Irving Wladawsky-Berger

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.

A Quarter Century into the Digital Age

The digital age was born around 25 years ago with the public release of the Netscape browser in 1994.  The browser made it much, much easier for the average person to access information over the Internet, sparking the explosive growth of users, websites and online applications.  This was a truly historical point, marking the transition from the industrial economy of the past couple of centuries to a new kind of digital economy.   Ever since those days, the digital age has continued to advance driven by a stream of new technologies, and business models, from smartphones and cloud to big data and machine learning.

A few months ago, the McKinsey Global Institute published Twenty-five Years of Digitization, a report offering ten insights to help business leaders navigate this still relatively new digital world.  Let me first list the ten insights, and then briefly comment on a few of them.

• Large economic potential is linked to digitization - and much of it is yet to be captured;
• Digital superstars are rising far beyond the US big four and China’s big three;
• Digital natives are calling the shots;
• Digital changes everything - even industry boundaries;
• Agile is the new way to compete;
• Playing the platform economy is an in-the-money option;
• Self-cannibalization and innovation are a necessity for digital reinvention;
• Going after the right M&A is key;
• Effective management of digital transformation is vital - but challenging; and
  
• Leveraging, and transitioning from, digital to new frontier technologies is an imperative.

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.

The Transformative Power of Blockchain

“Blockchain matters because no business operates in isolation,” says Blockchain for Business, a recently published book by Jai Arun, Jerry Cuomo, and Nitin Gaur.  “Multiple institutions can achieve more together than any single institution can alone.  By implementing business processes that leverage the collective knowledge of the group, processes can be orders of magnitude more cost-efficient.  New processes that were not possible before blockchain, can be created.  This opens new opportunities and creates competitive advantage for many businesses.”

Since if fist came to light over a decade ago as the public, distributed ledger for the Bitcoin cryptocurrency, people have struggled to understand what blockchain is about and what it’s good for.  This is not unusual for potentially transformative technologies in their early stages, as was the case with the Internet in the early-mid 1990s.  The key question is whether blockchain has the potential to become a truly transformative technology over time.  Most everyone agrees.  According to Gartner, the business value-add of blockchain will be more than $176 billion by 2025, and will exceed $3.1 trillion by 2030.

There are two fairly distinct blockchains camps, - one primarily focused on blockchain as the underlying platform for cryptocurrencies, the other on the use of blockchain in the business world.  The cryptocurrency camp, - best characterized by Bitcoin, - is mostly based on public permissionless blockchains, which operate anonymously and require some kind of proof-of-work or proof-of-stake systems.  The second camp, - best characterized by Hyperledger, - is mostly interested in business applications, and is based on the use of private or public permissioned blockchains networks to handle interactions between institutions that are known each other.

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.

From “Buy What We Have” to “Connected Customer Strategies”

Adapting to the ongoing digitization of the economy is one of the key challenges faced by just about every company around the world.  While there are many ways a company can leverage digital technologies to differentiate itself from competitors, two are particularly prominent: streamline operations to develop higher quality products at competitive prices, and provide a superior, cost-effective customer experience through digital channels

The explosive growth of mobile devices means that a company can be engaged with its customers whether they are at home, at work, or shopping in a store.  But, digital customers can be fickle and hard to satisfy.  A successful business will try to make each of its clients feel special by understanding and addressing their unique requirements, and by providing excellent customer service and quickly resolving problems when they come up.  This is hard, which is also why it may very well be the most important way for a business to stand out from its competitors.

“Thanks to new technologies that enable frequent, low-friction, customized digital interactions, companies today are building much deeper ties with customers than ever before,” said a recent Harvard Business Review (HBR) article, The Age of Continuous Connection by Nicolaj Siggelkow and Christian Terwiesch, in its opening paragraph.  “Instead of waiting for customers to come to them, firms are addressing customers’ needs the moment they arise - and sometimes even earlier.  It’s a win-win: Through what we call connected strategies, customers get a dramatically improved experience, and companies boost operational efficiencies and lower costs.”

Most companies engage with their customer fleetingly, using what the authors call the buy what we have model.  In it, companies provide high-quality offerings at competitive prices, but it’s up to customers to identify their needs and seek out the appropriate products or services to meet them.  The key problem with the traditional buy what we have journey is that company and customer are poorly connected throughout.

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 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.

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.

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.

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 Rise of Global, Superstar Firms, Sectors and Cities

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

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

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

The World Bank’s Human Capital Index

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

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

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

Human Capital and the Changing Nature of Work

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

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

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

Blockchain Beyond the Hype

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

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

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

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

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

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

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

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

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

The Jobs Outlook for 2022

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

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

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

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

Blockchain’s Potential for Environmental Applications

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

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

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

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

The 2018 MIT Inclusive Innovation Challenge

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

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

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

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

The Impact of AI on the World Economy

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

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

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

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

A Biological Approach to Management

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

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

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

The Economic Value of Artificial Intelligence

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

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

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

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

Automation and the Changing Demand for Workforce Skills

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

AI - the Creation of a Human-Centric Engineering Discipline

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

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

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

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

What Machine Learning Can and Cannot Do

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

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

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

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

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

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

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

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

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

Education, Work and US Leadership in the 21st Century

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

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

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

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

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

The 2018 Inclusive Internet Index

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

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

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

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

From Horseless Carriages to Driverless Vehicles

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

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

Human-Machine Work Teams

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

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

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

Improving the Odds of Success for Corporate Transformations

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

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

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

The Promise and Current State of Blockchain

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

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

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

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

The Growing Importance of Resilience in an Increasinsgly Turbulent World

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

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

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

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

A Framework for the Safe Management of Digital Identities and Data

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

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

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

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

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

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

Customer Experience - the Key Competitive Differentiator in the Digital Age

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

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

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

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