Irving Wladawsky-Berger

The Rise of Global, Superstar Firms, Sectors and Cities

In the 1990s, the Internet was supposed to usher a much more open, decentralized, democratic economy and society.  Startups with innovative business models where now able to reach customers anywhere, anytime.  Companies, from the largest to the smallest, could now transact with anyone around the world.  Vertically integrated firms became virtual enterprises, increasingly relying on supply chain partners for many of the functions once done in-house.  Experts noted that large firms were no longer necessary and would in fact be at a disadvantage in the emerging digital economy when competing against agile, innovative smaller companies.

Some even predicted that the Internet would lead to the decline of cities.  People could now work and shop from home; be in touch with their friends over e-mail, video calls, and text messaging; and get access to all kinds of information and entertainment online.  Why would anyone choose to live in a crowded, expensive, crime-prone urban area when they could lead a more relaxing, affordable life in an outer suburb or small town?

But, as we well know, it hasn’t quite worked out as expected.  Instead, we’ve seen the rise of the global superstar company, the unicorn startup, and the winner-take-all city.  As it’s turned out, the Internet’s universal reach and connectivity has led to increasingly powerful network effects and to the rise of platform economies.

The World Bank’s Human Capital Index

Last week I wrote about a recently published World Bank report, The Changing Nature of Work.  The report highlights the increasing importance of investing in human capital by both the public and private sectors.  All around the world, the demand for workers who are good at complex problem-solving, interactions with clients and colleagues, and able to adapt to new tools and technologies is rising, while the demand for less advanced skills that can be replaced by technology is declining.  Neglecting investments in human capital, it warned, can dramatically weaken a country’s competitiveness.

“The world is healthier and more educated than ever…,” notes the World Bank in a companion report, The Human Capital Project.  “But a large and unfinished agenda remains.  Life expectancy in the developing world still lags far behind that of rich countries…  Worldwide, more than 260 million children and youth are not in school. Meanwhile, nearly 60 percent of primary school children in developing countries fail to achieve minimum proficiency in learning.”

Why should countries invest in human capital?  Can early health care and education prepare children to succeed and prosper as adults in a rapidly changing world?  What are the barriers to nurturing human capital and how can countries overcome them?  To address these important questions, the World Bank introduced the Human Capital Index (HCI).  The HCI is designed to quantify the amount of human capital that a child born today can expect to attain by age 18 given the prevailing health and education conditions in the country where the child lives.  It will be updated periodically and refined as data measures improve.  This video nicely explains the HCI and what it measures. 

Human Capital and the Changing Nature of Work

People have long feared that machines are coming for our jobs.  Throughout the Industrial Revolution there were periodic panics about the impact of automation on work, going back to the so-called Luddites, - textile workers who in the 1810s smashed the new machines that were threatening their jobs.

Automation anxieties have understandably accelerated in recent years, as our increasingly smart machines are now being applied to activities requiring intelligence and cognitive capabilities that not long ago were viewed as the exclusive domain of humans.  But on balance, such fears appear to be unfounded, noted the World Bank in a comprehensive recent report on The Changing Nature of Work.  Our problem is not that there won’t be enough work in the future.  Our key problem is that, in many countries, the workforce isn’t prepared for our fast unfolding future.

In a recent article in Foreign Affairs, World Bank Group President Jim Yong Kim warned that the world is facing a Human Capital Gap.  “Governments in pursuit of economic growth love to invest in physical capital - new roads, beautiful bridges, gleaming airports, and other infrastructure.  But they are typically far less interested in investing in human capital, which is the sum total of a population’s health, skills, knowledge, experience, and habits.  That’s a mistake, because neglecting investments in human capital can dramatically weaken a country’s competitiveness in a rapidly changing world, one in which economies need ever-increasing amounts of talent to sustain growth.” 

Blockchain Beyond the Hype

A recent issue of The Economist included a special report on cryptocurrencies and blockchains.  The Economist’s overall conclusion was that “Bitcoin has been a failure as a means of payment, but thrilling for speculators.”  Its assessment of blockchain was somewhat more positive.  “For blockchains, the jury is still out,… For all the technology’s potential, though, most attempts to use it remain tentative,…  The advantages of blockchains are often oversold.” 

In 2016, blockchain made the list of the World Economic Forum’s Top Ten Emerging Technologies.  That same year, blockchain also made its first appearance in Gartner’s yearly hype cycles.  Even The Economist has been guilty of overselling when it featured blockchain in the cover of its October 31, 2015 issue with the tag line “The Trust Machine: How the technology behind Bitcoin could change the world.” 

Is there too much hype surrounding blockchain?  Absolutely,… but not surprising.  All potentially transformative technologies are oversold in their early stages.  Remember the dot-com bubble of the late 1990s.  Blockchain is still in its early phases of experimentation and adoption.  Much work remains to be done on standards, platforms, interoperability, applications and governance. 

But, does blockchain have the potential to become a truly transformative technology over time?  Yes, said McKinsey in a recent article on the strategic value of blockchain beyond the hype.  The article starts out by acknowledging that all the hype around blockchain makes it difficult to nail down not just blockchain's long term strategic value, but also what it actually is and what it isn’t in the present. To make sure everyone is on the same page, the article first recaps what it calls “the nuts and bolts of blockchain.”  Let me start by summarizing blockchain’s nuts and bolts.

Redefining Work - Leveraging Human Capabilities in a Future of Expanding Automation

How will labor markets evolve in our 21st century digital economy?  What’s the likely future of jobs, given that our increasingly smart machines are now being applied to activities requiring intelligence and cognitive capabilities that not long ago were viewed as the exclusive domain of humans?  How will AI, robotics and other advanced technologies transform the very nature of work?

Over the past few years, a number of papers, reports and books have addressed these very important questions.  They generally conclude that AI will have a major impact on jobs and the very nature of work.  For the most part,  they view AI as mostly augmenting rather than replacing human capabilities, automating the more routine parts of a job and increasing the productivity and quality of workers, so they can focus on those aspect of the job that most require human attention.  Overall, few jobs will be entirely automated, but automation will likely transform the vast majority of occupations. 

A recent McKinsey report noted while there will likely be enough work to maintain full employment by 2030, the transition will be very challenging, “on a scale not seen since the transition of the labor force out of agriculture in the early 1900s in the United States and Europe, and more recently in in China.”  The report estimated that “up to 375 million workers, or 14 percent of the global workforce, may need to change occupations - and virtually all workers may need to adapt to work alongside machines in new ways.”

Given these predictions about the changing nature of work, what should companies do?  How should firms prepare for a brave new world where we can expect major economic dislocations along with the creation of new jobs, new business models and whole new industries, and where many people will be working alongside smart machines in whole new ways?

The Jobs Outlook for 2022

A few weeks ago, the World Economic Forum (WEF) released The Future of Jobs Report 2018.  The report takes an in-depth look at the world of work in what the WEF calls the Fourth Industrial Revolution. The report is based on a survey of over 300 chief human resource officers and top strategy executives from large global companies across 12 industry sectors.  These companies collectively represent more than 15 million employees, and conduct business in 20 developed and emerging economies which collectively account for about 70% of global GDP.

First introduced at its 2016 annual Davos Forum, the WEF positions the Fourth Industrial Revolution within the historical context of three previous industrial revolutions.  The First, - in the last third of the 18th century, - ushered the transition from hand-made goods to mechanized, machine-based production based on based on steam and water power.  The Second, - a century later, - revolved around steel, railroads, cars, chemicals, petroleum, electricity, the telephone and radio, leading to the age of mass production.  The Third, - starting in the 1960s, - saw the advent of digital technologies, computers, the IT industry, and the automation of process in just about all industries.

“Now a Fourth Industrial Revolution is building on the Third…” wrote WEF founder and chairman Klaus Schwab in a December, 2015 Foreign Affairs article.  “The possibilities of billions of people connected by mobile devices, with unprecedented processing power, storage capacity, and access to knowledge, are unlimited.  And these possibilities will be multiplied by emerging technology breakthroughs in fields such as artificial intelligence, robotics, the Internet of Things, autonomous vehicles, 3-D printing, nanotechnology, biotechnology, materials science, energy storage, and quantum computing.”

The Future of Jobs 2018 examines the potential of these advanced technologies to eliminate and create new jobs, as well as to improve the current workplace and prepare people for emerging jobs. Rather than looking at the future of work over the longer term, the report chose to examine the 2018-2022 period, which is within the planning horizon of the companies and executives that participated in the survey.

Blockchain’s Potential for Environmental Applications

In mid-September, the World Economic Forum (WEF) released Building Block(chain)s for a Better Planet, a report that examined how blockchain technologies could be harnessed to address serious environmental issues, better manage our shared global environment and help drive sustainable growth and value creation.  The report outlines some of the world’s most-pressing environmental challenges and highlights eight blockchain-based game changers that could lead to transformative solutions to these pressing problems.

The WEF report includes a short blockchain tutorial.  Blockchain is essentially “a new, decentralized and global computational infrastructure that could transform many existing processes in business, governance and society.”  Blockchain technologies promise to significantly improve the efficiency and security of business transactions and data sharing among participants in a global ecosystem, such as supply chains, financial services, and similar complex applications involving multiple institutions.

But, the hype surrounding blockchain, can tempt solution designers to try to use it for applications that it’s not suitable for.  The report recommends that designers first consider three key questions:

• Will blockchain solve your actual problem; is a distributed architecture vital to your solution?
• Have you built the right ecosystem of stakeholders; do they understand and trust how blockchain is being used?
• Can you acceptably manage the downside risks or unintended consequences, e.g., scalability, performance, implementation risks?

The 2018 MIT Inclusive Innovation Challenge

In 2016, MIT’s Initiative on the Digital Economy launched its first annual Inclusive Innovation Challenge (IIC).  The key objective of the IIC is to help create greater shared prosperity around the world by awarding over one million dollars in prizes each year to to entrepreneurs that are leveraging technology advances to reinvent the future of work.

“The IIC believes that Inclusive Innovation is an economic and moral imperative, and that the key question of our era isn’t what technology is going to do to our economy and society, but what we will do with technology.  By identifying and promoting the powerful global community of future of work visionaries, the IIC proactively accelerates the technology-driven solutions enabling greater economic opportunity for working people around the world facing the challenge of rapidly advancing digital progress.”

Entrepreneurs from around the globe compete for awards in four categories: income growth and job creation, skills development and opportunity matching, technology access, and financial inclusion. Approximately 1,500 organizations registered to compete this year, and 500 judges reviewed their applications.   

In 2018, rather than having one global competition as in the previous two years, there were five separate regional competitions, - North America, Latin America, Europe, Africa, and Asia, - each region selecting its own category winners.  A Grand Prize winner was then selected in each category from among the regional winners and each awarded $250,000.  The winners were announced at a gala event that took place in MIT on November 8.

The Impact of AI on the World Economy

A few weeks ago I wrote about the economic value of AI based on a recently published report by PwC.  The report’s overriding finding was that AI technologies and applications will increase global GDP by up to 14% between now and 2030, the equivalent of an additional $15.7 trillion contribution to the world’s economy.  According to PwC, AI is the biggest economic opportunity over the next 10-15 years.

I now want to discuss a September, 2018 report by the McKinsey Global Institute on the impact of AI on the world economy.

The McKinsey report is based on simulation models of the impact of AI at the country, sector, company and worker levels.  It looked at their adoption of five broad categories of AI technologies: computer vision, natural language, virtual assistants, robotic process automation, and advanced machine learning.  Its data sources included survey data from approximately 3,000 firms in 14 different sectors; around 400 potential AI use cases across a variety of industries and functions; AI’s potential to automate and transform 800 existing occupations in 46 countries; and economic data from a number of organizations including the United Nations, the World Bank, the Organization for Economic Co-operation and Development (OECD), and the World Economic Forum.

Below is a summary of the report’s key findings. 

A Biological Approach to Management

Darwinian principles seem to apply in business almost as much as in biology.  After analyzing the longevity of more than 30,000 public US firms over a 50-year span, Martin Reeves, Simon Levin, and Daichi Ueda noted in a 2016 HBR article, The Biology of Corporate Survival, that companies are disappearing faster than ever before.  “Public companies have a one in three chance of being delisted in the next five years, whether because of bankruptcy, liquidation, M&A, or other causes.  That’s six times the delisting rate of companies 40 years ago… Neither scale nor experience guards against an early demise.”

Biological systems have long been an inspiration in the study of complex systems.  In their HBR article, the authors argued that companies are not just like biological species, but in some important respects they’re actually identical to biological species.  Companies and biological systems are both what’s known as complex adaptive systems, that is, systems in which a perfect understanding of their individual components does not automatically lead to a perfect understanding of their overall system behavior.

Companies are an example of sociotechnical systems, that is, systems that have to deal with  complex technologies and infrastructures, and the even more complex issues associated with human and organizational behaviors.  Other examples include cities, government agencies, industries and economies.  The dynamic nature of their technology and human components, as well as their intricate interrelationships renders such systems increasingly unpredictable and accounts for their emergent behavior and unintended consequences.

The Economic Value of Artificial Intelligence

 PwC recently released a report on the potential economic value of AI to different regions and industry sectors around the world.  The report defined AI, “as a collective term for computer systems that can sense their environment, think, learn, and take action in response to what they’re sensing and their objectives.”  By this broad definition, AI includes the automation of physical and cognitive tasks; assisting people to perform tasks better and faster; helping humans make better decisions; and automating decision making with no human intervention.

The report’s overriding finding is that AI is the biggest commercial opportunity for companies, industries and nations over the next few decades.  PwC estimates that AI advances will increase global GDP by up to 14% between now and 2030, the equivalent of an additional $15.7 trillion contribution to the world’s economy.

Around $6.6 trillion of the expected GDP growth will come from productivity gains, especially in the near term.  These include the continued automation of routine tasks, and the development of increasingly sophisticated tools to augment human capabilities.  Companies that are slow to adopt these AI-based productivity improvements will find themselves at a serious competitive disadvantage.

But, over time, increased consumer demand for AI-enhanced offerings will overtake productivity gains and result in an additional $9.1 trillion of GDP growth by 2030.  Moreover,  network effects, - that is, the more data and better insights companies are able to gather, the more appealing the products and services they’ll be able to develop, - will further increase consumer demand.  AI front-runners will gain an enormous competitive advantage through their ability to leverage this rich supply of customer data to shape product developments and business models, making it harder for slower moving competitors to catch up. 

Automation and the Changing Demand for Workforce Skills

In 2015, the McKinsey Global Institute launched a multi-year study to explore the potential impact of automation technologies on jobs, organizations and the future of work.  In the intervening three years, the study has published a number of report on the subject.

The first report, published in November, 2015, explored whether we can look forward to vast improvements in productivity and quality of life, or whether automation will mostly threaten jobs, disrupt organizations, and strain the social fabric.  Based on an analysis of around 2,000 work activities across 800 different occupations, the report concluded that “fewer than 5 percent of occupations can be entirely automated using current technology.  However, about 60 percent of occupations could have 30 percent or more of their constituent activities automated.”  In other words, relatively few jobs will be entirely automated, but automation will likely transform the vast majority of occupations.

McKinsey’s December, 2017 report analyzed whether there will be enough work in the future.  The overall conclusion was that a growing technology-based economy will create a significant number of new occupations, - as has been the case in the past, - which will more than offset declines in occupations displaced by automation.  However, “while there may be enough work to maintain full employment to 2030 under most scenarios, the transitions will be very challenging - matching or even exceeding the scale of shifts out of agriculture and manufacturing we have seen in the past.”

Let me now discuss the more recent report published earlier this year, which examined the changes in skills required of human workers over the next 10-15 years.  To do so, the study analyzed how the total number of hours worked in 25 different skill areas has changed between 2002 and 2016 and estimated the expected change in hours worked by 2030.  This was done for the US and 5 Western European countries.  I’ll focus my discussion on the US skill changes, as the European changes were similar.

Cryptocurrencies and Blockchains - How Are They Doing After 10 Years?

Bitcoin was created about a decade ago, with the release of the original design paper, Bitcoin: A Peer-to-Peer Electronic Cash System, under the pen name of Satoshi Nakamoto.  Bitcoin aimed to become a decentralized cryptocurrency and digital payment system that would enable the secure exchange of digital money without the need for central government authorities or financial intermediaries.  It introduced the blockchain, - which most everyone agrees is a truly brilliant architecture built on decades-old fundamental research in cryptography, distributed data, distributed computing, game theory and other advanced technologies.

How are they doing after 10 years?  The September 1 issue of The Economist includes a special report on cryptocurrencies and blockchains, with nine articles on the subject. The report’s overview article doesn’t mince words in articulating its overall assessment: “Bitcoin and other cryptocurrencies are useless.  For blockchains, the jury is still out.”

“Bitcoin has been a failure as a means of payment, but thrilling for speculators,” it further added.  Its price has been subject to wild fluctuations over the past two years.  It rose from around $600 in September, 2016, to $6,000 in September, 2017, reaching a peak of over $19,000 in December, 2017, before falling to around $7,000 earlier this year.  It’s been hovering between $6,500 and $7,500 for the past few months.  Other cryptocurrencies have experienced similar price fluctuations.  “That has made a few people very rich (just 100 accounts own 19% of all existing bitcoin), encouraged others to play for quick gains and left some nursing substantial losses.”

Solow Paradox 2.0: The Lagging Impact of Technology Advances on Productivity Growth

Fewer workers is one of the key reasons for our stagnant economic growth.  Over the coming decades, the labor force is expected to shrink in most parts of the world as fertility rates continue to decline, especially in advanced and emerging economies.  The US labor force is growing very slowly while fertility rates keep hitting record lows.

Our aging economies are thus dependent on productivity gains to drive long-term economic growth, future prosperity and higher standards of living.  Which is why few topics have generated as much concern among economists and policy makers as the sharp decline in productivity growth in the US and other advanced economies over the past decade, - despite accelerating technology advances.

“Labor productivity growth is near historic lows in the United States and much of Western Europe,” noted Solving the Productivity Puzzle, a report published earlier this year by the McKinsey Global Institute (MGI).  The report analyzed the productivity-growth declines across a sample of seven countries, - France, Germany, Italy, Spain, Sweden, the United Kingdom, and the United States, - which represent about 65% of the GDP of advanced economies.  Their average productivity growth in the 5 years between 2000 and 2004 was 2.4%.  A decade later, - in the 5 year period between 2010 and 2014, - their average productivity growth had declined to .5%.  While starting to pick up recently, productivity growth remains at or below 1 percent in many of the countries in the study, still quite low relative to historical levels.

The Myth of the Washed-Up, Middle-Age Entrepreneur

Steve Jobs, Bill Gates, and Mark Zuckerberg were in their early 20s; Sergei Brin and Larry Page were 25; and Jeff Bezos was just 30 when they founded their world leading high-tech companies.  Their companies are truly in a class by themselves, even having their own, somewhat frightful acronym - FAMGA.  Their founders’ ages are not only evidence that young people can be highly successful entrepreneurs, but it’s led to the belief that they’re uniquely capable of coming up with big, transformational ideas.

“Young people are just smarter,” Zuckerberg told the audience of a 2007 VC conference, adding that successful start-ups should only employ young people with technical expertise.  In 2010, PayPal’s co-founder Peter Thiel created the Thiel Fellowships, a program that provides $100,000 over two years to would-be entrepreneurs 23 and under so they can drop out of school to work on their ideas.  “The stereotype of an entrepreneur is a college drop-out,” said a recent Economist article.

“Silicon Valley has become one of the most ageist places in America,” noted a 2014 New Republic article on The Brutal Ageism of Tech, where years of experience are considered obsolete.  “People under 35 are the people who make change happen,” said VC Vinod Khosla at a 2011 conference.  “People over 45 basically die in terms of new ideas” because they “keep falling back on old habits.”  Entrepreneur and VC Paul Graham said in a 2013 interview that investors tend to be biased against older founders.  “The cutoff in investors’ heads is 32… after 32, they start to be a little skeptical.”

These various statements remind me of the 1960s counterculture saying don’t trust anyone over 30.  But, are they justified?  Are young entrepreneurs more likely to produce high-growth firms?  Can middle-age founders in their 40s be successful? 

AI - the Creation of a Human-Centric Engineering Discipline

AI is rapidly becoming one of the most important technologies of our era.  Every day we can read about the latest AI advances from startups and large companies, including applications that not long ago were viewed as the exclusive domain of humans.  Over the past few years, the necessary ingredients have finally come together to propel AI beyond the research labs into the marketplace: powerful, inexpensive computer technologies; huge amounts of data; and advanced algorithms.

Machine learning  and related algorithms like deep learning, have played a major role in AI’s recent achievements.  Machine learning gives computers the ability to learn by ingesting and analyzing large amounts of data instead of being explicitly programmed.  It’s enabled the construction of AI algorithms that can be trained with lots and lots of sample inputs, which are subsequently applied to difficult AI problems like language translation, natural language processing, and playing championship-level Go.

Over the past several decades, AI has been besieged by rounds of hype which over-promised, under-delivered, and nearly killed the field.  Once more, a recent Gartner article positioned machine and deep learning at the top of their hype cycle, - when all the excitement, publicity and promising potential often leads to a peak of inflated expectations, risking falling into the trough of disillusionment if the technology fails to deliver.  Now that AI is finally reaching a tipping point of market acceptance, it’s particularly important to be cautious and not repeat past mistakes.

In Artificial Intelligence - The Revolution Hasn’t Happened Yet, UC Berkeley professor Michael I. Jordan aims to inject such a note of caution.  “AI has now become the mantra of our current era…  The idea that our era is somehow seeing the emergence of an intelligence in silicon that rivals our own entertains all of us  -  enthralling us and frightening us in equal measure.  And, unfortunately, it distracts us…  Whether or not we come to understand intelligence any time soon, we do have a major challenge on our hands in bringing together computers and humans in ways that enhance human life.”

What Machine Learning Can and Cannot Do

Artificial intelligence is rapidly becoming one of the most important technologies of our era.  Every day we can read about the latest AI advances from startups and large companies.  Over the past few years, the necessary ingredients have come together to take AI across the threshold: powerful, inexpensive computer technologies; huge amounts of data; and advanced algorithms, especially machine learning.  Machine learning has enabled AI to get around one of its biggest obstacles, - the so-called Polanyi’s paradox.

Explicit knowledge is formal, codified, and can be readily explained to people and captured in a computer program.  But, tacit knowledge, a concept first introduced in the 1950s by scientist and philosopher Michael Polanyi, is the kind of knowledge we’re often not aware we have, and is therefore difficult to transfer to another person, let alone capture in a computer program.

“We can know more than we can tell,” said Polanyi in what’s become known as Polanyi’s paradox.  This common sense phrase succinctly captures the fact that we tacitly know a lot about the way the world works, yet aren’t able to explicitly describe this knowledge.  Tacit knowledge is best transmitted through personal interactions and practical experiences.  Everyday examples include speaking a language, riding a bike, and easily recognizing many different people, animals and objects.

Machine learning, and related advances like deep learning, have enabled computers to acquire tacit knowledge by being trained with lots and lots of sample inputs, thus learning by analyzing large amounts of data instead of being explicitly programmed.  Machine learning methods are now being applied to vision, speech recognition, language translation, and other capabilities that not long ago seemed impossible but are now approaching or surpassing human levels of performance in a number of domains.

The Transformative Power of Analytics - The Houston Astros: a Case Study

In 2013, the Houston Astros finished the season with a 51-111 record, - the worst record in the history of the franchise, having also lost over 100 games in the previous two years.  In 2017, the Astros won 101 games, finished first in the American League West Division, and went on to win their first ever World Series.  How did the Astros go from worst team record to first World Series victory in only four years?

The answer, in a nutshell, is analytics.  In December, 2011 the Astros hired Jeff Luhnow from the St Louis Cardinals as their General Manager.  As VP of scouting and player development in St Louis, Luhnow built a strong analytics department and enjoyed great success as one of the top talent producers in baseball, helping the Cardinals win the 2011 World Series as well as reach the playoffs from 2012 to 2015.  Luhnow then brought his analytics and management skills to Houston and proceeded to transform the team into a top contender in a short few years.

Today, just every major sports team makes extensive use of sports analytics, but none more so than Major League Baseball (MLB) teams.  Baseball has long been a game of statistics, collecting and analyzing vast amounts of data going back to the founding of the MLB in the years following the Civil War. 

Top MLB players are constantly compared not just to other contemporaneous players, but to other top players across the long history of the game, - a history that was very nicely depicted in Baseball, Ken Burns excellent documentary miniseries. Babe Ruth, Willie Mays, Mickey Mantle, Hank Aaron and countless other players are never forgotten in the world of baseball statistics.  For those of us who are fans, - as I am, not surprisingly given my Cuban roots, - baseball’s rich history is a major part of our enjoyment of the game. 

Education, Work and US Leadership in the 21st Century

A few weeks ago, the Council on Foreign Relations (CFR) released The Work Ahead - Machines, Skills, and U.S. Leadership in the Twenty-First Century, a report by a CFR-sponsored Task Force to assess the future of work and its implications for the US economy and national security.  The Task Force was led by Penny Pritzker, - former Secretary of Commerce and founder and chair of PSP Partners, and by John Engler, - former Governor of Michigan and interim President of Michigan State University.

Their comprehensive report, - over 150 pages and 276 citations, - explored in great depth the changing nature of work as global competition and advanced technologies like AI, robotics, and smart IoT devices transform jobs and careers.  It states right up front that “The most important challenge facing the United States - given the seismic forces of innovation, automation, and globalization that are changing the nature of work - is to create better pathways for all Americans to adapt and thrive.  The country’s future as a stable, strong nation willing and able to devote the necessary resources and attention to meeting international challenges depends on rebuilding the links among work, opportunity, and economic security.”

For much of the 20th century, technology and globalization helped lead America to its leadership position in the world stage.  Underpinning this US leadership was the American Dream, - the promise that anyone can get ahead and achieve success and prosperity through talent and hard work.  But this promise has been eroding for decades.  For many of its citizens, the American Dream has been steadily disappearing.

Technology advances have led to massive innovations and efficiencies, enabling companies to get the same work done with significantly fewer people.  At the same time, many US-headquartered companies are truly global, doing business all over the world.  Beyond outsourcing jobs to countries with lower labor costs they’ve been investing and adding jobs where the demand is highest, - which has often been in faster growing emerging markets.

The US thus faces two major challenges: “creating new work opportunities, better career paths, and higher incomes for its people, while developing a workforce that will ensure U.S. competitive success in a global economy that will continue to be reshaped by technology and trade.” 

The 2018 Inclusive Internet Index

Earlier this year, the Economist Intelligence Unit (EIU) published the Inclusive Internet Index 2018.  Now in its second year, the Index analyzes and compares the current state of the Internet across 86 countries, ranging from advanced to developing economies and covering 91% of the world’s population.  Its key objective is to help policymakers understand the key factors necessary for the wide, inclusive use of the Internet in their respective countries.

Countries are evaluated based on 54 indicators organized around four major categories:

• Availability,  which captures the quality of breadth of the infrastructure available for Internet access, including network availability, access points for landline and mobile connections, and the basic electricity infrastructure needed to support Internet connectivity in urban and rural areas.
• Affordability, which measures the cost of Internet access relative to income, the competitive environment for wireless and broadband operators and the measures taken to decrease costs and promote access.
• Relevance, which looks at the availability of Internet content in the local language(s) and the value of being connected to get access to relevant services like news, entertainment, health advice and business and financial information.
• Readiness, which examines the capacity to take advantage of accessing the Internet, including level of literacy, educational attainment, cultural acceptance, privacy and security, and trust in the sources of online information.

Each country is assigned a score for each of the four categories, as well as an overall index score. 

From Horseless Carriages to Driverless Vehicles

Autonomous vehicles (AVs) may well be the quintessential symbol of our AI/robotics age.  Cars are a major part of our daily lives.  A self-driven car is a concept that requires little explanation, something we can all quickly grasp.  It wasn’t that long ago that the notion on an AV driving us around while we read or sleep would have felt like the stuff of science fiction.  Having experimental AVs coursing through public roads in Silicon Valley, Pittsburgh and Phoenix is concrete evidence that our smart machines are achieving human-like intelligence, raising a number of important questions:  how long before AVs are all around us?; how will they impact our lives?; what unintended consequences might we have to deal with?; and what should be done to ensure that they arrive as safely and smoothly as possible?

These are among the questions addressed in the March 1 issue of The Economist, which includes a comprehensive special report on autonomous vehicles with seven articles on the subject.  The special report starts out with the assumption that whatever technological hurdles lie in their way will be eventually overcome.  But there are wider economic, social and public policy issues to be explored, starting with: what can we learn from the transition to horseless carriages in the 20th century that can now be applied to the transition to driverless cars?

Human-Machine Work Teams

Will there be enough work in the future?  What’s the likely impact of our continuing technology advances on jobs?  How will they impact productivity?  These are all-important questions to reflect on as our increasingly smart technologies are now being applied to activities that not long ago were viewed as the exclusive domain of humans.

While no one really knows the answer to these questions, most studies of the subject have concluded that relatively few occupations, 10% or less, will be entirely automated and disappear over the next 10 - 15 years.  Instead, a growing percentage of occupations will significantly change as technologies automate the more routines tasks within those occupations.  People will still be involved, but their jobs will be transformed by the advanced tools they now have to master.  Moreover, a growing technology-based economy will likely create all kinds of new occupations, which will more than offset declines in occupations displaced by automation, - as has been the case over the past couple of centuries.

One would also expect that technology advances will increase the productivity of the workers involved in these new or transformed occupations.  But, if we look at the past 10-15 for guidance, we’ll find that productivity growth has significantly declined over this timeframe, notwithstanding huge technology advances like smartphones, cloud computing, big data and artificial intelligence.  Economist have proposed competing explanations for the declining productivity growth, but have so far failed to reach consensus.  Understanding this productivity puzzle may well hold the key to future productivity improvements and long-term economic growth.

Improving the Odds of Success for Corporate Transformations

Looking back upon my long career, one of the major factors shaping my views of strategy and innovation are the powerful transformational forces that I saw buffeting the IT industry over much of that time.  It’s frankly sobering how many once powerful IT companies are no longer around or are shadows of their former selves.  The carnage might be more pronounced in the fast-changing IT industry, but no industry has been immune.  It’s all part of what Joseph Schumpeter called creative destruction over 75 years ago, - “the process of industrial mutation that incessantly revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one.”

For a startup a transformative innovation is all upside, an opportunity to take-on established companies with new products that offer significantly better capabilities and/or lower costs.  Startups hope that their compelling new offerings will help them establish a foothold in the marketplace and, over time, become leaders in their industry.

But change is often difficult for established companies.  Over the years, they’ve amassed a number of valuable assets and extensive organizations.  Already consumed with managing their existing operations, - e.g., products and services; supply chain and channel partners; sales and marketing; revenues, profits and cash; -  they may see a transformative innovation as more of a threat or distraction than an opportunity. 

The Promise and Current State of Blockchain

I recently participated in a blockchain panel at a conference of senior supply chain executives from a variety of companies.  I welcomed the opportunity to learn how these real-world executives feel about blockchain, as well as how to best communicate the promise and current state of this emerging and potentially transformative technology.  Let me share a few of my observations.

Before our panel started, the executives were asked how they viewed blockchain.  In general, they felt that there was too much hype surrounding blockchain, were skeptical about its value to supply chain systems, but expressed a wait-and-see attitude.  They were then asked how many were conducting blockchain prototypes in their companies.  The majority raised their hands. 

I thought that their views and subsequent questions and comments during the panel succinctly captures the current state of blockchain in the business world: skeptical, turned off by the hype, but hedging their bets by getting on the learning curve with prototypes and experimentation.

The moderator first asked each panelist to address the audience’s skepticism about blockchain.  What, in our opinion, is the key value of blockchain?  I said that I look at the value of blockchain through two complementary lenses: enhancing the security of the Internet, and reducing the inefficiencies and overheads in applications, like supply chains, involving multiple institutions.

The Growing Importance of Resilience in an Increasinsgly Turbulent World

In March of 2017, Northeastern University launched a new interdisciplinary initiative, the Global Resilience Institute (GRI).  GRI is a university-wide research and educational effort to advance the resilience of individuals, communities, economies and societies around the world by strengthening their capacity to adapt to an increasingly turbulent world.  Its key mission is to develop and deploy practical tools, applications and skills to help effectively respond to human-made and naturally-occurring disruptions and disasters.

GRI’s Founding Director is Northeastern professor Stephen Flynn.  In his Director’s Welcome web page, Flynn writes that “Building resilience within and across multiple levels, from individual to societal, requires a comprehensive effort that matches the complexity of the increasingly interdependent systems and networks we all rely on… Our aim is to serve as both a channel and a catalyst for experts in industry, academia, and government to collaborate on solving the world’s most pressing resilience challenges.  These include both slowly emerging disruptions as well as shocks and sudden disasters.”

Most everyone agrees that dealing with our increasingly turbulent, complex world us requires significant enhancements to the resilience of our societies and systems.  But, five critical barriers stand in the way:

• Widespread risk illiteracy and a limited understanding of the new dependencies and interdependencies that pervade our more connected lives;
• Inadequate designs for embedding resilience into systems, networks, and infrastructure at multiple levels;
• Pervasive economic disincentives for investing in proven resilience measures;
• Inadequate governance frameworks and policy guidance to foster resilience; and
• A lack of adequate training and education to support the development and implementation of comprehensive capabilities for advancing resilience.

A Framework for the Safe Management of Digital Identities and Data

Identity plays a major role in everyday life.  Think about going to an office, getting on a plane, logging to a website or making an online purchase.  Identity is the key that determines the particular transactions in which we can rightfully participate as well as the information we’re entitled to access.   But, we generally don’t pay much attention to the management of our identity credentials unless something goes seriously wrong.

For much of history, our identity systems have been based on face-to-face interactions and on physical documents and processes.  But, the transition to a digital economy requires radically different identity systems.  In a world that’s increasingly governed by digital transactions and data, our existing methods for managing security and privacy are proving inadequate.  Data breaches, large-scale fraud, and identity theft are becoming more common.  In addition, a significant portion of the world’s population lacks the credentials needed to participate in the digital economy.  Our existing methods for managing digital identities are far from adequate.

As explained in A Blueprint for Digital Identity, - a 2016 report by the World Economic Forum, - identity is essentially a collection of information or attributes associated with a specific individual.  These attributes fall into three main categories:  inherent - attributes intrinsic to an individual, - e.g., age, height, date of birth, fingerprints, color of eyes, retinal scans;  assigned - attributes attached to but not intrinsic to the individual - e.g., e-mail address, telephone numbers, social security, drivers license, passport number; and accumulated - attributes gathered or developed over time - e.g., health records, job history, home addresses, schools attended.

While mostly associated with individuals, identities can also be assigned to legal entities like corporations, partnerships and trusts; to physical entities like cars, buildings, smartphones and IoT devices; and to digital entities like patents, software programs and data sets.

Data attributes are generally siloed within different private and public sector institutions, each using its data for its own purposes.  But to reach a higher level of privacy and security, we need to establish trusted data ecosystems, which requires the exchange and sharing of data across a variety of institutions.  The more data sources a trusted ecosystem has access to, the higher the probability of detecting fraud and identity theft while reducing false positives.  In addition, an ecosystem with a variety of data sources can help foster economic inclusiveness by certifying the identities and credit worthiness of poor people with no banking affiliation.

However, safeguarding the data used to validate identities creates security and privacy issues of its own.  It’s unsafe to gather all the needed attributes within one institution or central data location, making it a target for data breaches.  But, it’s also highly unfeasible, as few institutions will let their critical data out of their premises.

Customer Experience - the Key Competitive Differentiator in the Digital Age

About five years ago, McKinsey conducted an online survey of over 850 CEOs and other senior executives to find out how their companies were faring in their implementation of digital technologies and strategies.  The data showed that executives were generally optimistic, but they still had much to do to achieve their digital business objectives.  Organizational alignment and leadership were the critical factors in the success or failure of their digital strategies.

More specifically, the survey asked them about their progress in embracing five major digital trends: big data and advanced analytics, digital engagement of customers, digital engagement of employees and external partners, automation, and digital innovation.  While all five trends were important, executives said that digital customer engagements was their most important competitive differentiator and where they were expecting the largest financial returns.

This is not surprising, even more now than at the time of the survey.  A company can differentiate itself from competitors in one of two key ways: by providing a superior customers experience or by offering the lowest prices.  For companies that prefer the former, digital channels are, far and away, the most cost-effective way of reaching out to their clients.  The explosive growth of mobile devices means that you can be engaged with your customers whether they are at home, at work, in your store or anywhere else.

But, digital customers can be fickle and hard to satisfy.  It’s quite difficult to keep up with their fast changing behaviors and expectations.  New products and services are hitting the market faster than ever, brand loyalty keeps decreasing, and the increased competition continues to shift power from institutions to individuals.

Human + Machine: The Impact of AI on Business Transformation

Last week I wrote about the impact of blockchain technologies on business transformation.  I now want to turn my attention to the more immediate impact of AI on business, based on Human + Machine: Reimagining Work in the Age of AI, a recently published book by Accenture executives Paul Daugherty and Jim Wilson.

The book reminds us that technology has long been transforming business.  Electricity and standardized processes ushered the first wave of business transformations about 100 years ago.  Manufacturing was decomposed into a set of processes and reassembled into the assembly line, leading to the age of mass production.

Several decades later, IT and process automation brought us the second wave of business transformation.  Mainframes and data processing software enabled the automation of back-office processes in the 1960s and 1970s.  Front-office automation followed in the 1980s with the advent of PCs and client-server systems.  A decade later, advances in networking and ERP software led to  business process reengineering and management. 

“Now, the third wave involves adaptive processes,” write Daugherty and Wilson.  “This third era, which builds on the previous two, will be more dramatic than the earlier revolutions enabled by assembly lines and digital computers, and will usher in entirely new, innovative ways of doing business… This adaptive capability is being driven by real-time data rather than by an a priori sequence of steps.   The paradox is that although these processes are not standardized or routine, they can repeatedly deliver better outcomes.”

Blockchain and Business Process Transformation

One can look at the transformational impact of IT on companies and industries through a variety of lenses, - e.g., products and services, revenue and profit, sales and support, market strategy.  One of the most important such lenses is the impact of IT on business processes, that is, on how work is actually done across the various functions of an organization. 

Examining the impact of IT over the past several decades through a process point of view, one can identify three distinct phases: first came process automation in the early years of IT, followed around the 1990s by enterprise-wide process reengineering and management.  The third phase, is now starting, focused on the processes and transactions that determine how institutions interact with each other around the world.  Let me discuss each of these phases. 

The Vital Role of Culture in Business

The January-February issue of the Harvard Business Review spotlights The Culture Factor, with five articles on the subject.  “Culture is the tacit social order of an organization: It shapes attitudes and behaviors in wide-ranging and durable ways,” notes the issue’s lead article.  “Cultural norms define what is encouraged, discouraged, accepted, or rejected within a group.  When properly aligned with personal values, drives, and needs, culture can unleash tremendous amounts of energy toward a shared purpose and foster an organization’s capacity to thrive.”

In Who Says Elephants Can't Dance?, - his excellent chronicle of the IBM transformation he led as Chairman and CEO from 1993 to 2002, - Lou Gerstner wrote: “I came to see in my time at IBM that culture isn’t just one aspect of the game - it is the game.  In the end, an organization is nothing more than the collective capacity of its people to create value.  Vision, strategy, marketing, financial management - any management system, in fact - can set you on the right path and can carry you for a while.  But no enterprise - whether in business, government, education, health care or any area of human endeavor - will succeed over the long haul if those elements aren't part of its DNA.” 

The Complex Relationship Between Science and the Public

More than ever, scientific and technological innovations touch every aspect of our lives and influence the choices we make in a wide variety of areas.  A 2015 study by the Pew Research Center revealed that most Americans have a positive view of science and of the accomplishments of scientists.  But, despite broadly similar views about the place of science in the country, there were large differences between the general public and scientist across a number of important issues, including genetically modified foods, vaccine safety and climate change.

Two years ago, the American Academy of Arts and Sciences launched The Public Face of Science, an initiative whose objective is to better understand the complex relationship between scientists and the public in America.  A few weeks ago, the Academy released Perceptions of Science in America, the first in a series of reports from the Public Face of Science initiative.  The report aims to analyze the public’s perception of scientists, explore how opinions vary among different populations, and shed light on how public opinions are shaped.  Its analysis is based on survey data from multiple sources, including the Pew Research Center, the National Science Board Science and Engineering Indicators, the National Opinion Research Center, Research!America and ScienceCounts.

What do we mean by science?  “The term science encompasses a range of disciplines in the physical, social, and life sciences, along with applied fields, such as engineering and medicine,” notes the report. “Science can be defined as ‘the use of evidence to construct testable explanations and predictions of natural, as well as the knowledge generated through this process.’”  But, one must keep in mind that the word science is interpreted differently by different individuals, - e.g., medical treatments, technological advances, fundamental research.  “When interpreting the available survey data, it is important to consider the range of responses a question might prompt.”

A Framework for Building AI Capabilities

After decades of promise and hype, artificial intelligence has finally reached a tipping point of market acceptance.  AI is seemingly everywhere.  Every day we can read about the latest AI advances and applications from startups and  large companies.  AI was the star of the 2018 Consumer Electronic Show earlier this year in Las Vegas.

But, despite its market acceptance, a recent McKinsey report found that AI adoption is still at an early, experimental stage, especially outside the tech sector.  Based on a survey of over 3,000 AI-aware C-level executives across 10 countries and 14 sectors, the report found that 20 percent of respondents had adopted AI at scale in a core part of their business, 40 percent were partial adopters or experimenters, while another 40 percent were still waiting to take their first steps.

The report adds that the gap between the early AI adopters and everyone else is growing.  While many companies have yet to be convinced of AI’s benefits, leading edge firms are charging ahead.  Companies need to start experimenting with AI and get on the learning curve, or they risk falling further behind. 

AI will likely become the most important technology of our era as it’s improved upon over time, but we’re still in the early stages of deployment.  It’s only been in the last few years that complementary innovations, especially machine learning have taken AI from the lab to early marketplace adopters.  And, history shows that even after technologies start crossing over into mainstream markets, it takes considerable time, - often decades, - for the new technologies and business models to be widely embraced by companies and industries across the economy.

Blockchain and the Promise of an Open, Decentralized Internet

The January 21 issue of the NY Times Magazine included a very good article, Beyond the Bitcoin Bubble, by science writer Steven Johnson.  His article aims to explain what blockchain, cryptocurrencies and related technologies are all about as well as their potential impact on the economy and society.  These are not only complex but esoteric subjects, as nicely illustrated in this funny video.  While blockchain and Bitcoin have been frequently in the news lately, beyond knowing that they have something to do with money, most people don’t really know what they are or why they should care.

This is not surprising.  In their early years, it’s hard for most people to appreciate the potential of a transformative technology.  There’s generally a considerable time-lag between the time we start reading articles about their exciting potential and their adoption by companies and industries across the marketplace.  This is particularly the case for general purpose technologies, like electricity, the Internet and blockchain, which are the most potentially transformative due to their widespread use.   Their deployment time-lags are longer because attaining their full benefits requires a number of complementary co-inventions and investments, including additional technologies, applications, processes, business models, and regulatory policies.

Will There Be Enough Work in the Future?

Will there be enough work in the future?  Opinions are fairly divided between those who believe that technology advances will reduce human jobs, and those who believe that technology advances will produce as many jobs as they displace.  It’s easier to predict the jobs that will be automated away by technology, but much more difficult to predict the new jobs that these same technologies will create.  In the end, we don’t really know.

In December of 2017, the McKinsey Global Institute published Jobs Lost, Jobs Gained: Workforce Transition in a Time of Automation, a report that directly addresses this question.  The McKinsey study examined in great detail the work that’s likely to be displaced by automation through 2030, as well as the jobs that are likely to be created over the same period.  It analyzed data from 46 countries comprising almost 90 percent of global GDP, focusing particularly on six countries: China, Germany, India, Japan, Mexico and the US.  For each of this six countries, the study modeled the potential for employment changes in more than 800 occupations based on different scenarios for the pace of automation adoption and for future labor demand.

The report’s overall conclusion is that a growing technology-based economy will create a significant number of new occupations, - as has been the case in the past, - which will more than offset declines in occupations displaced by automation.  However, “while there may be enough work to maintain full employment to 2030 under most scenarios, the transitions will be very challenging - matching or even exceeding the scale of shifts out of agriculture and manufacturing we have seen in the past.”

The Challenges of Executing a Transformational Strategy

The 2018 MIT CIO Symposium will take place on May 23.  This year’s Symposium will be focused on the challenges of executing a digital transformation strategy.   It’s no longer enough to have formulated a digital strategy for the business.  As a number of recent reports have indicated, leading edge companies are pulling ahead of everyone else by pushing the boundaries of digitization.  And, having a strong digital foundation gives those companies a leg up in embracing data analytics, AI, blockchain, and other advanced technologies, further widening their advantage.  “The time for merely thinking digital has passed; the future belongs to the doers,” notes the Symposium’s website.

Why do so many organizations struggle to implement their well formulated strategies, especially transformational strategies like the transition to digital, that encompass new technologies, business models and management practices?  This is a question I’ve long been thinking about, having lived through IBM’s near-death experience in the early1990s.

Is it that in spite of all their efforts in strategy formulation, their management was unable to anticipate the new technology and market changes and were caught by surprise?  Or it is that, as if actors in a kind of Greek tragedy, they saw the changes coming and understood what had to be done, but were somehow unable to execute their strategies?

MIT’s Donald Sull has been exploring these questions for over 20 years.  As this 2015 HBR article notes, we know a lot more about strategy than about translating strategy into results.  “Books and articles on strategy outnumber those on execution by an order of magnitude…  A recent survey of more than 400 global CEOs found that executional excellence was the number one challenge facing corporate leaders in Asia, Europe, and the United States, heading a list of some 80 issues, including innovation, geopolitical instability, and top-line growth.  We also know that execution is difficult.  Studies have found that two-thirds to three-quarters of large organizations struggle to implement their strategies.” 

AI and the Productivity Paradox

AI is being successfully applied to tasks that not long ago were viewed as the exclusive domain of humans, matching or surpassing human level performance in more and more domains.  But, at the same time, productivity growth has significantly declined over the past decade, and income has continued to stagnate for the majority of Americans.  This puzzling contradiction is addressed in Artificial Intelligences and the Modern Productivity Paradox, a working paper recently published  by the National Bureau of Economic Research (NBER).

The paper’s authors, - MIT professor Erik Brynjolfsson, MIT PhD candidate Daniel Rock, and University of Chicago professor Chad Syverson, - note that “aggregate labor productivity growth in the U.S. averaged only 1.3% per year from 2005 to 2016, less than half of the 2.8% annual growth rate sustained from 1995 to 2004…  What’s more, real median income has stagnated since the late 1990s and non-economic measures of well-being, like life expectancy, have fallen for some groups.”

This productivity puzzle isn’t confined to AI.  “Few topics in economics today in most large economies generate as much debate as the productivity puzzle,” said McKinsey in a March, 2017 report.  “In  the United States, productivity growth has declined sharply since 2004 yet digital technology has been widely apparent during this period…  The answer to this puzzle holds the key to future prosperity because now more than ever our economy depends on productivity improvements for long-term economic growth.  Economists have proposed competing explanations for declining productivity growth and so far have failed to reach a consensus.” 

The Rise of Winner-Take-All Urbanism

Earlier this year, Richard Florida published Why America’s Richest Cities Keep Getting Richer in The Atlantic.  Florida is a leading urban studies author and researcher, professor at the University of Toronto and Director of Cities at its Martin Prosperity Institute.

“The most important and innovative industries and the most talented, most ambitious, and wealthiest people are converging as never before in a relative handful of leading superstar cities that are knowledge and tech hubs,” wrote Florida.  “This small group of elite places forge ever forward, while most others struggle, stagnate, or fall behind.  This process is one I like to call winner-take-all urbanism.”

Winner-take-all urbanism is another manifestation of the winner-take-all economics of the past few decades, in which a relatively small number of players reap a very large share of the rewards, upending whole industries with their outsized returns.

AI and The Future of Work

Last month I attended AI and the Future of Work, a conference hosted by MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and its Initiative on the Digital Economy (IDE).  The two-day agenda included over 20 keynotes and panels on AI-related topics, and around 60 speakers and panelists from academia and business.

New technologies have been replacing workers and transforming economies over the past two centuries.  But, over time, these same technologies led to the creation of whole new industries and new jobs.  While the technologies of the industrial economy helped to make up for our physical limitations, the technologies of the digital economy are now enhancing our cognitive capabilities.  They’re being increasingly applied to activities that not long ago were viewed as the exclusive domain of humans.  Will the AI revolution play out like past technology revolutions, - short term disruptions followed by long-term benefits, - or will this time be different?

Conference participants generally agreed that AI will have a major impact on jobs and the very nature of work.  But, for the most part, they viewed AI as mostly augmenting rather than replacing human capabilities, automating the more routine parts of a job and increasing the productivity and quality of workers, so they can focus on those aspect of the job that most require human attention.  Overall, a small percentage of jobs will be fully automated, while many more will be significantly transformed.

Conference participants also generally agreed that the more advanced AI-based transformations will not happen rapidly, but are likely decades away.  Much progress has been recently made in the ability to extract features from all the data we now have access to, as well as in machine learning algorithms that give computers the ability to learn by ingesting large amounts of data instead of being explicitly programmed.  While such statistical pattern recognition approaches can be applied to many tasks, they’re no substitute for model formation, the main approach used by humans, - from toddlers to physicists, - to understand how the world works.  We’re a long way from the development of AIs that truly learn and reason like people. 

The Emergence of Industry 4.0

Over the past few years, Industry 4.0, - aka the 4th Industrial Revolution, - has been the subject of several articles, studies, and surveys, as well as a book published earlier this year by Klaus Schwab, founder and executive chairman of the World Economic Forum (WEF).  They all attempt to describe and put a name to the disruptive changes taking place all around us within the context of the past 250 years.

“Industrial revolutions are momentous events,” said A Strategist Guide to Industry 4.0, - a 2016 article in strategy+business.  “By most reckonings, there have been only three.”  The First Industrial Revolution, - starting in the last third of the 18th century, - introduced new tools and manufacturing processes based on steam and water power, ushering the transition from hand-made goods to mechanized, machine-based production.  The Second starting a century later, brought us steel, cars, chemicals, petroleum, electricity, the telephone and radio while creating the age of mass production.  The Third, - following World War II - saw the advent of computers, digital technologies, the IT industry, and the automation of process in just about all industries.

There’s general agreement that the 4th Industrial Revolution is primarily driven by a fusion of once separate technologies that when joined together are integrating the physical and digital worlds.  But, there’s a spectrum of opinions as to the scope of its impact, some arguing that Industry 4.0 applies primarily to manufacturing technologies and industries, while others argue that it’s profoundly transforming our economies and societies.

A Historical Perspective on the Digital Economy

A few months ago, the magazine strategy+business (s+b) published a roundtable discussion between s+b editor-in-chief Art Kleiner, economic historian Carlota Perez, and PwC UK partner Leo Johnson.  The discussion was centered on Perez’s theory of technology revolutions and economic cycles.  In her 2002 influential book, Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages and in several other publications, Perez framed the digital economy of the past few decades from a historical perspective.  If we examine the long term historical big picture, patterns emerge which can help guide our understanding and planning for the future. 

According to Perez, since the onset of the Industrial Revolution we’ve had 5 major technology-driven economic cycles, each one lasting roughly 50-60 years.  First was the age of machines, factories and canals starting in 1771.  This was followed by the age of steam, railways, iron and coal, starting in 1829; steel, electricity and heavy engineering in 1875; oil, automobiles, and mass production in 1908; and our present ICT-based digital age starting in 1971.

Each economic cycle is composed of two very different periods, each lasting roughly 20 - 30 years.  The installation period is the time of creative destruction, when new technologies emerge from the labs into the marketplace, entrepreneurs start many new businesses based on the new technologies, VCs encourage business model experimentation, and the new ventures attract considerable investments and financial speculation.  Inevitably this all leads to a financial bubble, which eventually bursts in spectacular fashion leading to a time of crisis.

After the crash, comes the deployment period, which Perez views as a time of economic transformation and institutional recomposition.  The now well accepted technologies and business paradigms become the norm; infrastructures and industries start getting better defined and more stable; and production capital drives long-term growth and expansion by spreading and multiplying the successful business models.

Human-AI Decision Systems

I recently read The Human Strategy, a very interesting article on Human-AI decision systems by MIT Media Lab professor Sandy Pentland.  Pentland is the faculty director of MIT Connection Science, - with which I’m associated as a Fellow, - as well as director of the Human Dynamics Lab.  In 2014 he published Social Physics: How Social Networks Can Make Us Smarter.

“Perhaps the most critical function of any organization or society is its decision systems,” wrote Pentland.  “In modern societies, decision systems provide a leader the ability to make informed and timely decisions, supported by a complex enterprise of distributed information and communication systems that provide situational awareness.  Traditionally, decision systems have been confined to individual physical domains, such as logistics, physical plant, and human resources, and more recently virtual domains such as cyber, by both policy and technology, resulting in challenges with the integration of information across disparate domains.”

But, despite the increasingly complex decisions that organizations are called upon to make, decision-making remains human-intensive and anecdotal.  Few organizations have applied social network analysis to help them scale the size and expertise of the decision-making group.  Nor have they integrated the large amounts of data, analytical tools and powerful AI systems now at our disposal into their decision making systems. 

Where is Technology Taking the Economy

I recently read Where is Technology Taking the Economy?, an article in McKinsey Quarterly by W. Brian Arthur, - External Professor at the Santa Fe Institute , Visiting Researcher at PARC, and former faculty member at Stanford University.

According to Arthur, the digital revolution has morphed through three distinct eras over the past several decades.  The first era, in the 1970s and 1980s, brought us Moore’s law and the dramatic advances in semiconductor technologies.  From mainframe and supercomputers to PCs and workstation, IT was now being used in a wide variety of applications, from financial services and oil exploration, to computer-aided design and office systems.  “The economy for the first time had serious computational assistance.”

Then came the second era in the 1990s and 2000s, which enabled us to link computers together, share information, and connect digital processes.  “Everything suddenly was in conversation with everything else,” giving rise to “the virtual economy of interconnected machines, software, and processes…, where physical actions now could be executed digitally.”

We’re now in the third era, which began roughly in the 2010s.  It’s brought us smartphones, ubiquitous sensors, IoT devices and oceans and oceans of data.  Powerful computers and intelligent algorithms are enabling us to make sense of all that data by searching for patterns and doing  something with the results, including computer vision, natural-language processing, language translation, face recognition and digital assistants.

The MIT 2017 Inclusive Innovation Challenge

The MIT Initiative on the Digital Economy (IDE) was organized in 2013 by Erik Brynjolfsson and Andy McAfee to examine the impact of digital technologies on the world.  Understanding the future of work and jobs is one of the major areas of research being address by IDE.  What will the workforce of the future look like?, Where will jobs come from in the coming years, especially for the workers must impacted by automation?,  How can we accelerate the transformation of institutions, organizations, and human skills to keep up with the quickening pace of digital innovation?

To help come up with breakthrough, real-world answers to these tough questions, IDE launched the MIT Inclusive Innovation Challenge (IIC) last year.  The Challenge aims to identify, celebrate and award prizes to organizations around the world that are developing innovative approaches for improving the economic opportunities of middle- and base-level workers.

Now in its second year, the IIC introduced the winners of its 2017 competition at an event held on October 12 as part of Boston’s annual HUBweek.  Over $1 million was awarded to the winners in four categories: job creation and income growth, skills development and matching, technology access and financial inclusion.  The grand prize winner in each category received $150,000, while the three runners up in each category received $35,000.  The awards were funded with support from from Google.org, The Joyce Foundation, ISN and Joseph Eastin.

A Novel Approach for Measuring the True Impact of the Digital Economy

Gross domestic product (GDP), the basic measure of a country’s overall economic output, is generally used by governments to inform their policies and decisions.  “What we measure affects what we do; and if our measurements are flawed, decisions may be distorted,” noted a 2009 Commission convened to look at the adequacy of GDP as an indicator of economic performance, - which was led by Nobel-prize winning economists Joseph Stiglitz and Amartya Sen.

GDP is essentially a measure of production.  While suitable when economies were dominated by the production of physical goods, GDP doesn’t adequately capture the growing share and variety of services and the development of increasingly complex solutions in our 21st century digital economy.

Digital Spillover, a recent report co-developed by Huawei and Oxford Economics, explores how to better define and measure the true impact of the digital economy.  It argues that the scope of the digital economy is expanding and proposes a novel way of measuring its impact.

“A truly digital economy is one in which businesses from across the industrial spectrum are investing in digital and making the most productive use of it,” notes the report.  “The mechanisms by which this is happening are complex and evolving.  Over and above the direct productivity boost that companies enjoy from digital technologies, a more profound chain of indirect benefits also takes place, as the impact spills over within a firm, to its competitors, and throughout its supply chain.  These digital spillover effects materialize through numerous channels, and are integral to understanding the role digital technologies play in the economy.”

The Evolution Toward Digital Supply Chains

Two years ago, a group of leading CEOs from around the world met in China to discuss the major issues facing their companies in the global marketplace.  The meeting was organized by the Center for Global Enterprise (CGE), - the nonprofit research institution founded by former IBM Chairman and CEO Sam Palmisano to study the contemporary corporation, globalization, economic trends, and their impact on society.  The CEOs at the meeting in China concluded that their highest research priority was the evolution toward Digital Supply Chains (DSC), and its potential to transform organizations and the conduct of business around the world.  In response, the Digital Supply Chain Institute (DSCI) was launched a few months later.

In October of 2016, the DSCI, in partnership with CREATe.org  published its first research paper, Digital Supply Chains: A Frontside Flip.  The paper explains the key differences between traditional and digital supply chains, and provides practical advice to help companies prepare for the digital supply chains of the future

The traditional supply chain has evolved over the years as a crucial process used by many companies for the production, handling, and/or distribution of their products or services.  Traditional supply chains generally comprise five separate back-office functions: transport, warehousing, purchasing, marketing and finance.  Their key objective has been to improve the efficiency of getting goods and services from suppliers to customers, through a series of intermediate steps including manufacturing, distributors and retailers.  But, these supply chains were quite fragmented, with limited interactions and information sharing among their various functions and steps.

Over the past couple of decades, the explosive growth of the Internet has made it much easier for companies to transact with each other around the world.  The connectivity and universal reach of the Internet has enabled companies to integrate and better coordinate all their various processes, as well as to go beyond the boundaries of the firm and develop highly sophisticated global supply chains.  Vertically integrated firms have evolved into virtual enterprises, increasingly relying on supply chain partners for many of the manufacturing and services functions once done in-house.

The DSCI paper argues that it’s time to focus on the transformation of the whole supply chain process, not just in further increases in efficiency.  Their traditional linear nature is increasingly misaligned with the more networked nature of today’s global production.

Jobs, Skills and Education

Earlier this year the communications firm Burson-Marsteller along with research firm PSB interviewed 1,500 Americans from all walks of life to shed light on their feelings about the current state of the economy and their expectations for the future.  “Americans are concerned about the present, but optimistic about the future,” was the survey’s overriding finding.  “However, opinions about the economy and the future are somewhat driven by education level.”

While only 33% said that the country is headed in the right direction, 67% said that they were somewhat or very optimistic about the future.  To a large extent, this optimism is based on the belief that their jobs are safe over the next 5 years, with 62% believing that they won’t be laid off and 60% that a machine couldn’t replace them.  In their opinion, the high cost of living, unemployment and jobs, the gap between rich and poor and taxes are the top issues facing the US economy, ahead by a fairly wide margin over corporate corruption, loss of manufacturing jobs, and falling or stagnant wages.

Their feelings about the future differed by educational level.  71% of Americans with a college education or more said that they have the right skills to succeed in the 21st century, compared to 42% of those with a high school education or less.  Only 14% of survey participants with a college education worried that their jobs could be automated within five years, compared with 30% of those with high school or less.  And 55% of Americans with college or more felt that technology will improve overall employment compared to 45% of those with high school or less.

The True State of Globalization

The July-August issue of the Harvard Business Review features an article by NYU and IESE professor Pankaj Ghemawat, - Globalization in the Age of Trump: Protectionism Will Change How Companies Do Business - But Not in the Ways You Think.  Ghemawat has long been conducting research on globalization, and has written extensively on the subject.  His latest book, The Laws of Globalization, was published in October of 2016.

“Business leaders are scrambling to adjust to a world few imagined possible just a year ago,” notes the article’s opening sentence.  “The myth of a borderless world has come crashing down.  Traditional pillars of open markets - the United States and the UK - are wobbling, and China is positioning itself as globalization’s staunchest defender.  In June 2016, the Brexit vote stunned the European Union, and the news coverage about globalization turned increasingly negative in the U.S. as the presidential election campaign progressed.”

What should companies do?  Should they pack up and return home?  Is this the end of globalization, only a dozen years after Thomas Friedman’s The World is Flat became a prominent international best-seller?  “Not according to my research,” says Ghemawat. 

Artificial Intelligence is Ready for Business; Are Businesses Ready for AI?

AI is now seemingly everywhere.  In the past few years, the necessary ingredients have come together to propel AI beyond the research labs into the marketplace: powerful, inexpensive computer technologies; advanced algorithms and models; and most important, oceans and oceans of data.

“Artificial intelligence is getting ready for business, but are businesses ready for AI?,” asks McKinsey in a recently published report - Artificial Intelligence: the Next Digital Frontier.  “AI adoption outside of the tech sector is at an early, often experimental stage,” is the report’s succinct answer.  “Few firms have deployed it at scale.”

The report is based on a survey of over 3,000 AI-aware C-level executives across 10 countries and 14 sectors.  Only 20 percent of respondents had adopted AI at scale in a core part of their business.  40 percent were partial adopters or experimenters, while another 40 percent were essentially contemplators.

AI encompasses a broad rage of technologies and application.  It’s often viewed as the leading edge of IT.  As soon as AI is successfully applied to a problem, the problem is no longer a part of AI.  The McKinsey report is focused on five technologies being increasingly deployed in business, that most everyone agrees are part of AI: robotics and autonomous vehicles, computer vision, language, virtual agents, and machine learning.

Platforms, Blockchains, and the Evolution of Trust

“In primitive economies, people traded mostly with members of their village and community,” wrote David Brooks in a June, 2014 NY Times OpEd, - The Evolution of Trust.  “Trust was face to face.  Then, in the mass economy we’ve been used to, people bought from large and stable corporate brands, whose behavior was made more reliable by government regulation.  But now there is a new trust calculus, powered by both social and economic forces.”

Brooks’ article focused on the rise of the on-demand economy, - aka, the collaborative, sharing, peer-to-peer economy, - and in particular on the surprising success of Airbnb.  Rooms for rent in boarding houses and child and pet care services are nothing new.  What’s new is the impact of technology, platforms and blockchain in particular, on the growing on-demand economy.  Companies are being disrupted as consumers are now able to deal with each other bypassing traditional hotels and taxi services.  All kinds of on-demand products and services are now coming to market.

The on-demand economy wouldn’t be possible without the mobile devices and platforms that enable peer-to-peer transactions among individuals any time and place; the digital payment systems that reliably and securely broker the transactions between buyers and sellers; and the social reputation systems, where people rank buyers and sellers, - a critical requirement for the smooth functioning of collaborative markets like Airbnb.  In the digital economy, our online reputations follow us everywhere, whether we are the renters or the ones renting, the service providers or the service consumers.

What’s the Core Priority of Corporate Managers?

In a recent Harvard Business Review article, - The Error at the Heart of Corporate Leadership, - Harvard Business School professors Joseph Bower and Lynn Paine raised a number of important questions. What’s the core priority of corporate’s management, the near-term gains of shareholders or the long-term health of the company?  Do shareholders generally share common investment objectives?  Can a governance model focused on maximizing shareholder value threaten a company’s overall health and financial performance?  These questions go to the heart of what a corporation is all about as well as to the very nature of modern capitalism.

The idea that maximizing shareholder value should be the primary goal of corporate managers is relatively recent, first put forth by academic economists in the 1970s, - most notably by University of Chicago economist and Nobel Prize recipient Milton Friedman.  In a 1970 NY Times Magazine article, The Social Responsibility of Business is to Increase its Profits, Professor Friedman wrote:

“In a free-enterprise, private-property system, a corporate executive is an employee of the owners of the business.  He has direct responsibility to his employers.  That responsibility is to conduct the business in accordance with their desires, which generally will be to make as much money as possible while conforming to the basic rules of the society, both those embodied in law and those embodied in ethical custom.”  He believed that business concerns beyond making a profit, - such as “promoting desirable social ends,” or “providing employment, eliminating discrimination, avoiding pollution and whatever else,” amounted to “preaching pure and unadulterated socialism.”