, envisioning written political obituaries of the "red" or conservative-leaning states, envisioning a brave new world dominated by fashionably blue bastions in the Northeast or a brave new world dominated by fashionably blue bastions in the Northeast or California. But political fortunes are notoriously fickle, while economic trends tend California. But political fortunes are notoriously fickle, while economic trends tend to be more enduring.to be more enduring.

““These trends point to a U.S. economic future dominated by four These trends point to a U.S. economic future dominated by four growth corridors that are generally less dense, more affordable, growth corridors that are generally less dense, more affordable, and markedly more conservative and pro-business: the Great and markedly more conservative and pro-business: the Great Plains, the Intermountain West, the Third Coast (spanning the Plains, the Intermountain West, the Third Coast (spanning the Gulf states from Texas to Florida), and the Southeastern Gulf states from Texas to Florida), and the Southeastern industrial belt. industrial belt.

““Overall, these corridors account for 45% of the nation's land Overall, these corridors account for 45% of the nation's land mass and 30% of its population. Between 2001 and 2011, job mass and 30% of its population. Between 2001 and 2011, job growth in the Great Plains, the Intermountain West and the growth in the Great Plains, the Intermountain West and the Third Coast was between 7% and 8%—nearly 10 times the job Third Coast was between 7% and 8%—nearly 10 times the job growth rate for the rest of the country. Only the Southeastern growth rate for the rest of the country. Only the Southeastern industrial belt tracked close to the national average.industrial belt tracked close to the national average.

““Historically, these regions were little more than resource colonies or low-wage Historically, these regions were little more than resource colonies or low-wage labor sites for richer, more technically advanced areas. By promoting policies that labor sites for richer, more technically advanced areas. By promoting policies that encourage enterprise and spark economic growth, they're catching up.”encourage enterprise and spark economic growth, they're catching up.”

Source: Joel Kotkin, Source: Joel Kotkin, Wall Street JournalWall Street Journal, 0225.13, 0225.13

““We are in no danger of runningWe are in no danger of running out of new combinations to try. out of new combinations to try.

Even if technology froze today, we Even if technology froze today, we have more possible ways of have more possible ways of

configuring the different configuring the different applications, machines, tasks, and applications, machines, tasks, and

distribution channels to create distribution channels to create new processes and products than new processes and products than we could ever exhaust.”we could ever exhaust.” —Erik Brynjolfsson and —Erik Brynjolfsson and

Andrew McAfee, The Race Against the Machine: How the Digital Revolution Is Andrew McAfee, The Race Against the Machine: How the Digital Revolution Is Accelerating Innovation, Driving Productivity and Irreversibly Transforming Accelerating Innovation, Driving Productivity and Irreversibly Transforming

Employment and the Economy Employment and the Economy

Muhammad Yunus:Muhammad Yunus: ““All human All human beings are beings are

entrepreneursentrepreneurs.. When we were When we were in the caves we were all self-employed in the caves we were all self-employed

. . . finding our food, feeding . . . finding our food, feeding ourselves. That’s where human history ourselves. That’s where human history

began . . . As civilization came we began . . . As civilization came we suppressed it. We became labor suppressed it. We became labor

because they stamped us, ‘You are because they stamped us, ‘You are labor.’ We forgot that we are labor.’ We forgot that we are entrepreneurs.”entrepreneurs.” —Muhammad Yunus/

The News Hour/PBS/1122.2006

““Human Human creativitycreativity

is the ultimate is the ultimate economic economic

resource.”resource.” —Richard Florida—Richard Florida

USA 1996-2007USA 1996-2007

HighestHighest rate rate entrepreneurial activity entrepreneurial activity

(firms founded): (firms founded): Ages Ages 55-6455-64

LowestLowest rate: Ages rate: Ages 20-3420-34

Source: Dane Stangler, Kauffman Foundation (Source: Dane Stangler, Kauffman Foundation (EconomistEconomist))

““The average age of a The average age of a startup founder is 40. startup founder is 40. And And high-high-ggrowth starturowth startupps s are nearlare nearlyy twice as twice as

likellikelyy to be launched b to be launched byy ppeoeopple over 55 as ble over 55 as byy ppeoeopple 20-34le 20-34.”.” —Vivek Wadhwa, —Vivek Wadhwa,

Kauffman foundation (Kauffman foundation (TimeTime/0325.13)/0325.13)

““The prospect of contracting a gofer on an aThe prospect of contracting a gofer on an a la carte basis is enticing.la carte basis is enticing. For instance, wouldn’t it For instance, wouldn’t it

be convenient if I could outsource someone to be convenient if I could outsource someone to write a paragraph here, explaining the history of write a paragraph here, explaining the history of outsourcing in Americaoutsourcing in America? ? Good idea! I went ahead Good idea! I went ahead and commissioned just such a paragraph from Get and commissioned just such a paragraph from Get Friday, a ‘virtual personal assistant- firm based in Friday, a ‘virtual personal assistant- firm based in Bangalore. … The paragraph arrived in my in-box Bangalore. … The paragraph arrived in my in-box ten days after I ordered it. It was 1,356 words. ten days after I ordered it. It was 1,356 words.

There is a bibliography with eleven sources. … At There is a bibliography with eleven sources. … At $14 an hour for seven hours of work, the cost came $14 an hour for seven hours of work, the cost came

to $98. …”to $98. …” —Patricia Marx, “Outsource Yourself,” —Patricia Marx, “Outsource Yourself,” The New Yorker, The New Yorker, 01.14.201301.14.2013 (Marx describes in detail contracting out everything associated (Marx describes in detail contracting out everything associated

with hosting her book with hosting her book club club —including the provision of “witty” —including the provision of “witty” comments on Proust, since she hadn’t had time to read the book—comments on Proust, since she hadn’t had time to read the book—

excellent comments only set her back $5;excellent comments only set her back $5; the the writer/contractor turned out to be a 14-writer/contractor turned out to be a 14-yyear-old ear-old ggirl irl

from New Jersefrom New Jerseyy.).)

ADDICTION ADDICTION BY DESIGNBY DESIGN

Machine GamblingMachine Gambling

66% revenue66% revenue85% profit85% profit

Source:Source: Natasha Dow Schüll, Natasha Dow Schüll, Addiction By Addiction By Design: Machine Gambling in Las VegasDesign: Machine Gambling in Las Vegas

Machine GamblingMachine Gambling

““Pleasing” odor #1 vs. Pleasing” odor #1 vs. “pleasing” odor #2: “pleasing” odor #2:

+45%+45% revenue revenue

Source:Source: “Effects of Ambient Odors on Slot-Machine Useage in “Effects of Ambient Odors on Slot-Machine Useage in Las Vegas Casinos,” reported in Las Vegas Casinos,” reported in Natasha Dow Schüll, Natasha Dow Schüll, Addiction By Design: Machine Gambling in Las VegasAddiction By Design: Machine Gambling in Las Vegas

““When Friedman slightly curved When Friedman slightly curved the right angle of an entrance the right angle of an entrance

corridor to one property, he was corridor to one property, he was ‘amazed at the magnitude of ‘amazed at the magnitude of

change in pedestrians’ change in pedestrians’ behavior’ (the percentage who behavior’ (the percentage who entered increased from entered increased from oneone--thirdthird to nearly to nearly twotwo--thirdsthirds).”).” ——

Natasha Dow Schüll, Natasha Dow Schüll, Addiction By Design: Machine Gambling in Addiction By Design: Machine Gambling in Las VegasLas Vegas

THE MYTH OF THE MYTH OF AMERICAN DECLINE AMERICAN DECLINE AND THE GROWTH AND THE GROWTH

OF AOF A NEW ECONOMYNEW ECONOMY

Daniel Gross, Daniel Gross, The Myth The Myth of American Decline and of American Decline and

the Growth of a New the Growth of a New EconomyEconomy

““Not Dead Yet”Not Dead Yet”

BRIC/2011: $11T/$4K per capitaBRIC/2011: $11T/$4K per capitaUSA/2011: $16T/$48K per capitaUSA/2011: $16T/$48K per capita

USA/2000: 4% population/30% world GDPUSA/2000: 4% population/30% world GDPUSA/2010: 4% populattion/28% world GDPUSA/2010: 4% populattion/28% world GDP

USA productivity: ’07/1.7%; ’08/2.1%; ’09/5.4%; ’10/2.4%; USA productivity: ’07/1.7%; ’08/2.1%; ’09/5.4%; ’10/2.4%; ’11/4.1%’11/4.1%

FDIC institutions: 4Q/2008/-$38B; 2Q/2011/+$29BFDIC institutions: 4Q/2008/-$38B; 2Q/2011/+$29B

1/2008 to 9/2011: USA consumer savings 0% to 6%/$2.1T saved1/2008 to 9/2011: USA consumer savings 0% to 6%/$2.1T saved

Foreign Direct Investment: 2003: $64B; 2008: $328B; 2009: Foreign Direct Investment: 2003: $64B; 2008: $328B; 2009: $134B; 2011: $200B+$134B; 2011: $200B+

Exports/2009: USA $1.53T ($1.06T goods, $0.47T services); Exports/2009: USA $1.53T ($1.06T goods, $0.47T services); Germany $1.36T; China $1.33TGermany $1.36T; China $1.33T

USA/Refined petroleum products/1Q 2011: Imports 2.16M BPD; USA/Refined petroleum products/1Q 2011: Imports 2.16M BPD; Exports 2.49M BPDExports 2.49M BPD

““New economy”: Apple (>Exxon) + Google + Facebook ~ $1T New economy”: Apple (>Exxon) + Google + Facebook ~ $1T market capmarket cap

Source: Daniel Gross, Source: Daniel Gross, The Myth of American Decline and the Growth of a New EconomyThe Myth of American Decline and the Growth of a New Economy

iPad/$4 billioniPad/$4 billion of $300 billion negative of $300 billion negative USA trade balance with USA trade balance with

China (2011)China (2011)

Cost/Profit Components:Cost/Profit Components:

Total labor 7%Total labor 7%

(Chinese labor: 2%)(Chinese labor: 2%)Materials 31%Materials 31%

Distribution: 15%Distribution: 15%Profit: 47%Profit: 47%

Landed iPad cost: Landed iPad cost: $275 = $275 = ImImpputeduted USA negative trade balance with ChinaUSA negative trade balance with China

((ActualActual China cost: $10) China cost: $10)

Source: Personal Computing Industry Centre (Source: Personal Computing Industry Centre (EconomistEconomist))

CostCost**/Profit Components:/Profit Components:

Total labor 7%Total labor 7%

(Chinese labor: 2%)(Chinese labor: 2%)Materials 31%Materials 31%

Distribution: 17%Distribution: 17%Profit: 47%Profit: 47%

Landed iPad cost: Landed iPad cost: $275 = $275 = ImImpputeduted USA negative trade balance with ChinaUSA negative trade balance with China

((ActualActual China cost: $10) China cost: $10)

*Biggest non-USA component: *Biggest non-USA component: KoreaKoreaSource: Personal Computing Industry Centre (Source: Personal Computing Industry Centre (EconomistEconomist))

Q3 2011/BLSQ3 2011/BLS

+3.1/+3.1/Non-farm productivity growthNon-farm productivity growth

+3.8/+3.8/Non-farm outputNon-farm output

+0.6/+0.6/Non-farm hours workedNon-farm hours worked

+5.4/+5.4/Manufacturing productivityManufacturing productivity

++4.74.7//Manufacturing outputManufacturing output

-0.6//Manufacturing hours workedManufacturing hours worked

Source: Bureau of Labor Statistics/03 November 2011Source: Bureau of Labor Statistics/03 November 2011

It’s Getting a Little Strange OutIt’s Getting a Little Strange Out**

DelFly/lighter than yourDelFly/lighter than your wedding ringwedding ring ((EconomistEconomist 0602) 0602)

Oscar Pistorius’ sprinting Oscar Pistorius’ sprinting acumen/approved foracumen/approved for

LondonLondon ((WSJWSJ 0602) 0602)

*See Ray Kurzweil, *See Ray Kurzweil, The Singularity Is Near: The Singularity Is Near: When Humans Transcend Biology When Humans Transcend Biology;; key keychapter, “GNR: Three Overlapping Revolutions” chapter, “GNR: Three Overlapping Revolutions” (GNR: (GNR: GeneticsGenetics, , NanotechnoloNanotechnology, gy, RoboticsRobotics) )

““In some sense you can In some sense you can argue that the science argue that the science

fiction scenario is already fiction scenario is already starting to happen. starting to happen. The The computers are in control. computers are in control.

We We jjust live in their ust live in their worldworld.”.” —Danny Hillis, Thinking —Danny Hillis, Thinking

Machines Machines

““Unless mankind Unless mankind redesigns itself by redesigns itself by changing our DNAchanging our DNA

through altering our through altering our genetic makeup, genetic makeup,

comcompputeruter-g-generated enerated robots will take overrobots will take over

the worldthe world.”.” – Stephen Hawking– Stephen Hawking

THE SHAREHOLDER THE SHAREHOLDER VALUE MYTH: HOW VALUE MYTH: HOW

PUTTING SHAREHOLDERS PUTTING SHAREHOLDERS FIRST HARMS INVESTORS, FIRST HARMS INVESTORS,

CORPORATIONS, ANDCORPORATIONS, AND THE PUBLIC THE PUBLIC

Lynn Stout, professor of Lynn Stout, professor of corporate and business corporate and business law, Cornell Law school, law, Cornell Law school, author author The Shareholde,r The Shareholde,r Value Myth: How Putting Value Myth: How Putting Shareholders First Harms Shareholders First Harms Investors, Corporations, Investors, Corporations,

and the Publicand the Public

““The notion that corporate law The notion that corporate law requires directors, executives, and requires directors, executives, and employees to maximize shareholder employees to maximize shareholder wealth simply isn’t true. There is no wealth simply isn’t true. There is no

solid legal support for the claim solid legal support for the claim that directors and executives in that directors and executives in U.S. public corporations have an U.S. public corporations have an

enforceable legal duty to maximize enforceable legal duty to maximize shareholder wealth. The idea is shareholder wealth. The idea is

fable.”fable.” —Lynn Stout, professor of corporate and business law, —Lynn Stout, professor of corporate and business law, Cornell Law school, in Cornell Law school, in The Shareholder Value Myth: How Putting The Shareholder Value Myth: How Putting Shareholders First Harms Investors, Corporations, and the PublicShareholders First Harms Investors, Corporations, and the Public

““Courts uniformly refuse to actually Courts uniformly refuse to actually impose sanctions on directors or impose sanctions on directors or

executives for failing to pursue one executives for failing to pursue one purpose over another. In particular, purpose over another. In particular, courts refuse to hold directors of courts refuse to hold directors of

public corporations legally public corporations legally accountable for failing to maximize accountable for failing to maximize

shareholder wealth.”shareholder wealth.” —Lynn Stout,—Lynn Stout, professor of corporate and business law, Cornell Law school, professor of corporate and business law, Cornell Law school,

in in The Shareholder Value Myth: How Putting Shareholders FirstThe Shareholder Value Myth: How Putting Shareholders First Harms Investors, Corporations, and the Public Harms Investors, Corporations, and the Public

““What about shareholders’ rights to sue What about shareholders’ rights to sue corporate officers and directors for corporate officers and directors for

breach of fiduciary duty if they fail to breach of fiduciary duty if they fail to maximize shareholder wealth? Such a maximize shareholder wealth? Such a

right turns out to be illusory. Executives’ right turns out to be illusory. Executives’ and directors’ duty of loyalty to the and directors’ duty of loyalty to the

corporation bars them from using their corporation bars them from using their corporate positions to enrich themselves corporate positions to enrich themselves at the firm’s expense, but unconflicted at the firm’s expense, but unconflicted directors remain legally free to pursue directors remain legally free to pursue

almost any other goal.”almost any other goal.” —Lynn Stout,—Lynn Stout, professor of corporate and business law, Cornell Law school, professor of corporate and business law, Cornell Law school,

in in The Shareholder Value Myth: How Putting Shareholders FirstThe Shareholder Value Myth: How Putting Shareholders First Harms Investors, Corporations, and the Public Harms Investors, Corporations, and the Public

““From a legal perspective, shareholders do not, From a legal perspective, shareholders do not, and cannot, own corporations. and cannot, own corporations. Corporations Corporations

are indeare indeppendent leendent leggal entities that own al entities that own themselvesthemselves, just as human beings own , just as human beings own

themselves. … Shareholders own shares of themselves. … Shareholders own shares of stock. A share of stock is simply a contract stock. A share of stock is simply a contract

between the shareholder and the corporation, a between the shareholder and the corporation, a contract that gives the shareholder very limited contract that gives the shareholder very limited

rights under limited circumstances. In this rights under limited circumstances. In this sense, stockholders are no different from sense, stockholders are no different from

bondholders, suppliers, and employees. All bondholders, suppliers, and employees. All have contractual relationships with the have contractual relationships with the

corporate entity. None ‘owns’ the company corporate entity. None ‘owns’ the company itself.”itself.”

—Lynn Stout, professor of corporate and business law, —Lynn Stout, professor of corporate and business law, Cornell Law school, in Cornell Law school, in The Shareholder Value Myth: How Putting The Shareholder Value Myth: How Putting Shareholders First Harms Investors, Corporations, and the PublicShareholders First Harms Investors, Corporations, and the Public

““[a corporation] can be [a corporation] can be formed to conduct or formed to conduct or promote any lawful promote any lawful

business or purpose”business or purpose”

—from Delaware corporate code (no mandate—from Delaware corporate code (no mandate for shareholder primacy), for shareholder primacy), perper Lynn Stout, Lynn Stout, professor of corporate and business law, professor of corporate and business law,

Cornell Law school, in Cornell Law school, in The Shareholder Value Myth:The Shareholder Value Myth: How Putting Shareholders First Harms Investors, How Putting Shareholders First Harms Investors,

Corporations, and the PublicCorporations, and the Public

““On the face of it, On the face of it, shareholder value is the shareholder value is the

dumbest idea in the world. dumbest idea in the world. Shareholder value is a Shareholder value is a

result, not a strategy. … result, not a strategy. … Your main constituenciesYour main constituencies are are yyour emour empploloyyees, ees, yyour our

customers and customers and yyour our pproductsroducts.”.” —Jack Welch, —Jack Welch, FTFT, 0313.09, page 1 , 0313.09, page 1

““Too Much Cost, Not Enough Value”Too Much Cost, Not Enough Value”““Too Much Speculation, Not Enough Too Much Speculation, Not Enough

Investment”Investment”““Too Much Complexity, Not Enough Simplicity”Too Much Complexity, Not Enough Simplicity”

““Too Much Counting, Not Enough Trust”Too Much Counting, Not Enough Trust”““Too Much Business Conduct, Not Enough Too Much Business Conduct, Not Enough

Professional Conduct”Professional Conduct”““Too Much Salesmanship, Not Enough Too Much Salesmanship, Not Enough

Stewardship”Stewardship”““Too Much Focus on Things, Not Enough Focus Too Much Focus on Things, Not Enough Focus

on Commitment”on Commitment”““Too Many Twenty-first Century Values, Not Too Many Twenty-first Century Values, Not

Enough Eighteenth-Century Values”Enough Eighteenth-Century Values”““Too Much ‘Success,’ Not Enough Character”Too Much ‘Success,’ Not Enough Character”

Source: Jack Bogle, Source: Jack Bogle, Enough!Enough! (chapter titles) (chapter titles)

““Managers have lost dignity over the Managers have lost dignity over the past decade in the face of widespread past decade in the face of widespread institutional breakdown of trust and institutional breakdown of trust and self-policing in business. To regain self-policing in business. To regain

society’s trust, we believe that society’s trust, we believe that business leaders must embrace a way business leaders must embrace a way

of looking at their role that goes of looking at their role that goes beyond their responsibility to the beyond their responsibility to the shareholders to include a civic and shareholders to include a civic and

personal commitment to their duty as personal commitment to their duty as institutional custodians. In other words, institutional custodians. In other words,

it is time that management became a it is time that management became a profession.”profession.” —Rakesh Khurana & Nitin Nohria, “It’s Time To Make —Rakesh Khurana & Nitin Nohria, “It’s Time To Make

Management a True Profession,” HBR/10.08Management a True Profession,” HBR/10.08

““On the face of it, On the face of it, shareholder value is the shareholder value is the

dumbest idea in the world. dumbest idea in the world. Shareholder value is a Shareholder value is a

result, not a strategy. … result, not a strategy. … Your main constituenciesYour main constituencies are are yyour emour empploloyyees, ees, yyour our

customers and customers and yyour our pproductsroducts.”.” —Jack Welch, —Jack Welch, FTFT, 0313.09, page 1 , 0313.09, page 1

Documents

RECENT READING Tom Peters/11 July 2013. FILTER BUBBLE