Irving Wladawsky-Berger

How We Learned to Prosper Using Fewer Resources

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

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

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

How to Support the Widespread Adoption of AI

“Artificial intelligence is reshaping business - though not at the blistering pace many assume,” notes Building the AI-Powered Organization, an article in Harvard Business Review by McKinsey partners Tim Fountaine, Brian McCarthy, and Tamim Saleh in its opening sentence.  A number of studies agree that AI is the biggest commercial opportunity for companies and industries over the next 10-15 years.  Two recent reports by McKinsey and PwC respectively concluded that AI has the potential to incrementally increase global economic output between now and 2030 by an additional $13 to $15 trillion.

“Yet, despite the promise of AI, many organizations’ efforts with it are falling short,” adds the HBR article.  Surveys with thousands of executives, showed that most firms are only using AI in ad hoc pilots or applying it to a single business process.  Only 8% of firms are engaged in practices that support widespread adoption.

Why the slow progress?  “At the highest level, it’s a reflection of a failure to rewire the organization.  In our surveys and our work with hundreds of clients, we’ve seen that AI initiatives face formidable cultural and organizational barriers…  at most businesses that aren’t born digital, traditional mindsets and ways of working run counter to those needed for AI.”

To support the widespread adoption of AI, companies must make three fundamental shifts.

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

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

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

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

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

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

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

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

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

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

The State of AI in the Enterprise

A few months ago, Babson College professor Tom Davenport gave a talk on the state of AI in the enterprise at the annual conference of MIT’s Initiative on the Digital Economy.  His talk was based on two recent US surveys conducted by Deloitte, the first one in 2017 followed by a second in 2018.  Davenport was a co-author of both reports.

The 2017 survey was focused on the responses of 250 US executives who were leading the applications of AI in their companies.  The larger 2018 survey reached out to 1,100 IT (46%) and line-of-business (54%) executives from US-based companies (64% at the C-level) and 10 different industries.  All of these respondents were early AI adopters compared with their counterparts in an average company, - 90% were directly involved in their company’s AI projects, and 75% said that they had an excellent understanding of AI.

Davenport started his talk by summarizing the key findings in the Deloitte surveys:

• 20-30% of enterprises are early adopters, having implemented at least one AI prototype or production application;
  
• Many projects are in pilots but some are already in production;
• Relatively simple low hanging fruit projects prevail over more ambitious and complex moon shots;
  
• Only 24% cited “reducing headcount through automation” as one of their top AI priorities;
• The great majority of respondents believe that AI leads to moderate or substantial changes in job roles and skills;
• Implementation, integration, data issues and talent top the list of challenges faced by early adopters;
  
• Further AI growth is inevitable.

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.

Using Agile Processes to Develop AI-Based Solutions

In the waterfall development model, a project is broken down into a set of sequential phases, - e.g., conception, analysis, design, construction, testing, deployment, maintenance, - each dependent on the completion of the previous phase.  Such a model is most appropriate for complex engineering projects, like bridges, airplanes and computer hardware.  It’s also quite applicable for mission critical software and infrastructure projects where security, availability, scalability and other ilities are paramount.

But, a serial, orderly approach isn’t appropriate for projects where time-to-market, usability, and flexibility are the highest priorities.  For such projects, an agile development approach is much more suitable.  Key to the agile approach is the involvement of the product’s target end-users early in the process to make sure that the product under development is responsive to their requirements. 

Agile development is evolutionary in nature, based on the early availability of experimental prototypes, user feedback, continuous improvements, and rapid responses to technology, market and user requirements.  It’s not surprising that agile software development took off in the 1990s, when the explosive rise of the Internet brought millions and millions of users online, lots of new user-oriented applications, and short time-to-market cycles in response to fierce competition.

Our increasingly sophisticated smart machines bring with them a whole new set of development challenges.  These AI-based systems are generally software-intensive, user-centric, and data-driven.  Their complexity goes way up the smarter we want the systems to be.  The behavior of an AI system based on machine learning depends on the information we use to train its algorithms rather than on the precise set of software instructions that tell the computer what to do.

AI systems are designed to sense, respond and adapt to a changing environment.  Their development feels sort of like bringing up and sending a teen-ager into the world.  Given that we want them to be able to handle unanticipated situations, they must have a fair degree of flexibility and autonomy.  But, it’s hard to be sure that the system (or the teen-ager) will always behave the way we’d like them to.

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.”

The Business Value of Augmented Reality

I recently heard a very interesting presentation at the 2019 MIT CIO Symposium, - Why Companies Need an Augmented Reality Strategy, - by Harvard professor Michael Porter and James Heppelmann, CEO of PTC, an industrial software company.  The talk was based on their HBR article A Manager’s Guide to Augmented Reality.

We generally think of products as physical entities built with a combination of mechanical and electrical components, - e.g., appliances, cars, agricultural machines, industrial equipment, - some quite simple and some highly complex.  But, in a 2014 HBR article, Porter and Heppelmann introduced the notion of smart connected products (SCPs), a whole new class of products that, in addition to mechanical and electrical components, use digital components like microprocessors, sensors, data storage, software, and connectivity in a wide variety of ways.  Smart connected products are radically reshaping companies and exposing them to new competitive opportunities and threats, while altering industry boundaries and creating entirely new industries.

Through its first five decades, IT helped companies become significantly more productive, by automating many of their processes, reengineering their overall operations, and reaching out beyond the boundaries of the enterprise.  But, their actual products were largely unaffected.  This is all now changing with the rise of the Internet of Things (IoT), cloud computing, Big Data, AI, and other innovations whose combined impact is driving the new era of smart connected products.

As the world’s digital and physical infrastructures converge, IT is now an integral part of the products themselves. Digital technologies are designed right into the products.  Massive amounts of product-usage data can now be gathered, stored and analyzed by a variety of applications in the product’s cloud counterpart.  The cloud might also include a digital twin of each specific physical product, that is, a highly realistic 3-D digital model that monitors the real-time status of the digital twin’s physical counterpart, - e.g., a jet engine, elevator or turbine, - and helps make important decisions such as the need for preventive maintenance to prevent a product failure.

Is the Digital Revolution At Last Shaking Up Banking?

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

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

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

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

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

The Economic Value of Digital Identity

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

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

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

The Evolution of Work in Urban Labor Markets

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

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

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

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

The Puzzling Personal Productivity Paradox

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

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

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

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

Blockchain - the Networked Ecosystem is the Business

A few weeks ago I attended two back-to-back blockchain events in Toronto, - the Blockchain Research Institute All-Member Summit followed by the inaugural Blockchain Revolution Global conference.  Both events included a number of excellent talks and panels.  One of the presentations I particularly enjoyed was Scaling Blockchain for the Enterprise: Emerging Business Models, by IBM’s Andrew Martin and Smitha Soman.  Their presentation was based on their recently published report Building your blockchain advantage.

Since it first came to light a decade ago as the public, distributed ledger for the Bitcoin cryptocurrency, people have struggled to understand what blockchain is all about and what it’s truly good for.  This is not unusual for potentially transformative technologies in their early stages, as was the case with the Internet, and is still the case with AI.  The key question is whether blockchain has the potential to become a truly transformative technology over time.  With few exceptions, the answer is positive.

The essence of blockchain, notes the report, is that the unit of competition is the networked ecosystem, no longer a single enterprise.  “As blockchain adoption continues to gather momentum, organizations must approach their blockchain strategies with the same rigor and commitment as any other new and transformative strategies.  They can’t just fall back on prototypes alone.  They need to build a robust business case for blockchain that includes a fair incentive model to attract all the partners required for the success of their networks.”

A Novel Method for Measuring the Value of Free Digital Services

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

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

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

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

Once Lumbering, Now Innovative Incumbents Hit Their Stride

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

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

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

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

AI and the Evolution of History

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

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

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

The Third Wave of the Digital Economy - Promises and Challenges

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

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

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

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

The Top Ten Emerging Technologies of 2018

For the past several years, the World Economic Forum (WEF) has published an annual list of the Top Ten Emerging Technologies that would be potentially disruptive over the next three to five years while also providing significant benefits to economies and societies.  Starting in 2017, the Top Ten list has been chosen in collaboration with Scientific American and published in an issue of the magazine.

The Top Ten Emerging Technologies 2018 was released in September, 2018.  The final list was selected by a steering group of experts from an initial list with more than 50 submissions.  Here are the 10 technologies comprising the 2018 list, along with the reasons cited in the report for their selection:

What Might Work Look Like in 2030?

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

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

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

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

AI and our Social Interactions

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

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

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

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

Globalization in Transition

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

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

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

The Productivity Paradox: Digital Abundance and Scarce Genius

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

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

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

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

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

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

Impact of an Aging Population on Economic Growth and Automation

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

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

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

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

The global labor force grew at an average of 1.8% per year between 1960 and 2005, but since then it’s been growing at just 1.1% per year.  The labor force is still growing in some developing countries like India, Nigeria and the Philippines, but it’s already shrinking in China, Japan and Germany.  In the US, the labor force is growing very slowly, - 0.5 percent per year over the past decade, compared with 1.7 percent from 1960 to 2005.  Given the continuing decline in fertility rates in most parts of the world, the global labor force is expected to slip further in the coming decades.

The New, Interdisciplinary, MIT Schwarzman College of Computing

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

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

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

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

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

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

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

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

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

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

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

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

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

Fostering an Innovation Culture: Talent, Discipline and Leadership

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

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

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

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

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

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

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

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

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

The Business Value of Resilience

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

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

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

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

The Current State of AI Adoption

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Rise of Global, Superstar Firms, Sectors and Cities

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

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

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

Beyond Machine Learning: Capturing Cause-and-Effect Relationships

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.  AI technologies  are approaching or surpassing human levels of performance in vision, speech recognition, language translation, and other human domains. Machine learning advances, like deep learning, have played a central role in AI’s recent achievements, giving computers the ability to be trained by ingesting and analyzing large amounts of data instead of being explicitly programmed.

Deep learning is a powerful statistical technique for classifying patterns using large training data sets and multi-layer AI neural networks.  It’s essentially a method for machines to learn from all kinds of data, whether structured or unstructured, that’s loosely modeled on the way a biological brain learns new capabilities.  Each artificial neural unit is connected to many other such units, and the links can be statistically strengthened or decreased based on the data used to train the system.  Each successive layer in a multi-layer network uses the output from the previous layer as input.

Machine learning can be applied to just about any domain of knowledge given our ability to gather valuable data in almost any area of interest.  But, machine learning methods are significantly narrower and more specialized than humans.  There are many tasks for which they’re not effective given the current state-of-the-art.  In an article recently published in Science, professors Erik Brynjolfsson and Tom Mitchell identified the key criteria that help distinguish tasks that are particularly suitable for machine learning from those that are not.  These include:

• Tasks that map well-defined inputs to well-defined outputs, - e.g., labeling images of specific animals, the probability of cancer in medical record, the likelihood of defaulting on a loan application;
• Large data sets exist or can be created containing such input-output pairs, - the bigger the training data sets the more accurate the learning;
• The capability being learned should be relatively static, - If the function changes rapidly, retraining is typically required, including the acquisition of new training data; and
• No need for detailed explanation of how the decision was made, - the methods behind a machine  learning recommendation, - subtle adjustments to the numerical weights that interconnect its huge number of artificial neurons, - are difficult to explain because they’re so different from those used by humans.

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.

It's All About the Business Model, - Not the Technology

In Reinvent Your Business Model, his recently published book, Mark Johnson argues that digital transformation and business model innovation are not the same thing.  New technology alone, - no matter how transformative, - is not enough to propel a business into the future.  The business model wrapped around the technology is the key to its success or failure.  Johnson is Senior Partner in Innosight, the strategy consulting firm he cofounded with Harvard Business School professor Clayton Christensen in 2000.  He’s been conducting research on business model innovation for over a decade, and in 2010 published Seizing the White Space.

Business model innovation has long been the domain of disruptive startups looking to compete against established companies by changing the rules of the game, - and, hopefully, creating new markets and reshaping entire industries.  But, it’s no longer enough for established companies to just roll out improved products and services based on their once-reliable business models.

“Building a great business and operating it well no longer guarantees you’ll be around in a hundred years, or even twenty,” notes Johnson.  “In 1965, the average length of time a company remained on the S&P 500 was thirty-three years.  By 1990, it had dropped to twenty years; in 2012, it was just eighteen.  Based on the 2017 churn rate, it is forecast that half of the S&P 500 will be replaced over the next ten years.”

Examples abound.  In the 1970s, Xerox PARC famously developed, - but didn’t commercialize, - some of the key innovations of the PC era, including the graphical user interface, the mouse and local area networks.  In 2010, Blockbuster filed for bankruptcy, a victim of Netflix’s new business models.  Done in by Apple’s iPhone and Android-based smartphones, the Blackberry, - once the undisputed leader in its market, - fell into a death spiral from which it never recovered.