Irving Wladawsky-Berger

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.”

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.

The Simple, Economic Value of Artificial Intelligence

I recently attended a very interesting talk, - Exploring the Impact of Artificial Intelligence: Prediction versus Judgment, - by University of Toronto professor Avi Goldfarb.  The talk was based on recent research conducted with his UoT colleagues Ajay Agrawal  and Joshua Gans.  In addition to an in-depth paper aimed at a research audience, they’ve explained their work in two more general interest articles, one in the Harvard Business Review and the second in the MIT Sloan Management Review.

In their opinion, “the best way to assess the impact of radical technological change is to ask a fundamental question: How does the technology reduce costs?  Only then can we really figure out how things might change.”  For example, the semiconductor revolution can be viewed as being all about the dramatic reductions in the cost of arithmetic calculations.  Before the advent of computers, arithmetic was done by humans with the aid of various kinds of devices, from the abacus to mechanical and electronic calculators.

Then came digital computers, which are essentially powerful calculators whose cost of arithmetic operations has precipitously decreased 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 revolution can be described as reducing the cost of communications and of search, thus enabling us to easily find and access all kinds of information, - including documents, pictures, music and videos.

How does this framing now apply to our emerging AI revolution?  After decades of promise and hype, AI seems to have finally arrived, - driven by the explosive growth of big data,  inexpensive computing power and storage, and advanced algorithms like machine learning that enable us to analyze and extract insights from all that data.  Agrawal, Fans and Goldfarb provide an elegant answer to this question in their HBR article.  “Machine intelligence is, in its essence, a prediction technology, so the economic shift will center around a drop in the cost of prediction.”

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.

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 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.”

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.”

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.

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.

The Evolution of the Firm

A few weeks ago I discussed The Rise of the Global Superstar Company based on a recent special report on the subject by The Economist.  The report noted that the decade-long trend toward increasingly concentrated global firms is somewhat surprising. 

“The rise of the giants is a reversal of recent history…  In the 1980s and 1990s management gurus pointed to the demise of size as big companies seemed to be giving way to a much more entrepreneurial economy.  Giants such as AT&T were broken up and state-owned firms were privatised.  High-tech companies emerged from nowhere.  Peter Drucker, a veteran management thinker, announced that ‘the Fortune 500 [list of the biggest American companies] is over.’ That chimed with the ideas of Ronald Coase, an academic who had argued in ‘The Nature of the Firm’ (1937) that companies make sense only when they can provide the services concerned more cheaply than the market can.”

Professor Coase’s views on the firm changed quite a bit over the years.  In 1937 he published The Nature of the Firm, a seminal article which along with other major contributions earned him the 1991 Nobel Prize in economics.  In the article, Professor Coase provided a simple answer to the question: Why do firms exist?  He explained that, in principle, a firm should be able to find the cheapest, most productive goods and services by contracting them out in an efficient, open marketplace.  However, markets are not perfectly fluid.  Transaction costs are incurred in obtaining goods and services outside the firm, such as searching for the right people, negotiating a contract, coordinating the work, managing intellectual property and so on.  Thus, firms came into being to make it easier and less costly to get work done.

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.

Is Design Thinking the “New Liberal Arts”?

Design thinking has become an increasingly popular topic of discussion over the past decade.  It was featured in the September, 2015 issue of the Harvard Business Review with several articles on the subject.  Design is no longer just for physical objects, e.g. cars, bridges, shoes, jewelry, smartphones.  Design thinking is now being applied to abstract entities, - e.g. systems, services, information and organizations, - as well as to devise strategies, manage change and solve complex problems.

The application of design thinking beyond products isn’t new.  Nobel laureate Herbert Simon discussed the concept in his 1969 classic The Sciences of the Artificial.  IDEO, a firm best known for pioneering this expanded view of design, traces its roots back to 1978.  The School of Design in London’s Royal College of Art has long been expanding the boundaries of industrial design.  Stanford’s Institute of Design, - better known as the d.school, - was launched in 2004 as a graduate program that integrates business, the social sciences, the humanities and other disciplines into more traditional engineering and product design.

The d.school’s website nicely explains its design-thinking point of view:  “Students and faculty in engineering, medicine, business, law, the humanities, sciences, and education find their way here to take on the world’s messy problems together.  Human values are at the heart of our collaborative approach… Along the way, our students develop a process for producing creative solutions to even the most complex challenges they tackle…  Our deliberate mash-up of industry, academia and the big world beyond campus is a key to our continuing evolution.”

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.”

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.”

The Continuing Evolution of Service Science

Service Science emerged in the mid-2000s as an academic discipline aimed at applying technology and science to the service sector, - by far the largest sector of the US economy and of most economies around the world.  Since its inception, IBM has played a major role in the continuing evolution of service science, coining the term Service Science, Management and Engineering (SSME) to highlight the interdisciplinary nature of this emerging discipline.  I was personally involved in these efforts during my last few years at IBM.  Subsequently, it’s continued to be one of the main subjects I’ve talked about in seminars and written about in this blog.

A few weeks ago I had an interesting conversation on the the state of service science with analysts from an IT research organization who were preparing a report on the subject for their clients.  Our discussion led me to reflect on the evolution of service science over the past several years.  I think that we are hearing a bit less about it these days.  But is that because we’ve become tired of the subject and moved on, or because the application of science and technology to services is now so well accepted that it’s no longer a topic of debate?  I very much think it’s the latter.

The Smart London Plan

Urbanization is one of the major forces shaping the 21st century - right up there with the digital technology revolution and globalization.  Over half of the world’s population lives in urban areas, and as the 2014 UN World Urbanization Prospect noted: “The continuing urbanization and overall growth of the world’s population is projected to add 2.5 billion people to the urban population by 2050,” with the proportion of the population living in urban areas increasing to 66 percent by 2050.

Just about every study that’s benchmarked the competitiveness of major urban areas ranks London, - along with New York, - as the world’s two top cities.  But despite, - or perhaps because of, - their leadership positions, both cities face major challenges as they deal with economic growth and a growing population.

London has been much in the news lately.  First came the election of Sadik Khan last May, - the first Muslim mayor not only of London but of a major Western capital, followed by the recent Brexit referendum, where London voted to Remain in the EU by an overwhelming 60% of its vote while the UK as a whole voted 52% to Leave.

At this point, it’s very hard to predict where Brexit is heading over the next few months, let alone what it’s long term consequences might be.  But, given its many strengths I have little doubt that London will remain among the world’s very top cities.  So, let me put Brexit aside for the moment and discuss instead what London has been doing to address its major population and economic challenges.

Learning to Work with Our Increasingly Smart Machines

A few weeks ago I attended the annual conference of MIT’s Initiative on the Digital Economy.  The day-long conference featured a number of interesting talks on the impact of digital technologies on business, the economy and society.  Tom Davenport, - Babson College professor and IDE Fellow, - gave one such talk on technology and jobs, based on his recently published book Only Humans Need Apply: Winners and Losers in the Age of Smart Machines, co-authored with HBR editor Julia Kirby.

Davenport started his talk by noting that over the past two centuries we’ve seen three distinct stages of automation, based on the kinds of jobs that were replaced by machines.  The machines of the first automation era “relieved humans of work that was manually exhausting,” making up for our physical limitations, - steam engines and electric motors enhanced our physical power while railroads, cars and airplanes helped us go faster and farther.

Next came the automation of jobs involving routine tasks that could be well described by a set of rules and were thus prime candidates for IT substitution.  “Era Two automation doesn’t only affect office workers.  It washes across the entire services-based economy that arose after massive productivity gains wiped out jobs in agriculture, then manufacturing.”  It threatened many transactional service jobs that “are so routinized that they are simple to translate into code,” from bank tellers to airline reservations clerks.

We’ve now entered the third era of automation.  Our increasingly smart machines are “now breathing down our necks…  This time the potential victims are not tellers and tollbooth collectors, much less farmers and factory workers, but rather all those knowledge workers who assumed they were immune from job displacement by machines,…” including, - as Davenport and Kirby poignantly remind us, - “People like the writers and readers of this book.”

The Public Face of Science

Last month, the American Academy of Arts and Sciences launched a three year initiative to address the complex relationship between scientists and the public.  The Public Face of Science project will explore the interactions of the general public with science, technology, medicine and related areas.

Throughout the 20th century, polls have consistently indicated strong public support for science and technology, especially during the Cold War decades following World War 2.  But recent polling data, - and in particular, a recent study conducted by the Pew Research Center, -  reveals a more complex relationship between the public and science.  While scientific achievements are still recognized and valued, there is a large opinion gap between the general public and scientists on a number of scientific issues.

The Pew findings were released in a January, 2015 report based on two surveys of science-related issues conducted in collaboration with the American Association for the Advancement of Science (AAAS).  The first survey is based on a representative sample of 2,002 general public adults and was conducted by landline and mobile phones.  The second survey was conducted online, and is based on a representative sample of 3,748 US-based scientists who are members of AAAS.

Why Some Work Groups Thrive While Others Falter

I recently read an interesting article published earlier this year in the NY Times Magazine by reporter Charles Duhigg, - What Google Learned From its Quest to Build the Perfect Team.  The article is focused on Project Aristotle, an initiative Google embarked on in 2012 that examined hundreds of Google teams in their quest to learn why some work groups thrive while others falter. 

“[M]any of today’s most valuable firms have come to realize that analyzing and improving individual workers - a practice known as employee performance optimization -  isn’t enough,” writes Duhigg.   “As commerce becomes increasingly global and complex, the bulk of modern work is more and more team-based… at many companies, more than three-quarters of an employee’s day is spent communicating with colleagues.”

“In Silicon Valley, software engineers are encouraged to work together, in part because studies show that groups tend to innovate faster, see mistakes more quickly and find better solutions to problems,” he later adds.  “Studies also show that people working in teams tend to achieve better results and report higher job satisfaction.  In a 2015 study, executives said that profitability increases when workers are persuaded to collaborate more.  Within companies and conglomerates, as well as in government agencies and schools, teams are now the fundamental unit of organization.  If a company wants to outstrip its competitors, it needs to influence not only how people work but also how they work together.”

The article devotes several paragraphs to the pioneering research on group performance by MIT professor Tom Malone, CMU professor Anita Woolley and their various collaborators over the past 8 years.  Their work addressed a very intriguing set of questions: Do groups exhibit characteristic levels of intelligence which can be measured and used to predict the group’s performance across a wide variety of cognitive tasks?  If so, can you devise tests to measure the group’s intelligence using methodologies and statistical techniques similar to those that have been applied to measure the IQs of individuals over the past hundred years?

Anticipating the Future of the IT Industry

How can we best anticipate the future of a complex, fast changing industry like IT?  Which hot technology innovations, - e.g., artificial intelligence, the blockchain, cloud computing, - will end up having a big impact and which are destined to fizzle?  What can we learn from the IT industry’s 60-year history that might help us better prepare for whatever lies ahead?

A major way of anticipating the future of any economic or social entity, - be it a company, industry, university, government agency or city, - is to explore and learn from its history.  While there’s no guarantee that historical patterns will continue to apply going forward, they might well be our most important guides as we peer into an otherwise unpredictable future.

I’ve been involved with computers since the early 1960s, first as a student at the University of Chicago, then in my long career at IBM, and subsequently through my relationship with a number of companies and universities.  I’ve thus had a ringside seat from which to observe the journey the IT industry’s been on since those early days.

Let me share some of my personal impressions of this journey through the lens of three key areas, each of which has played a major role throughout IT’s history, and will continue to do so well into its future: data centers, transaction processing, and data analysis.

The MIT Inclusive Innovation Competition

About three years ago, MIT launched the Initiative on the Digital Economy (IDE), a major effort focused on the broad changes brought about by the relentless advances of digital technologies.  As its website explains:

“While digital technologies are rapidly transforming both business practices and societies and are integral to the innovation-driven economies of the future, they are also the core driver of the great economic paradox of our time.  On one hand, productivity, wealth, and profits are each at record highs; on the other hand, the median worker in America is poorer than in 1997, and fewer people have jobs.  Rapid advances in technology are creating unprecedented benefits and efficiencies, but there is no economic law that says everyone, or even a majority of people, will share in these gains.

The future of work and jobs is one of the major areas being addressed by IDE.  What will the workforce of the future look like?; Where will jobs come from in the coming years?; Will the nature of work be significantly different in the digital economy?; How can we accelerate the transformation of institutions, organizations, and human skills to keep up with the quickening pace of digital innovation?

To help come up with breakthrough answers to these very challenging questions, IDE just launched its first annual Inclusive Innovation Competition.  The competition aims to identify, celebrate and award prizes to “organizations that are inventing a more sustainable, productive, and inclusive future for all by focusing on improving economic opportunity for middle- and base-level income earners.” 

The Coming Robotics Revolution

Hi, Robot: Work and Life in the Age of Automation, reads the Hamlet-inspired cover of the July, 2105 issue of Foreign Affairs.  The clever cover highlights the issue’s main theme, artificial intelligence and robotics, - a set of technologies that some fear could one day threaten our very existence. 

As its introductory article reminds us “new technologies have been revolutionizing the world for centuries, transforming life and labor and enabling an extraordinary flourishing of human development.  Now some argue that advances in automation and artificial intelligence are causing us to take yet another world-historical leap into the unknown.” 

Speculations about a future radically transformed by technology are nothing new.  But, with AI and robots seemingly everywhere, the concerns surrounding their long term impact may well be in a class by themselves.  Like no other technologies, AI and robots force us to explore the boundaries between machines and humans.  Will they turn out like other major innovations, e.g., steam power, electricity, cars, - highly disruptive in the near term, but ultimately beneficial to society?  Or, will we see a more dystopian future, as smart machines increasingly encroach on activities requiring intelligence and cognitive capabilities that not long ago were the exclusive domain of humans?  Opinions abound, but in the end, we don’t really know.

What’s the Value of a Liberal Arts Education in Our 21st Century Digital Economy?

A few years ago, I came across a very interesting article about the efforts of Roger Martin to transform business education.  At the time, Martin was the Dean of the Rotman School of Management at the University of Toronto.  He had long been advocating “that students needed to learn how to think critically and creatively every bit as much as they needed to learn finance or accounting.  More specifically, they needed to learn how to approach problems from many perspectives and to combine various approaches to find innovative solutions.”

“Learning how to think critically - how to imaginatively frame questions and consider multiple perspectives - has historically been associated with a liberal arts education, not a business school curriculum, so this change represents something of a tectonic shift for business school leaders.”  Achieving this goal would require business schools to move into territory “more traditionally associated with the liberal arts: multidisciplinary approaches, an understanding of global and historical context and perspectives, a greater focus on leadership and social responsibility and, yes, learning how to think critically.”

Similarly, in a 2006 report, the National Academy of Engineering called for reforming engineering education.  “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.”

Shaping the Future of the Internet

The Winter 2016 issue of Dædalus, the Journal of the American Academy of Arts and Sciences, is devoted to the Internet.  Its 7 articles explore some of the major issues shaping the Internet’s future.  The issue was curated by Harvard professor Yochai Benkler and MIT research scientist David Clark, whose joint introduction and individual papers frame the overall discussion.

“The Internet was born in 1983,” write Benkler and Clark in the Introduction.  That year saw the adoption of TCP/IP, - the communications protocol that defines the Internet to this day.  Its technical directions and overall culture have been largely determined by the academic and research communities, who were also its early users.  With strong government support, they developed a system that “only a researcher could love: general, abstract, optimized for nothing, and open to exploration of more or less anything imaginable using connected computers.”

The Internet has since evolved “from a network that primarily delivered email among academics and government employees, to a network over which the World Wide Web arose, to the video and mobile platform it has become - and the control network for embedded computing that it is fast becoming.”  From its niche research beginnings, it’s become the universal platform for collaborative innovation, enabling startups, large institutions, and everyone in between to quickly develop and bring to market many new digital products and services.

What design choices have led to the Internet as we know it?  Could it still evolve into a fundamentally different platform than the one we use today?  What decisions could significantly shape its future?  These are among the very important questions explored in the Dædalus issue.  In their Introduction, Benkler and Clark summarized some of the key themes that emerged from the various papers, - a few of which I’d now like to discuss.

Leadership in a Changing World

What are the critical competencies needed to lead in our fast-changing business environment?  This question was the focus of a study jointly conducted by the professional services firm Heidrick & Struggles and the University of Oxford Said Business School.  The study was based on over 150 conversations with CEOs from a wide range of sectors around the world.  Its findings were recently published in The CEO Report: Embracing the Paradoxes of Leadership and the Power of Doubt.

“Declining tenure rates and levels of public trust suggest that CEO leadership has not kept pace with increased expectations,…” notes the report in its opening paragraphs.  “Indeed, the role of the CEO is becoming more complex as competing and increasingly vocal stakeholders permeate organizations.”  Leading in today’s world requires new thinking: “past experience is no longer a reliable guide for future action…  success as a CEO today hinges on continual growth in the role, even more than on the preparation beforehand.”

The key challenge confronting CEOs, - and senior executives in general, - is the relentless pace of change.  But, while speed is the major dimension of change, it’s not the only one.  Beyond speed, executives must also focus on scope and significance.  They must carefully consider a few fundamental questions about each:

Corporate Survival: Lessons from Biology

Having lived through IBM’s near-death experience in the early 1990s, respect for the forces of the marketplace is edged deep in my psyche.  It’s frankly sobering how many once powerful IT companies are no longer around or are shadows of their former selves.  The carnage might be more pronounced in the fast-changing IT industry, but no industry is immune.  It would seem as if darwinian principles apply in business almost as much as they do in biology. 

A few weeks ago I discussed a paper published last year, The Mortality of Companies, by physicist Geoffrey West and his collaborators at the Santa Fe Institute.  Based on their extensive analysis of data about publicly traded US companies, they were surprised to discover that a typical firm lasts about ten years before it gets merged, acquired or liquidated, and that a firm’s mortality rate is independent of its age, how well established it is or what it does.  While beyond the scope of their study, the authors speculated that biological ecosystems are likely to shed valuable insights into their findings.

Such similarities between business and biological ecosystems was the focus of The Biology of Corporate Survival, a paper recently published in the Harvard Business Review (HBR) by Martin Reeves, Simon Levin, and Daichi Ueda. 

“Some business thinkers have argued that companies are like biological species and have tried to extract business lessons from biology, with uneven success,” note the authors.  “We stress that companies are identical to biological species in an important respect: Both are what’s known as complex adaptive systems.  Therefore, the principles that confer robustness in these systems, whether natural or manmade, are directly applicable to business.”

Why People and Companies Die While Cities Keep Growing

The Winter issue of strategy+business includes a very interesting article on theoretical physicist Geoffrey West, - The Fortune 500 Teller.  Dr. West is Distinguished Professor and Past President of the Santa Fe Institute (SFI), a non-profit research institute dedicated to the study of complex systems.  Prior to joining the SFI in 2003, he was the leader and founder of the high energy physics group at Los Alamos National Lab.

About 20 years ago, West got interested in whether some of the techniques and principles from the world of physics could be applied to complex biological and social systems.  In particular, he wondered if we could apply empirical, quantifiable and predictive scientific methods to help us better understand complex biological organisms and social organizations like cities and companies.

In the 1990s, his attention first turned to biology.  There are enormous variations in the characteristics of living creatures, their live spans, pulse rates, metabolism, and so on.  How do these characteristics change with body size?  Why do human beings live roughly 80 to 100 years, while mice live only two to three years.  Are there some common principles that apply to all living creatures regardless of size?  Can we find empirical mathematical models that might allow scientists to ask big questions about life, aging and death?

Learning to Apply Data Science to Business Problems

This past semester I was involved in an interesting course at MIT’s Sloan School of Management, - Analytics Labs (A-Lab).  A-Lab’s objective is to teach students how to use data sets and analytics to address real-world business problems.  Companies submit project proposals prior to the start of the class, including the business problem to be addressed and the data on which the project will be based. Students are then matched with the project they’re most interested in and grouped into teams of 3-4 students.

A-Lab received over 20 project proposals from different companies, of which 13 were selected by the students.  Each project team was assigned a research mentor to provide guidance as appropriate.  I mentored a 3-student team that worked on a project sponsored by MasterCard.  The students explored the possibility of improving on predictions of the economic performance of emerging markets by coupling existing economic indicators data with consumer behavior based on MasterCard’s transaction data.  This is a particularly interesting project because economic data in emerging markets is often not as reliable as the data in more advanced markets.

But more important for the students, the various A-Lab projects served as a concrete learning experience on what data science is all about, - how to leverage messy, incomplete, real-world data to shed light on a complex and not-so-well-defined problem.

The Rise of the T-Shaped Organization

The concept of T-shaped skills was first introduced over 20 years ago, but its importance, - to both individuals and organizations, - has continued to rise.  A growing number of articles have been extolling the value of T-shaped professionals, that is, individuals who combine deep cognitive, analytical, and/or technical skills in a specific discipline, with broad multidisciplinary, social skills.

As described by IDEO CEO Tim Brown:  “The vertical stroke of the T is a depth of skill that allows them to contribute to the creative process.  That can be from any number of different fields: an industrial designer, an architect, a social scientist, a business specialist or a mechanical engineer.  The horizontal stroke of the T is the disposition for collaboration across disciplines.”

T-shaped skills are increasingly valued in the marketplace.  For example, a recent paper by Harvard professor David Deming showed that labor markets have been rewarding individuals with strong social skills, that is, with interpersonal skills that facilitate interactions and communications with others.  Deming’s research showed that since 1980, social-skill intensive occupations have enjoyed most of the employment growth across the whole wage spectrum.  Employment and wage growth have been particularly strong in jobs requiring both high cognitive and high social skills.  But since 2000, they have fallen in occupations with high cognitive but low social skill requirements, - “suggesting that cognitive skills are increasingly a necessary but not sufficient condition for obtaining a high-paying job.”

Disruptive Innovation Revisited

Disruptive innovation was first introduced by Harvard Business School professor Clayton Christensen in a 1995 Harvard Business Review (HBR) paper co-written with Joseph Bower.  The concept was further elaborated and popularized in Christensen’s 1997 bestseller The Innovator's Dilemma, and in the articles and books he’s written or co-authored since then.  Few business concepts have been as long and widely used.

But as often happens with serious ideas once they become popular, disruptive innovation has joined the pantheon of trendy, overused business buzzwords, - stretched and applied way beyond its intended meaning.  It’s even led to a rather strong backlash in a 2014 New Yorker article by Harvard history professor Jill Lepore.

20 years after its introduction, Christensen has revisited his original concept in What is Disruptive Innovation?, co-written with Michael Raynor and Rory McDonald, and just published in HBR’s December issue.  The authors aim to define what disruptive innovation is and what it isn’t, as well as to update the theory based on the experiences of the past two decades.

“The theory of disruptive innovation… has proved to be a powerful way of thinking about innovation-driven growth…,” they write.  “Unfortunately, disruption theory is in danger of becoming a victim of its own success.  Despite broad dissemination, the theory’s core concepts have been widely misunderstood and its basic tenets frequently misapplied.  Furthermore, essential refinements in the theory over the past 20 years appear to have been overshadowed by the popularity of the initial formulation.  As a result, the theory is sometimes criticized for shortcomings that have already been addressed.”

The Continuing Evolution of Cloud Computing

I’ve been closely following cloud computing for a number of years, having posted my first cloud blog in March of 2008.  A few months later I gave a presentation at a  cloud-based conference.  The overall sense at that conference was that something big and profound was emerging in the IT industry.  Cloud computing could well be The Next Big Thing - one of those massive changes that the IT industry goes through from time to time that really shake things up, - like the advent of personal computers in the 1980s and the Internet in the 1990s. 

Cloud has fulfilled those early expectations, having made much progress in a relatively short time.  As I look back at its evolution over the past few years, I think of cloud as having gone through three main, overlapping phases.

Early adopters viewed cloud as computing-on-demand.  Next, cloud introduced a much more disciplined approach to the architecture and management of large IT infrastructures.  And by now, cloud has become accepted as a major transformational force in IT, in business, and in the economy in general.

Let me discuss each of these phases. 

The Growing Value of Social Skills

I recently read a very interesting paper, The Growing Importance of Social Skills in the Labor Market, by Harvard professor David Deming.  Deming’s paper shows that over the past several decades, labor markets have been increasingly rewarding social skills, that is, interpersonal skills that facilitate interactions and communications with others.  He presents evidence that since 1980, social-skill intensive occupations have enjoyed most of the employment growth across the whole wage spectrum, and that employment and wage growth have been particularly strong in jobs that require both high cognitive and high social skills. 

Deming’s paper builds on the work of MIT economist David Autor, a leading authority on the impact of technological change on the US labor market.  A few weeks ago I wrote about a paper he published this past summer on the history and future of workplace automation.  In that paper, Autor summarized the sharp polarization of job opportunities that’s taken place in the US over the past two decades, noting that job opportunities have significantly expanded in both high-skill and low-skill occupations, while they have significantly contracted for mid-skill jobs. 

The reason for this polarization is that automation has been most successful when applied to mid-skill routine tasks, that is, tasks or processes that follow precise, well understood procedures that can be well described by a set of rules.  The occupations most susceptible to automation have included blue-collar physical activities such as manufacturing and other forms of production, as well as white-collar, information-based activities like accounting, record keeping, and many kinds of administrative tasks.  As a result, these occupations have experienced the biggest declines in employment opportunities and earnings. 

On the other hand, non-routine tasks are much harder to describe by a set of rules that a machine can follow.  These include manual jobs in fast-food restaurants, janitorial services and health-care aides that require relatively low skills and education, as well as high-skill jobs that involve expert problem solving and complex communications requiring strong cognitive skills and a college education.

Two-Speed IT Architectures

I recently read an interesting McKinsey article, Organizing for digital acceleration: Making a two-speed IT operating model work.  The article argues that born digital  Internet companies, - e.g., Amazon, Google, Facebook, - are setting the standards in the quest to understand and optimize the customer experience, challenging traditional companies to keep up.  These older companies have to both leverage the back-end systems, processes and overall capabilities that have served them well over the years, while adopting innovative consumer-facing technologies to enhance their customers’ experiences.

The article starts out by summarizing the best practices of born digital companies:

• Provide the appropriate information and platforms so customers can find what they need quickly and reliably. 
• Align technology and production systems with business goals. 
• Limit the size of application-development teams. 
• Focus on agile product development to enhance customer engagements. 
• Encourage product managers to think about digital experiences rather than discrete applications or components. 
• Collect and learn from consumer data.
• Engage in frequent testing and experimentation.
• Don’t be afraid to fail.
  

Design Thinking and the Workplace Experience

Design has long played a major role in product innovation.  But in the last few years, a shift has been underway bringing design to the very core of the business.  “The Evolution of Design Thinking: It’s no longer just for products. Executives are using this approach to devise strategy and manage change,” read the cover of the Harvard Business Review’s September issue, which featured several articles on the subject. 

Leading-edge companies are leveraging design thinking to translate technological advances into compelling customer experiences in order to seize market share from more traditional competitors.  As noted in one of the HBR articles, design-centric organizations are adamantly focused on their customer’ needs, rather than on their internal operational efficiencies.

A second HBR article explored a different kind of application.  To help overcome the stiff resistance often encountered by disruptive innovations, - both within one’s own organization and in the marketplace, - the article proposed that design thinking should be applied to their actual introduction, - a process it calls intervention design.

I would now like to turn to another recent article, Management by Design, by professors Mark Gruber, Nick de Leon, Gerry George and Paul Thomson.  As the authors argue, design principles should be applied within the management domain itself, to help rethink business processes, workflows and the overall structure of the organization and thus create what it calls a New Workplace Experience (NWX).

Bridging the World’s Knowledge Divide: Technology + Competition, Skills and Institutions

A few months ago I attended the 12th annual Brookings Blum Roundtable on Global Poverty, a meeting that brought together around 50-60 policy and technical experts from government, academia, business, investors and NGOs.  This year’s event was focused on the impact of digital technologies on economic development in emerging and developing countries.  It was organized around six different sessions, each of which explored the links between technology and development through a different lens. 

Prior to the event, the Brookings Institution commissioned a policy brief for each session to help set the stage for the ensuing discussions.  I wrote one of the briefs, - Will the Digital Revolution Deliver for the World’s Poor.  I would now like to discuss another brief which I found quite interesting, - Will the Spread of Digital Technologies Spell the End of the Knowledge Divide?, by Deepak Mishra, lead economist at the World Bank.  Dr. Mishra is also co-director of the World Development Report 2016: Internet for Development, which will shortly be released by the World Bank.

The Importance of Empathy in Our Services-Centric, People-Oriented Economy

A few days ago I read an excellent article in the Harvard Business Review, - Empathy Is Still Lacking in the Leaders Who Need It Most, - by Ernest Wilson, Dean of the Annenberg School of Communications and Journalism  at the University of Southern California (USC).  The article is based on the Third Space, a research project to better understand the key competencies companies are looking for, and whether these talent requirements are being adequately addressed by universities.

The study conducted face-to-face, in-depth conversations with dozens of senior executives across companies in a broad range of industries and consulted with a number of other academic institutions.  It gathered additional data through online surveys and partnered with Korn Ferry, - the world’s largest executive search firm, - which gave them access to data on almost 1,900 executives with a broad range of professional backgrounds. 

Their initial findings were published in a working paper, The one-trillion dollar global talent gap: What it is and what we can do about it.  Their research discovered that talent is the greatest competitive challenge companies face.  More specifically, beyond the traditional skills typically provided by engineering and business schools, companies need a new kind of talent that is currently undersupplied in the workforce.

Future leaders must be strong in quantitative, technical and business skills.  But these must be complemented with a unique set of attitudes, perspectives, experiences and other so called softer skills.  Good leaders need to be good strategic thinkers and must have strong social and communications skills.  Finding and retaining talented individuals with these capabilities is a challenge regardless of geography or industry.

Intervention Design: Overcoming Stiff Resistance to a New, Disruptive Innovation

The September issue of the Harvard Business Review features a spotlight on The Evolution of Design Thinking.  With four articles on the subject, HBR’s overriding message is that design is no longer just for physical products, being increasingly applied to customer experiences, innovation, business strategy, and complex problem solving. 

Last week I discussed this expanded view of design thinking based on one of the articles, - Design Thinking Comes of Age.  I now want to turn my attention to a second article in the HBR issue, Design for Action, which applies design thinking not to the actual artifact being designed, - whether product, service, business strategy, or complex system, - but to a very different kind of problem: the introduction of the designed artifact into the world.

When first introduced, disruptive innovations are likely to encounter stiff resistance, both within one’s own organization and in the marketplace, - otherwise we wouldn’t call them disruptive.  The article argues that we should apply design thinking to the launch of the disruptive innovation itself - a process they call intervention design. 

Design for Action was co-written by Tim Brown and Roger Martin.  Tim Brown is the CEO of IDEO, - the firm most associated with this more general view of design.  Roger Martin is in the faculty of the Rotman School of Management at the University of Toronto, where he served as Dean from 1998 to 2013.  He’s a strategic advisor to CEOs around the world and the author of a number of books.

The Evolution of Design Thinking

Design Thinking is the featured topic in the September issue of the Harvard Business Review with four articles on the subject.  “It’s no longer just for products.  Executives are using this approach to devise strategy and manage change,” reads the tagline in its cover.

The application of design thinking beyond products, - in innovation, problem solving and business strategy, - isn’t new.  Nobel laureate Herbert Simon discussed the concept in his 1969 classic The Sciences of the Artificial.  IDEO, a firm best known for pioneering this expanded view of design, traces its roots back to 1978.  The School of Design in London’s Royal College of Art has long been pushing the boundaries of industrial design.  Stanford’s Institute of Design, aka the d.school, was founded a dozen years ago.  “But many companies still aren’t sure how it can improve their business,” notes Adi Ignatius, HBR’s editor-in-chief.

It’s not surprising that many companies don’t understand what is meant by design thinking, let alone its potential value to their business.  It’s much easier to appreciate the role of design when it comes to physical objects: cars, bridges, buildings, dresses, shoes, jewelry, smartphones, laptops, and so on.  But, it’s considerably harder to appreciate its importance when it comes to more abstract entities like systems, services, information and organizations.  Their very nature is vague.  You can’t touch them.  Yet, they account for the bulk of the growing complexity in our daily lives.

Realizing the Potential of the Digital Economy

A few weeks ago I attended the 12th annual Brookings Blum Roundtable on Global Poverty.  Organized by the Brookings Institution, the Blum Roundtable brings together policy and technical experts from government, academia, business, investors and NGOs from all over the world.  This year’s event, - Disrupting Development with Digital Technologies, - explored the impact of the digital economy on the ways business and development organizations now engage with emerging and developing countries.

In a pre-Roundtable blog, Kemal Derviş, - Brookings VP, Global Economy and Development, - identified three major trends with the potential to redefine global development over the next 10 years: the growing adoption of digital payments serving people everywhere with near-frictionless transactions; the spread of Internet connectivity and digital literacy; and the harnessing of data to better serve the poor and to generate new knowledge.

 Discussions were organized around six different sessions over three days.  Each session looked at the impact of digital technologies on economic development through a different lens, including electronic payments, access to information and the future of work.  Prior to the Roundtable, Brookings commissioned a policy brief for each session to help set the stage for the ensuing presentations and discussions.

Is a Backlash Brewing Against Silicon Valley’s Perceived Insularity?

“Empire of the geeks… and what could wreck it” was the featured cover story of The Economist’s July 25 issue.  “Silicon Valley should be celebrated.  But its insularity risks a backlash,” reads the tag line of its leader article.  Last year’s anger at the private buses that take employees who live in San Francisco to their workplaces 30 or 40 miles south, is but one of the signs that a backlash might be brewing.

You can sense the reaction against the perceived excesses of its culture in a pair of recent articles, - Silicon Valley’s Youth Problem in the NY Times Magazine, and What Silicon Valley Thinks of Women in Newsweek, the latter visually making its point in its cover.  You can feel it in HBO’s no-holds-barred namesake TV series. And then there is Peter Thiel, - one of Silicon Valley’s most successful, outspoken, and wealthy entrepreneurs and VCs, - who adds fuel to the backlash fire with his controversial views on a wide range of subjects, including women, democracy, capitalism and education.  

Becoming a 21st Century Digital Tinkerer

I like to tell people that the key to being and/or sounding smart is to hang out with smart people.  And, one of the names that would quickly comes to mind if asked to recommend who to hang out with is John Seely Brown, aka JSB.  JSB, - who’s been a friend for over 25 years, - was chief scientist at Xerox and director of its Palo Alto Research Center (PARC) until June of 2000.  He is now the independent co-chairman of the Deloitte Center for the Edge and a visiting scholar at USC.  But, as noted in his personal website, his chief occupation is Chief of Confusion,“helping people ask the right questions, trying to make a difference through my work- speaking, writing, teaching.” 

Transform our learning paradigms for the digital age is one of the main topics JSB has long been exploring, most recently in Cultivating the Entrepreneurial Learner in the 21st Century, - an article based on a presentation of the same title.  Entrepreneurial learners aren’t necessarily entrepreneurs, but like entrepreneurs, they are “constantly looking for new ways, new resources, new peers and potential mentors to learn new things.” 

Why is it so important to foster a more entrepreneurial style of learning?  Our educational system has long been based on the assumption that learning is mostly about acquiring knowledge as well as critical skills to enable us to apply whatever we learned in school throughout our careers, say 30 - 40 years.

Man, Machine, and Work

The June, 2015 issue of the Harvard Business Review includes a spotlight on Man and Machine: Knowledge Work in the Age of the Algorithm.  Another example that, as a recent article observed, “Artificial intelligence is suddenly everywhere…”

People have long worried about the impact of technology on society, whether discussing 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.  But the concerns surrounding AI may well be in a class by themselves.  Like no other technology, AI forces us to explore the very boundaries between machines and humans.

Some experts fear that at some future time, sentient, superintelligent AI machines might pose an “existential risk” that “could spell the end of the human race.”  Others are dismissive of such dire concerns while agreeing that we must work hard to ensure that our complex AI systems do what we want them to do. 

Whether right or wrong, these long term worries are still decades into the future.  Much more immediate is the impact of our smart machines on jobs and the economy.  Will AI turn out like other major innovations, - e.g., steam power, electricity, cars, - highly disruptive in the near term, but ultimately beneficial to society?  Or, will our smart machines take over not just low-skilled tasks but high-skilled ones too?  What will life be like in such an AI future, when highly intelligent machines, - many far surpassing human cognitive capabilities, - are all around us?

Open Letter on the Digital Economy

A few days ago I received an e-mail from MIT professor Erik Brynjolfsson inviting me to consider supporting an Open Letter on the Digital Economy that a small group had recently drafted.  As he explained in the e-mail: 

“We met out in Silicon Valley to discuss how technology was driving an economic transformation.  We concluded that the effects on wealth and income are already significant, and emerging technologies promise greater effects in the coming decade.  While the economic pie is bigger than ever, wages for the median worker in America and other advanced nations have stagnated, increasing inequality.  With more powerful technologies available than ever before, the challenge before us is to create a better society than ever before.  We decided to draft an open letter expressing some of our ideas.” 

Since the steam engine in the late 18th century, we’ve had major waves of innovation every 50-60 years, including railroads, steel, electricity and heavy engineering in the 19th century; and automobiles, airplanes and telecommunications in the 20th century.  

Each such wave was accompanied by periods of creative destruction, when older industries and jobs where displaced by the new technology-based innovations.  Over time, these innovations transformed the economy, re-shaped the institutions of society, and ultimately led to the creation of all kinds of new, more productive industries and many new kinds of jobs.  

“Previous technological innovation has always delivered more long-run employment, not less.  But things can change,” is the tag line of an article on the future of jobs published in January of 2014 in The Economist.  “Nowadays, the majority of economists confidently wave such worries away.  By raising productivity, they argue, any automation which economises on the use of labour will increase incomes.  That will generate demand for new products and services, which will in turn create new jobs for displaced workers…”

“For much of the 20th century, those arguing that technology brought ever more jobs and prosperity looked to have the better of the debate…  Yet some now fear that a new era of automation enabled by ever more powerful and capable computers could work out differently.  They start from the observation that, across the rich world, all is far from well in the world of work. The essence of what they see as a work crisis is that in rich countries the wages of the typical worker, adjusted for cost of living, are stagnant.”

Game-Changing Basic Research: Benefits and Challenges

In April, MIT released The Future Postponed: Why Declining Investment in Basic Research Threatens a U.S. Innovation Deficit, - a report on both the long term benefits of basic research and the impact on those benefits of declining federal investments.

“2014 was a year of notable scientific highlights,” notes the report in its introduction, “including: the first landing on a comet, which has already shed important light on the formation of the Earth; the discovery of a new fundamental particle, which provides critical information on the origin of the universe; development of the world’s fastest supercomputer; a surge in research on plant biology that is uncovering new and better ways to meet global food requirements.” 

“None of these, however, were U.S.-led achievements,” it then adds.  “The first two reflected 10-year, European-led efforts; the second two are Chinese accomplishments, reflecting that nation’s emergence as a science and technology power.  Hence the widespread concern over a growing U.S. innovation deficit, attributable in part to declining public investment in research.” 

According to the American Association for the Advancement of Science (AAAS), the percentage of the federal annual budget devoted to R&D has fallen from 9.1%, ($16.2 billion), in 1968 to 3.4%, ($131.0 billion), in 2015.  As it has for many decades, the US continues to lead the world in total R&D outlays, which includes all the R&D carried out in companies, universities, research institutes, government labs and so on.  But other countries, China in particular, are catching up. 

In 2014, says R&D magazine, the US spent $465 billion in R&D, while China was second at $284 billion.  But, it estimates that given the current rate of growth and investments, China will be spending $600 billion in R&D and surpass the US around 2022 due to its faster growing economy.

Reflections on Ten Years of Blogging

The Internet era was born around 20 years ago, when Netscape went public in August of 1995 and caught the world by storm with its highly successful IPO.  For many people, myself included, the Netscape IPO marked the passage of the Internet from a network primarily used by universities, research labs and the technical community in general, to the universal platform for content, communications and commerce it’s since become. 

The Internet ushered the rise of a highly connected economy all around us, whose magnitude and implications were nicely captured by NY Times columnist Tom Friedman in his 2005 bestseller The World is Flat.  But it’s a measure of how fast things are changing that, as Friedman observed in a conference in 2011, the environment he described was already out of date.  In a short 6 years, we were already transitioning from the connected world of PCs, browsers and web servers he wrote about, to the hyperconnected world of smart mobile devices, cloud computing and broadband wireless networks, a transition that in 2015 is now in full swing.  

Friedman’s words came to mind as I was reflecting on the evolution of blogging since I posted my first blog in May of 2005.

According to Wikipedia, something like blogging got its start with the advent of the Internet era in the mid 1990s, although the term weblog, later abbreviated to blog, didn’t come into being until the latter part of the decade.  In its early years, blogging was mostly viewed as something geeks did, - technical knowledge was required since the tools were fairly primitive, - or as a narcissistic endeavor, - early blogs often felt like online personal diaries.

This all started to change in the early 2000s.  The advent of easy-to-use tools and platforms brought blogging to a much larger, non-technical population.  People started to blog about a wide variety of topics, from politics and journalism to business and technology.  Internal company blogs, only accessible to employees, began to appear.  By the mid 2000s, blogging was growing quite rapidly and had made its way from the fringes to the Internet mainstream.

Imagination as a Journey of Survival and Discovery

On April 18 I participated in a TEDx conference at my local community library in Westport, CT.  TEDx programs were developed to help independent organizations create their own TED-like events in the spirit of TED’s mission, - ideas worth spreading.  As is the case with TED, TEDx talks should be carefully curated, compelling and short, lasting no more than 18 minutes. 

Imagination was the overall theme of the Westport Library conference.  Five different speakers applied the theme to their various talks, ranging from Imagining Risk to Boredom - A Source of Inspiration.   Imagination as a Journey of Survival and Discovery was the title of my own talk.  I’ve given many seminars and taught semester-long courses on the subject.  But, boiling down your key message into a meaningful, 18-minutes-or-less TED talk is quite a challenge.  It forces you to think really hard about the very essence of the story you are telling, as well as why the audience should be interested in what you have to say. 

I focused my talk on the role of imagination during times of major economic and societal change.  At such times, when survival and discovery are at a premium, imagination is badly needed to help us accept the need to deal with a fast-changing present, as well as to encourage us to take the necessary actions and embark on a journey toward an uncertain, and possibly perilous future.

I followed the wise advice once given by Leo Tolstoy - Describe your village, and you will describe the world.  Part of my talk was about Westport, the village or town where I’ve lived for the past 24 years, and in particular about our community library with which I’ve been closely associated for most of those years.  But first, I talked about IBM, which while not quite a village, was for me a kind of extended family for over 40 years, from the time I joined in 1970, - right after school and shortly after my 25th birthday, - until my retirement in 2007, followed by an additional 3 years as a consultant.

The Promise and Challenges of Health Analytics

On February 24 I attended a workshop in MIT on the Future of Health Analytics.  The event was sponsored by MIT Connection Science, a recently organized research initiative aimed at leveraging data science to quantify and analyze human behaviors, and to leverage the new insights thus obtained in key societal applications, including healthcare, transportation and finance.  Connection Science, - with which I’m affiliated as a Fellow, - was founded by Media Lab professor Alex “Sandy” Pentland.  He’s the author of several books, including the recently published Social Physics: How Good Ideas Spread.  

I’ve worked with Pentland for the past several years, and have previously written about his research on Reinventing Society in the Wake of Big Data, as well as his work with the World Economic Forum and others on the creation of trust frameworks for the sharing and protection of personal data.  In his opening remarks at the workshop, Pentland talked about Big Data and Health.  The little data breadcrumbs that we leave behind as we move around in the world can now be reality mined to help us better understand our behaviors and thus improve our lives and health.  He discussed several applications of big data to health, based on research at his MIT Human Dynamics Lab, as well as startups he’s involved in.

Following Pentland, Dr. Dennis Ausiello talked about Quantitative Human Phenotyping and the opportunities it offers to transform the practice of medicine.  Ausiello is professor of medicine at the Harvard Medical School, Chief Emeritus of Medicine at Massachusetts General Hospital (MGH), and director and co-founder of the Center for Assessment Technology and Continuous Health (CATCH), a joint MGH-MIT initiative aimed at finding new ways of measuring the human condition, i.e., phenotypes.

The Big Data Era Is Here

In mid-January, the Transportation Research Board (TRB) of the National Academies held its 94th Annual Meeting in Washington DC.  The conference attracted around 12,000 transportation researchers and practitioners from around the world.  During the meeting, the TRB Executive Committee held a policy session on the impact of big data on transportation systems.  I was one of three panelists invited to the session.  Each of us made a short presentation which was then followed by an extensive discussion between the panel and the Committee.

Big data and related information-based disciplines, - e.g., data science, artificial intelligence, - are everywhere.  Why are we so excited about them?  Is it mostly hype, or is there something truly profound going on?  In my presentation, I tried to briefly address these questions based on three key observations.