Irving Wladawsky-Berger

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

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

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

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

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

Jobs, Skills and Education

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

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

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

The True State of Globalization

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

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

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

Blockchain and the Future of the Firm

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

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

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

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

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

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

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

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

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

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

Platforms, Blockchains, and the Evolution of Trust

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

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

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

Automation Anxieties - We’ve Been Here Before

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

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

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

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

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

What’s the Core Priority of Corporate Managers?

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

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

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

Global Arbitrage and the Productivity Puzzle

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

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

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

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

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

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

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

The Productivity Puzzle

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

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

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

Robocalypse Now? Technology, Productivity and Employment

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

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

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

Automation fears have understandbly accelerated in recent years, as our increasingly smart machines are now being applied to activities requiring intelligence and cognitive capabilities that not long ago were viewed as the exclusive domain of humans. “Previous technological innovation has always delivered more long-run employment, not less.  But things can change,” said a 2014 Economist article.  “Nowadays, the majority of economists confidently wave such worries away…  Yet some now fear that a new era of automation enabled by ever more powerful and capable computers could work out differently.”

On the Governance of Blockchain: Lessons from the Internet

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

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

I totally agree.

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

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

Serious AI Challenges that Require Our Attention

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

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

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

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

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

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

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

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

Mobile Payments - a Long, Relentless War of Attrition

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

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

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

Global Trends and Business Strategy

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

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

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

The Emerging Data Economy

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

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

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

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

Peering into a Confusing, Paradoxical Future

Every four years since 1997, the US National Intelligence Council has been publishing a Global Trends report on the key trends that will shape the world over the following twenty years.  This unclassified strategic report is intended to provide the incoming administration and other senior leaders with a framework for long-range policy assessment.

Global Trends 2030: Alternative Worlds, released four years ago, identified four overarching megatrends that are expected to shape and transform the world by 2030: the empowerment of individuals - which will accelerate the reduction of poverty and raise the standard of living around the world; the diffusion of power among states, - which will shift power to networks and coalitions in a multipolar world; changing demographic patterns especially rapid aging, urbanization, and increased migration; and growing demands on resources such as food, water and energy, - which might lead to scarcities.

The latest Global Trends report, Paradox of Progress, was released earlier this year.  It begins by explaining its title. “We are living a paradox: The achievements of the industrial and information ages are shaping a world to come that is both more dangerous and richer with opportunity than ever before… The progress of the past decades is historic - connecting people, empowering individuals, groups, and states, and lifting a billion people out of poverty in the process.  But this same progress also spawned shocks like the Arab Spring, the 2008 Global Financial Crisis, and the global rise of populist, anti-establishment politics.  These shocks reveal how fragile the achievements have been, underscoring deep shifts in the global landscape that portend a dark and difficult near future.”

Globalization at a Crossroads

Earlier this year I wrote about the future of global firms, based on two recent articles on the subject, - The retreat of the global company in The Economist, and The Global Enterprise: Where to Now? in Foreign Affairs by IBM’s former CEO Sam Palmisano.  As both articles noted, global firms boomed over the past few decades, driven by IT advances that made it much easier to do business around the world and by the fierce global competition that forced companies to become more efficient and lower costs.

But now, “The biggest business idea of the past three decades is in deep trouble,” said The Economist.  Palmisano pointed out that the world stands at a major crossroads.  “A rising chorus of nationalism echoes across developed countries; it calls for tighter borders and restrictions on immigration.  Global trade negotiations have essentially ceased, and regional trade deals face strong headwinds of opposition.”

How should business address the very important issues now surrounding globalization?  This question was explored in two recent Harvard Business Review articles, - People Are Angry About Globalization. Here’s What to Do About It, by NYU and IESE professor Pankaj Ghemawat, and An Agenda for the Future of Global Business by BCG strategists Martin Reeves and Johann Harnoss.

Not Your Grandfather’s Manufacturing

The February 23 issue of the NY Times Magazine was devoted to the future of work, and in particular, to what it called the new working class.  The Oxford Dictionary defines working class as “The social group consisting of people who are employed for wages, especially in manual or industrial work.”  The working class includes many traditional blue-collar occupations, - factory workers, truck drivers, electricians, plumbers, - but it also now includes an increasing number of white-collar and service jobs.

While the new working class comprises a variety of jobs, most discussions on the topic are generally centered on the deindustrialization of America over the past few decades, as well as on the ensuing decline in both the number and the wages of manufacturing jobs.  “Now when politicians invoke the working class, they are likely to gesture, anachronistically, to an abandoned factory,” notes the issue’s introductory article.

The ongoing transformation of manufacturing jobs was the subject of Learning to Love Our Robot Co-Workers by Kim Tingley.  Her article nicely explained the major changes taking place in US manufacturing, especially the appearance of a new generation of flexible, programmable, moderately priced robots that are designed to safely collaborate with humans, - each doing what they do best.

Robotics is an exciting, fast moving discipline, which is playing an increasingly important role in the future of manufacturing.  All computers are defined by what their brains, - that is, their hardware and software, - are capable of computing and controlling.  Robots are computers that have both a brain and a body.  A robot’s capabilities are defined by what its brains and body can jointly do.

What’s the Value of Blockchain?

A few weeks ago I attended the IBM Blockchain Summit 2017 in New York.  The Summit included a number of talks and panels, and - most important, - presentations of concrete blockchain use cases IBM has been working on in a number of areas, including finance, supply chains and healthcare.  Such real-world use cases are particularly important when introducing a new, complex set of technologies in the marketplace.  They’re the best way of getting on the learning curve for the new technologies as well as of explaining the kinds of problems they help us address.

Blockchain has the potential to transform our economic and social systems, but such a transformation is still many years away.  Like the Internet, blockchain is a foundational technology, whose adoption process is gradual, incremental and steady, unlike the hockey stick adoption we typically associate with disruptive technologies as defined by Clayton Christensen 20 years ago.  Foundational transformations must overcome many barriers, - technological, organizational, governance, political, - which is why they take a long time to play out.

Lest we forget,  TCP/IP, - the communications protocol that defines the Internet to this day, - was first adopted in the mid-1980s.  So were the protocols used in e-mail, - the first widely used Internet application which enabled its user base in government, universities and research institutions to easily communicate with each other.  It wasn’t until the late 1980s that the Internet was first opened up to public, commercial users.  General Internet adoption took off a few years later with the advent of the World Wide Web and the Web browser, and its transformational power wasn’t fully evident until the late 2000s, with the explosive growth of smartphones and the mobile Internet.

The concrete uses cases presented by IBM as it defines its blockchain strategy brought to mind the approach we took in the early days of formulating IBM’s Internet strategy, which I was closely involved with.

Technology, Capitalism and “The American Way of Life”

Much has been written about our digital technology revolution and its impact on economies, societies and individuals.  But few have brought the kind of historical perspective to the subject that helps us better understand our turbulent times like scholar and consultant Carlota Perez in her 2002 book Technological Revolutions and Financial Capital:  The Dynamics of Bubbles and Golden Ages and related publications.

According to Perez, over the past few centuries, we’ve had a technology revolution every 40 to 60 years, starting with the Industrial Revolution in 1771, which was characterized by the emergence of machines, factories and canals.  This was followed by the age of steam, coal, iron and railways starting in 1829; steel, electricity and heavy engineering in 1875; and oil, autos and mass production in 1908.  Our present age of information and communication technologies (ICT), whose starting point she pegs at 1971, is the fifth such major revolution.

Technology revolutions have two distinct periods, each lasting 20 to 30 years.  The installation period is the time of creative destruction, when new technologies emerge from the lab into the marketplace, entrepreneurs start many new businesses based on these new technologies, VCs encourage experimentation with new business models, and the new ventures attract considerable investments and financial speculation.  Inevitably, this all leads to bubbles and crashes like the recent dot-com and subprime mortgage bubbles.

After the crash, comes the deployment period, a time of institutional recomposition.  The now well accepted technologies become the norm.  New paradigms emerge for guiding innovation and for determining winners and losers in the marketplace.  Over time, these new paradigms significantly transform the economy and everything around it, as well as re-shaping social behavior and the institutions of society.

Last year Perez published Capitalism, Technology and a Green Global Golden Age: The Role of History in Helping to Shape the Future.  In this recent article, she argued that we’re going through a crucial moment in history similar to the 1930s, which requires the kind of creative thinking and bold measures for which we justifiably celebrate statesmen like Franklin Delano Roosevelt and John Maynard Keynes.  Let me summarize her argument.

The State of American Jobs

Last year, the Pew Research Center, - in association with the Markle Foundation, - conducted a survey about the state of American jobs to shed light on how people think about work and about the skills and training needed to get ahead in our fast changing digital economy.  The survey was based on landline and mobile telephone interviews with a national sample of over 5000 adults.  Their analysis and conclusions were published last October in a highly detailed 94 page report, - The State of American Jobs.

“Tectonic changes are reshaping U.S. workplaces as the economy moves deeper into the knowledge-focused age,” said the report in its opening paragraph.  “These changes are affecting the very nature of jobs by rewarding social, communications and analytical skills.  They are prodding many workers to think about lifetime commitments to retraining and upgrading their skills.  And they may be prompting a society-wide reckoning about where those constantly evolving skills should be learned - and what the role of colleges should be.”

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

Should the US Slowly Phase Out the $100 Bill?

Last November, Indian Prime Minister Narendra Modi shocked the country by banning the use of all 500- ($7.50) and 1,000-rupee ($15) notes, effectively removing over 85 percent of India’s currency in circulation.  Modi said that the ban was intended to curb corruption, tax evasion and the flow of counterfeit money, as well as disrupting terrorist organizations.  By December 30, people had to deposit their banned bills in bank accounts, or exchange them for the new bills the government was printing.

But, the Indian government didn’t print enough replacement notes in time for a smooth change, creating a cash shortage that disrupted large sectors of the economy.  In response, Indians started switching to electronic payments faster than had been anticipated.   Modi’s bold move will likely accelerate India’s push towards digital payments, - a major long-term benefit.

In a recent Washington Post article - What the U.S. can learn from India’s move toward a cashless society, - academic and writer Vivek Wadhwa noted that India has built a digital payments infrastructure that’s enabling it to skip two generations of financial technologies.  When it comes to digital payments, India may be actually leapfrogging the US, based on practical innovations, hard work, and, - necessity being the mother of invention, - the urgent need to step up to a major problem, - a decade ago, nearly half the Indian population had no identification whatsoever.

Wadhwa explained that “When you are born in a village without hospitals or government services, you don’t get a birth certificate.  If you can’t prove who you are, you can’t open a bank account or get a loan or insurance; you are doomed to be part of the informal economy - whose members live in the shadows and don’t pay taxes.”  He then described some of the key steps taken by the Indian government to address this problem.

The Growing Importance of Storytelling in the Business World

Communications have played a major role in my work over the past few decades.  During this period, I was involved in organizing emerging business opportunities at IBM, including the Internet and Linux.  Since retiring from IBM in 2007, I’ve continued my involvement with technology-based initiatives, - AI and blockchain most recently, - through my consulting and academic activities as well as the weekly blogs that I’ve now been writing for the past dozen years.

Over the years I’ve learned that when trying to explain a complex new concept or product, it’s important to engage in a kind-of conversation with your intended audience, where you talk about what's in your mind, your questions, your doubts, what you know and what you don't know.  In short, you’re telling a story about the concept, product or brand you’re trying to explain.

While storytelling is mostly associated with entertainment, education and culture, it also plays a major role in business.  Storytelling is widely used in the development of brands for products as well as companies.  And, the more powerful, important and complex the brand messages you are trying to convey, the more important it is that you do so by telling a compelling but simple, - i.e., understandable, - story.

Good storytelling is particularly important when introducing a complex and potentially disruptive offering in the marketplace whose value is not well understood.  It’s a natural way of explaining what the new innovation is all about.  As a concrete example, let me share my experiences with the development of IBM’s Internet and e-business strategy in the mid-late 1990s.

Automation and Employment in the 21st Century

Ever since the advent of industrialization over 200 years ago, there’ve been periodic fears about the impact of technology-based automation on jobs.  In the 1810s, for example, the so-called Luddites smashed the new machines that were threatening their textile jobs.  But each time those fears arose in the past, technology advances ended up creating more jobs than they destroyed.

Automation anxieties have understandably accelerated in recent years, as AI-related innovations, including robots and smart machines of all kinds, are now being applied to activities requiring intelligence and cognitive capabilities that not long ago were viewed as the exclusive domain of humans.  The concerns surrounding AI’s long term impact on jobs may well be in a class by themselves.

There’s a broad consensus that AI will have a major impact on jobs and the very nature of work, but it’s much less clear what that impact will be.  Will AI play out like past technology innovations, - highly disruptive in the near term, but ultimately leading to the creation of new jobs, whole new industries, and a rising standard of living?  Or will this time be different, as AI-based innovations end up replacing a large portion of the workforce, - leading to mass unemployment, economic dislocations and social unrest?

The Fourth Industrial Revolution - Risks and Benefits

The Fourth Industrial Revolution (4IR) was the central theme of the 2016 World Economic Forum (WEF) that took place a year ago in Davos, Switzerland.  It continued to play a prominent role in the recently concluded 2017 annual meeting.   Coming to grips with the impact of the fourth industrial revolution was among the top leadership priorities discussed at this year’s meeting.

Over the past year, Klaus Schwab, - WEF founder and executive chairman, - has described his vision for the fourth industrial revolution in a number of articles as well as a book on the subject.  Dr. Schwab positions the 4IR within the historical context of three previous industrial revolutions.  The First, - starting in the last third of the 18th century, - introduced new tools and manufacturing processes based on steam and water power.  The Second, - a century later, - saw the advent of steel, oil, electricity, mass production and associated inventions including the telephone, light bulbs phonographs and cars.  The Third, - starting in the middle of the last century, - has brought us digital technologies, computers, the IT industry, and the automation of processes in just about all industries.

The 4IR builds on our ongoing digital revolution.  It “entails nothing less than a transformation of humankind,” wrote Dr. Schwab in his book’s introduction  “We are at the beginning of a revolution that is fundamentally changing the way we live, work, and relate to one another.  In its scale, scope and complexity, what I consider to be the fourth industrial revolution is unlike anything humankind has experienced before…”

“Consider the unlimited possibilities of having billions of people connected by mobile devices, giving rise to unprecedented processing power, storage capabilities and knowledge access.  Or think about the staggering confluence of emerging technology breakthroughs, covering wide-ranging fields such as artificial intelligence (AI), robotics, the internet of things (IoT), autonomous vehicles, 3D printing, nanotechnology, biotechnology, materials science, energy storage and quantum computing, to name a few.  Many of these innovations are in their infancy, but they are already reaching an inflection point in their development as they build on and amplify each other in a fusion of technologies across the physical, digital and biological worlds.”

The Future of the Globally Integrated Enterprise

“In Retreat: Global companies in the era of protectionism,” reads the cover of the January 28 issue of The Economist.  In The retreat of the  global company, The Economist defines global firms as those where over 30% of their sales come from outside their home region.  For the past three decades, global firms have been the “apex predators of the global economy,” directing the global flow of goods, services and capital, shaping ecosystems, orchestrating supply chains and owning most of the world’s intellectual capital.  But now, “The biggest business idea of the past three decades is in deep trouble.”

Global firms boomed as China was opening up, Europe was integrating, the Soviet bloc was collapsing, and barriers to global trade and investment were coming down.  IT advances made it much easier to do business around the world.  Global competition forced companies to become more agile and efficient by unbundling functions and re-engineering processes. 

The world was waking up to the growing economic power of emerging markets.  “Governments in the rich world dreamed of their national champions becoming world-beaters.  Governments in the emerging world welcomed the jobs, exports and technology that global firms brought.  It was a golden age.”

The Economist referenced The Globally Integrated Enterprise, a 2006 Foreign Affairs article by IBM’s then CEO Sam Palmisano.  Palmisano’s article started out by looking at the evolution in the way companies have conducted business around the world.

The Emerging, Unpredictable Age of AI

In a 1930 essay, Economic Possibilities for our Grandchildren, English economist John Maynard Keynes wrote about the onset of “a new disease” which he called technological unemployment, that is “unemployment due to our discovery of means of economising the use of labour outrunning the pace at which we can find new uses for labour.”

“But this is only a temporary phase of maladjustment,” he added.  “I would predict that the standard of life in progressive countries one hundred years hence will be between four and eight times as high as it is.”  Keynes believed that technological progress would lead us to a brighter future.  By 2030, people would likely work no more than 15 hours a week, and their biggest problem would be how to use their leisure and freedom from economic cares.

I was reminded of Keynes’ predictions while reading a number of recent articles on our emerging age of AI.  After decades of unfulfilled promises and hype, AI seems to be reaching a tipping point.  The necessary ingredients are finally coming together: lots and lots of data, with the volume of data pouring in expected to double every three years or so; advanced machine learning algorithms that extract insights and learn from all that data; drastically lowered technology costs for collecting, storing and analyzing these oceans of information; and access to an increasing variety of data-driven, cloud-based AI applications.

A small, but prominent group of technologists and scientists have warned that at some future time, a superintelligent AI might pose an existential threat to humanity.  But while dismissing such dire concerns, most experts worry that the real threat is that AI advances could lead to widespread economic dislocation and social unrest.  “Contrary to the more fantastic predictions for AI in the popular press, the Study Panel found no cause for concern that AI is an imminent threat to humankind…” was the conclusion of a recent study by AI experts.  “At the same time, many of these developments will spur disruptions in how human labor is augmented or replaced by AI, creating new challenges for the economy and society more broadly.”

The Competitive Value of Data: From Analytics to Machine Learning

A recent article by NY Times reporter Steve Lohr discussed the rising importance of data as a major competitive differentiator.  The article noted that regulators, policy makers and academics in Europe and the US are increasingly concerned that the vast data assets of digital giants could become a competitive barrier to startups and innovation.

Such competitive concerns have long applied to hardware and software platforms in the IT industry.  IBM’s System 360 family of mainframes, announced in 1964, became the premier platform for commercial computing over the following 25 years by developing a 3rd party ecosystem of add-on hardware, software and services.  In 1969, the US Department of Justice launched an antitrust suit against IBM based on its market dominance, which was eventually dropped in 1982 given the changing environment in the IT industry.

In the 1980s and 1990s, the explosive growth of personal computers was largely driven by the Wintel platform based on Microsoft’s Windows operating systems and Intel’s microprocessors, which attracted an even  larger ecosystem of hardware and software developers.  It also attracted the attention of the Justice Department and several states, which sued Microsoft in the 1990s, - a lawsuit that was eventually settled.  More recently, the European Commission has accused Google of unfair competition based on its Android mobile operating system.

The competitive concerns surrounding these various platforms are closely linked to the concept of network effects.  Scale significantly increases a platform’s value.  The more 3rd party applications and 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… and on and on and on.

AI and Public Policy

Two years ago, Stanford University launched the One Hundred Year Study of AI (AI100), “to study and anticipate how the effects of artificial intelligence will ripple through every aspect of how people work, live and play.”  One of its key missions is to convene a Study Panel of experts every five years to assess the then current state of the field, as well as to explore both the technical advances and societal challenges over the next 10 to 15 years. 

The first such Study Panel recently published Artificial Intelligence and Life in 2030, a report that examined the likely impact of AI on a typical North American city by the year 2030.  The report is organized into three main sections: Section I described the key research trends influencing AI’s future; Section II examined the key economic sectors that are most likely to be impacted by AI;  and Section III looked into issues surrounding AI and public policy.  Over the past several weeks I’ve discussed the first and second sections.  I now would like to turn my attention to Section III: Prospects and Recommendations for AI Public Policy.

“Throughout history, humans have both shaped and adapted to new technologies.  This report anticipates that advances in AI technologies will be developed and fielded gradually - not in sudden, unexpected jumps in the techniques themselves - and will build on what exists today, making this adaptation easier… The measure of success for AI applications is the value they create for human lives.  Going forward, the ease with which people use and adapt to AI applications will likewise largely determine their success.”

The report then adds some very important warnings.  “Conversely, since AI applications are susceptible to errors and failures, a mark of their success will be how users perceive and tolerate their shortcomings.  As AI becomes increasingly embedded in daily lives and used for more critical tasks, system mistakes may lead to backlash from users and negatively affect their trust.”

The Internet, Blockchain, and the Evolution of Foundational Innovations

Last year, a panel of global experts convened by the World Economic Forum selected blockchain as one of the Top Ten Emerging Technologies for 2016, based on its potential to fundamentally change the way economies work.  But, how transformative will blockchain turn out to be?  How long is the transformation likely to take?  And how will it compare with the Internet-based transformation of the past few decades?

These questions were nicely addressed in The Truth about Blockchain, a recent Harvard Business Review article by Harvard professors Marco Iansiti and Karim Lakhani.  Their article starts off with one of the best descriptions I’ve seen of the long-term promise of blockchain.

“Contracts, transactions, and the records of them are among the defining structures in our economic, legal, and political systems.  They protect assets and set organizational boundaries.  They establish and verify identities and chronicle events.  They govern interactions among nations, organizations, communities, and individuals.  They guide managerial and social action.  And yet these critical tools and the bureaucracies formed to manage them have not kept up with the economy’s digital transformation.  They’re like a rush-hour gridlock trapping a Formula 1 race car.  In a digital world, the way we regulate and maintain administrative control has to change…”

“With blockchain, we can imagine a world in which contracts are embedded in digital code and stored in transparent, shared databases, where they are protected from deletion, tampering, and revision.  In this world every agreement, every process, every task, and every payment would have a digital record and signature that could be identified, validated, stored, and shared.  Intermediaries like lawyers, brokers, and bankers might no longer be necessary.  Individuals, organizations, machines, and algorithms would freely transact and interact with one another with little friction.”

AI, Automation and the US Economy

On December 20, the White House released Artificial Intelligence, Automation, and the Economy, a new report that investigates how AI will likely transform job markets over time, and recommends policy responses to address AI’s impact on the US economy.  This report is further evidence that AI is now reaching the kind of market impact that requires well thought out strategies by both business and government.

AI research has been around since the early days of IT, - having gone through ups and downs over the past several decades.  But it’s recent advances based on machine learning and related data-driven technologies are still relatively new, making it hard to predict where AI will be heading over time.

Harder still is predicting AI’s longer term economic and societal impact.  Think of anticipating the impact of the emerging Internet revolution back in the mid 1990s, before the advent of e-commerce and social media, let alone smartphones, IoT and cloud computing in the 2000s.  That’s roughly where we are with AI today.

But, there’s one big difference.  The Internet generated a fair degree of hype, especially in the dot-com era of the late 1990s, but the overriding feelings were ones of hope and excitement.  Not so with AI.  Along with admiration for its considerable accomplishments, - from assisting in the treatment of rare forms of cancer to self-driving cars, - there are serious fears that AI advances will lead to massive job losses, economic dislocations and social unrest.

Securing the Foundations of our Digital Economy

In February of 2016, the President issued an Executive Order establishing the Commission on Enhancing National Cybersecurity within the Department of Commerce.  The Commission was charged with “recommending bold, actionable steps that the government, private sector, and the nation as a whole can take to bolster cybersecurity in today’s digital world, and reporting back by the beginning of December.”

On December 1 the Commission issued its final Report on Securing and Growing the Digital Economy.  The Commission urged the incoming administration to strengthen cybersecurity efforts within its first 100 days.  The report includes 16 major recommendations and related actions that can hopefully serve as a useful framework for significantly enhancing the Nation’s cybersecurity.

The widespread success of the Internet in the 1990s has led to a historical transition from the industrial age of the past two centuries to an economy and society increasingly based on global, digital interactions.  This transition has significantly accelerated over the past decade with the advent of billions of smartphones, tens of billions of IoT devices and huge amounts of data, all now connected via Internet-based broadband networks.

At the same time, Internet threats have been growing.  Large-scale fraud, data breaches, and identity thefts are becoming more common.  Companies are finding that cyber-attacks are costly to prevent and recover from.  As we move from a world of physical interactions and paper documents, to a world primarily governed by digital data and transactions, our existing methods for protecting identities and data are proving inadequate.

The 2016 MIT Inclusive Innovation Awards

Several months ago, MIT’s Initiative on the Digital Economy launched its first annual Inclusive Innovation Competition (IIC).  MIT is one of the world’s top research universities, renowned as well for its entrepreneurial culture.  But the objectives of the IIC are quite different and unique.  Instead of competing on creating the most advanced technologies or compelling startups, the IIC is focused on innovations aimed at improving the economic opportunities of middle- and low-income earners around the world.

Applicants competed for $1 million in prizes to be awarded in four main categories: Skills, - how employees were re-skilled for new types of jobs; Matching, - how qualified people were connected to new types of jobs; Humans + Machines, - how technology is used to augment human labor; and New Models, - how new business models revolutionize labor markets and job opportunities.  In each of these categories, the grand prize winner was awarded $125,000, while each of the four runner-ups received $25,000.  In addition, four additional $25,000 Judges Choice Awards were given to organizations deemed by the judges to be uniquely inventive.

I’m a Fellow of the Initiative on the Digital Economy, and served as one of the judges in the competition.  243 applications were received from around the world, from which we selected 24 finalists.  I personally reviewed around 20 applications, and was really impressed not only by their innovative ideas, but also by their courage and determination as they addresed some of society’s toughest problems.

How Will AI Likely Impact How We Work, Live and Play?

A few weeks ago I discussed whether AI is finally reaching a tipping point, mostly based on a recently published report, - Artificial Intelligence and Life in 2030.  The report was developed by a study panel of AI experts convened by the One Hundred Year Study of AI (AI100), an initiative launched at Stanford University in December, 2014 “to study and anticipate how the effects of artificial intelligence will ripple through every aspect of how people work, live and play.”  To better understand the future impact of AI on everyday lives, the panel focused the study on the likely influence of AI on a typical North American city by the year 2030.

The report is organized into three main sections.  Section I, - What is Artificial Intelligence?, - describes how researchers and practitioners define AI, as well as the key research trends that will likely influence AI’s future.  Section II looks into AI’s overall impact on various sectors of the economy, while the third Section, examines AI issues related to public policy.

My previous discussion was primarily focused on Section I.  I’d now like to turn my attention to Section II, - AI by Domain.  To help analyze where AI might be heading, the study panel narrowed its explorations to the eight domains most likely to be impacted by AI:

Towards a Trusted Framework for Identity and Data Sharing

I recently participated in a Treasury Identity Forum organized by the US Treasury Department in Washington, DC.  The Forum focused “on the critical role of legal identity for financial inclusion, economic development, and anti-money laundering/counter financing of terrorism (AML/CFT) safeguards, and the development of new technology identification/authentication solutions to help achieve these goals.”  It brought together stakeholders from governments, financial service companies, FinTech startups and technologists to better understand how emerging technologies and legal frameworks can help us develop the required digital identity systems.

I was a member of a panel on how government, business and research communities can collaborate in developing workable identity solutions.  Let me summarize the points I made in my introductory remarks.

From time immemorial, 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.  As the economy and society move toward a world where interactions are primarily governed by digital data and transactions, our existing methods of managing identity and data security are proving inadequate.  Large-scale fraud, identity theft and data breaches are becoming common, and a large fraction of the world’s population lacks the credentials needed to be part of the digital economy.

The Rise of the Global Superstar Company

The September 17 issue of The Economist included a special report on the rise of so called superstar companies.  “Disruption may be the buzzword in boardrooms, but the most striking feature of business today is not the overturning of the established order,” notes The Economist.  “It is the entrenchment of a group of superstar companies at the heart of the global economy.”

This trend toward consolidation and growing size is evidenced by a few worldwide statistics: 10% of public companies generate 80% of all profits; firms with over $1 billion in annual revenues are responsible for 60% of total global revenues; and the rate of mergers and acquisitions is more than twice what it was in the 1990s.  The trend is particularly prominent in the US: in the 20 years from 1994 to 2013, the share of GDP generated by the 100 biggest companies rose from 33% to 46%; the five largest banks now account for 45% of all banking assets, up from 25% in 2000; and new firm formation has been going down since the late 1970s, leading to an overall decline in young- and medium-aged companies over the years.

The current rise of large companies is somewhat unexpected.  Following the Great Depression and WW2, the country welcomed the stability promised by corporate capitalism.  Big, multinational companies dominated most industries, - from GM, Ford and Chrysler in cars to Esso/Exxon, Mobil and Texaco in oil and gas.  It was an era characterized by bureaucratic corporate cultures, focused on organizational power and orderly prosperity.

This all started to change a few decades later with the advent of a more innovative, fast moving entrepreneurial economy.  The 1980s saw the rise of young, high-tech companies, - e.g., Microsoft, Apple, Oracle, Sun Microsystems; telecommunications was deregulated; ATT was broken up; and Silicon Valley became the global hub for innovation, emulated by regions around the world.

Blockchains: the Promise of More Frictionless, Trusted Economies

Why do firms exist?  Ronald Coase, - the eminent British economist and University of Chicago professor, - addressed this question in The Nature of the Firm, - a seminal paper published in 1937 which along with other major achievements earned him the 1991 Nobel Prize in economics.

Professor Coase explained that, in principle, a firm should be able to find the cheapest, most productive, highest quality goods and services by contracting them out in an efficient, open marketplace.  However, markets are not perfectly fluid.  Transaction costs are a kind of friction incurred in obtaining goods and services outside the firm, such as searching for the right supply chain partners, establishing a trusted relationship, negotiating a contract, coordinating the work, managing intellectual property and so on.  Firms came into being to make it easier and less costly to get work done.

A recent IBM report, - Fast forward: Rethinking enterprises, ecosystems and economies with blockchains, - harks back to Coase’s paper to analyze the potential value of blockchains.  The report notes that while transaction costs are lower within firms, “in recent years as enterprises have scaled, the added complexity of operations has grown exponentially while revenue growth has remained linear.  The result?  At a certain point, organizations are faced with diminishing returns.  Blockchains have the potential to eradicate the cost of complexity and ultimately redefine the traditional boundaries of an organization.”

Why Are We Stuck in a World of Slow Economic Growth? Nobody Seems to Know for Sure

Over the past few centuries, the natural sciences, - e.g. physics, chemistry, biology, - have developed a variety of principles and models.  These have enabled them to analyze and predict the behavior of our highly complex physical systems under widely different conditions.  But the situation is quite different in the social sciences, - e.g., economics, sociology, political science.  It’s much more difficult to make accurate predictions in social systems, - whose key components are people, organizations and their intricate interactions, - because of their highly fluctuating behaviors.

I was reminded of this major distinction between physical and social systems by a recent issue of Foreign Affairs, which focused on How to Survive Slow  Growth.  “[G]rowth has ground to a halt almost everywhere, and economists, investors, and ordinary citizens are starting to confront a grim new reality: the world is stuck in the slow lane and nobody seems to know what to do about it,” notes its introductory article.  Several prominent authors wrote about various aspects of the economic slowdown.  But in the end, they didn’t arrive at consensus reasons for the slow growth or how long it will likely last, - years or decades.

In the lead article, - The Age of Secular Stagnation: What It Is and What to Do About It, - Harvard economics professor Larry Summers wrote:  “As surprising as the recent financial crisis and recession were, the behavior of the world’s industrialized economies and financial markets during the recovery has been even more so.”  Back in 2009, almost no one would have predicted that we would still be in a period of slow economic growth, that inflation would be around one percent and interest rates would hover around zero.  But, “nearly seven years into the U.S. recovery, markets are not expecting normal conditions to return anytime soon.”

Blockchain Can Reshape Financial Services… But it Will Take Significant Time and Investment

In early August, the World Economic Forum (WEF) published two excellent reports: A Blueprint for Digital Identity, - which I wrote about a few weeks ago, - and The future of financial infrastructure: An ambitious look at how blockchain can reshape financial services, - which I’d now like to discuss.

“Distributed ledger technology (DLT), more commonly called blockchain, has captured the imaginations, and wallets, of the financial services ecosystem,” notes the report, citing a few statistics as evidence: over 90 central banks are engaged in DLT discussions around the world; more than 24 countries have already launched blockchain-based initiatives; 80% of banks predict that they’ll launch blockchain projects by 2017; over 90 financial and technology companies have already joined blockchain consortia; over the past 3 years; more than 2,500 patents have been filed; and $1.4 billion has been invested in blockchain-based startups over the same time span.

In addition, a panel of global experts convened by the WEF selected blockchain as one of the Top Ten Emerging Technologies for 2016 because of its potential to fundamentally change the way markets and governments work, - further evidence that blockchain is reaching a tipping point of market acceptance.

But, significant hurdles must be overcome before the advent of large-scale blockchain infrastructures.  The WEF correctly warns that this will take time.  Not only are there major technology and standards issues to be worked out, but the industry will have to collaborate with governments around the world to develop the appropriate legal frameworks and regulatory environments.

STEM Literacy and Jobs

In the early days of the Industrial Revolution, it’s been estimated that around 12% of the world's population was literate. Literacy rates increased throughout the 19th century, as people started moving from the countryside to towns and cities for the job opportunities opening up in the newly industrialized societies.  Many of these new jobs, especially the higher paying ones, required the ability to read and write.  With the rise of universal education in almost all countries around the world, literacy rates steadily increased in the 20th century, - from roughly 20% in 1900 to around 80% in 2000.  In the 21st century, we not only have the challenge of eliminating illiteracy altogether, but we must now also focus on STEM (Science, Technology, Engineering, Math) literacy.

Most everyone would agree that there is a big difference between being proficient at reading and writing and being a playwright, a literary critic, a book editor or a journalist.  The skills requirement are radically different.  But, students don’t often appreciate the difference between achieving a modicum of STEM literacy and pursuing a STEM profession.  Many avoid taking STEM courses because they have no intention of majoring in a STEM discipline.  While everyone agrees that basic literacy is critical for just about any job, we don’t quite have the same level of appreciation that being STEM literate is increasingly important to qualify for a wide variety of jobs in our information-based knowledge economy.

Over the past few years, an increasing number of studies have argued that a good education for students majoring in STEM disciplines should include the so-called softer competencies more associated with the liberal arts in addition to harder, more technical skills.  For example, a 2006 report by the National Academy of Engineering on the need to reforming engineering education noted that “New graduates were technically well prepared but lacked the professional skills for success in a competitive, innovative, global marketplace.  Employers complained that new hires had poor communication and teamwork skills and did not appreciate the social and nontechnical influences on engineering solutions and quality processes.”

Automation Technologies and the Future of Work

Last year, McKinsey launched a multi-year study to explore the potential impact of automation technologies on jobs, organizations and the future of work.  “Can we look forward to vast improvements in productivity, freedom from boring work, and improved quality of life?,” its initial article on the study asked, or “Should we fear threats to jobs, disruptions to organizations, and strains on the social fabric?”

Most jobs involve a number of different tasks or activities.  Some of these activities are more amenable to automation than others.  But just because some of the activities have been automated, does not imply that the whole job has disappeared.  To the contrary, automating parts of a job will often increase the productivity and quality of workers by complementing their skills with machines and computers, as well as by enabling them to focus on those aspect of the job that most need their attention.

Given that few jobs or occupations will be entirely automated in the near-mid future, the study focused instead on the kinds of activities within jobs that are more likely to be automated, as well as how those jobs and business processes will then be redefined.  It did so by analyzing the extensive data in O*NET, a web-based application sponsored by the US Department of Labor which includes the most comprehensive information on US occupations.

The study analyzed around 2,000 work activities across 800 different occupations, and grouped them into 18 different capabilities, - 3 of them social in nature; 10 cognitive; and 5 physical, - and then assessed the automation potential of each.  It found that 45% of work activities could be automated using existing, state-of-the-art technologies.

Digital Identity - the Key to Privacy and Security in the Digital World

From time immemorial, 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 no longer adequate.  Data breaches, identity theft and large-scale fraud are becoming more common.  In addition, a significant portion of the world’s population lacks the necessary digital credentials to fully participate in the digital economy.

Last month, the World Economic Forum (WEF) published an excellent report, A Blueprint for Digital Identity.  The report lays out a framework for the creation of digital identity systems, discusses the benefits that these systems would bring to its various stakeholders, and argues that financial institutions should lead their development.  It also includes a primer on identity, which clearly explains what identity is all about.

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.  While all around us, we generally don’t pay much attention to our identity credentials unless something goes wrong.  But, it’s a highly complex and interesting subject, which the report helped me better understand.  Let me summarize some of what I learned.

“Why is identity important?,” the primer starts out by asking.  “In an increasingly borderless and digital world, privacy and security cannot be ensured through the construction of walls around sensitive information.  Identity is the new frontier of privacy and security, where the very nature of entities is what allows them to complete some transactions but be denied from completing others.  To understand the importance of identity and the criticality of strong identity protocols that protect against cyber-risk and suit the needs of transacting parties, it is essential to understand what identity is, and its role in enabling transactions.”

Blockchain - Once Again, the Technological Genie Has Been Unleashed from its Bottle

Skeptics needing further reassurance that the Blockchain is truly reaching a tipping point can rest easier with the recent publication of Don Tapscott’s Blockchain Revolution, co-written with his son Alex.  Don, whom I’ve long known, has a knack for identifying what The Next Big Thing will likely be, and shortly thereafter writing a book to explain it to everybody else.  The Digital Economy, written in the mid 1990s, anticipated the unbundling of vertically integrated companies into business webs, giving rise to the open networked enterprise.  His 2006 Wikinomics predicted the rise of mass collaboration in what became known a few years later as the On Demand Economy.  Blockchain Revolution, his 16th book, now explains “How The Blockchain Will Transform Everything From Banking To Government To Our Identities.”

The book is organized into three main sections.  The first explains the blockchain from two complementary points of view: as a major next step in the evolution of the Internet; and as the architecture underpinning bitcoin, the best known and most widely held digital currency.  The second and longest section describes how blockchain could potentially transform financial services, companies, government, the Internet of Things, and other key areas.  The last section summarizes the major challenges that must be overcome as well as the governance required to fulfill the promise of blockchain.

“It appears that once again, the technological genie has been unleashed from its bottle,” write the authors in their opening paragraph.  “Summoned by an unknown person or persons with unclear motives, at an uncertain time in history, the genie is now at our service for another kick of the can - to transform the economic power grid and the old order of human affairs for the better.  If we will it.”

Is There a STEM Crisis or a STEM Surplus?

Is the US facing a critical shortage of STEM skills?  Do we have enough STEM workers to meet the demands of the labor market?  Are enough young people choosing STEM careers so we can meet future demands?

Such serious concerns have been expressed in a number of national studies over the past two decades.  In 2005, for example, the National Innovation Initiative listed “Build the Base of Scientists and Engineers” as one of its top recommendations, noting that “unless the United States takes swift action, the demand for S&E talent will far outstrip supply.  The number of jobs requiring technical training is growing at five times the rate of other occupations.”

Two years later, a major National Academies study, Rising Above the Gathering Storm, called for increasing America’s STEM talent pool by providing 25,000 new 4-year competitive undergraduate scholarships to US citizens enrolled in the physical sciences, the life sciences, engineering and math; and by funding 5,000 new graduate fellowships each year for US citizens pursuing graduate studies in areas of national need.

And in 2012, a report by the President’s Council of Advisors on Science and Technology wrote: “Economic projections point to a need for approximately 1 million more STEM professionals than the U.S. will produce at the current rate over the next decade if the country is to retain its historical preeminence in science and technology.  To meet this goal, the United States will need to increase the number of students who receive undergraduate STEM degrees by about 34% annually over current rates.”

Is the Blockchain Now Reaching a Tipping Point?

A few weeks ago, the World Economic Forum (WEF) published its annual list of the Top Ten Emerging Technologies for 2016.  The technologies on the list have been worked on for years.  But their inclusion in the Top Ten List indicates that each has now reached a market acceptance tipping point where its impact can be meaningfully felt.  The Blockchain is one of the technologies in this  year’s list, selected by the WEF panel of global experts because of its emerging potential to fundamentally change the way markets and governments work. 

What does it mean for an infrastructure technology like the blockchain to have reached such a tipping point?  The WEF report compared the 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.”

I agree with this comparison and find it useful to help us understand how blockchains might evolve over the years. So, I’d like to compare the state of the blockchain in 2016 to the state of the Internet 25 years ago or so.

The Long Term Impact of AI on Jobs - Some Lessons from History

The June 25 issue of The Economist includes a special report on artificial intelligence.  AI has been making extraordinary progress in the past few years.  It’s ironic that after years of frustration with AI’s missed promises, many now worry that its mighty power is now upon us while we still don’t know how to properly deploy it.  Some fear that at some future time, a sentient, superintelligent general AI might pose an existential threat to humanity.  But while being dismissive of such dire concerns, many experts worry that the real threat is that AI advances could lead to widespread economic dislocation.

People have long worried about the impact of technology on society, be it railroads, electricity, and cars in the Industrial Age, or the Internet, mobile devices and smart connected products now permeating just about all aspect of our lives.  The Economist reminds us that these worries have been with us ever since the advent of industrialization two centuries ago.  Eminent English economist David Ricardo first raised the machinery question in 1821, that is, the “opinion entertained by the labouring class, that the employment of machinery is frequently detrimental to their interests”.

Automation anxieties continued to resurface in the 20th century, right along with accelerating technology advances.  In a 1930 essay, English economist John Maynard Keynes wrote about the onset of “a new disease” which he named technological unemployment, that is, “unemployment due to our discovery of means of economising the use of labour outrunning the pace at which we can find new uses for labour.”  But each time those fears arose in the past, technology innovations ended up creating more jobs than they destroyed, causing the majority of economists to confidently wave away the machinery question.

Automation fears 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.  The concerns surrounding AI’s long term impact may well be in a class by themselves.  Like no other technology, AI forces us to explore the very boundaries between machines and humans.

What impact will AI have on jobs?  Could our smart machines lead to mass unemployment?  What will life be like in such an AI future?  “After 200 years, the machinery question is back. It needs to be answered, notes The Economist.”  What can we learn from history that will help us better respond to AI’s technological advances?

Vehicle Automation - a Very Important Priority for AI Research

In September, 2014, I attended an MIT conference that explored the major progress that’s taken place in artificial intelligence, robotics and related technologies over the past several years. Autonomous vehicles was one of the main areas of discussion.  With most other topics, there was considerable consensus, but not so with self-driving cars.  While some thought that fully autonomous vehicles will be all around us within a decade or so, others were not quite so sure, myself included, due to the many highly complex technical, economic and societal issues that must be worked out.

I was reminded of this meeting a few weeks ago when I read that a Florida man had been killed while driving a Model S Tesla in autopilot mode.  The accident is still under investigation, but it appears that the Tesla struck a tractor-trailer truck that was making a left turn in front of its path.  Neither the driver or the Tesla’s autopilot noticed that a truck was suddenly crossing its lane of traffic, perhaps because the white truck was hard to spot against a bright sky.

This accident has led to a renewed discussion of the current state-of-the-art of vehicle automation, the approaches being pursued by different companies, and the prospects for the near- and long-term future.