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.

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 Identity Challenge: On the Internet, Nobody Knows You're a Dog

I recently read Better Identity in America, a report by the Better Identity Coalition, an organization launched earlier this year to focus on promoting the development and adoption of solutions for identity verification and authentication.  The report outlines a policy agenda for improving the privacy and security of digital systems and help combat identity fraud.

How bad is the problem?  Data breaches, large-scale fraud, and identity theft are becoming more common.  In 2017, there were 16.7 million victims of identity fraud in the US, causing a loss of $16.8 billion; there was a 44.7% increase in US data breaches between 2016 and 2017 and a 30% rise in online shopping fraud; 179 million personal information records were exposed to data breaches, 69% of which were identity theft incidents.

The report notes that “the ability to offer high-value transactions and services online is being tested more than ever, due in large part to the challenges of proving identity online. The lack of an easy, secure, reliable way for entities to verify identities or attributes of people they are dealing with online creates friction in commerce, leads to increased fraud and theft, degrades privacy, and hinders the availability of many services online.”

As the report reminds us, the extent of this challenge was famously captured back in 1993 by Peter Steiner’s New Yorker cartoon with the caption On the Internet, nobody knows you’re a dog.  25 year later, the cartoon still perfectly describes the identity challenge.  If anything, the challenge is even more serious in 2018, given that the volume and variety of online transaction services are greater than ever before.

The Emergence of a Blockchain-Based Token Economy

Blockchain Revolution, published in May of 2016 by Don Tapscott and Alex Tapscott, was one of the first books that explained the promise of blockchain technologies to the general public.  Its central argument is that for nearly four decades, the Internet has been great for reducing the costs of searching, collaborating, and exchanging information.  But, it has serious limitations for business and economic activity.  “Doing business on the Internet requires a leap of faith,” the book noted.  The Internet was designed to move information, but it’s lacked the necessary trust, security and privacy safeguards to move assets of value.  With blockchain, we’re now seeing the emergence of such an Internet of value.  “Now for the first time ever we have a native digital medium for value, through which we can manage, store, and transfer any asset.”

An updated edition of Blockchain Revolution was published this past June.  A lot has happened with blockchain in the intervening two years.  The updated edition explains some of these recent developments in a new preface, including cryptoassets, permissioned networks, identity and supply chains.  I’ll focus my comments on the emergence of a blockchain-based token economy, driven by the explosive growth in the value and variety of cryptoassets.

When the original book was published, the entire cryptoasset market had a value of $9 billion, - mostly dominated by the value of Bitcoin at $7 billions, while Ether, the Ethereum cryptocurreny, had reached around $1 billion.  The new edition estimates that as of March, 2018, the cryptoasset market was around $400 billion in size, a value subject to rapid fluctuations.  In addition, a wide variety of cryptoassets have been developed over the past two years. 

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.

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. 

The Impact of AI on R&D and Innovation

For the past couple of centuries, general-purpose technologies (GPTs) have been the key drivers of productivity and economic growth, - thanks to their rapid improvements in price and performance, pervasive applications, and ability to spur complementary innovations across a wide variety of industries.  The steam engine, electricity and the internal combustion engine are prominent examples of GPTs in the 18th and 19th century.  More recently, semiconductors, computers and the Internet have led to the digital revolution of the past several decades.

Beyond innovations in existing sectors, the rapidly improving price/performance of GPTs have led over time to the creation of whole new applications and industries.  For example, the steady declines in the price of electricity-generated power and the improvement in the efficiency of electric motors led to the radical transformation of manufacturing in the early part of the 20th century with the advent of the assembly line.  It also led to the creation of the consumer appliance industry, e.g., refrigerators, dishwashers, washing machines.  Similarly, as the semiconductor industry took off, it led to the historical transition from the industrial economy of the past two centuries to our ongoing digital economy.

How about artificial intelligence?  Beyond its use by leading edge technology companies, we’re still in the early stages of AI deployment.  It’s only been in the last few years that major advances in machine learning have taken AI from the lab to early adopters in the marketplace.  While considerable innovations and investments are required for its wider deployment, AI is likely to become one of the most important GPTs in the 21st century. 

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?

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.

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 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.”

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 Impact of Automation on Everyday Life

This past October, the Pew Research Center released Automation in Everyday Life, a report on what Americans think about advanced technologies like artificial intelligence and robotics, and the impact they expect them to have on their everyday lives.  The report is based on a national survey of 4,135 randomly selected American adults.  To help gauge their opinion on such complex topics, the survey questions were framed around four specific scenarios related to these advanced technologies: driverless cars, workplace automation, robot caregivers, and computer algorithms that evaluate and hire job applicants.

“Americans anticipate significant impacts from various automation technologies in the course of their lifetime,” noted the report.  “Although they expect certain positive outcomes from these developments, their attitudes more frequently reflect worry and concern over the implications of these technologies for society as a whole.”

Let me discuss some of the report’s key findings.

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.

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.

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.”

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. 

Blockchain and the Future of the Firm

Last week I wrote about Machine, Platform, Crowd: Harnessing Our Digital Future, a recently published book by MIT’s Andy McAfee and Erik Brynjolfsson.  The book is organized into three main sections: Mind and Machine, Product and Platform, and Core and Crowd, each based on  a business trend that’s significantly reshaping the business world.  I based my discussion on the Mind and Machine section, which I found to be an excellent explanation of recent advances in artificial intelligence.

I now want to focus my attention on the provocative questions raised in the book’s latter chapters.  “In this era of powerful new technologies, do we still need companies?”  How are firms likely to evolve given the continuing advances of Internet-based technologies, as well as emerging blockchain-based technologies such as distributed ledgers and smart contracts?  “Are companies passé” in an increasingly decentralized economy?

Following the Great Depression and WW2, the country welcomed the stability promised by corporate capitalism.  Big, multinational companies dominated most industries, - GM, Ford and Chrysler in cars, Citibank, American Express and Morgan Stanley in finance, and Esso/Exxon, Mobil and Texaco in oil and gas.  It was an era characterized by bureaucratic corporate cultures, - not unlike military hierarchies - focused on organizational power and orderly prosperity.

This all started to change a few decades later with the advent of a more innovative, fast moving entrepreneurial economy.  The 1980s saw the rise of young, high-tech companies, - e.g., Microsoft, Apple, Oracle, Sun Microsystems, - and Silicon Valley became the global hub for innovation, emulated by regions around the world.  Turtlenecks and jeans replaced blue suits, white shirts, and ties.

The advent of the Internet and e-commerce pushed all these trends into hyperdrive in the 1990s.  The Internet made it much easier for companies to transact with each other around the world.  Vertically integrated firms evolved into virtual enterprises, increasingly relying on supply chain partners for many of the functions once done in-house.  Management experts noted that large firms were no longer necessary and would in fact be at a disadvantage in the Internet era when competing against agile, innovative smaller companies.

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.

Automation Anxieties - We’ve Been Here Before

“The rise of artificial intelligence is the great story of our time,” notes What to Do When Machines Do Everything in its Preface.  The book was published earlier this year by Malcolm Frank, Paul Roehrig and Ben Bring of Cognizant’s Center for the Future of Work.  “Artificial intelligence has left the laboratory (and the movie lot)…  It’s pervading all the institutions that drive our global economy.… And this is just the beginning.”

The book deals with a number of important questions: When machines do everything, what am I going to do?; Will a robot take my job away?; How are humans going to make a living?; What will my industry look like in 10 years?; Will my children be better off than I am?

Overall, the authors are optimistic about the future, reminding us that we’ve been here before.  Automation anxieties have been with us ever since the 1810s, when the so-called Luddites smashed the new weaving machines that were threatening their textile jobs, and they continued to resurface over the past two centuries right along with advances in technology.

Over 60 years ago, the advent of computers led to a new round of automation fears.  In 1965, Newsweek devoted a special issue to The Challenge of Automation, which it called  “the most controversial economic concept of the age.  Businessmen love it.  Workers fear it.  The government frets and investigates and wonders what to do about it.

In his 1964 Bill creating the National Commission on Technology, Automation and Economic Progress, President Lyndon Johnson said that “Technology is creating both new opportunities and new obligations for us, opportunity for greater productivity and progress - obligation to be sure that no workingman, no family must pay an unjust price for progress.”

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.” 

Global Arbitrage and the Productivity Puzzle

A couple of weeks ago I wrote about The Productivity Puzzle, - namely that despite our continuing technology advances, the US and other developed economies have experienced a sharp decline in productivity growth over the past 10 to 15 years.  In an article published earlier this year, McKinsey offered three main possible explanations for this productivity growth decline: the difficulty of measuring productivity in the digital economy, the shortage of demand and investment opportunities, and the impact, - or lack thereof, - of technological innovations. “So far, though, economists have failed to reach consensus on the causes of the  productivity growth slowdown or indeed the relative significance of the various arguments,” wrote McKinsey.

But another article, The New Class War by Michael Lind, offers a different, rather provocative explanation for our low productivity growth.  According to Lind, the last few decades have seen the rise of managerial elites in advanced economies which have been pursuing profits by methods other than technology-driven innovation and productivity growth.  Instead, they’ve been taking advantage of the significant reductions of trade barriers over this timeframe to pursue profits based on global labor arbitrage, as well as tax arbitrage and other forms of financial engineering.

Lind references James Burnham, John Kenneth Galbraith, and other political scientists and economists whose works I’m not familiar with.  I found the article quite interesting because Lind’s explanation for our declining economic and productivity growth seems quite different from those of most other economists.  Let me attempt to summarize his key arguments. 

Is Blockchain Ready to “Cross the Chasm”? Lessons from the Internet

Earlier this year, IBM conducted the largest study to date on the state of adoption of blockchain.  The study interviewed almost 3,000 C-Suite executives from over 80 countries and 20 industries to learn about their company’s blockchain plans.  Survey responses where classified into three groups: explorers, investigators and passives.

Explorers are already involved in blockchain pilots and experiments.  They make up an average of 8 percent across all industries, with higher activity in certain industries like financial services.  25 percent of organizations are investigators, - considering but not yet ready to deploy blockchains, and 67 percent are passives, - not considering blockchains so far.

These survey results are not surprising for a technology as new and complex as blockchain.  With such technologies, you typically have a relatively small number of early adopters.  A larger number of followers are waiting to learn from the early adopters’ experiences, while the laggards are not yet sure what the technology is about or if it applies to them.

The Productivity Puzzle

Last year, Foreign Affairs focused its March/April issue on Surviving Slow Growth.  “The first decade of the twenty-first century was a time of unprecedented economic growth,” said the issue’s introductory article.  “The rich world got richer, and the developing world raced ahead: by 2007, the emerging-market growth rate had hit 8.7 percent… Then came the fall… growth has ground to a halt almost everywhere, and economists, investors, and ordinary citizens are starting to confront a grim new reality: the world is stuck in the slow lane and nobody seems to know what to do about it.”

Economic growth has two main components, productivity growth and the growth of the labor force.  Fewer workers is one of the key reasons for our stagnant economic growth.  The labor force is still growing in some developing countries like India, Nigeria and the Philippines, but it’s already shrinking in China, Japan and Germany.  In the US, the labor force is growing very slowly.

Over the coming decades, the labor force is expected to shrink in most parts of the world as fertility rates continue to decline.  The 2016 US fertility rate was the lowest it’s ever been.  Increasing productivity is thus more crucial than ever to promote economic growth.  But, in the US and other advanced economies, productivity growth has significantly slowed down over the past few decades.

Robocalypse Now? Technology, Productivity and Employment

Is the Robocalypse upon us?, asked MIT economist David Autor in his presentation at a recent Forum of European central bankers.  His presentation was based on a paper co-written with Utrecht University economist Anna Salomons.  “Is productivity growth inimical to employment?,” they asked in the paper’s abstract.  “Canonical economic theory says no, but much recent economic theory says maybe  - that is, rapid advances in machine capabilities may curtail aggregate labor demand as technology increasingly encroaches on human job tasks.”

Fears that machines will put humans out of work are not new.  Throughout the Industrial Revolution there were periodic panics about the impact of automation on jobs, going back to the Luddites, - textile workers who in the 1810s smashed the new machines that were threatening their jobs.  But, “In the end, the fears of the Luddites that machinery would impoverish workers were not realized, and the main reason is well understood,” noted a 2015 article on the history of technological anxiety.

“The mechanization of the early 19th century could only replace a limited number of human activities.  At the same time, technological change increased the demand for other types of labor that were complementary to the capital goods embodied in the new technologies.  This increased demand for labor included such obvious jobs as mechanics to fix the new machines, but it extended to jobs for supervisors to oversee the new factory system and accountants to manage enterprises operating on an unprecedented scale.  More importantly, technological progress also took the form of product innovation, and thus created entirely new sectors for the economy, a development that was essentially missed in the discussions of economists of this time.”

Automation fears have understandbly 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. “Previous technological innovation has always delivered more long-run employment, not less.  But things can change,” said a 2014 Economist article.  “Nowadays, the majority of economists confidently wave such worries away…  Yet some now fear that a new era of automation enabled by ever more powerful and capable computers could work out differently.”

On the Governance of Blockchain: Lessons from the Internet

A few weeks ago, the World Economic Forum (WEF) released Realizing the Potential of Blockchain, a report by Don Tapscott and Alex Tapscott, authors of Blockchain Revolution which was published in May, 2016, and founders of the recently launched Blockchain Research Institute.  “The last few decades brought us the internet of information.  We are now witnessing the rise of the internet of value,” wrote the authors in the report’s Executive Summary.  “Like the first generation of the internet, this second generation promises to disrupt business models and transform industries… pulling us into a new era of openness, decentralization and global inclusion.”

“However, this extraordinary technology may be stalled, sidetracked, captured or otherwise suboptimized depending on how all the stakeholders behave in stewarding this set of resources - i.e. how it is governed,” they add in a strong note of caution.  “How we govern the internet of information as a global resource serves as a model for how to govern this new resource: through a multi-stakeholder approach using what we call global governance networks.”

I totally agree.

The Internet and World Wide Web brought a badly needed culture of collaboration and standards to the IT industry.  In the 1980s, it was quite difficult to get different IT systems to talk to each other.  Just sending an e-mail across two different applications from two different vendors was quite a chore, as was sharing information across disparate systems.  Then in the 1990s, the open Internet protocol, - TCP/IP, - was widely embraced by the general IT marketplace, making it possible to interconnect systems and applications from any vendors.  Internet e-mail protocols, - SMTP, MIME, POP, IMAP, - enabled people to easily communicate with anyone on any system.  At the same time,  the Web’s open standards, - HTML, HTTP, URLs enabled any PC connected to the Internet to access information on any web server anywhere in the world.

A similar story played out with Unix.  In the 1980s, Unix became a popular operating system for technical workstations, supercomputers and other kinds of systems.  Every vendor had developed its own version of Unix, - IBM’s AIX, Sun’s Solaris, HP’s HP-UX, and several others, - and they were all somewhat different and incompatible, making it difficult to port applications across these different flavors of Unix.  Attempts to unify UNIX were not successful.  Finally, in the 1990s Linux emerged as a Unix-like operating system that over time was embraced by just about all vendors.

Serious AI Challenges that Require Our Attention

Several weeks ago Vanity Fair published and article by NY Times columnist Maureen Dowd, - Elon Musk’s Billion-Dollar Crusade to Stop the AI Apocalypse.  “Elon Musk is famous for his futuristic gambles, but Silicon Valley’s latest rush to embrace artificial intelligence scares him,” noted Dowd.  “And he thinks you should be frightened too.”

Entrepreneur and inventor Elon Musk is one of a number of world-renowned technologists and scientists who have expressed serious concerns that AI might be an existential threat to humanity, a group that includes Stephen Hawking, Ray Kurzweil and Bill Gates.  But, the vast majority of AI experts do not share their fears.  A few months ago, Stanford University’s One Hundred Year Study of AI project published a report by a panel of experts assessing the current state of AI.  Their overriding finding was that:

“Contrary to the more fantastic predictions for AI in the popular press, the Study Panel found no cause for concern that AI is an imminent threat to humankind.  No machines with self-sustaining long-term goals and intent have been developed, nor are they likely to be developed in the near future.  Instead, increasingly useful applications of AI, with potentially profound positive impacts on our society and economy are likely to emerge between now and 2030, the period this report considers.  At the same time, many of these developments will spur disruptions in how human labor is augmented or replaced by AI, creating new challenges for the economy and society more broadly.”

Just because experts conclude that, - at least for the foreseeable future, - AI does not pose an imminent threat to humanity, doesn’t mean that such a powerful technology isn’t accompanied by serious challenges that require our attention.

Become a More Productive, Empathetic, Creative Person With the Help of AI-Based Tools

Despite dramatic advances in technology, most of the world’s economies have been stuck in a long period of slow growth and slow productivity.  This is one of the most serious challenges in our 21st century economy.  Opinions abound, but there’s little consensus on its causes, and, nobody seems to know what to do about it, or how long it will likely last, - years or decades. 

In a recent article in the MIT Sloan Management Review, MIT Research Fellow Michael Schrage proposed a provocative and counterintuitive approach for enhancing innovation and productivity through man-machine collaborations.  Schrage’s approach has been more influenced by behavioral economics than by technology or algorithmic advances.  Instead of just asking how can people create more valuable innovation?, why not also ask How can innovation create more valuable people?  Don’t just leverage advanced technologies, - e.g., bots, software agents, and digital assistants, - to automate away a large portion of the workforce, but also focus on enhancing innovation and productivity by leveraging technology to create higher-performance versions of employees.

“Designing and training smarter algorithms may be cheaper and easier than retraining smart people,” wrote Schrage.  “Advocates of autonomous systems and machine learning typically innovate to minimize or marginalize human involvement in business processes.  For them, people are part of the problem, not the solution.  Organizations that take productivity seriously, however, understand that false dichotomies make poor investments: Smarter machines can - and should - be keys to unlocking greater returns from human capital.”

Mobile Payments - a Long, Relentless War of Attrition

Apple Pay was launched in September of 2014.  It was positioned as a relatively easy, secure way to pay for purchases in physical stores as well as online using a variety of supported Apple devices including iPhones, iPads, Macs and Apple Watch. The general reaction to Apple’s announcement was quite positive:  “[F]or now, at least, analysts believe if there is any company to persuade consumers of the mobile wallet’s value, it is Apple.” 

The payments community welcomed Apple with open arms, hoping that Apple could play a major role in bringing together the different players in the fragmented payments ecosystem, much as it had previously done with iTunes for music and the App Store for smartphone apps.  Apple Pay embraced a number of industry standards, including NFC (near-field communications) for contact-less payments; secure element, a dedicated device chip; and network-based tokenization to protect sensitive financial data.  In addition, Apple collaborated with the credit card networks, several banks and a number of merchants in the development and deployment of Apple Pay, as well as publishing the APIs so developers could embed Apple Pay services in their own apps.  

“Say hello to Apple Pay. It’s the new kid on the payments block and, depending on how things unfold, it could be the new Gorilla in the mobile payments ecosystem,” wrote Karen Webster, CEO of Market Platform Dynamics and an expert in digital payments.  But, she also added a prescient note of caution: “As great as this sounds, there are two limitations of Apple Pay right now for consumers and merchants.  And it’s that old chicken and egg issue that gets in the way of every new payments system.”  The chicken and egg issue is that there aren’t enough consumer devices supporting Apple Pay, and there aren’t enough merchants that accept it.  Both, consumers and merchants, have to be convinced to move away from payment systems they know well and feel comfortable with.

Global Trends and Business Strategy

McKinsey recently published an article on the long-term trends that are reshaping the business environment.  The article reminds us that the trend is your friend is an old adage that applies to business strategy as well as to investing.  The ability to anticipate where your industry is moving and to begin to reposition your company ahead of competitors is one of the most important elements of business strategy.  Companies that correctly identify and ride the tailwinds of technology and market trends will generally perform significantly better than companies for whom these same trends become tough headwinds.

But, the full version of the phrase is “The trend is your friend, until the end when it bends.”  Identifying long term trends is easier said than done.   Major trends involve competing complex forces interacting with each other, making it even more difficult to ascertain where things are heading.  And, they will eventually change direction.  It can be just as painful to miss an important trend as to stay with the trend once it starts to bend.

The article discusses nine major global forces organized into three distinct mega-trends: global growth shifts, accelerating industry disruption, and a new societal deal.  Let’s take a closer look at each of them.

The Emerging Data Economy

The Economist’s May 6 issue referred to data in its cover as the world’s most valuable resource.  Its lead article, - Data is giving rise to a new economy, - called it the fuel of the future.  “Data are to this century what oil was to the last one: a driver of growth and change.”  Both oil refineries and data centers fulfill similar roles: “producing crucial feedstocks for the world economy.”  Much of modern life would not exist without the cars, plastics and drugs made possible by oil refineries.  Similarly, data centers “power all kinds of online services and, increasingly, the real world as devices become more and more connected.”

Data has been closely intertwined with our digital technology revolution since the early days of the IT industry.  Data processing was the term then used to describe the applications of IT to automate highly structured business processes, e.g., financial transactions, inventory management, airline reservations.  Over time, increasingly sophisticated applications were developed to better manage key business operations along with their associated data, including enterprise resource planning, customer relationship management and human resources.  Beyond their use in operations, the information generated by these various applications was collected in data warehouses, and a variety of business intelligence tools were used to analyze the data and generate management reports.

These commercial applications dealt mostly with structured information in those early years of computing.  But at the same time, the scientific community was developing tools for managing, analyzing and visualizing the high volumes of much more unstructured data generated by their  experiments and observations.  Physicists, astronomers, biologists, and other scientists and engineers were developing methodologies and architectures for dealing with very large volumes of unstructured data, as well as analytical techniques, like data mining, for discovering patterns and extracting insights from all that data.

Then came the explosive growth of the Internet in the mid-1990s.  Ever since, digital technologies have been permeating just about every nook and cranny of the economy, society and our personal lives.  Data is now being generated by just about everything and everybody around us, including the growing volume of online and offline transactions, web searches, social media interactions, billions of smart mobile devices and 10s of billions of smart IoT sensors.