Irving Wladawsky-Berger

How to Transform a “Big, Old” Company into an Agile Digital Business

“As consumers, we take digital technologies for granted…  We don’t wonder why these things are possible; we simply expect them,” said Designed for Digital a recently published book by Jeanne Ross, Cynthia Beath, and Martin Mocker in its Preface.  But, “the adoption of digital technologies in business doesn’t come as naturally,” - especially if you’re working in a big, old company.  “Inserting digital technologies to enhance operations and create new value propositions has proven extremely challenging.”

Previous research has uncovered this puzzling digital adoption discrepancy between individuals and organizations.  While in our personal lives we’ve enthusiastically embraced digital technology advances, many corporations and institutions still struggle to do so.  At a conference several years ago I learned about a so-called Sunday-night/Monday-morning syndrome, the name given to the frustration of  employees of large companies who, at home on Sunday night, have access to the latest devices and applications, but at work on Monday morning, have to use the more primitive devices and applications that their IT departments support.

A quarter century into the digital age, - as a recent McKinsey study found, - the digital maturity of traditional incumbent firms is still relatively low, - under 25% of their overall potential.  Big, established companies, in particular, have been slow to embrace digital organizational practices, - only 26% of worldwide sales are made through digital channels, 30% of internal operations have been digitally automated, and 25% of supply chains interactions have been digitized.

“If digital technologies are consistently making our consumer lives easier, why aren’t they making it easier to succeed in business?,” asks Designed for Digital.  “Why are business leaders anxious about digital disruption rather than marveling about how quickly they can provide new, exciting, and ever-improving digital solutions to their customers?”  Based on five years of research at nearly 200 large companies, - including executive interviews, surveys of business leaders, and in-depth case studies, - the authors overriding conclusion is that “Big, old companies are simply not designed for digital.”

Leveraging AI to Transform Government

“AI has the potential to help address many of our country’s pervasive problems and advance our safety, health and well-being,” said The Future Has Begun, a report on the impact of AI on government by the Partnership for Public Service and the IBM Center for the Business of Government.  “That promise has great potential to transform government.”

 “The most challenging problems AI may help us solve - from fighting terrorists to serving vulnerable populations - will involve government.  More immediately, though not less consequentially, AI will change the way public servants do their jobs.  Because of the impact AI could have on how governments ensure our safety and further our well-being, government leaders are likely to play a significant role in dictating future directions for AI.”

To illustrate how government is using AI, the report is focused on four concrete use cases being pursued by different agencies.  In-depth interviews were conducted with 14 people from 10 organizations, including the various government agencies involved, as well as their collaborators in non profits and universities.  Let me briefly summarize each of these four case studies.

The MIT Work of the Future Report

“The world now stands on the cusp of a technological revolution in artificial intelligence and robotics that may prove as transformative for economic growth and human potential as were electrification, mass production, and electronic telecommunications in their eras,” said the MIT Task Force on the Work of the Future in its recently released interim report.  The final report will be issued after conducting additional research over the next year.

The Task Force was convened in the spring of 2018 by MIT President Rafael Reif to address what may well be the most critical question of the digital economy: as emerging technologies raise aggregate economic output and the wealth of nations, will they also enable people to attain higher living standards, better working conditions, greater economic security, and improved health and longevity?

The report’s overriding conclusion is that the likelihood that AI and automation will wipe out major workforce sectors in the near future is exaggerated.  However, we’ve already seen important reasons for concern, especially the rising polarization of employment and wage distribution over the past few decades, which has disproportionately benefited high-skilled professionals while reducing opportunities for mid- and low-skilled workers.

“[A] critical challenge is not necessarily a lack of jobs, but the low quality of many jobs and the resulting lack of viable careers for many people, particularly workers without college degrees.  With this in mind, the work of the future can be shaped beneficially by new policies, renewed support for labor, and reformed institutions, not just new technologies. Broadly, the task force concludes, capitalism in the U.S. must address the interests of workers as well as shareholders.”

How We Learned to Prosper Using Fewer Resources

“We have finally learned how to tread more lightly on our planet.  It’s about time,” writes Andrew McAfee in the Introduction to his new book, More from Less: The Surprising Story of How We Learned to Prosper Using Fewer Resources - and What Happens Next.   McAfee is Co-Director of MIT’s Initiative on the Digital Economy and a Principal Research Scientist at the MIT Sloan School of Management, as well as a prolific writer of books and articles.

“For just about all of human history our prosperity has been tightly coupled to our ability to take resources from the earth,” he adds.  “So as we became more numerous and prosperous, we inevitably took more: more minerals, more fossil fuels, more land for crops, more trees, more water, and so on.  But not anymore.  In recent years we’ve seen a different pattern emerge: the pattern of more from less.”

What led to the emergence of a more-from-less pattern?  The book provides a provocative answer to this question.  The most important forces responsible for the change are technological progress and capitalism.

Despite Their Huge Upside Potential, Why Do Most Platforms Fail?

“For anyone who follows the world of business, it is now common knowledge that the most valuable firms on the planet and the first companies to surpass the trillion-dollar mark in value (albeit temporarily) are platforms,” note Michael Cusumano, Annabelle Gawer, and David Yoffie in their recently published book The Business of Platforms: Strategy in the Age of Digital Competition, Innovation, and Power.  “Moreover, in a recent list of more than two hundred current and former ‘unicorns’ - startups with valuations of $1 billion or more - we estimated that platforms made up between 60 and 70 percent.”

What do we mean by platform?  The book offer a simple definition.  “Platforms, in general, connect individuals and organizations for a common purpose or to share a common resource.”  A platform strategy differs from a product strategy in that it relies on an external ecosystem to generate complementary innovations and/or business transactions.  The effect is much greater potential for innovation and growth than a single product-oriented firm can generate alone.

Platforms are all about network effects.  The more products or services a platform offers, the more users it will attract, helping it then attract more offerings, which in turn brings in more users, which then makes the platform even more valuable.  Moreover, the larger the network, the more data is available to customize offerings to user preferences and better match supply and demand, further increasing the platform’s value.

The Future of Work in the US: A Tale of Multiple Americas

The US economy looks good by almost any measure.  Unemployment is at historical low levels, forcing employers to raise wages and become more aggressive about hiring and training workers.  There are more job openings than unemployed people to fill them.  Inflation remains low.  A number of recent studies have concluded that over the next decade, our technology-based economy will create a significant number of new occupations which will more than offset declines in occupations displaced by automation.  Not only is the US economy doing well in the present, but despite our increasingly smart machines, automation fears appear to be unfounded.

Why then are so many Americans so anxious about the future?  Why do they feel left behind despite our growing economy?  Why is the country so polarized?

A recent McKinsey report, The Future of Work in America: People and place, today and tomorrow, sheds light on these very important questions.  For the past several years, McKinsey has been conducting research on the impact of automation technologies on the future of work at a national level.  This new report is radically different, based on a detail analysis of more than 3,000 US counties and over 300 cities.

Overall, the study found that while in aggregate the country is doing well, there are actually multiple Americas, each with widely different economic patterns and on sharply different trajectories, some of which are doing quite well and face an exciting future, while others are not in good shape and risk falling further behind.

The report is organized into five main sections, and includes a number of tables, graphs and maps.  Let me summarize the key findings of each of the sections.

The Digitalization of the American Workforce

“Over the past half century, wave after wave of digital innovation has ensured that digitalization - the diffusion of digital technologies into nearly every business and workplace and pocket - has been remaking the U.S. economy and the world of work,” said the Brookings Institution in Digitalization and the American Workforce.  The digitalization of everything has both increased the potential of individuals and societies while contributing to widespread anxiety about its impact on jobs and economic inequalities.  “And yet, for all of the evidence that big changes are underway, surprisingly little data exists to track the spread of digital adoption.”

The Brookings report quantified the spread of digitalization by analyzing the changes in the digital content of 545 occupations between 2002 and 2016 based on O*NET, the most comprehensive data on US occupations sponsored by the US Department of Labor.  O*NET includes highly detailed, task-level information on hundreds of occupations, and includes quantitative measures of knowledge requirements, skills, tools, education, training and work activities. In addition, the report collected information on the occupations’ particular industries, pay, growth rates, geographical locations and distribution across educational and demographic groups.

Each of the 545 occupations, - which encompassed 90% of the US workforce, - was assigned a digital score between 0 and 100 based on the required digital skills.  High digital occupations are those with scores over 60.  They include software developers - with a score of 91; computer system analysts - 79; and financial managers, - 61.  Medium digital occupations scored between 33 and 60, and include lawyers - 58; automative service technicians - 55; office clerks - 55; and registered nurses - 55.  Low digital occupations have a score below 33, and include security guards - 31; restaurant cooks, - 18; construction laborers - 17; and personal care aides - 14.

The (Updated) Purpose of the US Corporation

In September of 1997, the Business Roundtable (BRT), - an association of CEOs of major US companies, - issued a Statement on Corporate Governance which argued that “the paramount duty of management and of boards of directors is to the corporation’s stockholders; the interests of other stakeholders are relevant as a derivative of the duty to stockholders.”

Last week, the BRT released an updated statement on the Purpose of a Corporation which moves away from it’s previous commitment to shareholder primacy to now emphasize a “Commitment to All Stakeholders” and to “An Economy that Serves All Americans.”  This new statement, which was signed by almost 200 CEOs, now places shareholder interests on the same level as the interests of all its other stakeholders, including customers, employees, suppliers, and communities.

The recent BRT statement has received mostly positive coverage. “Top CEOs Say Companies Have Obligations to Society.  Business Roundtable urges firms to take into account employees, customers and community,” said the WSJ in its article’s headline.  Speaking of the change, Fortune noted that over the past decade “Capitalism, at least the kind practiced by large global corporations, was under assault from all sides, and CEOs were getting the message loud and clear…  Capitalism, it seemed, was desperately in need of a modifier.”  The only note of discord I came across was the response of the Council of Institutional Investors, which expressed concerns that the BRT statement “undercuts notions of managerial accountability to shareholders.”

I applaud the BRT statement.  “This is great,” I thought when first reading it, followed by “what took you so long?”  Given the suffering brought upon so many Americans by the 2008 global financial crisis, - the worst since the Great Depression, - restoring trust in business and in our capitalist economic system should have been a top priority for corporate leaders.  To help appreciate why it’s taken the BRT over a decade to affirm that companies have obligations to society, let’s take a look at the prevalent economic and political context of the past 50 years.

The Challenges of Automation in a Fast Changing Economy

“Technological innovation should be embraced,” notes Automation and a Changing Economy, a recently published report by The Aspen Institute.  “Automation has been a largely positive economic and social force, and looking forward,   automation will be necessary to feed, house, and raise the living standards of a growing and aging population.” 

But, “While it is important to note that automation’s overall, long-run effect on the economy has been positive - more jobs, more growth, higher living standards - this does not negate the disruptive impact of automation on individuals and communities, which results from displacements, changing skill needs, and income inequality.”

The report is divided into two sections: The Case for Action, which explores how automation impacts the economic security and opportunity of American workers, and Policies for Shared Prosperity, which outlines a policy agenda for addressing automation’s challenges and opportunities.  Let me summarize the key conclusions and recommendations in each of the report’s two sections.

Free-Market, Free-Trade Capitalism at a Crossroads

The global economy has undergone considerable change over the past few decades.  Results have been mixed.  On the positive side, the digital revolution has significantly improved the quality of life of billions around the world.  Technology advances like the smartphone and wireless Internet, and the economic development brought about by globalization have led to the dramatic decline of extreme poverty around the world.  However, these benefits have been accompanied by major dislocations in labor markets, benefiting workers in low-cost developing nations while severely impacting workers in advanced economies.

The promise of technological progress and globalization as drivers of growth and prosperity has been a reality for many, but it’s also created winners and losers within nations, leading to rising economic inequality and fueling the rise of populism in countries around the world.  What’s going on?  I believe that part of the answer to this very important question can be found by exploring the evolution of free-market, free-trade capitalism over the past couple of centuries.

Adam Smith, the 18th century Scottish economist and philosopher, is the father of free market, free trade capitalism, and one of the major figures of The Enlightenment.  In the The Wealth of Nations, considered the first modern work of economics, he explained that in a free market, an individual pursuing his own self-interests tends to also promote the good of his community as a whole, and that the free market, while appearing chaotic and unrestrained, is actually guided to produce the right results by a so-called invisible hand.

The past 200 years have seen many variants of capitalism.  They’re all generally based on the private ownership of the means of production, their operation for profit, and the accumulation of capital.  Neoclassical economics, first formulated in the 19th century, led to a laissez-faire model of capitalism, based on the premise that given all relevant information people will make rational decisions, markets on their own will achieve the right results, and government interventions, - e.g., regulations, subsidies, tariffs, - are almost always a bad idea.

Faith in neoclassical economies was shattered in the 1930s by the Great Depression, giving rise to Keynesian economics, named for British economist John Maynard Keynes.  His pragmatic, mixed model of capitalism, based on a predominantly private sector economy but with an appropriate role for the public sector, dominated economic thinking for the next four decades.

Decision Making in Our Increasingly Complex Organizations

A couple of years ago I attended a seminar by University of Toronto professor Avi Goldfarb, who along with UoT colleagues has been conducting research on the economic value of AI.  Goldfarb explained that the best way to assess the impact of a new radical technology is to look at how the technology reduces the cost of a widely used function.  For example, computers are essentially powerful calculators whose cost of digital operations has been dramatically going down over the past several decades thanks to Moore’s Law.  Over the years, we’ve learned to define all kinds of tasks in terms of such digital operations, e.g., inventory management, financial transactions, word processing, photography.  Similarly, the economic value of the Internet can be described as reducing the cost of communications and of search, enabling us to find and access all kinds of information, - including text, pictures music and videos.

Viewed through this lens, our AI revolution can be viewed as reducing the cost of predictions.  The explosive growth of big data and advanced algorithms like machine learning have enabled us to analyze and extract insights from all that data.  Prediction means anticipating what will happen in the future, and given the widespread role of predictions in business, government and our everyday life, AI is already having a major impact on all kinds of human activities.  Decision making, the process of identifying and choosing alternatives, will be particularly impacted by advances in AI.

Decisions typically involve two main tasks: predictions and judgement.  Judgement is the part of decision making that, unlike prediction, cannot be explicitly described to and performed by a machine and is based on human factors like expertise, intuition, and unconscious feelings.  As machine predictions become inexpensive and commonplace, the human judgement that leverages and complements prediction will become more valuable, especially given the increasing complexity of the decisions our institutions are called upon to make.

“It’s the best and worst of times for decision makers,” said McKinsey in a recent article, Untangling your Organization’s Decision Making.  “Swelling stockpiles of data, advanced analytics, and intelligent algorithms are providing organizations with powerful new inputs and methods for making all manner of decisions.”  On the other hand, “Corporate leaders also are much more aware today than they were 20 years ago of the cognitive biases - anchoring, loss aversion, confirmation bias, and many more - that undermine decision making without our knowing it.”

Is the Digital Revolution At Last Shaking Up Banking?

Digital transformations have generally followed three distinct stages.  First comes the use of IT to improve the productivity and quality of back- and front-end processes.  Distribution is next, leveraging the universal reach and connectivity of the Internet for online transactions and access to information. Finally, the transformation reaches a tipping point when technologies radically change the business model and user experience, leading to a fundamental disruption of the industry.

While such a digital journey is ultimately inevitable, the pace varies widely across industries.  The IT industry has been the most disrupted, - often by its own digital creations in a kind of sorcerer’s apprentice scenario.  Over my long career, I’ve seen many once powerful IT companies done in by technology and market changes, and either disappear altogether or become shadows of their former selves.

Beyond IT, few industries have felt the impact of digital forces like media.  In less than two decades, the global recorded music industry has lost over half its revenues, while the drop in newspaper advertising revenue in the US has been even steeper.  Retail has also undergone major changes with the rise of e-commerce, as has telecommunications with the transition to mobile phones.  How about banking, which has long been a leading user of information technologies?

The May 4 issue of The Economist addressed this question in a special report on Banking and Technology, with several articles on the subject.  “Over the past two decades people across the world have seen digital services transform the economy and their lives,” said its lead article.  “Taxis, films, novels, noodles, doctors and dog-walkers can all be summoned with a tap of a screen.  Giant firms in retailing, carmaking and the media have been humbled by new competitors.  Yet one industry has withstood the tumult: banking.”

But, adds The Economist, “technology is at last shaking up banking.”  Two major forces are driving this shake-up: the dramatic growth of digital payments in China and other Asian countries; and the rise of mobile banking in the West, especially among young digital natives.

The Economic Value of Digital Identity

Identity plays a major role in our everyday life.  It’s the key that determines the particular transactions in which we can rightfully participate as well as the information we’re entitled to access.  Think about going to an office, getting on a plane, logging on to a website or making an online purchase.  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 have become 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.

A few weeks ago, the McKinsey Global Institute released Digital Identification: A key to inclusive growth, a comprehensive report at over 100 pages that examined the state of digital identities around the world.  The report analyzed the value creation potential of good digital IDs for individuals and institutions, as well as the potential risks and challenges.  McKinsey’s analysis was informed by nearly 100 concrete uses of digital IDs in seven countries: Brazil, China, Ethiopia, India, Nigeria, the UK and the US.

The Evolution of Work in Urban Labor Markets

A few weeks ago I heard a very interesting keynote by MIT economist David Autor at the annual conference of MIT’s Initiative on the Digital Economy.  The talk was based on his recently published paper Work of the Past, Work of the Future.

Professor Autor started his talk by noting that over the last four decades, the US and other industrial economies have seen a remarkable rise in wage inequality by educational attainment, with the earnings of the most-educated increasing, and the earnings of the least-educated falling in real terms.  Since the 1980s, the earnings of those with a four year college degree have risen by 40% to 60%, while the earnings of those with a post-college education have risen 80% to 100%.  At the same time, the earnings of those with a high school education or less have fallen among men and barely changed among women.

At the same time, the supply of educated workers in the US has vastly increased.  From the 1960s to the present, the share of the labor force with college or post-college degrees rose from 12% to 39%,  while the share of those with some college rose from 13% to 28%.  But the share of those with a high school degree or less fell from 75 to 33%.  Based on the law of supply-demand, one would expect that the wages of college-educated workers would have fallen in recent years because there are so many of them, while the wages of those with a high school education or less would have risen because they are a significantly smaller share of the labor force.  But, as we well know, this has been far from the case.

Autor addressed this seeming paradox in his keynote by analyzing a number of potential explanations.  Eroding union penetration, falling minimum wages, and the rise of China, India and other low labor-cost countries have certainly contributed to the wage erosion of non-college workers.  But his main focus was the changing structure of work in the US and other advanced economies, as well as how these changes have unfolded across different geographic regions, from low density rural areas to high density cities.

The Puzzling Personal Productivity Paradox

The past 10-15 years have seen a number of technology advances, from smartphones to machine learning.  Yet, despite these impressive advances, for most of this period economies around the world have been stuck in an era of slow productivity growth.  Opinions abound, but in the end, there’s no consensus on the causes of this apparent productivity paradox, on how long the slowdown will likely last, or on what to do about it.

Some economists have argued that secular stagnation is the reason behind this unusual situation, caused primarily by a reluctance of companies to invest and of consumers to spend.  Others feel that aging populations around the world, accompanied by slow or declining labor forces are major factors.  A few contend that there’s been a fundamental decline in innovation and productivity over the past few decades, compared to the period between 1870 and 1970.

Yet others argue that there have always been time lags between technology advances and their impact on economic growth.  While technologies may advance rapidly, humans and institutions change slowly.  And, the more transformative the technologies, - as is currently the case, - the longer it takes for their impact to be felt across economies and societies.

But, companies and industries are not the only ones trying to adjust to our fast changing digital economy.  Individuals are also experiencing their own version of the productivity paradox.  Personal computers, along with applications like word processing and spreadsheets introduced a whole new category of personal productivity tools in the 1980s. A decade later, the Internet and World Wide Web enabled people to easily communicate with each other, and gave them access to all kinds of information and applications.

A Novel Method for Measuring the Value of Free Digital Services

Gross domestic product (GDP) is the basic measure of a country’s overall economic output, based on the market value of all the goods and services the country produces.  Most measures of economic performance used by government officials to inform their policies and decisions are based on GDP figures.  But, concerns have long been raised about the adequacy of GDP-based measurements given the major changes that’ve taken place in economies around the world over the past several decades.

GDP is essentially a measure of production.  While suitable when economies were dominated by the production of physical goods, GDP does not adequately capture the growing share of services and increasingly complex solutions that characterize advanced economies.  Nor does it reflect important economic activity beyond production, such as income, consumption and living standards.

In 2008, a Commission on the Measurement of Economic Performance and Social Progress led by Nobel-prize winning economists Joseph Stiglitz and Amartya Sen was convened to look at the limits of GDP as an indicator of economic performance and progress.  “What we measure affects what we do; and if our measurements are flawed, decisions may be distorted.” noted its report.  The Commission recommended complementing classical measures of GDP and economic production with additional measurements that captured people’s well being, as well as factoring in measurements to help reflect the evolution of the economy into the future.

Over the past decade, a new set of concerns have arisen with the rise of the Internet-based digital economy.  How do you measure the value of the increasing amounts of free information goods available over the Internet, including Wikipedia articles, Google maps, Facebook interactions, smartphone apps and You Tube videos?

Once Lumbering, Now Innovative Incumbents Hit Their Stride

Last year, IBM’s Institute for Business Value published Incumbents Strike Back, a global C-suite study based on survey data from over 12,800 executives from 112 countries,  including CEOs, CFOs, CMOs, COOs, CIOs, and CHROs.

“Two decades after the Internet became a platform for transformation, we’re still wondering how it all might turn out,” notes the report.  “The signals aren’t always clear.  Today, winner-take-all organizations are on the rise, but collaborative ecosystems are flourishing as well.  Even in industries where competitive concentration is increasing, innovation hasn’t – as would be expected – flatlined.”

Overall, respondents were evenly divided on whether the key to the future are established markets or new ones.  But they stand in close agreement on two areas:  68% of executives said that their organizations will emphasize customer experience over products, and 63% said that they will continue to expand their networks of business partners.

There was also general agreement on the top external forces that will have the greatest impact in the 2-3 year timeframe: 69% said market factors such as increased competition and changing customer preferences; 63% said technological factors; and 61% said people skills, reflecting the increased importance of talent and ideas.

AI and the Evolution of History

Several weeks ago, I wrote about the event I attended on February 28 to celebrate the launch of MIT’s new Schwarzman College of Computing, - MIT’s strategic response to the rise of artificial intelligence, - a technology that will reshape “geopolitics, our economy, our daily lives and the very definition of work” in the decades to come.  The all-day celebration featured talks and panels on a wide variety of topics, some focused on innovative applications of AI technologies, others on the challenging issues raised by these powerful technologies.

A few weeks later I wrote about one of these challenging issues, the impact of AI on our social interactions, based on the talk by MIT professor Sherry Turkle on Rethinking Friction in Digital Culture, and a related article by Yale professor Nicholas Christakis on How AI Will Rewire US.

I now want to discuss the interview conducted by NY Times columnist Thomas Friedman with former US Secretary of State Dr. Henry Kissinger at the February 28 event.  The interview, - which can be seen in this video, - was based on a June, 2018 article by Dr. Kissinger in The Atlantic, - How the Enlightenment Ends: “Philosophically, intellectually - in every way - human society is unprepared for the rise of artificial intelligence.”

The Third Wave of the Digital Economy - Promises and Challenges

“The next wave of digital innovation is coming.  Countries can welcome it, prepare for it, and ride it to new heights of innovation and prosperity, or they can ignore the changing tide and miss the wave,” writes Robert Atkinson in The Task Ahead of Us.  Atkinson is founder and president of the Information Technology and Innovation Foundation (ITIF), a think tank focused on science and technology policy.

We’re now entering the third wave of the digital economy, says Atkinson.  The first was based on personal computing, the Internet, Web 1.0, and e-commerce.  The second brought us Web 2.0, big data, smartphones and cloud computing.  The emerging third wave promises to be significantly more connected, - including higher bandwidth and a wide variety of devices; more automated, - with more work being done by machines while integrating the physical and digital worlds; and more intelligent, - leveraging huge volumes of data and advanced algorithms to help us understand and deal with our increasingly complex world.

“Building and adopting the new connected, automated, and intelligent technology system will lead to enormous benefits globally, not least of which will be robust rates of productivity growth and improvements in living standards.  Moreover, these technologies will help address pressing global challenges related to the environment, public health, and transportation, among others.”

We’re in the early stages of this third wave.  5G, IoT, robotics, AI and other promising technologies are being embraced by early marketplace adopters, but their full-scale impact is still 5 to 10 years away.  We’re in a period not unlike the late 1980s, when it was clear that IT was on the brink of a major transition, but the Internet revolution didn’t arrive until the mid-1990s.

What Might Work Look Like in 2030?

A number of recent studies have taken a close look at the the future of work over the next 10 to 15 years.  For example, a December, 2017 report by McKinsey examined which jobs will likely be displaced by automation through 2030, as well as which jobs are likely to be created over the same period, based on data from 46 countries.

McKinsey’s overall conclusion was that a growing technology-based economy will create a significant number of new occupations 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.”

Another report, The Changing Nature of Work, - published by the World Bank in October, 2018, - concluded that our problem is not that there won’t be enough work in the future.  The key problem is that, in many countries, the workforce isn’t prepared for a fast unfolding future. Neglecting investments in human capital, - i.e., the sum total of a population’s health, skills, knowledge, and experience, - will dramatically weaken a country’s competitiveness.

Let me now discuss Eight Futures of Work Scenarios and their Implications, a white paper published in January, 2018 by the World Economic Forum (WEF) in collaboration with the Boston Consulting Group (BCG).  The paper considered eight distinct scenarios on what work might be like by 2030, based on different combinations of three of the most impactful and uncertain variables affecting the future of work: the rate of technological change; the evolution of learning in the workforce; and the magnitude of talent mobility across geographies.

AI and our Social Interactions

I recently wrote about the event I attended on February 28 to celebrate the launch of MIT’s Schwarzman College of Computing.  This new interdisciplinary College is MIT’s response to the rise of artificial intelligence, - a profoundly powerful technology that will likely reshape our economy, society and personal lives in the decades to come.  But, attaining AI’s broad potential will require not only a continuing slew of technological innovations, but equally important research on the challenges our society must get ready for, including ethical issues, workplace disruptions, and human interactions.

The event included keynotes and panels on a wide variety of topics. One of the talks I found most interesting was Rethinking Friction in Digital Culture by Sherry Turkle, MIT Professor of the Social Studies of Science and Technology.

In her many writings and seminars, - including her 2011 best-seller Alone Together, -  Professor Turkle has long been warning about the impact of technology on our social interactions.  “Most of us here today were introduced to the idea of friction free as a really good thing,” she said at the start of her keynote.  “It’s an aesthetic of engineering efficiency, so why shouldn’t it be a really good thing?”  This idea that technical things should be friction free and smooth easily bleeds into other domains. “Efficiency becomes aspirational - in politics, in business, in education, and in our thinking about relationships.”

She cited a few examples of the kinds of friction free interactions she’s come across in her research on technology and social relationships.  People of all ages have told her that they prefer to text rather than talk, - even when interacting with a colleague in the next cubicle or office.  People have said to her that they prefer to text their spouse rather than have a face-to-face conversation.  Friction free, she explained, “is usually tied up with a hope for greater efficiency and less vulnerability.”

Globalization in Transition

I recently read two interesting reports on the state of globalization in 2019, one by The Economist, the second by the McKinsey Global Institute.  They both agree on the facts, - globalization and global trade have been undergoing considerable changes since the 2008 global financial crisis.  But, they differ in their explanations of the reasons for the changes.

“The golden age of globalisation, in 1990-2010, was something to behold,” notes The Economist in its overview article, The steam has gone out of globalization.  “Commerce soared as the cost of shifting goods in ships and planes fell, phone calls got cheaper, tariffs were cut and the financial system liberalised.”

But now, we’ve transitioned to a so-called Slobalisation era.  “Globalisation has slowed from light speed to a snail’s pace in the past decade for several reasons.  The cost of moving goods has stopped falling.  Multinational firms have found that global sprawl burns money and that local rivals often eat them alive.  Activity is shifting towards services, which are harder to sell across borders: scissors can be exported in 20ft-containers, hair stylists cannot.  And Chinese manufacturing has become more self-reliant, so needs to import fewer parts.”  Finally, we have the well-publicized trade wars and tariffs of the past two years.  All in all, the “rules of commerce are being rewritten around the world.”

The Productivity Paradox: Digital Abundance and Scarce Genius

Despite the relentless advances of digital technologies, productivity growth has been declining over the past decade.  Investment and interest rates have remained low, and income has continued to stagnate for the majority of workers in the US and other developed economies. The world seems to be stuck in a period of slow growth and no one is quite sure what’s causing this apparent contradiction.

Opinions abound.  Harvard economist Larry Summers has argued that secular stagnation is the reason behind this unusual situation, caused primarily by a reluctance of companies to invest and of consumers to spend, with the ensuing excessive savings acting as a drag on economic growth.

Some contend, - most prominently Northwestern University economist Robert Gordon, - that over the past few decades there’s been a fundamental decline in innovation and productivity.  Perhaps our current technologies, advanced and exciting as they might be, aren’t as transformative as the technologies from the period between 1870 and 1970, when we experienced high productivity growth and a rising standard of living. 

Demographic change, namely, the declining population and labor force growth around the world, is another potential cause for slow economic growth.  The global labor force grew at an average of 1.8% per year between 1960 and 2005, but since then it’s been growing at just 1.1% per year, and it’s already shrinking in a number of countries, including Japan, Germany and China.

Another possible explanation, - advanced by Erik Brynjolfsson, Daniel Rock, and Chad Syverson in AI and the Modern Productivity Paradox, - is that the paradox is primarily due to a time lag between technology advances and their impact on the economy.  We’ve been living in a time of major transformative technologies, - the Internet, smartphones, IoT, big data, AI - whose deployment and impact on productivity growth are still lagging.

While technologies may advance rapidly, humans and our institutions change slowly.  Moreover, the more transformative the technologies, the longer it takes for them to be embraced by companies and industries across the economy.  Leading edge firms are already benefiting from these advances, but most are still in the early learning stages.  Translating technological advances into productivity gains requires major transformations in business processes, organization and culture, - and these take time.

Impact of an Aging Population on Economic Growth and Automation

The world has been stuck in an era of slow economic growth over the past decade.  Top economists and policy makers have proposed a number of explanations for the economic slowdown, but in the end, there’s no consensus on the reasons, on how long the slowdown will likely last, or on what to do about it.

Economic growth has two main components, productivity growth and the growth of the labor force.  One potential theory for our stagnant economic growth is demographics, namely, the rapidly declining population and labor force growth around the world.

The current world’s population is 7.7 billion.  The UN Population Division estimates that global population will reach 9.8 billion with a growth rate of 0.5% in 2050, and will peak around 2100 at roughly 11.2 billion with a growth rate of 0.1%.  Other projections estimate that the population will grow slower, peak at 9.4 billion around 2070 and then decline to around 9 billion in 2100.  Some project that after reaching a peak of 9 billion, the world’s population will decline back to today’s levels, - around 7 billion, - by 2100.

Japan and a few other countries are already experiencing population declines.  The US population is currently around 330 million, and is projected to reach 390 million in 2050 and 450 million in 2100.  While the US birthrate hit a historic low in 2017, the population has continued to grow due to immigration.  The US growth rate is 0.71% including immigration and 0.43% without.

The global labor force grew at an average of 1.8% per year between 1960 and 2005, but since then it’s been growing at just 1.1% per year.  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, - 0.5 percent per year over the past decade, compared with 1.7 percent from 1960 to 2005.  Given the continuing decline in fertility rates in most parts of the world, the global labor force is expected to slip further in the coming decades.

The New, Interdisciplinary, MIT Schwarzman College of Computing

A few months ago, MIT president Rafael Reif announced the launch of the Stephen A. Schwarzman College of Computing in an e-mail to the MIT community.  “This new College is our strategic response to a global phenomenon – the ubiquity of computing and the rise of AI…” wrote President Reif.  “To state the obvious, AI in particular is reshaping geopolitics, our economy, our daily lives and the very definition of work.  It is rapidly enabling new research in every discipline and new solutions to daunting problems.  At the same time, it is creating ethical strains and human consequences our society is not yet equipped to control or withstand.”

The new College of Computing is made possible by a $350 million foundational gift from Stephen Schwarzman, - chairman, CEO and co-founder of Blackstone, one of the world’s leading investment firms.  The gift is part of a  $1 billion commitment to reshape MIT to help it better address the continuing advances in technology in our 21st century digital economy, as well as their many applications , - both the exciting opportunities and the difficult challenges.

The College is envisioned as an interdisciplinary hub across all five of MIT’s existing schools: Engineering; Science; Humanities, Arts, and Social Sciences; Architecture and Planning; and the Sloan School of Management.  This MIT News announcement succinctly summarized the key objectives of the new College:

• reorient MIT to bring the power of computing and AI to all fields of study at MIT, allowing the future of computing and AI to be shaped by insights from all other disciplines

• create 50 new faculty positions that will be located both within the College and jointly with other departments across MIT - nearly doubling MIT’s academic capability in computing and AI

• give MIT’s five schools a shared structure for collaborative education, research, and innovation in computing and AI

• educate students in every discipline to responsibly use and develop AI and computing technologies to help make a better world; and

• transform education and research in public policy and ethical considerations relevant to computing and AI.

Is Blockchain Now Transitioning to a More Mundane and Useful Era?

Blockchain was created around a decade ago as the public, distributed ledger for the Bitcoin cryptocurrency.  Most everyone agrees that it’s a truly brilliant architecture, built on decades-old fundamental research in cryptography, distributed data, distributed computing, game theory and other advanced technologies.  When it first came to light, blockchain had no broader goals beyond supporting Bitcoin.  But, as was the case with the Internet and World Wide Web, blockchain soon transcended its original objectives.

In 2016, blockchain made the list of the World Economic Forum’s Top Ten Emerging Technologies.  The WEF report compared blockchain to the Internet, noting that “Like the Internet, the blockchain is an open, global infrastructure upon which other technologies and applications can be built.  And like the Internet, it allows people to bypass traditional intermediaries in their dealings with each other, thereby lowering or even eliminating transaction costs.”

That same year, blockchain made its first appearance in Gartner’s yearly hype cycles, where it’s remained for the past three years.  In 2017, Gartner noted that “blockchain might seem like it’s just around the corner.  However, most initiatives are still in alpha or beta stage… Long-term, Gartner believes this technology will lead to a reformation of whole industries.”

In a recent article in the MIT Technology Review, associate editor Mike Orcutt succinctly summarized the current state of blockchain: “In 2017, blockchain technology was a revolution that was supposed to disrupt the global financial system.  In 2018, it was a disappointment.  In 2019, it will start to become mundane… After the Great Crypto Bull Run of 2017 and the monumental crash of 2018, blockchain technology won’t make as much noise in 2019.  But it will become more useful.”

Fostering an Innovation Culture: Talent, Discipline and Leadership

Innovation has been a hot topic for the past few decades.  Companies all over the world have integrated innovation into their overall strategies and marketing campaigns.  Nations and regions have launched innovation initiatives in an attempt to attract such companies and their accompanying well-paying jobs.  But, as I have learned over my long career, managing innovation initiatives is actually quite hard, much harder than it may at first appear.

I was reminded of this point as I read The Hard Truth About Innovative Cultures, an article in a recent issue of the Harvard Business Review (HBR) by Harvard professor Gary Pisano.  “A culture conducive to innovation is not only good for a company’s bottom line,” writes Pisano.  “It also is something that both leaders and employees value in their organizations.”

Most everyone agrees that such a culture entails five key behaviors: tolerance for failure, willingness to experiment, psychological safety, collaborative, and non-hierarchical. “But despite the fact that innovative cultures are desirable and that most leaders claim to understand what they entail, they are hard to create and sustain.  This is puzzling.  How can practices apparently so universally loved - even fun - be so tricky to implement?”

The reason, answers Pisano, is that these much-liked behaviors are only one side of the coin, which must be counterbalanced with some tougher behaviors.  People love solving problems that seem impossible to everyone else and creating something qualitatively different from anything that came before.  But they will rarely perform at the necessary levels if they’re relaxed and happy.  True innovation is, in fact, not all that much fun.  In order to reach inside yourself and find that something extra needed for true innovation, you have to feel the stress that comes when you know that what you’re doing is absolutely crucial.  In the end, necessity is the mother of true innovation.

Despite All the Bad News that Surround Us… Is the World Actually Getting Better?

The World is Getting Quietly, Relentlessly Better is the title of a recent WSJ article by its chief economics commentator Greg Ip.  “If you spent 2018 mainlining misery about global warming, inequality, toxic politics or other anxieties, I’m here to break your addiction with some good news: The world got better last year, and it is going to get even better this year,” writes Ip in his opening paragraph.  “Poverty around the world is plummeting; half the world is now middle class; and illiteracy, disease and deadly violence are receding.  These things don’t make headlines because they are gradual, relentless and unsurprising.”

Ip’s article is mostly based on data insights from Our World in Data, a comprehensive website that quantifies the evolution of our global living conditions over the past few centuries.  Our World in Data (OWID) is an initiative of the Oxford Martin Programme on Global Development, led by economist Max Roser at the University of Oxford.

OWID analyzes longitudinal data on how the world has been changing over decades and centuries, as well as explaining the causes and consequences of those changes in its accompanying articles. It makes extensive use of visual aids, including interactive graphs and maps, to present its analyses and insights.  Its content is organized into 15 main sections:  population, health, food, energy, environment, technology, growth & inequality, work & life, public sector, global connections, war & peace, politics, violence & rights, education, media and culture, -  each of which further includes a number of subsections.

Let me summarize just a few of the key trends in the OWID website.

The Business Value of Resilience

The Industrial Revolution was “as much about process innovations that reduced waste and increased productivity as it was about the application of new technologies,” wrote Roger Martin in a recent Harvard Business Review (HBR) article, - The High Price of Efficiency.  Martin is professor and former dean of the Rotman School of Management at the University of Toronto, as well as an accomplished writer.

The belief that efficiency is fundamental to competitive advantage has turned management into a science, - taught in every business school on the planet, - whose objective is the elimination of waste, - whether of time, materials, or capital.

“Why would we not want managers to strive for an ever-more-efficient use of resources?,” asks Martin.  Of course we do.  But, an excessive focus on efficiency can produce startlingly negative effects.  To counterbalance such potential negative effects, companies should pay just as much attention to a less appreciated source of competitive advantage: resilience,  - “the ability to recover from difficulties - to spring back into shape after a shock.”

“Think of the difference between being adapted to an existing environment (which is what efficiency delivers) and being adaptable to changes in the environment.  Resilient systems are typically characterized by the very features - diversity and redundancy, or slack - that efficiency seeks to destroy.”

The Current State of AI Adoption

AI is seemingly everywhere.  In the past few years, the necessary ingredients have finally come together to propel AI beyond early adopters to a broader marketplace: powerful, inexpensive computer technologies; advanced algorithms; and huge amounts of data on almost any subject.  Newspapers and magazines are full of articles about the latest advances in machine learning and related AI technologies.

Two recent reports concluded that, over the next few decades, AI will be the biggest commercial opportunity for companies and nations.  AI advances have the potential to increase global GDP by up to 14% between now and 2030, the equivalent of an additional $14-15 trillion contribution to the world’s economy, and an annual average contribution to productivity growth of about 1.2 percent.

Over time, AI could become a transformative, general purpose technology like the steam engine, electricity, and the Internet. AI marketplace adoption will likely follow a typical S curve pattern, - that is, a relatively slow start in the early years, followed by a steep acceleration as the technology matures and firms learn how to best leverage AI for business value.

To get a better sense of the current state of AI adoption, McKinsey recently conducted a global online survey on the topic, garnering responses from over 2,000 participants across 10 industry sectors, 8 business functions and a wide range of regions and company sizes.  The survey asked about their progress in deploying nine major AI capabilities, including machine learning, computer vision, natural language text and speech processing, and robotic process automation.

The True Value of a Good Education: Adaptability to a Changing Environment

In his 1945 seminal report, Science The Endless Frontier, presidential science advisor Vannevar Bush laid out the blueprint for R&D in post-war America: “New knowledge can be obtained only through basic scientific research” conducted in universities and research labs, which is then applied to develop new products by the private sector and new and improved weapons by the defense sector.  The report was quite influential and led to the considerable expansion of university research, much of it supported by the National Science Foundation (NSF), the National Institutes of Heath (NIH) and other US federal agencies.

A number of large industrial companies also embraced these recommendations, and launched or expanded corporate research labs, such as ATT’s Bell Labs, GE Research, Xerox PARC and IBM Research.  Their job was to push the frontiers of knowledge by conducting both basic and applied research, which would hopefully lead to innovative technologies and products.

A doctoral degree or PhD has long been a key requirement for most research positions, whether in universities, or corporate and government labs.  Thus, since the post-war expansion of research, the number of PhDs has been steadily rising.  NSF data shows that the number of PhD recipients per year from US universities has more than quintupled, - from under 10,000 in 1958 to almost 55,000 in 2017.  Over three quarter of the degrees awarded in 2017 were in STEM disciplines: 23% in Life Sciences, 18% in Engineering, 17% in Social Sciences, 11% in Physical Sciences, and 7% in Math and Computer Sciences. Beyond STEM, 10% of 2017 PhDs were in the Humanities and Arts, 9% in Education, and 5% in various other disciplines.

Data from the US Bureau of Labor Statistics (BLS) shows the value of a PhD education.  According to 2017 BLS data, recipients of doctoral degrees had among the lowest unemployment rates at 1.5%, compared with 2.2% of those with master’s degrees, 2.5% with bachelor degrees and an overall unemployment rate of 3.6%.  Holders of doctoral degrees were also among the best paid, with weekly median earnings of $1,743, slightly below holders of professional degrees, - e.g. physicians, dentists, lawyers, - at $1,836, and higher than the $1,401 for master’s degrees and $1,173 for bachelor’s.

Given these positive figures, one would expect near universal agreement on the value of obtaining a PhD.  But, in fact, this is not the case.  A number of recent articles have been questioning whether a PhD is worth it.  Let’s take a closer look at their arguments.

Will Humans Be Better Off in an Increasingly AI-Based Future?

After decades of promise and hype, artificial intelligence is finally becoming one of the most important technologies of our era.  As AI continues to both spread and advance, will it enhance human capacities or will it lessen human autonomy and agency?  By 2030, will most people be better or worse off than they’re today?

To shed light on these very important questions, the Pew Research Center canvassed AI experts this past summer, including technologists developers, and business and policy leaders.  It received almost 1,000 responses, which formed the basis for its recently published report Artificial Intelligence and the Future of Humans.

Overall, 63% of respondents were optimistic, predicting that most individuals will mostly be better off, while 37% thought otherwise.  But optimistic or not, most experts expressed concerns about the long-term impact of AI on “the essential elements of being human.”

The survey further asked the experts to explain their hopes and fears, to sketch out how they envision human-machine interactions over the coming decade, and what actions should be taken to assure the best possible future. Many answered, and their detailed comments are organized into the report’s three main sections: concerns; suggested solutions; and expectations for the future.  Let me briefly discuss the key points in each section.

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.” 

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. 

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.

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.”

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.

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. 

Getting on the AI Learning Curve: A Pragmatic, Incremental Approach

After decades of promise and hype, AI is now seemingly everywhere.  Over the past several years, the necessary ingredients have come together to propel AI beyond the research labs into the marketplace: powerful, inexpensive computer technologies; huge amounts of data; and advanced algorithms including machine learning.

While AI is likely to become one of the most important technologies of our era, we’re still in the early stages of deployment, especially outside leading-edge technology companies.  But, as AI continues its rapid progress, it’s not too early to ask a few important questions: What is AI’s overall value to the economy? What are the biggest application opportunities?; and, what are AI’s most serious challenges and limitations?

To address these questions, McKinsey recently published a discussion paper on the marketplace potential of AI.  The paper is particularly focused on machine learning and related technologies, and is based on a detail analysis of more that 400 use cases across 19 industries and 9 business functions.

“Two-thirds of the opportunities to use AI are in improving the performance of existing analytical use cases,” is the paper’s overriding finding.  This is a very interesting insight.  AI is now being successfully applied to tasks that not long ago were viewed as the exclusive domain of humans, - machine translation, natural language processing, defeating the world’s top Go players, - but only 16% of the use cases studied by McKinsey are greenfield cases, where only machine learning techniques can be used.  For the vast majority of the use cases, 69%, the key value of machine learning is to improve performance beyond that provided by traditional analytic techniques.  And, in the remaining 15% of cases, machine learning provided limited additional performance over existing analytical methods. 

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.”